Current Protocols in Protein Science: 001

Current Protocols in Protein Science Edited by: John E. Coligan (Bethesda, Maryland); Ben M. Dunn (University of Florida); David W. Speicher (The Wistar Institute); Paul T. Wingfield (Bethesda, Maryland); Past Editor: Hidde L. Ploegh Series Editor: Gwen Taylor

Copyright © 2007 by John Wiley and Sons, Inc. ISBN: 978-0-471-11184-9 0-471-11184-8 - Looseleaf 0-471-14098-8 - CD-ROM Last updated: 26 Apr 2007

PREFACE roteins are like people: they are highly diverse, with very few traits shared by the entire population. Even close relatives often have very different structural (physical) characteristics and behave quite differently. This heterogeneity presents challenges for scientists interested in studying protein structure and function. The need for a readily accessible, updatable reference for protein methods has grown in tandem with the ongoing protein science renaissance driven by advances in molecular biology, genetics, structural biology, and related disciplines. Using recombinant methods, it is now practical to overexpress both normal and reengineered forms of a protein and to create chimeric proteins with unique new properties. The ability to express proteins usually found in low abundance at high levels and to manipulate their amino acid sequences provides unprecedented opportunities to study a vast array of proteins and their associated biological processes (both in vitro and in vivo). The combination of biochemical, biophysical, and high-resolution structural analyses of proteins with manipulation of the associated genes (in cultured cells or an entire mammalian organism) has already led to spectacular advances in many areas of biotechnology and molecular medicine.

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Because proteins are so diverse, a wide range of techniques must often be considered and tested empirically for a particular protein, and each researcher’s arsenal must include both recent innovations and techniques that have been used for many years. Moreover, methods appropriate for purifying and initially characterizing the protein(s) associated with a biological activity of interest are often not suitable for analyzing the corresponding overexpressed recombinant protein. This manual includes both specific detailed protocols as well as strategies for adapting methods to particular projects at hand. It is designed to meet the needs of scientists with little prior experience of protein isolation and characterization, including graduate students and scientists trained in other biological disciplines. At the same time, the broad range of techniques presented here should ensure that even seasoned experts will find new and useful approaches and will benefit from the manual’s convenient compilation of standard information and methods that typically must be culled from many sources. The quarterly updating service will allow ongoing expansion of the core volume to include chapters covering critical topics that were deferred due to initial space limitations and to keep pace with the “cutting edge” of developments in this fast-moving field.

HOW TO USE THIS MANUAL Format and Organization This publication is available in looseleaf, CD-ROM and online formats. For looseleaf subscribers, binders are provided to accommodate the growth of the manual via the quarterly updates. The looseleaf binder format allows easy insertion of new pages, units, and chapters that are added. The index and table of contents are updated with each supplement. CD-ROM subscribers receive a completely new disc every quarter and should dispose of their outdated discs. The online version is available through institutional license and offers many useful features such as downloadable PDF files of units, enhanced text searching for contents from all Current Protocols titles, real-time updating, and superior color graphics. The material covered in all three versions is identical. Subjects in this manual are organized by chapters and sections, and protocols are contained in units. Units generally describe a method and include one or more protocols with

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listings of materials, steps and annotations, recipes for unique reagents and solutions, and commentaries on the “hows” and “whys” of the method; there are also “overview” units containing theoretical discussions that lay the foundation for subsequent protocols. Page numbering in the looseleaf version reflects the modular arrangement by unit; for example, page 2.3.5 refers to Chapter 2 (Computational Analysis), UNIT 2.3 (Protein Secondary Structure Prediction), page 5 of that particular unit. Many reagents and procedures are employed repeatedly throughout the manual. Instead of duplicating this information, cross-references among units are used extensively. Crossreferencing helps to ensure that lengthy and complex protocols are not overburdened with steps describing auxiliary procedures needed to prepare raw materials and analyze results. Certain units that describe commonly used techniques and recipes (e.g., gel electrophoresis, ion-exchange chromatography) are cross-referenced in other units that describe their application. Thus, whenever it is necessary to isolate or identify a protein band in a protocol, the appropriate unit in Chapter 10—describing various procedures for gel electrophoresis—is cross-referenced (i.e., UNIT 10.1). For some widely used techniques (such as dialysis), readers are cross-referenced to APPENDIX 3.

Introductory and Explanatory Information Because this publication is first and foremost a compilation of laboratory techniques in protein science, we have not provided extensive instructive material. We have, however, included explanatory information where required to help readers gain an intuitive grasp of the procedures. Chapter 1 provides an overview of strategies for protein purification and characterization, to help the reader plan for all aspects of an experimental study. Some subsequent chapters begin with special overview units that describe the state of the art of the topic matter and provide a context for the procedures that follow. Chapter and unit introductions describe how the protocols that follow connect to one another, and annotations to the actual protocol steps describe what is happening as a procedure is carried out. Finally, the Commentary that closes each protocol unit describes background information regarding the historical and theoretical development of the method, as well as alternative approaches, critical parameters, troubleshooting guidelines, anticipated results, and time considerations. All units contain cited references and many indicate key references to inform users of particularly useful background reading, original descriptions, or applications of a technique. Protocols Many units in the manual contain groups of protocols, each presented with a series of steps. The basic protocol is presented first in each unit and is generally the recommended or most universally applicable approach. Alternate protocols are provided where different equipment or reagents can be employed to achieve similar ends, where the starting material requires a variation in approach, or where requirements for the end product differ from those in the basic protocol. Support protocols describe additional steps that are required to perform the basic or alternate protocols; these steps are separated from the core protocol because they might be applicable to other uses in the manual, or because they are performed in a time frame separate from the basic protocol steps.

Preface

Reagents and Solutions Reagents required for a protocol are itemized in the materials list before the procedure begins. Many are common stock solutions, others are commonly used buffers or media, while others are solutions unique to a particular protocol. Recipes for the latter solutions are provided in each unit, following the protocols (and before the commentary) under

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the heading Reagents and Solutions. It is important to note that the names of some of these special solutions might be similar from unit to unit (e.g., SDS sample buffer) while the recipes differ; thus, make certain that reagents are prepared from the proper recipes. On the other hand, recipes for commonly used stock solutions and buffers are provided once in APPENDIX 2E. These universal recipes are cross-referenced parenthetically in the materials lists rather than repeated with every usage.

Commercial Suppliers Throughout the manual, we have recommended commercial suppliers of chemicals, biological materials, and equipment. In some cases, the noted brand has been found to be of superior quality or it is the only suitable product available in the marketplace. In other cases, the experience of the author of that protocol is limited to that brand. In the latter situation, recommendations are offered as an aid to the novice protein science experimenter in obtaining the tools of the trade. Experienced investigators are therefore encouraged to experiment with substituting their own favorite brands. Addresses, phone numbers, and facsimile numbers of all suppliers mentioned in this manual are provided in the SUPPLIERS APPENDIX.

Safety Considerations Anyone carrying out these protocols may encounter the following hazardous or potentially hazardous materials: (1) radioactive substances, (2) toxic chemicals and carcinogenic or teratogenic reagents, and (3) pathogenic and infectious biological agents. Readers are referred to APPENDIX 2A and APPENDIX 2B for guidelines regarding use, storage, and disposal of these materials. Although cautionary statements are included in the appropriate units with cross-references to this appendix, we emphasize that users must proceed with the prudence and precaution associated with good laboratory practice, and that all materials must be used in strict accordance with local and national regulations. Reader Response Most of the protocols included in this manual are used routinely in our own laboratories. These protocols work for us; to make them work for you we have annotated critical steps and included critical parameters and troubleshooting guides in the commentaries to most units. However, the successful evolution of this manual depends upon readers’ observations and suggestions. Consequently, a self-mailing reader-response survey can be found at the back of the manual (and is included with each supplement); we encourage readers to send in their comments. Feedback can also be submitted online from the Current Protocols homepage at http://www.currentprotocols.com using the “Contact Us” link. ACKNOWLEDGMENTS This manual is the product of dedicated efforts by many of our scientific colleagues who are acknowledged in each unit and by the hard work by the Current Protocols editorial staff at John Wiley and Sons. The publisher’s commitment and continuing support for a protein science manual, particularly through the efforts of Brian Crawford and Paul Majchrzyk, was essential for realizing this ambitious project. We are extremely grateful for the critical contributions by Virginia Chanda (Series Editor) who kept the editors and the contributors on track and played a key role in bringing the entire project to completion. Other skilled members of the Current Protocols staff who contributed to the project include Janet Blair, Hazel Chan, Elizabeth Konkle, Alice Ro, and Kathy Wisch. The extensive copyediting required to produce an accurate protocols manual was ably handled by Rebecca Barr, Karen Hopkin, Arline Keithe, Kathy Morgan, Connie Parks, and Allen Ranz, and electronic illustrations were prepared by Gae Xavier. Finally, we

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would like to thank two people who played important roles in initiating this project. Sarah Greene initially conceived this project and had a critical role in developing it. John A. Smith made important contributions during the early planning phases of the project. Helpful advice and criticisms of the project during early planning stages were provided by Fred Ausubel, Charles S. Craik, Bruce A. Cummingham, Mark Hermodson, Tony E. Hugli, Robert Kingston, Albert Light, Paul Matsudaira, David Moore, George Rose, and Kevin Struhl. Mark Hermodson read and critiqued the entire manual and individual chapters were thoughtfully reviewed by John Freed and John Kay. Finally, we are especially grateful to our many colleagues, who contributed material to this manual, for sharing their procedures and experiences.

RECOMMENDED BACKGROUND READING Branden, C. and Tooze, J. 1999. Introduction to Protein Structure, 2nd edition. Garland Publishing, New York. Easy-to-read overview of basic structural principles of proteins with extensive illustrations. Fersht, A. 1998. Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and Protein Folding. 3rd edition. W. H. Freeman. Well established and clear review of protein structure and function, with an emphasis on enzymes. Hunte, C., von Jagow, G. and Schagger, H., editors. 2003. Membrane Protein Purification and Crystallization: A Practical Guide (2nd edition). Academic Press. Deals with specific problems encountered when purifying membrane-associated proteins. Link, A.J. (editor). 1998. 2-D Proteome Analysis Protocols. Humana Press. Extensive treatment of 2-D gel analysis and its role in proteomics, including preparations of samples from diverse sources, alternative 2-D gel analysis methods and data analysis/interpretation. Petsko, G.A. and Ringe, D. 2004. Protein Structure and Function. New Science Press Ltd., London. An updated look at protein structure in the genomics age. Includes discussion of sequence-to-structure and structure-to-function correlations. Speicher, D.W. (editor). 2004. Proteome analysis: Interpreting the Genome. Elsevier. Overview of most current proteomics methods and supporting technologies. Voet, D. and Voet, J.G. 2005. Biochemistry, 3rd edition. John Wiley & Sons. An updated biochemistry textbook whose protein-related chapters provide a comprehensive background in biochemistry.

John E. Coligan, Ben M. Dunn, Hidde L. Ploegh, David W. Speicher, and Paul T. Wingfield

Preface

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FOREWORD

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ver the past five to ten years we have witnessed a revolution in biological research which is fueled by our increasing capacity to decipher biological information of three types: the digital information embedded in DNA with its four-letter alphabet; the three-dimensional information represented by proteins, the major executors of biological function; and the four-dimensional information of complex biological systems and networks representing the temporal and spatial interaction of multiple components. The analysis of complex systems and networks in their entirety is essential for scientists to understand the molecular basis of fascinating processes such as growth, development, and differentiation, and to extract the biological meaning of emergent properties such as consciousness, memory, and the ability to learn which evolved over billions of years. Not surprisingly, the linear nucleotide sequence in DNA has been the first type of information to be deciphered. Large-scale genetic mapping and DNA sequencing rapidly generate enormous amounts of biological information. In many ways, programs such as the human genome project represent some of the earliest attempts to understand biological complexity. Successful completion of the human genome and similar projects will pose the challenge of interpreting the information contained in billions of nucleotides and of explaining how the interplay of the products of perhaps 100,000 genes results in the myriad of biological phenotypes. Meeting these challenges will require new interdisciplinary strategies which draw from the expertise of scientists from disciplines as different as molecular biology, biochemistry, engineering, chemistry, applied mathematics, and computer science. Protein science is central to such integrated strategies. Separation of proteins from complex mixtures, followed by structural and functional analyses, control of protein function, post-translational processing, and modification and formation of macromolecular complexes of proteins and other biomolecules are but a few examples of topics which are essential for all those scientists who attempt to interpret the linear DNA sequence in terms of the three- and four-dimensional information of complex biological systems and networks. The following chapters detail the necessary protocols for this endeavor, provided by experts in experimental protein science. Two attractive features set this manual apart from other collections of research protocols. First, Current Protocols in Protein Science will be continuously updated and expanded by quarterly additions to the core edition. Secondly, this volume as well as the updates are also available on CD-ROM. Numerous cross-references within the manual by hypertext links, context based searching, and provisions for making individualized notebooks containing frequently used protocols are attractions of the CD-ROM version which are in tune with the increasing dependence on computers in biological research laboratories.

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Protein science is a diverse, experimentally challenging, and rapidly evolving discipline. This book provides expert guidance in experiment design and execution and promises to remain up-to-date, in contents and in format, for many years to come. Leroy E. Hood1 and Ruedi Aebersold1 1 Seattle, Washington

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Table of Contents

Preface Foreword

Chapter 1 Strategies of Protein Purification and Characterization Unit 1.1 Overview of Protein Purification and Characterization Unit 1.2 Strategies for Protein Purification Unit 1.3 Protein Purification Flow Charts Unit 1.4 Purification of Glutamate Dehydrogenase from Liver and Brain Unit 1.5 Overview of the Physical State of Proteins within Cells

Chapter 2 Computational Analysis Introduction Unit 2.1 Computational Methods for Protein Sequence Analysis Unit 2.2 Hydrophobicity Profiles for Protein Sequence Analysis Unit 2.3 Protein Secondary Structure Prediction Unit 2.4 Internet Basics Unit 2.5 Sequence Similarity Searching Using the BLAST Family of Programs Unit 2.6 Protein Databases on the Internet Unit 2.7 Protein Tertiary Structure Prediction Unit 2.8 Protein Tertiary Structure Modeling Unit 2.9 Comparative Protein Structure Prediction Unit 2.10 Protein Charge Determination Unit 2.11 Basic Protein Sequence Analysis

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Unit 2.12 Minimotif Miner: A Computational Tool to Investigate Protein Function, Disease, and Genetic Diversity

Chapter 3 Detection and Assay Methods Introduction Unit 3.1 Spectrophotometric Determination of Protein Concentration Unit 3.2 Quantitative Amino Acid Analysis Unit 3.3 In Vitro Radiolabeling of Peptides and Proteins Unit 3.4 Assays for Determination of Protein Concentration Unit 3.5 Kinetic Assay Methods Unit 3.6 Biotinylation of Proteins in Solution and on Cell Surfaces Unit 3.7 Metabolic Labeling with Amino Acids Unit 3.8 Analysis of Selenocysteine-Containing Proteins Unit 3.9 Solid-Phase Profiling of Proteins

Chapter 4 Extraction, Stabilization, and Concentration Introduction Unit 4.1 Overview of Cell Fractionation Unit 4.2 Purification of Organelles from Mammalian Cells Unit 4.3 Subcellular Fractionation of Tissue Culture Cells Unit 4.4 Desalting, Concentration, and Buffer Exchange by Dialysis and Ultrafiltration Unit 4.5 Selective Precipitation of Proteins Unit 4.6 Long-Term Storage of Proteins Unit 4.7 Extraction of Proteins from Plant Tissues

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Chapter 5 Production of Recombinant Proteins Introduction Unit 5.1 Production of Recombinant Proteins in Escherichia coli Unit 5.2 Selection of Escherichia coli Expression Systems Unit 5.3 Fermentation and Growth of Escherichia coli for Optimal Protein Production Unit 5.4 Overview of the Baculovirus Expression System Unit 5.5 Protein Expression in the Baculovirus System Unit 5.6 Overview of Protein Expression in Saccharomyces cerevisiae Unit 5.7 Overview of Protein Expression in Pichia pastoris Unit 5.8 Culture of Yeast for the Production of Heterologous Proteins Unit 5.9 Overview of Protein Expression by Mammalian Cells Unit 5.10 Production of Recombinant Proteins in Mammalian Cells Unit 5.11 Overview of the Vaccinia Virus Expression System Unit 5.12 Preparation of Cell Cultures and Vaccinia Virus Stocks Unit 5.13 Generation of Recombinant Vaccinia Viruses Unit 5.14 Characterization of Recombinant Vaccinia Viruses and Their Products Unit 5.15 Gene Expression Using the Vaccinia Virus/ T7 RNA Polymerase Hybrid System Unit 5.16 Choice of Cellular Protein Expression System Unit 5.17 Use of the Gateway System for Protein Expression in Multiple Hosts Unit 5.18 Wheat Germ Cell-Free Expression System for Protein Production

Chapter 6 Purification of Recombinant Proteins Introduction Unit 6.1 Overview of the Purification of Recombinant Proteins Produced in Escherichia coli Unit 6.2 Preparation of Soluble Proteins from Escherichia coli

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Unit 6.3 Preparation and Extraction of Insoluble (Inclusion-Body) Proteins from Escherichia coli Unit 6.4 Overview of Protein Folding Unit 6.5 Folding and Purification of Insoluble (Inclusion Body) Proteins from Escherichia coli Unit 6.6 Expression and Purification of GST Fusion Proteins Unit 6.7 Expression and Purification of Thioredoxin Fusion Proteins Unit 6.8 Automated Large-Scale Purification of a Recombinant G-Protein-Coupled Neurotensin Receptor

Chapter 7 Characterization of Recombinant Proteins Introduction Unit 7.1 Overview of the Characterization of Recombinant Proteins Unit 7.2 Determining the Identity and Purity of Recombinant Proteins by UV Absorption Spectroscopy Unit 7.3 Determining the Identity and Structure of Recombinant Proteins Unit 7.4 Transverse Urea-Gradient Gel Electrophoresis Unit 7.5 Analytical Ultracentrifugation Unit 7.6 Determining the CD Spectrum of a Protein Unit 7.7 Determining the Fluorescence Spectrum of a Protein Unit 7.8 Light Scattering Unit 7.9 Measuring Protein Thermostability by Differential Scanning Calorimetry Unit 7.10 Characterizing Recombinant Proteins Using HPLC Gel Filtration and Mass Spectrometry Unit 7.11 Rapid Screening of E. coli Extracts by Heteronuclear NMR

Chapter 8 Conventional Chromatographic Separations Introduction Unit 8.1 Overview of Conventional Chromatography Unit 8.2 Ion-Exchange Chromatography

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Unit 8.3 Gel-Filtration Chromatography Unit 8.4 Hydrophobic-Interaction Chromatography Unit 8.5 Chromatofocusing Unit 8.6 Hydroxylapatite Chromatography Unit 8.7 HPLC of Peptides and Proteins Unit 8.8 Expanded-Bed Adsorption Chromatography

Chapter 9 Affinity Purification Introduction Unit 9.1 Lectin Affinity Chromatography Unit 9.2 Dye Affinity Chromatography Unit 9.3 Affinity Purification of Natural Ligands Unit 9.4 Metal-Chelate Affinity Chromatography Unit 9.5 Immunoaffinity Chromatography Unit 9.6 Purification of Sequence-Specific DNA-Binding Proteins by Affinity Chromatography Unit 9.7 Purification of DNA-Binding Proteins Using Biotin/Streptavidin Affinity Systems Unit 9.8 Immunoprecipitation Unit 9.9 Overview of Affinity Tags for Protein Purification

Chapter 10 Electrophoresis Introduction Unit 10.1 One-Dimensional SDS Gel Electrophoresis of Proteins Unit 10.2 One-Dimensional Isoelectric Focusing of Proteins in Slab Gels Unit 10.3 One-Dimensional Electrophoresis Using Nondenaturing Conditions Unit 10.4 Two-Dimensional Gel Electrophoresis

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Unit 10.5 Protein Detection in Gels Using Fixation Unit 10.6 Protein Detection in Gels Without Fixation Unit 10.7 Electroblotting from Polyacrylamide Gels Unit 10.8 Detection of Proteins on Blot Membranes Unit 10.9 Capillary Electrophoresis of Proteins and Peptides Unit 10.10 Immunoblot Detection Unit 10.11 Autoradiography Unit 10.12 Overview of Digital Electrophoresis Analysis Unit 10.13 Capillary Electrophoresis of Peptides and Proteins Using Isoelectric Buffers

Chapter 11 Chemical Analysis Introduction Unit 11.1 Enzymatic Digestion of Proteins in Solution Unit 11.2 Enzymatic Digestion of Proteins on PVDF Membranes Unit 11.3 Enzymatic Digestion of Proteins in Gels for Mass Spectrometric Identification and Structural Analysis Unit 11.4 Chemical Cleavage of Proteins in Solution Unit 11.5 Chemical Cleavage of Proteins on Membranes Unit 11.6 Reversed-Phase Isolation of Peptides Unit 11.7 Removal of N-Terminal Blocking Groups from Proteins Unit 11.8 C-Terminal Sequence Analysis Unit 11.9 Amino Acid Analysis Unit 11.10 N-Terminal Sequence Analysis of Proteins and Peptides Unit 11.11 Determination of Disulfide-Bond Linkages in Proteins

Chapter 12 Post-Translational Modification: Glycosylation Introduction

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Unit 12.1 Overview of Glycoconjugate Analysis Unit 12.2 Metabolic Radiolabeling of Animal Cell Glycoconjugates Unit 12.3 Inhibition of N-Linked Glycosylation Unit 12.4 Endoglycosidase and Glycoamidase Release of N-Linked Oligosaccharides Unit 12.5 Detection of Glycophospholipid Anchors on Proteins Unit 12.6 Determining the Structure of Oligosaccharides N- and O-Linked to Glycoproteins Unit 12.7 Determining the Structure of Glycan Moieties by Mass Spectrometry Unit 12.8 Detection and Analysis of Proteins Modified by O-Linked N-Acetylglucosamine

Chapter 13 Post-Translational Modification: Phosphorylation and Phosphatases Introduction Unit 13.1 Overview of Protein Phosphorylation Unit 13.2 Labeling Cultured Cells with 32Pi and Preparing Cell Lysates for Immunoprecipitation Unit 13.3 Phosphoamino Acid Analysis Unit 13.4 Detection of Phosphorylation by Immunological Techniques Unit 13.5 Detection of Phosphorylation by Enzymatic Techniques Unit 13.6 Preparation and Application of Polyclonal and Monoclonal Sequence-Specific AntiPhosphoamino Acid Antibodies Unit 13.7 Assays of Protein Kinases Using Exogenous Substrates Unit 13.8 Permeabilization Strategies to Study Protein Phosphorylation Unit 13.9 Phosphopeptide Mapping and Identification of Phosphorylation Sites Unit 13.10 Use of Protein Phosphatase Inhibitors

Chapter 14 Post-Translational Modification: Specialized Applications Introduction Unit 14.1 Analysis of Disulfide Bond Formation

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Unit 14.2 Analysis of Protein Acylation Unit 14.3 Analysis of Protein Prenylation and Carboxyl-Methylation Unit 14.4 Analysis of Oxidative Modification of Proteins Unit 14.5 Analysis of Protein Ubiquitination Unit 14.6 Analysis of Protein S-Nitrosylation Unit 14.7 Tyrosine O-Sulfation Unit 14.8 Analysis of Protein Sumoylation Unit 14.9 Identifying and Quantifying Sites of Protein Methylation by Heavy Methyl SILAC

Chapter 15 Chemical Modification of Proteins Introduction Unit 15.1 Modification of Cysteine Unit 15.2 Modification of Amino Groups

Chapter 16 Mass Spectrometry Introduction Unit 16.1 Overview of Peptide and Protein Analysis by Mass Spectrometry Unit 16.2 Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Analysis of Peptides Unit 16.3 Sample Preparation for MALDI Mass Analysis of Peptides and Proteins Unit 16.4 In-Gel Digestion of Proteins for MALDI-MS Fingerprint Mapping Unit 16.5 Searching Sequence Databases Over the Internet: Protein Identification Using MS-Fit Unit 16.6 Searching Sequence Databases Over the Internet: Protein Identification Using MS-Tag Unit 16.7 Enzymatic Approaches for Obtaining Amino Acid Sequence: On-Target Ladder Sequencing Unit 16.8 Introducing Samples Directly into Electrospray Ionization Mass Spectrometers Using a Nanospray Interface Unit 16.9 Introducing Samples Directly into Electrospray Ionization Mass Spectrometers Using Microscale Capillary Liquid Chromatography

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Unit 16.10 Protein Identification Using a Quadrupole Ion Trap Mass Spectrometer and SEQUEST Database Matching Unit 16.11 De Novo Peptide Sequencing via Manual Interpretation of MS/MS Spectra

Chapter 17 Structural Biology Introduction Unit 17.1 Overview of Protein Structural and Functional Folds Unit 17.2 Electron Microscopy and Image Processing: An Essential Tool for Structural Analysis of Macromolecules Unit 17.3 Principles of Macromolecular X-Ray Crystallography Unit 17.4 Crystallization of Macromolecules Unit 17.5 Introduction to NMR of Proteins Unit 17.6 Probing Protein Structure and Dynamics by Hydrogen Exchange–Mass Spectrometry Unit 17.7 Introduction to Atomic Force Microscopy (AFM) in Biology Unit 17.8 Raman Spectroscopy of Proteins Unit 17.9 Crystallization of Integral Membrane Proteins

Chapter 18 Preparation and Handling of Peptides Introduction Unit 18.1 Introduction to Peptide Synthesis Unit 18.2 Synthesis of Multiple Peptides on Plastic Pins Unit 18.3 Synthetic Peptides for Production of Antibodies that Recognize Intact Proteins Unit 18.4 Native Chemical Ligation of Polypeptides Unit 18.5 Synthesis and Application of Peptide Dendrimers As Protein Mimetics Unit 18.6 Disulfide Bond Formation in Peptides Unit 18.7 Guide for Resin and Linker Selection in Solid-Phase Peptide Synthesis

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Chapter 19 Identification of Protein Interactions Introduction Unit 19.1 Analysis of Protein-Protein Interactions Unit 19.2 Interaction Trap/Two-Hybrid System to Identify Interacting Proteins Unit 19.3 Phage-Based Expression Cloning to Identify Interacting Proteins Unit 19.4 Detection of Protein-Protein Interactions by Coprecipitation Unit 19.5 Imaging Protein-Protein Interactions by Fluorescence Resonance Energy Transfer (FRET) Microscopy Unit 19.6 High-Throughput Screening for Protein-Protein Interactions Using Yeast Two-Hybrid Arrays Unit 19.7 Identification of Protein Interactions by Far Western Analysis Unit 19.8 Scintillation Proximity Assay (SPA) Technology to Study Biomolecular Interactions Unit 19.9 Identifying Protein Interactions by Hydroxyl-Radical Protein Footprinting Unit 19.10 Visualization of Protein Interactions in Living Cells Using Bimolecular Fluorescence Complementation (BiFC) Analysis Unit 19.11 Production and Use of Trimeric Isoleucine Zipper Fusion Proteins to Study Surface Receptor Ligand Interactions Unit 19.12 Fluorescence Quenching Methods to Study Lipid-Protein Interactions Unit 19.13 Overview of Biacore Systems and Their Applications Unit 19.14 Using Biacore to Measure the Binding Kinetics of an Antibody-Antigen Interaction Unit 19.15 Identifying Small-Molecule Modulators of Protein-Protein Interactions Unit 19.16 Determination of Protein Contacts by Chemical Cross-Linking With EDC and Mass Spectrometry

Chapter 20 Quantitation of Protein Interactions Introduction Unit 20.1 Overview of the Quantitation of Protein Interactions Unit 20.2 Measuring Protein Interactions by Optical Biosensors Unit 20.3 Analytical Centrifugation: Equilibrium Approach

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Unit 20.4 Titration Microcalorimetry Unit 20.5 Reduced-Scale Large-Zone Analytical Gel-Filtration Chromatography for Measurement of Protein Association Equilibria Unit 20.6 Size-Exclusion Chromatography with On-Line Light Scattering Unit 20.7 Analytical Ultracentrifugation: Sedimentation Velocity Analysis Unit 20.8 Spectroscopic Methods for the Determination of Protein Interactions Unit 20.9 Application of Amide Proton Exchange Mass Spectrometry for the Study of Protein-Protein Interactions Unit 20.10 Circular Dichroism to Study Protein Interactions Unit 20.11 Quantitative Determination of Protein Stability and Ligand Binding by Pulse Proteolysis

Chapter 21 Peptidases Introduction Unit 21.1 Proteases Unit 21.2 Papain-like Cysteine Proteases Unit 21.3 Overview of Pepsin-like Aspartic Peptidases Unit 21.4 Metalloproteases Unit 21.5 Purification and Characterization of Proteasomes from Saccharomyces cerevisiae Unit 21.6 Purification of the Eukaryotic 20S Proteasome Unit 21.7 Serpins (Serine Protease Inhibitors) Unit 21.8 Caspases Unit 21.9 Use of GFP as a Reporter for the Analysis of Sequence-Specific Proteases Unit 21.10 An Overview of Serine Proteases Unit 21.11 Over-Expression and Purification of Active Serine Proteases and Their Variants from Escherichia coli Inclusion Bodies Unit 21.12 Assaying Proteases in Cellular Environments Unit 21.13 Expression, Purification, and Characterization of Caspases

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Unit 21.14 Expression, Purification, and Characterization of Aspartic Endopeptidases: Plasmodium Plasmepsins and “Short” Recombinant Human Pseudocathepsin Unit 21.15 Zymography of Metalloproteinases Unit 21.16 Monitoring Metalloproteinase Activity Using Synthetic Fluorogenic Substrates Unit 21.17 Applications for Chemical Probes of Proteolytic Activity

Chapter 22 Gel-Based Proteome Analysis Introduction Unit 22.1 Overview of Proteome Analysis Unit 22.2 Protein Profiling Using Two-Dimensional Difference Gel Electrophoresis (2-D DIGE) Unit 22.3 Laser Capture Microdissection for Proteome Analysis Unit 22.4 Preparing Protein Extracts for Quantitative Two-Dimensional Gel Comparison Unit 22.5 Isolation of Organelles and Prefractionation of Protein Extracts Using Free-Flow Electrophoresis Unit 22.6 Protein Profiling by Microscale Solution Isoelectrofocusing (MicroSol-IEF)

Chapter 23 Non-Gel-Based Proteome Analysis Introduction Unit 23.1 Analysis of Protein Composition Using Multidimensional Chromatography and Mass Spectrometry Unit 23.2 Quantitative Protein Profile Comparisons Using the Isotope-Coded Affinity Tag Method Unit 23.3 Proteomic Analysis Using 2-D Liquid Separations of Intact Proteins From Whole-Cell Lysates Unit 23.4 Quantitative Protein Analysis Using Proteolytic [18O]Water Labeling Unit 23.5 Protein Microarrays

Chapter 24 Targeted Proteomics Introduction Unit 24.1 Human Serum and Plasma Proteomics

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Unit 24.2 Organelle Proteomics Unit 24.3 Glycoproteomics Using Chemical Immobilization

Chapter 25 Proteome Bioinformatics Introduction Unit 25.1 Bioinformatics Analysis for Interactive Proteomics

Chapter 26 Protein Engineering Introduction Unit 26.1 Misincorporation Proton-Alkyl Exchange (MPAX): Engineering Cysteine Probes into Proteins Unit 26.2 Combinatorial Recombination of Gene Fragments to Construct a Library of Chimeras Unit 26.3 Incorporation of Isotopically Enriched Amino Acids

Appendix 1 Useful Data 1A Characteristics of Amino Acids 1B Commonly Used Detergents 1C Conversion Factors and Half-Life Information for Radioactivity 1D Common Conversion Factors

Appendix 2 Laboratory Guidelines, Equipment, and Stock Solutions 2A Laboratory Safety 2B Safe Use of Radioisotopes 2C Centrifuges and Rotors 2D Standard Laboratory Equipment 2E Commonly Used Reagents

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Appendix 3 Commonly Used Techniques 3A Use of Protein Folding Reagents 3B Dialysis 3C Techniques for Mammalian Cell Tissue Culture 3D Importing Biological Materials 3E Silanizing Glassware 3F Protein Precipitation Using Ammonium Sulfate 3G Statistics: Detecting Differences Among Groups 3H Analyzing Radioligand Binding Data

Appendix 4 Molecular Biology Techniques 4 Molecular Biology Techniques 4A Media Preparation and Bacteriological Tools 4B Growth in Liquid or Solid Media 4C Preparation of Plasmid DNA 4D Introduction of Plasmid DNA into Cells 4E Purification and Concentration of DNA from Aqueous Solutions 4F Agarose Gel Electrophoresis 4G Southern Blotting 4H Hybridization Analysis of DNA Blots 4I Digestion of DNA with Restriction Endonucleases 4J The Polymerase Chain Reaction 4K Quantitation of Nucleic Acids with Absorption Spectroscopy 4L Growth and Manipulation of Yeast

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Appendix 5 Biophysical Methods: Data Analysis 5A Theoretical Aspects of the Quantitative Characterization of Ligand Binding Suppliers Selected Suppliers of Reagents and Equipment

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Overview of Protein Purification and Characterization AIMS AND OBJECTIVES Protein purification has an over 200-year history: the first attempts at isolating substances from plants having similar properties to “egg albumen,” or egg white, were reported in 1789 by Fourcroy. Many proteins from plants were purified in the nineteenth century, though most would not be considered pure by modern standards. A century later, ovalbumin was the first crystalline protein obtained (by Hofmeister in 1889). The year 1989 may not go down in history as a milestone in protein chemistry, but since then there has been a resurgence of interest in proteins after more than a decade of gene excitement. The aims of protein purification, up until the 1940s, were simply academic. To then, even the basic facts of protein structure were not fully appreciated, and pure proteins were needed just to study structure and test the rival theories of the pre-DNA days. During the Second World War, an acute need for blood proteins led to development of the Cohn fractionation procedure for purification of albumin and other proteins from serum (Cohn et al., 1946). This was the inception of large-scale protein purifications for commercial purposes; Cohn fractionation continues to be used to this day. As more proteins, and particularly enzymes, were purified and crystallized, they started to be used increasingly in diagnostic assays and enzymatic analyses, as well as in the largescale food, tanning, and detergent industries. Many enzymes used in industry are not in fact very pure, but as long as they do the job, that is sufficient. “Process” enzymes such as α-amylase, proteases, and lipases are pro-duced in ton quantities, mainly as secretion products in bacterial cultures, and may undergo only limited purification processes to mini-mize costs. At the other extreme, enzyme products for research and analysis require a high degree of purification to ensure that contaminating activities do not interfere with the intended use. Anyone familiar with molecular biology enzymes will appreciate how minute levels of contamination of DNase or RNase can completely destroy carefully planned experiments. The 1960s and 1970s could be described as the peak years for protein and enzyme research, and most of the methods used in protein puri-

Contributed by R.K. Scopes Current Protocols in Protein Science (1995) 1.1.1-1.1.6 Copyright © 2000 by John Wiley & Sons, Inc.

fication were established by then, at least in their principles. More recent developments have been mainly in instrumentation designed to optimize the application of each methodology. Developments in instrumentation have been stimulated by the rapid progress in molecular biology, because gene isolation has often been preceded by isolation of the gene product. Because such products can now be characterized sufficiently (i.e., partially sequenced) using minute amounts of protein, the need for large-scale or even moderate-scale procedures has decreased. Hence there has been an explosive development of modern equipment designed specifically for dealing with amounts of protein in the milligram to microgram range. On the other hand, structural studies using X-ray crystallography and nuclear magnetic resonance (NMR) require hundreds of milligrams of pure protein, so larger-scale equipment and procedures are still needed in the research laboratory. The nature of the proteins studied has also changed substantially. Whereas enzymes were once the most favored subjects, they have now been superceded by nonenzymatic proteins such as growth factors, hormone receptors, viral antigens, and membrane transporters. Many of these occur in minute amounts in the natural source, and their purification can be a major task. Heroic efforts in the past have used kilogram quantities of rather unpleasant starting materials, such as human organs, and ended up with a few micrograms of pure product. It is now more usual, however, to take the genetic approach: clone the gene before the protein has been isolated or even properly identified, then express it in a suitable host cell culture or organism. The expression level may be orders of magnitude higher than in the original source, which will make purification a relatively simple task. It can be useful to know beforehand some physical properties of the protein, to facilitate the development of a suitable purification protocol from the recombinant source. On the other hand, there are now several ways of preparing fusion proteins, which can be purified by affinity techniques without any knowledge of the properties of the target protein. Moreover, there are ways of modifying the expressed product to simplify purification further.

UNIT 1.1

Strategies of Protein Purification and Characterization

1.1.1 CPPS

Overview of Protein Purification and Characterization

Thus the approach to protein purification must first take into account the reason it is being done, as the methods will vary greatly with different requirements. At one extreme is the one-of-a-kind purification, in a well-financed and equipped laboratory, that is carried out to obtain a small amount of product for sequencing so that gene isolation can proceed. In this case, expense of equipment and reagents may be no problem, and a very low overall recovery of product can be acceptable, provided it is pure enough. At the other extreme are the requirements of commercial production of a protein in large amounts on a continuing basis, where high recovery and economy of processing are the chief parameters to be considered. There are many intermediate situations as well. Many publications in the area of protein research are entitled “Purification and characterization of…,” and describe a purification procedure in sufficient detail that it can be reproduced in another laboratory. The characterization section may include structural, functional, and genetic information, and carrying out such studies is likely to require at least milligram quantities of pure protein. Ideally the purification should involve a small number of steps, with good recovery at each step. If the recovery is poor (<50% at any step), however, there should be some indication of what happened to the missing activity. Has it been discarded in the other fractions for the sake of purity, or does it represent a true loss of activity? If the latter, then the end-product may be less than fully active despite apparent homogeneity indicated by standard analysis. The choice between recovery and purification at each step can be problematical; taking a narrow cut of a chromatographic peak may provide a very pure fraction, at the expense of losing a good deal of less pure active component on either side. In making such decisions, the objective of the exercise must be kept in mind: if yield is not important, then the choice of poor yield for the sake of purity may be logical. By far the most important requirement of a publication is reproducibility of the method reported. It is not sufficient to have carried out the process only once if it is expected that other investigators will want to repeat it. There are always factors that influence the process that may be overlooked at first, and which if varied slightly can have a major effect on the purification procedure. The reported process should always be repeated exactly as described before submitting the manuscript for publication. There is one exception, namely, the case where

purification was conducted simply to obtain enough protein for sequencing and gene isolation; if that was achieved, there should be no need to provide instructions for repetition.

SOURCES OF MATERIAL FOR PROTEIN PURIFICATION For many people embarking on a protein purification project, there is no choice of material. They are studying a particular biological tissue or organism, and the objective is to purify a protein from that source. However, there may be approaches that can make the project simpler. If, for instance, the source is difficult to obtain in large amounts, it may be best to carry out at least preliminary trials on a source species more readily obtained. The most obvious and relevant example is when the species being studied is Homo sapiens, and tissue samples are not readily available for practical or ethical reasons, or both. In this case, it is usual to go to where mammalian tissue is readily available (i.e., an abattoir) and work with bovine, ovine, or porcine sources. Alternatively, if quantity of tissue is not a problem, the humble laboratory rat may suffice. Once a protocol for purifying the protein from substitute sources has been worked out, it will be much easier to develop one using human material—the identical procedure may work satisfactorily. Proteins differ to a fairly small extent between species that have diverged within about 100 million years, a time frame that groups together most higher mammals. Thus the behavior of proteins derived from different animals with respect to the various fractionation procedures is likely to be similar, and a protocol worked out for pig tissues is likely to need only minor adjustments for application to human tissues. A second example is where the interest is mainly on the function of a protein, especially an enzyme, for which functions and actions have generally been strongly conserved through evolution. In that case, a preliminary screening of potential sources, or, better still, the literature, should come up with a raw material that is best suited to the investigator’s purposes. Considerations should include the following: (1) What functions are required of the end product? For instance, an enzyme having a low Km may be needed, so selecting the source with the highest activity may not suffice. (2) How convenient is it to grow or obtain the raw material, and are there problems concerning pathogenicity or extractability? (3) Does the quantity of the protein vary with growth conditions or age, and does it deteriorate in situ

1.1.2 Current Protocols in Protein Science

if left too long? Obviously one requires a source that reliably produces the highest amount of the desired protein per unit volume to maximize the chances of developing a good purification procedure. (4) What storage conditions are required for the raw material? It is important to consider that fresh raw material may not be immediately available whenever a purification is attempted. The above considerations are relevant to the traditional situation for commencing a protein purification project. It is becoming increasingly common, however, for proteins to be purified as recombinant products using techniques in which the gene is expressed in a host organism or in cultured cells. This of course requires the gene encoding the protein of interest to be available. Until the mid-1980s, such material was usually obtained by hybridization of an oligonucleotide synthesized according to amino acid sequence information. This required the protein to have been purified first, so the initial task of protein purification still needed to be done at least once. More recently, genetic techniques have permitted the isolation of many genes encoding known proteins, even though the proteins may never have been studied directly. Moreover, with the expansion of the Human Genome Project and related DNA sequencing efforts, many genes for both known and unknown proteins will become available and will be able to be expressed in recombinant form without ever being purified from the host species. As a result some completely new considerations for protein purification come into play, including the possibility of modifying the gene structure not only to increase expression level and alter the protein product itself to enhance a desired function, but equally importantly to aid in purification. Recombinant proteins may be expressed in bacteria, yeasts, insect cells, and animal tissue cultures. Further details may be found in UNIT 1.2.

DETECTION AND ASSAY OF PROTEINS During a protein purification procedure there are two measurements that need to be made, preferably for each fraction. Measurements both of the total protein and of the amount (usually bioactivity) of the desired protein must be made. Details of the most commonly used assay methods are given in Chapter 3. It is not possible to isolate a protein without a method of determining whether it is present; an assay, either quantitative or at least

semiquantitative, indicating which fraction contains the most of the desired protein is essential. Assays may range from the quick-and-easy type (e.g., instantaneous spectrophotometric measurement of enzyme activity) to long and tedious bioassays that may take days to produce an answer. The latter situation is very difficult, because by the time one knows where the protein is, it may be “was,” owing to degradation or inactivation. Moreover, this may not become clear until the next step has been completed and its products assayed. Any assay that is quick is therefore advantageous, even if it means a sacrifice of accuracy for speed. Measurement of total protein is useful, as it indicates the degree of purification at each step. However, unless the next step critically depends on how much protein is present, total protein measurement is not extremely important: a small sample can be put aside and measured later, when the purification is complete. It is, however, very important to know how much protein is present in the final, presumed pure sample, as this will indicate the specific activity (if the protein has an activity), which can be compared with other preparations. The general object is to obtain as high a specific activity as possible (taking into account recovery considerations), which means retaining as much of the desired protein as possible while ending up with as little total protein as possible.

METHODS FOR SEPARATION AND PURIFICATION OF PROTEINS The methods available for protein purification range from simple precipitation procedures used since the nineteenth century to sophisticated chromatographic and affinity techniques that are constantly undergoing development and improvement. Methods can be classified in several alternative ways—perhaps one of the best is based on the properties of the proteins that are being exploited. Thus the methods can be divided into four distinct but interrelated groups depending on protein characteristics: surface features, size and shape, net charge, and bioproperties.

Methods Based on Surface Features of Proteins Surface features include charge distribution and accessibility, surface distribution of hydrophobic amino acid side chains, and, to a lesser extent, net charge at a given pH (see discussion of net charge). Methods exploiting surface fea-

Strategies of Protein Purification and Characterization

1.1.3 Current Protocols in Protein Science

tures mainly depend on solubility properties. Differences in solubility result in precipitation by various manipulations of the solvent in which the proteins are solubilized. Methods for obtaining an extract containing the desired protein in soluble form are given in Chapter 4. The solvent, nearly always water containing a low concentration of buffer salts, can be treated to alter properties such as ionic strength, dielectric constant, pH, temperature, and detergent content, any of which may selectively precipitate some of the proteins present. Conversely, proteins may be selectively solubilized from an insoluble state by manipulation of the solvent composition. The surface distribution of hydrophobic residues is an important determinant of solubility properties; it is also exploited in hydrophobic chromatography, both in the reversed-phase mode (UNIT 11.6) and in aqueousphase hydrophobic-interaction chromatography (UNIT 8.4). Also included in this group is the highly specific technique of immunoaffinity chromatography, in which an antibody directed against an epitope on the protein surface is used to pull out the desired protein from a mixture.

Methods Based on Whole Structure: Size and Shape Although the size and shape of proteins can have some influence on solubility properties, the chief method of exploiting these properties is gel-filtration chromatography (UNIT 8.3). In addition, preparative gel electrophoresis makes use of differences in molecular size. Proteins range in size from the smallest classified as proteins rather than polypeptides, around 5000 Da, up to macromolecular complexes of many million daltons. Many proteins in the bioactive state are oligomers of more than one polypeptide (see UNIT 1.2), and these can be dissociated, though normally with loss of overall structure. Thus many proteins have two “sizes”: that of the native state, and that (or those) of the polypeptides in the denatured and dissociated state. Gel-filtration procedures normally deal only with native proteins, whereas electrophoretic procedures commonly involve separation of dissociated and denatured polypeptides.

Methods Based on Net Charge

Overview of Protein Purification and Characterization

The two techniques that exploit the overall charge of proteins are ion-exchange chromatography (by far the most important) and electrophoresis (Chapter 10). Ion exchangers bind charged molecules, and there are essentially only two types of ion exchangers, anion and

cation. The net charge of a protein depends on the pH—positive at very low pH, negative at high pH, and zero at some specific point in between, termed the isoelectric point (pI). It should be stressed that at the pI a protein has a great many charges; it just happens that at this pH the total negatives exactly equal the total positives. The most charged state (disregarding the charge sign) is in the pH range 6.0 to 9.0. This is the most stable pH range for most proteins, as it encompasses common physiological pH values. Ion exchangers consist of immobilized charged groups and attract oppositely charged proteins. They provide the mode of separation that has the highest resolution for native proteins. High-performance reversedphase chromatography has equivalent or even better resolution, but it generally involves at least partial denaturation during adsorption and so is not recommended for sensitive proteins such as enzymes. Protein purification using ion-exchange chromatography has mainly employed positively charged anion exchangers, for the simple reason that the majority of proteins at neutral pH are negatively charged (i.e., have a low isoelectric point). Details of methodology are found in UNIT 8.2.

Methods Based on Bioproperties (Affinity) A powerful method for separating the desired protein from others is to use a biospecific method in which the particular biological property of the protein is exploited. The affinity approach is limited to proteins that have a specific binding property, except that proteins are theoretically able to be purified by immunoaffinity chromatography (UNIT 9.1), which is the most specific of all affinity techniques. Most proteins of interest do have a specific ligand: enzymes have substrates and cofactors, and hormone-binding proteins and receptor molecules are designed to bind specifically and tightly to particular hormones and other factors. Immobilization of the ligand to which the protein binds (or of antibody to the protein) enables selective adsorption of the desired protein in the technique known as affinity chromatography (Chapter 9). There are also nonchromatographic modes of exploiting biospecific interactions.

CHARACTERIZATION OF THE PROTEIN PRODUCT Once a pure protein is obtained, it may be employed for a specific purpose, such as enzymatic analysis (e.g., glucose oxidase and lac-

1.1.4 Current Protocols in Protein Science

tate dehydrogenase), or as a therapeutic agent (e.g., insulin and growth hormone). However, it is normal, when a protein has been isolated for the first time, to characterize it in terms of structure and function. Several features are generally expected in characterization of a new protein. These include molecular weight, or at least the size of the subunit(s), determined by SDS-polyacrylamide gel electrophoresis (Chapter 10) and/or gel filtration (UNIT 8.3). Spectral properties such as the UV spectrum (Trp and Tyr content), circular dichroism (CD) spectrum (secondary structure), and special characteristics of proteins with prosthetic groups (e.g., quantitation and spectra) may be presented. The quantity and nature of carbohydrates on glycoproteins should be determined (Chapter 12). Also, if the gene has not already been reported, some amino-terminal sequence analysis should be given, if at all possible, along with the results of a database search for similar sequences (UNIT 2.1). Functional proteins should be demonstrated to have the appropriate function, and for enzymes detailed kinetic characterization is appropriate. Ultimately the full three-dimensional structure of the protein may be determined, which will require crystals: any successful crystallization attempts should be reported.

THE PROTEIN PURIFICATION LABORATORY The requirements for a protein purification laboratory cannot be exactly formulated because they depend greatly on the types and amounts of proteins being isolated. To cover all eventualities, it would be necessary to have one set of equipment to deal with submicrogram quantities and another set to deal with multigram quantities—a range of around 108! One laboratory dedicated to protein purification may not need small-scale equipment if, for example, it works with plasma proteins that are always available in large quantities. Another may have all the latest in high-performance equipment but not be able (nor need) to handle quantities of protein in excess of a few milligrams. If it is assumed that neither extreme in quantity is to be attempted, and that the laboratory is handling a variety of protein types and sources, then certain basic pieces of equipment are needed. Obtaining the starting material and making an extract of it require homogenization equipment and centrifuges to remove insoluble residues. Preliminary fractionation, when starting with a crude extract of tissue or cells,

requires equipment and materials that will not become clogged by particulates. Adsorbents and similar materials used at the first step should be relatively inexpensive so that when performance falls off after a few uses, owing to intransigent impurity buildup, they can be discarded. It is also relevant that a larger amount is handled at the initial step than later steps; therefore, reagent expense can be an important consideration. After the first one or two steps, the sample should be sufficiently clean and clear to enable use of high-performance equipment. High-performance liquid chromatography, or HPLC, is a term with a variety of meanings. To some it refers exclusively to reversed-phase chromatography; to others it includes all sorts of chromatography provided that the equipment is fully automated and high-performance adsorbents are used. A high-performance system designed specifically for proteins—introduced by Pharmacia Biotech (see SUPPLIERS APPENDIX) called Fast Protein Liquid Chromatography, or FPLC—uses standard protein chromatographies such as ion exchange, hydrophobic interaction, and gel filtration. Scaleup is possible with larger equipment based on the FPLC design, so that laboratory development can be quickly translated to large-scale production. FPLC is designed to separate proteins in their native active configuration, whereas reversed-phase HPLC often causes at least transient denaturation during adsorption and elution. Reversed-phase HPLC has a high resolving power, but it is best suited to peptides and proteins smaller than ∼30 kDa. Chromatography run with older-style low-pressure adsorbents is sometimes referred to as “low-performance” or “open-column” chromatography; neither of those descriptions is necessarily accurate. Simple fraction collector and monitoring equipment is needed. This equipment will be used for larger-scale operations (tens of milligrams of protein and upward), probably at an earlier stage in the protocol than with HPLC. Various columns, both prepacked with proprietary adsorbents and empty for self-packing, will be needed, with the sizes and types depending on the scale of operations. Several anionexchange columns (different sizes), one or two cation-exchange columns, and gel-filtration media are essential, along with a range of alternative adsorbents such as hydrophobic interaction materials, dyes, hydroxyapatite, and chromatofocusing and specialist affinity media. Fully equipped protein purification laboratories should also have preparative electropho-

Strategies of Protein Purification and Characterization

1.1.5 Current Protocols in Protein Science

resis and isoelectric focusing apparatuses for rare occasions when other techniques fail to give sufficient separation. In addition to equipment used in the actual fractionation processes, a variety of other items are needed. In particular it should be possible to change buffers quickly and to concentrate protein solutions with ease. These operations require such things as dialysis membranes (APPENDIX 3B), ultrafiltration cells, and gel-exclusion columns of various sizes (UNIT 8.3). Finally, equipment for assaying and analyzing the preparations is needed. Most such equipment is fairly standard in biochemical laboratories and includes spectrophotometers, scintillation counters, analytical gel and capillary electrophoresis apparatuses, immunoblotting materials, and immunochemical reagents. A listing of standard equipment is found in APPENDIX 2D.

Literature Cited Cohn, E.J., Strong, L.E., Hughes, W.L., Mulford, D.J., Ashworth, J.N., Melin, M., and Taylor, H.L. 1946. Preparation and properties of serum and plasma proteins. IV. A system for the separation into fractions of the proteins and lipoprotein components of biological tissues and fluids. J. Am. Chem. Soc. 68:459-475.

Key References Deutscher, M.P. (ed.) 1990. Guide to protein purification. Methods Enzymol. 182:1-894. Extensive collection of purification methods with some general protocols and examples. Janson, J.-C. and Ryden, L.G. 1989. Protein Purification: Principles, High Resolution Methods, and Applications. VCH Publishers, New York. A useful collection of methods and examples. Kennedy, J.F. and Cabral, J.M. (eds.) 1993. Recovery Processes for Biological Materials. John Wiley & Sons, New York. A useful introduction to the problems of large-scale methods. Kenny, A. and Fowell, S. (eds.) 1992. Practical protein chromatography. Methods Mol. Biol. 11:1-327. Extensive descriptions of affinity chromatographic techniques with protocols and recipes. Scopes, R.K. 1993. Protein Purification, Principles and Practice, 3rd ed. Springer-Verlag, New York and Heidelberg. General principles of all the main techniques used in purifying proteins. A useful laboratory handbook; does not include recipes or procedures for specific proteins.

Contributed by R.K. Scopes La Trobe University Bundoora, Australia

Overview of Protein Purification and Characterization

1.1.6 Current Protocols in Protein Science

Strategies for Protein Purification CLASSIFICATION OF PROTEINS As with most heterogeneous collections of things, proteins can be classified in several different ways, such as by function, by structure, or by physicochemical characteristics. Each protein species consists of identical molecules with exactly the same size, amino acid sequence, and three-dimensional shape. In this way a solution of a mixture of proteins differs from a solution of synthetic polymers or sheared DNA, both of which contain a complete spectrum of possible sizes centered around the average. The protein mixture has only discrete sizes of molecules corresponding to each type of protein present. Although we could classify proteins by size, it would be of limited use, as there is usually no obvious relationship between size and function. A more useful structural classification takes into consideration shape and oligomeric structure (Table 1.2.1). In part, structure reflects biological location and origin. Simple, fairly rigid protein molecules occur in the extracellular environment, more complex and readily deactivated molecules are found intracellularly, and hydrophobic proteins are associated with membranes. Classification by function is more relevant (Table 1.2.2). Proteins can be simply stores of amino acids, can be structural, or can have specific binding functions. The most “funcTable 1.2.1

UNIT 1.2

tional” proteins are enzymes, which have both binding and catalytic roles. In part, this reflects the degree to which the detailed structure is a requirement for the protein’s function, which in turn relates to conservation of structure through evolution. But as with every attempt at classification, there are going to be examples that do not fit the pattern well. Most proteins of interest to the pharmaceutical industry belong to the general class of binding proteins, for instance, hormones [e.g., insulin and bovine somatostatin (BST)], viral antigens (e.g., hepatitis B antigen), growth factors [e.g., interferons, interleukins and colony-stimulating factors (CSFs)], and antibodies.

STRATEGIES FOR PROTEIN PURIFICATION Soluble Extracellular Proteins The source of soluble extracellular proteins is the extracellular medium, whether it be an animal source such as blood or spinal fluid, or a culture medium in which bacterial, fungal, animal, or plant cultures have been grown. Generally these do not contain a large number of different proteins (blood is an exception), and the desired protein may be a major component, especially if produced as the result of recombinant expression. Nonetheless, the protein in the starting material may be quite dilute,

Classification of Proteins by Structural Characteristics

Structural characteristic

Examples

Comments

Monomeric

Lysozyme, growth hormone

Usually extracellular; often have disulfide bonds

Glyceraldehyde-3-phosphate dehydrogenase, catalase, alcohol dehydrogenase, hexokinase Aspartate carbamoyltransferase, pertussis toxin

Mostly intracellular enzymes; rarely have disulfide bonds

Mitochondrial ATPase, alkaline phosphatase

Readily solubilized by detergents

Integral

Porins, cytochromes P450, insulin receptor

Require lipid for stability

Conjugated

Glycoproteins, lipoproteins, nucleoproteins

Many extracellular proteins contain carbohydrate

Oligomeric Identical subunits

Mixed subunits Membrane-bound Peripheral

Contributed by R.K. Scopes Current Protocols in Protein Science (1995) 1.2.1-1.2.4 Copyright © 2000 by John Wiley & Sons, Inc.

Allosteric enzymes; different subunits have separate functions

Strategies of Protein Purification and Characterization

1.2.1 CPPS

Table 1.2.2

Classification of Proteins by Function

Function

Examples

Amino acid storage

Seed proteins (e.g., gluten), milk proteins (e.g., casein)

Structural Inert With activity Binding Soluble Insoluble With activity

Collagen, keratin Actin, myosin, tubulin Albumin, hemoglobin, hormones Surface receptors (e.g., insulin receptor), antigens (e.g., viral coat proteins) Enzymes, membrane transporters (e.g., amino acid uptake systems, ion pumps)

and a large volume may therefore need to be processed. The starting fluid may also contain many compounds other than proteins, whose behavior must be taken into account. The first stage should aim mainly to reduce the volume and get rid of as much nonprotein material as possible; some protein-protein separation is also useful, but not essential. No general rules can be given, but a batch adsorption method using an inexpensive material such as hydroxyapatite, ion-exchange resin, immobilized metal affinity chromatography (IMAC) medium, or affinity adsorbent is best, if feasible. Following the first step, the sample should be in a form that is amenable to standard purification processes such as precipitation and column chromatography (Chapter 8).

Intracellular (Cytoplasmic) Proteins

Strategies for Protein Purification

To obtain soluble intracellular proteins (which are mainly enzymes), cells must be broken open or lysed to release their soluble contents. The ease with which cell disruption can be accomplished varies considerably; animal cells are readily broken, as are many bacteria, but plants and fungi have tough cell walls. Methods for obtaining cell extracts are given in Chapter 4. The macromolecular soluble contents of cells are mainly proteins, with nucleic acids as a minor but significant component. Bacterial extracts may be viscous unless DNase is added to break down the long DNA molecules. Although chromatographic procedures can be applied to crude extracts, valuable highperformance materials should not be employed in the first step, as there are always compounds, including unstable proteins, that may bind to them and be difficult to remove.

Membrane-Associated Proteins There are two approaches to isolating a membrane-associated protein. In one method, the relevant membrane fraction can first be prepared and then used to isolate the protein. Alternatively, whole tissue can be subjected to an extraction that solubilizes the membranes and releases the cytoplasmic contents as well. The former is much better in that purification is accomplished by isolating the membranes: the specific activity of the solubilized membrane fraction will be much higher than in the second method. However, the process of purifying the membrane fraction may lead to substantial losses, and it may be difficult to scale up. If total recovery of the protein is more important than purity, a whole-tissue extract is likely to be more appropriate. Although this means that a greater degree of purification is needed, the fact that membrane proteins have, by definition, properties somewhat different from those of cytoplasmic proteins permits some very effective purification steps (e.g., hydrophobic chromatography or fractional solubility separation). Peripheral membrane proteins are only loosely attached and may be released by gentle conditions such as high pH, EDTA, or low (nonionic) detergent concentrations. Once in solution, some peripheral proteins no longer require the presence of detergent to maintain their solubility. Integral membrane proteins are much more difficult—they require high concentrations of detergent for solubilization (i.e., complete solubilization of the membrane is needed to release them) and generally are neither soluble nor stable in the absence of detergent. It is sometimes necessary to maintain

1.2.2 Current Protocols in Protein Science

natural phospholipids in association with the proteins in order to maintain activity. Even though the final objective may not require activity (e.g., amino acid sequencing), there is a need for some sort of assay during the purification process to determine where the protein is. If a particular band on a gel is known to be the desired protein, then no other assay is needed and loss of bioactivity can be allowed. Purification processes may be affected by the presence of detergents. The problem of association with detergent micelles makes purifying integral membrane proteins difficult; the close association of the different proteins originating from membranes often results in very poor separation in conventional fractionation procedures.

Insoluble Proteins Natural proteins that are insoluble in normal solvents are generally structural proteins, which are sometimes cross-linked by posttranslational modification. The first stage of purification is obvious—it involves extracting and washing away all proteins that are soluble, leaving the residue containing the desired material. Further purification in a native state, however, may be impossible; extracting away other proteins using more vigorous solvents or attempting to solubilize the target protein may destroy the natural structure. Cross-linked proteins such as elastin or aged collagen cannot be dissolved without breaking the cross-links, and the individual proteins may even be crosslinked together.

Insoluble Recombinant Proteins (Inclusion Bodies) A major new class of insoluble proteins are recombinant proteins expressed (usually in Escherichia coli) as inclusion bodies. These are dense aggregates found inside cells that consist mainly of a desired recombinant product, but in a nonnative state. Inclusion bodies may form for a variety of reasons, such as insolubility of the product at the concentrations being produced, inability to fold correctly in the bacterial environment, or inability to form correct, or any, disulfide bonds in the reducing intracellular environment. Their purification is simple, since the inclusion bodies can be separated by differential centrifugation from other cellular constituents, giving almost pure product; the problem is that the protein is not in a native state, and is insoluble. Some methods for obtaining an active product from inclusion bodies are described in UNIT 6.3.

Soluble Recombinant Proteins Recombinant proteins that are not expressed in inclusion bodies either will be soluble inside the cell or, if using an excretion vector, will be extracellular (or, if E. coli is the host, possibly periplasmic). They can be purified by conventional means. In some systems, expression is so good that the desired product is the major protein present and its purification is relatively simple. In systems where the expression level is low, the purification process can be tedious, though easier, it is hoped, than isolation from the natural source. It should be remembered that a procedure developed for isolating a protein from natural sources may not work successfully with the recombinant product, because the nature of the other proteins present influences many fractionation procedures. Because of the difficulties often experienced in purifying recombinant proteins, a variety of vector systems (see UNIT 6.1 and Sassenfeld, 1990, for some examples) have been developed in which the expressed prod-uct is a fusion protein containing an N-terminal polypeptide that simplifies purification. Such “tags” can be subsequently removed using a specific protease. A further advantage is that the expression level is dictated mainly by the transcription and translation signals for the fusion portion of the protein, which are optimized. Tags used include proteins and polypeptides for which there is a specific anti- body, binding proteins that will interact with columns containing a specific ligand, polyhistidine tags with affinity to immobilized metal columns, sequences that result in biotinylation by the host and enable purification on an avidin column, and sequences that confer insolubility under specified conditions (UNIT 6.1 & UNIT 6.5). Unstable proteins may be modified by the molecular biological technique of site-directed mutagenesis to remove the site of instability— for instance, an oxidizable cysteine. Such techniques are appropriate for commercial production of proteins, but may of course alter natural functioning parameters. Increased thermostability can be one modification, although it is not easy to predict mutations that will improve that parameter. Thermostable proteins originating from thermophilic bacteria do not need structural modification and, if expressed in large amounts, can be purified satisfactorily in one step by simply heat-treating the extract at 70°C for 30 min, which denatures virtually all the host proteins (e.g., see Oka et al., 1989). The host bacteria used for production of recombinant proteins are usually Escherichia

Strategies of Protein Purification and Characterization

1.2.3 Current Protocols in Protein Science

coli, or Bacillus subtilis; they may express proteins at 1% to over 50% of the cellular protein, depending on such variables as the source, promoter structure, and vector type. Generally the proteins are expressed intracellularly, but leader sequences for excretion may be included. In the latter case, the protein is generally excreted into the periplasmic space, which limits the amount that can be produced. Excretion from gram-positive species such as B. subtilis sends the product into the culture medium, with little feedback limitation on total expression level.

Literature Cited Oka, M., Yang, Y.S., Nagata, S., Esaki, N., Tanaka, M., and Soda, K. 1989. Overproduction of thermostable leucine dehydrogenase of Bacillus stearothermophilus and its one-step purification from recombinant cells of Escherichia coli. Biotechnol. Appl. Biochem. 11:307-316. Sassenfeld, H.M. 1990. Engineering proteins for purification. Trends Biotechnol. 8:88-93.

Key References Deutscher, M.P. (ed.) 1990. Guide to protein purification. Methods Enzymol. 182:1-894.

Janson, J.-C. and Ryden, L.G. 1989. Protein Purification: Principles, High Resolution Methods, and Applications. VCH Publishers, New York. A useful collection of methods and examples. Kennedy, J.F. and Cabral, J.M. (eds.) 1993. Recovery Processes for Biological Materials. John Wiley & Sons, New York. A useful introduction to the problems of large-scale methods. Kenny, A. and Fowell, S. (eds.) 1992. Practical protein chromatography. Methods Mol. Biol. 11:1-327. Extensive descriptions of affinity chromatographic techniques with protocols and recipes. Scopes, R.K. 1993. Protein Purification, Principles and Practice, 3rd ed. Springer-Verlag, New York and Heidelberg. General principles of all the main techniques used in purifying proteins. A useful laboratory handbook; does not include recipes or procedures for specific proteins.

Contributed by R.K. Scopes La Trobe University Bundoora, Australia

Extensive collection of purification methods with some general protocols and examples.

Strategies for Protein Purification

1.2.4 Current Protocols in Protein Science

Protein Purification Flow Charts Protein purification flow charts are presented to give a broad outline of the methods used for different types of proteins. They cannot give any detail, as the process appropriate for each protein will have its own variations at each stage. In most cases, the first stage is to obtain a solution containing the desired protein, after which it can be dealt with by the many separation techniques described in the following chapters. In some cases the insolubility of the desired protein can be exploited by removing soluble fractions. Purification procedures are commonly divided into three stages: (a) the primary steps, which deal with crude mixtures of proteins and other molecules present in the raw material; (b) the secondary processing, which generates a product near to homogeneity; and (c) the polishing steps, which remove minor contaminants, a process that is especially important for therapeutic proteins.

SOLUBLE RECOMBINANT PROTEINS Proteins expressed in a recombinant manner may be (1) soluble in the cytoplasm, (2) insoluble as inclusion bodies (see section on Insoluble Recombinant Proteins), (3) excreted from the cells into the culture medium, (4) excreted into the periplasmic space (e.g., in gram-negative bacteria), or (5) associated with organelles or membrane fractions. In addition they may be expressed (6) as the normal, mature, naturally occurring protein, (7) containing a natural leader peptide that would normally be processed, (8) as a fusion protein with a peptide that is not natural to the protein, or (9) lacking glycosylation or other post-translational modification, or incorrectly modified. Possibilities (1) to (5) affect the method of extraction used to obtain the starting material for purification. Cases (6) to (9) can affect the methods used for purification. The scheme for purifying soluble recombinant proteins is outlined in Figure 1.3.1. The first stage is to obtain a clarified solution containing the desired protein, with as little in the way of unwanted proteins as possible. For soluble cytoplasmic proteins, case (1), it is not normally possible to exclude any significant amount of unwanted soluble proteins, but in cases (2) to (5) the compartmentalization away from the cytoplasm allows such separation in the initial stage. It may be necessary to carry out a concenContributed by R.K. Scopes Current Protocols in Protein Science (1995) 1.3.1-1.3.7 Copyright © 2000 by John Wiley & Sons, Inc.

UNIT 1.3

tration step before proceeding, especially if the protein has been excreted into the culture medium. Normally ultrafiltration is used, although other techniques are possible, especially if the extract contains particulates that block ultrafiltration membranes. Recombinant expression in the cytoplasm of bacteria, followed by extraction via total cell disruption, results in large amounts of nucleic acids being solubilized with the protein. A number of treatments to remove nucleic acids are possible. Streptomycin is used to precipitate ribosomal material, and cationic polymers such as protamine (a basic protein) and polyethylenimine will form insoluble complexes (at low ionic strength) with nucleic acids. In addition, viscosity caused by DNA can be reduced by adding small amounts of DNase.

INSOLUBLE RECOMBINANT PROTEINS It has been found that many proteins expressed in bacteria (mainly in Escherichia coli) do not fold correctly, and as a result aggregation occurs, leading to large insoluble inclusion bodies within the cytoplasm of the cells (see Chapter 6). Although this creates major difficulties in obtaining satisfactory amounts of active native product, it greatly simplifies the initial stage of purification. The purification scheme for recombinant insoluble proteins is outlined in Figure 1.3.2. After cell disruption, inclusion bodies can be obtained in a fairly pure state by differential centrifugation. They must now be solubilized, however, and the active protein generated by encouraging correct folding. Solubilization is usually accomplished with guanidine hydrochloride and/or urea, and thiols such as 2-mercaptoethanol or glutathione are included to disrupt any disulfides that have formed and prevent more from forming. Folding the protein correctly may require a variety of additions to the solution, as well as slow removal of the denaturant. The latter can be carried out by simple dilution or by dialysis. Folding occurs best at low protein concentrations, so dilution may be adequate. If the native protein does contain disulfides, then it is important to create redox conditions such that some (but not excessive) oxidation of thiols can occur. A combination of oxidized and reduced glutathione is commonly used. In addition, the action of the enzyme protein disulfide isomerase, which can

Strategies of Protein Purification and Characterization

1.3.1 CPPS

cells with protein soluble in cytoplasm

cells with protein excreted to medium

cells with protein in periplasmic space

cells with protein organelleassociated

break up cells; centrifuge or ultrafilter to remove insolubles

centrifuge or filter to remove cells

gently treat with lysozyme to minimize cell lysis

separate organelles; perform differential centrifugation

treat to remove nucleic acids

remove protoplasts and cell debris

extract to solubilize proteins, remove insolubles

perform concentration step

starting material: soluble fraction containing the protein

perform purification steps use salt fractionation, ion-exchange chromatography, hydrophobic chromatography, affinity and pseudoaffinity chromatography, gel filtration, or other less conventional steps (i.e., preparative electrophoresis)

for tagged fusion protein, use affinity column chromatography and polishing steps

purified protein

Figure 1.3.1 Purification scheme for soluble recombinant proteins, which may be excreted or located in the periplasm, in the membrane fraction, or most commonly the cytoplasm. The first step is to obtain an extract containing the desired protein in soluble form. After this, conventional purification steps may be carried out, or affinity purification of tagged fused proteins can be performed.

Protein Purification Flow Charts

make and unmake disulfides by exchange reactions, has been found to be beneficial in many cases. If the native protein is of intracellular origin, it probably will not contain disulfides; it will, however, contain cysteines, so a full reducing potential should be maintained. Specific methodology is discussed in UNIT 6.5. Not all proteins can fold unassisted by other cellular components. Chaperonins are proteins whose role is to refold denatured proteins such as those that form during heat shock (Zeilstra-

Ryalls et al., 1991). The most studied, and just becoming commercially available as of 1995, are the E. coli chaperonins GroEL and GroES, both of which are needed, together with ATP, to renature many proteins. Proline residues can adopt two isomeric conformations in proteins, and the wrong conformation is switched to the correct one by the enzyme prolyl isomerase, aiding the process of protein folding. At present these are not large-scale prospects, both because of the cost of the chap-

1.3.2 Current Protocols in Protein Science

cells containing protein

disrupt cells perform differential centrifugation

wash inclusion bodies dissolve in denaturing agents

dilute with buffer or dialyze to dilute denaturing agents add appropriate reducing agents and/or folding factors concentrate perform purification procedure: remove unwanted proteins and incorrectly folded species (e.g., by ion-exchange chromatography, immunoaffinity methods, gel filtration)

purified protein

Figure 1.3.2 Purification scheme for insoluble recombinant proteins that are produced as inclusion bodies in the cytoplasm of host cells. The cells must be broken open, and then the insoluble inclusion bodies are separated by differential centrifugation. Solubilization is achieved by the use of denaturing solvents, and renaturation of the dissolved protein occurs on removal of the denaturant. Further polishing steps will be needed to remove small amounts of contaminating proteins as well as incorrectly folded species. Additional information can be found in UNIT 6.3-6.5.

eronins and because the agents operate best in vitro at very low protein concentrations. Once the proteins are folded, the purification process consists of removing small amounts of still incorrectly folded protein plus any other host proteins that were trapped with the original inclusion bodies. The former may be difficult, since incorrectly folded species have a size and charge similar to those of the correct product. However, subtle differences arising from the folded conformation can be exploited by chromatographic techniques. In ideal cases immunoaffinity techniques using antibodies specific for either the incorrectly folded form or the correct one can be used to resolve the mixture.

SOLUBLE NONRECOMBINANT PROTEINS There are so many sources of soluble proteins that it is not possible to give a complete overview of methods used to obtain starting extracts from which a desired protein can be isolated. The sources can be classified as either microorganisms, plants, or animals, as shown in Figure 1.3.3, but these in turn should be subdivided according to how the starting extract is obtained. In particular there is a distinction between extracellular and intracellular proteins. With the latter it is necessary to disrupt the cells and release the proteins, whereas with the former, if the extracellular fluid can be obtained directly, there need be no contamination with intracellular proteins. Extracellular

Strategies of Protein Purification and Characterization

1.3.3 Current Protocols in Protein Science

microorganisms

plants

animals

disrupt cells, e.g., using French press, bead mill, or sonication with 5-10 vol buffer

disrupt cells, e.g., using French press or by grinding with sand using 1-2 vol buffer

homogenize with 2-5 vol buffer or mince and stir with buffer

treat to remove nucleic acids

treat to remove phenols

centrifuge

starting extract (5-20 mg/ml protein)

extracellular proteins

perform initial fractionation: use salt fractionation, precipitation with organic solvents, affinity methods, and/or two-phase partitioning perform secondary fractionation: use ion-exchange chromatography, hydrophobic chromatography, affinity methods, other adsorbents, and/or gel filtration perform polishing step: use HPLC– reversed phase, HPLC– ion exchange, or isoelectric focusing

purified protein

Figure 1.3.3 Purification scheme for soluble proteins present in their natural host cells. Cells must be disrupted to release the proteins, usually in the presence of 2 to 10 ml of a suitable buffer per gram weight. After removal of insoluble material, the process will generally require several steps, using various standard fractionation procedures in a suitable order. For production of highly pure protein, a final polishing step may be required to remove final trace contaminants. Additional information can be found in UNIT 6.2.

Protein Purification Flow Charts

sources include microorganism culture medium, plant and animal tissue culture medium, venoms, milk, blood, and cerebrospinal fluids. Soluble proteins may also occur within organelles such as mitochondria; these may be best obtained by first isolating the organelle, then disrupting it to release the contents. The starting extract normally contains be-

tween 5 and 20 mg protein per milliliter, though lesser concentrations can be dealt with, especially if working on a small scale. It may be necessary to include a concentration step before starting the purification process in order to approach that level. There are exceptions to every rule, however, and very high protein concentrations can be handled, for example,

1.3.4 Current Protocols in Protein Science

whole tissue homogenize without detergent; centrifuge

supernatant

disrupt cells; gently isolate organelles

residue homogenize with detergent; centrifuge

supernatant containing membrane-associated proteins perform primary fractionation: use hydrophobic chromatography, precipitation with organic solvents, ion-exchange chromatography, and/or phase partitioning perform secondary fractionation: use ion-exchange chromatography, affinity methods, and/or gel filtration

residue containing insoluble proteins

suspend and extract with other solvents (e.g., ethanol, urea, or SDS)

purified protein

insoluble residue

perform polishing step: use HPLC or isoelectric focusing

purified protein

Figure 1.3.4 Purification scheme for membrane-associated and poorly soluble proteins (nonrecombinant). An initial purification can be achieved by isolation of organelles containing the desired protein. Membrane proteins are normally solubilized with a nonionic detergent, although loosely associated proteins may be extracted without detergent at high pH, with EDTA, or with small amounts of an organic solvent such as N-butanol. Normal fractionation procedures may need some modification if the detergent is required throughout to maintain the integrity of the protein.

with two-phase partitioning (Walter and Johansson, 1994). When isolating proteins on a large scale, the volumes being manipulated become of increasing concern, so maximizing protein concentration can be an important aim. The starting extract should be clarified, usually by centrifugation; on a large scale, ultrafiltration methods are becoming more widely used. Pretreatment of certain extracts to remove ex-

cessive amounts of nucleic acids, phenolics, and lipids may be necessary in order to obtain an extract that is amenable to standard fractionation procedures. Fractionation procedures can somewhat arbitrarily be divided into three steps: initial fractionation, secondary fractionation, and polishing. Initial processing, which deals with a large amount of extract that is not all protein, may

Strategies of Protein Purification and Characterization

1.3.5 Current Protocols in Protein Science

involve materials that become soiled and unable to be used many times. Consequently, preferred methods do not require expensive reagents or adsorbents such as are used in highperformance liquid chromatography (HPLC). Classic salt fractionation and the less-used organic solvent fractionation can achieve, if not a high degree of purification, a useful level of concentration and removal of much unwanted nonproteinaceous material. Alternatively, a highly selective affinity procedure may be used as the first step, but only if the affinity material is inexpensive to make and/or the extract is a simple, clear solution, as opposed to a turbid whole-cell homogenate. Secondary processing achieves the main purification, and may involve two or more steps in difficult situations. Ion-exchange and hydrophobic-interaction chromatography, gel filtration, and affinity techniques (see Chapter 8 and Chapter 9) are the main procedures. Finally, it may be necessary to remove traces of contaminants by “polishing,” using high-resolution procedures such as reversed-phase HPLC (UNIT 11.6) and isoelectric focusing (IEF; UNIT 10.2 & UNIT 10.4). Because every protein has unique characteristics, it is impossible to make general statements about procedures to be followed.

MEMBRANE-ASSOCIATED AND INSOLUBLE NONRECOMBINANT PROTEINS

Protein Purification Flow Charts

Proteins that are not physiologically soluble can be purified after extracting and removing soluble proteins, thereby achieving a substantial degree of purification at the extraction step (Fig. 1.3.4; also see UNITS 6.1 & 6.2). To carry out a purification it is nearly always necessary to obtain the desired protein in a soluble form, which will often require the addition of solubilizing agents such as detergents. Some proteins remain insoluble even with detergent treatment, and so can be substantially purified by removing the soluble fractions. Some membrane-associated proteins become partly solubilized during breaking up of the tissue, and recovery in the particulate fraction may be poor. In such cases it may be best to solubilize the whole tissue by including detergent in the homogenizing buffer. Extraction of insoluble residues using detergents can be done differentially; some proteins are released at low detergent concentration, whereas others require complete solubilization of the membrane fraction. Suitable detergents include nonionic (e.g., Triton) and weakly acidic types (e.g., cholic acid deriva-

tives). Strongly acidic detergents such as sulfate esters (e.g., sodium dodecyl sulfate) usually result in denaturation. Detergents can be removed either by adsorption of the protein on a column, and subsequent elution without detergent, by use of special detergent-adsorbing beads, or even by extraction with nonmiscible organic solvents in which the detergent partitions. On the other hand, many membrane proteins require the presence of detergent at all times in order to remain in solution and in a native conformation. These include most integral membrane proteins, for example, cytochrome: P450, transmembrane receptors, and transporters. The most sensitive proteins require a particular combination of natural lipids (in addition to the detergent) to maintain structural integrity. Purification methods include most of those used for soluble proteins, but some techniques are not recommended if detergent is needed at all times. For instance, ammonium sulfate precipitation will often cause a detergent-protein complex to come out of solution and float rather than sink on centrifugation—this can be useful, but the “floatate,” when redissolved, may have a high detergent content. Hydrophobic chromatography can be very useful, as membrane proteins are naturally hydrophobic. Integral membrane proteins that are completely insoluble in normal detergents may be solubilized by denaturation using compounds such as sodium dodecyl sulfate and guanidine hydrochloride. Some cross-linked proteins such as elastin are not soluble without disruption of the covalent linkages.

Literature Cited Walter, H. and Johansson, G. (eds.) 1994. Aqueous two-phase systems. Methods Enzymol. 228:1725. Zeilstra-Ryalls, J., Fayet, O., and Georgopoulos, C. 1991. The universally-conserved GroE (Hsp60) chaperonins. Annu. Rev. Microbiol. 45:301-325.

Key References Deutscher, M.P. (ed.) 1990. Guide to protein purification. Methods Enzymol. 182:1-894. Extensive collection of purification methods, with some general protocols and examples. Janson, J.-C. and Ryden, L.G. 1989. Protein Purification: Principles, High Resolution Methods, and Applications. VCH Publishers, New York. A useful collection of methods and examples.

1.3.6 Current Protocols in Protein Science

Kennedy, J.F. and Cabral, J.M. (eds.) 1993. Recovery Processes for Biological Materials. John Wiley & Sons, New York. A useful introduction to the problems of large-scale methods. Kenny, A. and Fowell, S. (eds.) 1992. Practical protein chromatography. Methods Mol. Biol. 11:1-327. Extensive descriptions of affinity chromatographic techniques with protocols and recipes.

Scopes, R.K. 1993. Protein Purification, Principles and Practice, 3rd ed. Springer-Verlag, New York and Heidelberg. General principles of all the main techniques used in purifying proteins. A useful laboratory handbook; does not include recipes or procedures for specific proteins.

Contributed by R. K. Scopes La Trobe University Bundoora, Australia

Strategies of Protein Purification and Characterization

1.3.7 Current Protocols in Protein Science

Purification of Glutamate Dehydrogenase from Liver and Brain

UNIT 1.4

Because of the wide variety of procedures that may be used to purify any given enzyme, it is not possible to provide a single protocol that includes them all. In this unit two protocols for the purification of glutamate dehydrogenase (GDH)—also referred to as + L-glutamate-NAD(P) oxidoreductase (deaminating; EC 1.4.1.3:GDH)—have been selected as examples because they present two very different approaches to the purification of the enzyme. In the first (see Basic Protocol 1), affinity chromatography using a column consisting of the allosteric inhibitor guanosine 5′-triphosphate (GTP) bound to Sepharose is described. Support protocols detail the preparation of the GTP-Sepharose needed for this procedure (see Support Protocol 1), assessment of incorporation of GTP into the GTP-Sepharose gel, if necessary (see Support Protocol 2), the activation of Sepharose 4B with CNBr, if necessary (see Support Protocol 3), and reuse and equilibration of the chromatography media (see Support Protocols 4 and 5). An alternative purification procedure (see Alternate Protocol) uses a bifunctional ligand composed of two NAD+ molecules linked together by a spacer arm to precipitate the enzyme. Protocols for the synthesis of the bis-NAD compound (see Support Protocol 7), resolution of NAD+ derivatives (see Support Protocol 8), and instructions for a pilot affinity study (see Support Protocol 9) complement this alternative approach. Of value to both studies are methods for concentrating the protein product (see Support Protocol 6), determining the protein concentration (see Basic Protocol 2), and assaying for GDH activity (see Basic Protocol 3). Although some of these techniques can be found elsewhere in this manual, instructions are included in this unit that are specific to the systems mentioned here. As discussed below, some tissues, including the brain but not the liver, contain an additional isoenzyme of glutamate dehydrogenase (GLUD2) as a relatively minor component. The procedures described here are for purifying the major form (GLUD1). Since this unit is concerned with the purification of mammalian glutamate dehydrogenase, a detailed review of its properties and functions is not appropriate. However, some of the essential properties are briefly summarized below. Further details can be found in the references cited. References to earlier work on the enzyme can be found in Tipton and Couée (1988) and are not repeated here. GDH catalyzes the reversible oxidative deamination of glutamate to 2-oxoglutarate (α-ketoglutarate). Either NAD+ or NADP+ can serve as cofactor, although the kinetic and regulatory behavior depend on which is used. The native enzyme is a hexamer of six identical subunits, each consisting of 505 amino acids, with approximate relative molecular mass of 56,700. However, at high protein concentrations, the hexamers may aggregate further to form high-molecular-weight polymers. The crystal structure of the enzyme has been recently determined (Peterson and Smith, 1999; Smith et al., 2001). The enzyme activity is allosterically modulated by a range of metabolites, including adenosine, guanosine, and inosine di- and trinucleotides, as well as L-leucine (see Couée and Tipton 1989a). The exact behavior of these compounds depends on the conditions under which activity is measured and whether NAD+ or NADP+ is used as cofactor. The kinetic behavior in the direction of glutamate oxidation is complex. Substrate inhibition by high concentrations of 2-oxoglutarate occurs in the reaction leading to NADPH oxidation, but this is not apparent in the reaction pathway leading to NADH oxidation. For that reason, activity determinations are usually performed in the direction of 2-oxoglutarate amination with NADH as the cofactor. Phospholipids, such as cardiolipin, also

Strategies of Protein Purification and Characterization

Contributed by Martha Motherway, Keith F. Tipton, Alun D. McCarthy, Ivan Couée, and Jane Irwin

1.4.1

Current Protocols in Protein Science (2002) 1.4.1-1.4.34 Copyright © 2002 by John Wiley & Sons, Inc.

Supplement 29

affect enzyme activity (Couée and Tipton, 1989b) and the enzyme is also inhibited by antipsychotic drugs such as perphenazine (Couée and Tipton, 1990a,b; Yoon et al., 2001). GDH is localized in the mitochondrial matrix, although small amounts of the activity have been detected associated with microtubules (Rajas and Rousset, 1993) and endoplasmic reticulum (Lee et al., 1999). A nuclear-associated activity has also been reported (McDaniel, 1995) and the enzyme has been found to be capable of functioning as an RNA-binding protein (Preiss et al., 1993). In addition to the ubiquitously distributed GDH isoenzyme (GLUD1), which is encoded by a gene on chromosome 10, brain, retina, and testis tissue also contain a second form of the enzyme (GLUD2), encoded on the X chromosome (see Shashidharan et al., 1994). This isoenzyme differs in its kinetic and regulatory properties from the major form of GDH (see e.g., Cho et al., 1995; Shashidharan et al., 1997). The possible functions of these two GDH isoenzymes in brain have been discussed by Plaitakis and Zaganas (2001). NOTE: Unless otherwise stated, all required chemicals can be obtained from SigmaAldrich or BDH. They should be of the highest purity available. BASIC PROTOCOL 1

PURIFICATION OF OX BRAIN OR LIVER GLUTAMATE DEHYDROGENASE BY AFFINITY CHROMATOGRAPHY ON GTP-SEPHAROSE The initial steps in this protocol involve the classical procedures of salt precipitation and ion-exchange chromatography. These are then followed by affinity chromatography on GTP Sepharose. GTP is an allosteric inhibitor of glutamate dehydrogenase when NAD+ is used as the substrate (see McCarthy and Tipton, 1985; Couée and Tipton, 1989a). The procedure described here is that of McCarthy et al. (1980). The steps involved are summarized in Figure 1.4.1. Materials Fresh ox (beef) brain (∼400 g) or liver (∼50 g) from slaughterhouse 0.32 M sucrose (optional; if tissue samples are to be stored before use) Homogenization buffer (see recipe), 4°C Ammonium sulfate [(NH4)2SO4] 20 mM and 200 mM sodium phosphate buffer, pH 7.4 (APPENDIX 2E), 4°C Affinity chromatography buffer (see recipe) 400 mM KCl in affinity chromatography buffer (see recipe for buffer) Glycerol

Purification of Glutamate Dehydrogenase from Liver and Brain

Scissors Waring blender or kitchen liquidizer Refrigerated centrifuge (e.g., Sorvall RC-5C) Dounce (hand-held) homogenizer with loosely-fitting pestle (clearance, ∼0.03 cm) 250 ml or 500 ml polycarbonate centrifuge tubes Dialysis tubing, 12,000 molecular weight cut-off (MWCO) Conductivity meter Gradient mixer (e.g., UNIT 8.2) Chromatography columns (see Support Protocol 5 for equilibration; column lengths may be varied somewhat according to availability): 40 × 3–cm packed to height of 30 cm with DEAE-cellulose (DE-52, Whatman) and equilibrated with 20 mM sodium phosphate buffer, pH 7.4 (see APPENDIX 2E for buffer) 30 × 2.5–cm packed to height of 22 cm with Sephadex G-25 (Pharmacia Biotech) and equilibrated with affinity chromatography buffer (see recipe for buffer)

1.4.2 Supplement 29

Current Protocols in Protein Science

ox brain (400 g)

homogenize for 1 min in 3 volumes (w/v) of 4 mM sodium phosphate buffer, pH 7.4, containing 0.5mM EDTA and 0.1 mM PMSF

ox liver (50 g)

homogenize for 1 min in 500 ml of 4 mM sodium phosphate buffer, pH 7.4, containing 0.5 mM EDTA and 0.1 mM PMSF

stage 1 (homogenate) add solid (NH4)2 SO4 (113 g/liter), stir for 20 min

centrifuge discard pellet

stage 2 (NH4)2 SO4 supernatant

add solid (NH4)2 SO4 (188 g/liter), stir for 20 min

stage 3 (dialyzed pellet)

centrifuge discard supernatant dialyse against 20 mM sodium phosphate buffer, pH 7.4 and apply to DEAE-cellulose column elute with 20 to 200 mM sodium phosphate, pH 7.4, gradient

stage 4 (DEAE eluate)

gel-filter into affinity chromatography buffer; pass through hydrazide-Sepharose; and apply to GTP Sepharose column

stage 5 (GTP-Sepharose eluate)

elute with 0 to 15 mM KCl gradient in affinity-chromatography buffer concentrate by ultrafiltration dialyze against 20 mM sodium phosphate buffer, pH 7.4 purified glutamate dehydrogenase

Figure 1.4.1 Flow chart for the purification of ox liver and brain glutamate dehydrogenase according to the Basic Protocol 1. Full details of the steps involved are given in the text.

6 × 2–cm packed to height of 4 cm with ∼10 ml of hydrazide-Sepharose 4B and equilibrated with affinity chromatography buffer (see Support Protocol 1, steps 1 to 3 for resin; see recipe for buffer) 6 × 2–cm packed to height of 4 cm with GTP-Sepharose 4B and equilibrated with affinity chromatography buffer (see Support Protocol 1, steps 1 to 7 for resin; see recipe for buffer)

Strategies of Protein Purification and Characterization

1.4.3 Current Protocols in Protein Science

Supplement 29

Additional reagents and equipment for dialysis (UNIT 4.4 and APPENDIX 3B), determining protein concentration (see Basic Protocol 2 and UNIT 3.4), assaying GDH activity (see Basic Protocol 3), and concentrating protein solutions by ultrafiltration (see Support Protocol 6) NOTE: Unless indicated otherwise, all steps are performed at 0° to 4°C in a cold room or refrigerated cabinet. Prepare and homogenize the tissue 1. Obtain one ox brain (∼400 g) or liver (∼50 g) from a freshly slaughtered animal transported to the laboratory on ice to be used as the starting material for the purification. The brain may be used fresh or stored at −70°C, using the procedure as follows: Place the fresh tissue in ice-cold 0.32 M sucrose solution and keep it in a −20°C freezer until it has frozen. Transfer to a −70°C freezer until required. Before use place the vessel containing the frozen material in a water bath at 37°C and allow it to thaw, with occasional agitation. The larger amount of enzyme in ox liver permits the use of a smaller amount of starting material (e.g., 50 g in the example purification shown here) to be used.

2a. For brain tissue: Divide the brain tissue into three equal portions, weigh, and suspend each portion in 3 volumes (w/v) of cold homogenization buffer. 2b. For liver tissue: Weigh the liver tissue and suspend in 10 volumes (w/v) of cold homogenization buffer. 3. Chop the tissue into smaller pieces using scissors, then homogenize in a Waring blender at full speed for 1 min. Retain a small aliquot of this homogenate for determining protein concentration by the biuret procedure (see Basic Protocol 2) and assaying GDH activity (see Basic Protocol 3). A kitchen liquidizer can be used instead of a Waring blender. However, extra care is then needed with any biohazard material (see Critical Parameters) since kitchen liquidizers can release aerosols. These are generally less powerful than the Waring blender and it is thus necessary to increase the homogenization time (e.g., 1.5 min using a Kenwood liquidizer at full speed). The volumes given in the subsequent steps refer to the preparation of GDH from brain. The corresponding values for liver are given in Table 1.4.6 (see Critical Parameters and Troubleshooting).

Precipitate with ammonium sulfate 4. Add solid (NH4)2SO4 slowly in four approximately equal amounts to the homogenates while they are still in the blender, homogenizing for 1 min between each addition, to yield a final 20% saturated solution (113 g/liter). Continue homogenization for another 30 sec at full speed after the final addition. For brain tissue, repeat this procedure with the other two tissue homogenates. 5. Combine the ammonium sulfate–treated homogenates in a beaker on ice and stir the mixture for 20 to 30 min. Centrifuge 30 min at 14,000 × g, 4°C. Carefully decant the supernatants and discard the pellets. Retain a small aliquot of this material for determining protein concentration by the biuret procedure (Basic Protocol 2) and assaying GDH activity (Basic Protocol 3). Purification of Glutamate Dehydrogenase from Liver and Brain

6. Add 188 g/liter of solid (NH4)2SO4 slowly with continuous stirring. Stir the mixture for an additional 20 to 30 min, then centrifuge again as described in step 5. This results in 50% saturation with ammonium sulfate.

1.4.4 Supplement 29

Current Protocols in Protein Science

7. Decant and discard the supernatants. Resuspend the pellets in 20 mM sodium phosphate buffer, pH 7.4, using a Dounce homogenizer, to yield a total volume of 200 to 250 ml (160 ml for liver). Retain a small aliquot of this material for determining protein concentration by the biuret procedure (Basic Protocol 2) and assaying GDH activity (Basic Protocol 3). The ammonium sulfate pellet can be resuspended by careful stirring if a Dounce homogenizer is not available.

8. Dialyze (UNIT 4.4 and APPENDIX 3B) for 18 hr against 20 volumes (v/v) of cold 20 mM sodium phosphate buffer, pH 7.4, with at least two changes of buffer, using an MWCO 12,000 dialysis membrane. 9. Centrifuge the material from the dialysis bag 30 min at 40,000 × g, 4°C, and retain the supernatants. Resuspend the pellets in the 20 mM sodium phosphate buffer, pH 7.4, and centrifuge again for 30 min at 40,000 × g, 4°C. Combine the supernatants with those from the first centrifugation and discard the pellets. Retain a small aliquot of this material for determining protein concentration by the biuret procedure (Basic Protocol 2) and assaying GDH activity (Basic Protocol 3). Extraction by resuspension and recentrifugation of the pellet after dialysis and centrifugation increases the yield of enzyme, since otherwise some glutamate dehydrogenase activity is lost in the pellet. As is common with salt precipitation in enzyme purification, this step does not give a high degree of purification of glutamate dehydrogenase. However, it is an effective way of reducing the volume of material for the subsequent steps, and it also removes some contaminants.

Perform chromatography on DEAE-cellulose 10. Pour the supernatant carefully onto the top surface of a 40 × 3–cm chromatography column packed to 30 cm with DEAE-cellulose, equilibrated with 20 mM sodium phosphate buffer, pH 7.4 (Support Protocol 5). After the protein solution has run into the column, continue to pass buffer through it and determine the absorbance of aliquots of the effluent at 280 nm (A280). Continue washing the column until the A280 of the effluent is <0.1. 11. Pass a 2-liter linear gradient from 20 to 200 mM sodium phosphate buffer, pH 7.4, through the column. Collect fractions (∼10 ml) and determine their A280 (see Basic Protocol 2), conductivity, and enzyme activities (see Basic Protocol 3). Pool the fractions containing glutamate dehydrogenase activity. Retain a small aliquot of this pooled material for determining protein concentration by the biuret procedure (Basic Protocol 2) and assaying GDH activity (Basic Protocol 3). The combined active fractions require concentration before the next step; this can be done either by ultrafiltration or ammonium sulfate precipitation. The authors have used both of these procedures and they work equally well.

12a. Concentrate protein by ultrafiltration: Concentrate the combined fractions containing GDH activity in an Amicon ultrafiltration cell using either an Ultracel Amicon YM 30 or Ultracel PLTK membrane, as described in Support Protocol 6, until the volume remaining in the cell is reduced to ∼20 ml. 12b. Concentrate protein by ammonium sulfate precipitation: Add 312 g/liter solid (NH4)2SO4 slowly, over a period of 30 min with continuous stirring (this brings the saturation to 50%). Continue stirring for a further 20 min, then centrifuge 20 min at 14,000 × g, 4°C. Discard the supernatant and resuspend the pellet in ∼20 ml of 20 mM sodium phosphate buffer, pH 7.4.

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Perform chromatography on GTP-Sepharose 13. Desalt the sample by passing the concentrated enzyme solution through a 30 × 2.5–cm chromatography column packed to 22 cm with Sephadex G-25, equilibrated with affinity chromatography buffer. The next chromatographic steps should be performed as quickly as possible, since the enzyme is slightly unstable in the affinity chromatography buffer.

14. Apply the enzyme solution to a 6 × 2–cm chromatography column packed to 4 cm with hydrazide-Sepharose 4B equilibrated with affinity chromatography buffer. Elute by passing an additional 50 ml of affinity chromatography buffer through the column. Glutamate dehydrogenase activity is not appreciably retarded by this material, and essentially complete recovery of the applied activity is obtained when the applied volume plus an additional 50 ml of buffer has been passed through the column. This procedure removes any components which bind nonspecifically to the Sepharose or to the spacer arm.

15. Apply the enzyme solution eluted from the above column to a 6 × 2–cm chromatography column packed to 4 cm with GTP-Sepharose 4B, equilibrated with affinity chromatography buffer. Use a flow rate of not more than ∼1 ml/min. Pass affinity chromatography buffer through the column until the A280 of the effluent has fallen to <0.01 (∼150 ml of buffer should be required). Once the enzyme solution has passed into the column the flow rate may be increased to 1.5 ml/min, and this flow rate should be used for step 16.

16. Elute the enzyme activity with a linear 400-ml gradient from 0 to 400 mM KCl in affinity chromatography buffer. Collect fractions (∼10 ml) and monitor the A280 (see Basic Protocol 2), conductivity, and enzyme activity (see Basic Protocol 3) in each. 17. Combine fractions containing glutamate dehydrogenase activity and concentrate by ultrafiltration, followed by osmotic dehydration, as described in Support Protocol 6, to yield a protein concentration of at least 3 mg/ml. 18. Dialyze (UNIT 4.4 and APPENDIX 3B) overnight against 3 liters of 20 mM sodium phosphate buffer, pH 7.4, using MWCO 12,000 dialysis membrane. Add glycerol to the dialyzed enzyme solution to yield a final concentration of 30% (v/v) and store in a sealed container, which has been flushed with N2, at 4°C. There may be some small variations between different preparations of the GTP-Sepharose in the sharpness of the peak of activity that emerges and in the salt concentration necessary to elute the enzyme. This may reflect varying concentrations of bound ligand, but does not affect the degree of purification obtained. SUPPORT PROTOCOL 1

PREPARATION OF GTP-SEPHAROSE Although GTP-agarose is available from Sigma, the preparation described in this protocol generally yields more satisfactory and reproducible results than commercial preparations. The steps involved are outlined in Figure 1.4.2. GTP is coupled to Sepharose 4B by the method of Jackson et al. (1973) as modified by Godinot et al. (1974), where L-glutamic acid γ-methyl ester is used as the spacer instead of ε-aminohexanoic acid methyl ester. After the spacer is coupled to the activated Sepharose and the ester group is treated with hydrazine (Godinot et al., 1974), a portion of the gel (∼10 ml) is retained and used as the hydrazide gel in the purification procedure.

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OH OH Sepharose CNBr

O C O

NH CH2OPOPOP

HO L-glutamic acid γ -methyl ester

COOH

GTP

O

NHCHCH2 CH2COCH3 OH

G

NaIO4

H2NNH2 COOH

O

HO

CH2OPOPOP

O

O

NHCHCH2 CH2CNHNH2 OH hydrazine derivative

O

O G

CH2OPOPOP

COOH

O

NHCHCH2 CH2CNHN OH GTP-Sepharose

O G

Figure 1.4.2 The chemical processes involved in the synthesis of GTP-Sepharose according to the method described in Support Protocol 1. The activation of Sepharose by cyanogen bromide (Support Protocol 3) is also shown in simplified form; alternative activated products are formed in this step.

Materials Cyanogen bromide–activated Sepharose 4B (Pharmacia Biotech, or see Support Protocol 3). Sodium bicarbonate buffer: prepare 0.1 M NaHCO3 and adjust pH to 9.0 with NaOH L-glutamic acid γ-methyl ester Hydrazine hydrate Guanosine 5′-triphosphate (GTP) 0.1 M disodium hydrogen phosphate adjusted to pH 5.0 with 1 M citric acid Sodium periodate Ethylene glycol

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Nitrogen source 50 mM sodium phosphate buffer, pH 6.8 (APPENDIX 2E) containing 5 mM EDTA Butanol Sintered-glass filter funnel (Pyrex, porosity G2) 70°C water bath Prepare the hydrazide gel 1. Wash ∼4.5 g of cyanogen bromide–activated Sepharose 4B in a sintered-glass filter funnel with ice-cold water, then wash with 100 ml ice-cold sodium bicarbonate buffer. Finally, suspend the resin in sodium bicarbonate buffer to a volume of 16 ml. The cyanogen bromide–activated Sepharose 4B contains dextran and lactose as stabilizers. It is necessary to remove these by washing. It is possible to activate Sepharose with cyanogen bromide in the laboratory as described in Support Protocol 3. This is a less expensive alternative, but cyanogen bromide is toxic and should be handled with great care.

2. Dissolve 1.12 g of L-glutamic acid γ-methyl ester in 4 ml sodium bicarbonate buffer. Add this solution to the ∼16 ml of the gel suspension and stir this mixture for 16 to 18 hr at 4°C. Finally, filter the mixture and wash it with cold water in a sintered-glass filter funnel before suspending it in water to a volume of 30 ml. The following steps should be performed in a fume hood.

3. Add 28 ml of hydrazine hydrate (99% to 100%) slowly to 30 ml of the suspended gel and heat the reaction mixture to 70°C in a water bath for 15 min. Allow the mixture to cool and then filter and wash with cold water on a sintered-glass filter. This material, the hydrazide gel, may be overlaid with butanol to impede bacterial growth, and stored at 4°C. Retain a 10-ml portion of this hydrazide gel for use in the purification procedure. After use, the hydrazide gel can be recycled as described in Support Protocol 3.

Prepare GTP-Sepharose 4. Dissolve 14.2 mg of GTP in 5 ml of 0.1 M disodium hydrogen phosphate that has been adjusted to pH 5.0 with 1 M citric acid. Dissolve 8.6 mg of sodium periodate in 0.4 ml of the same buffer. Mix these two solutions and incubate in the dark for 30 min at room temperature. Add 8 µl of ethylene glycol to remove the excess periodate, and incubate the mixture for a further 15 min. Bubble nitrogen through the solution for 5 to 10 min. Bubbling with N2 is effected by connecting a Pasteur pipet to a nitrogen cylinder by plastic tubing. The Pasteur pipet is placed in the tube containing the reacted mixture and the nitrogen is allowed to bubble through slowly. This procedure removes any formaldehyde produced during the reaction, which would compete with the oxidized GTP for reaction with the hydrazide gel.

5. Dilute the mixture from step 4 (oxidized GTP solution) to 10 ml with 0.1 M disodium hydrogen phosphate that has been adjusted to pH 5.0 with 1 M citric acid. If necessary, remove the butanol layer from the hydrazide gel (from step 3) by aspiration and wash the gel with cold water, then suspend it in 20 ml of cold water. Mix 10 ml of this gel suspension with the oxidized GTP solution and stir the mixture at room temperature for 2 hr. Purification of Glutamate Dehydrogenase from Liver and Brain

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6. Filter the gel on a sintered-glass funnel and wash with at least 100 ml of cold 50 mM sodium phosphate buffer, pH 6.8, containing 5 mM EDTA. Retain washings if step 8 is to be performed. Store the resultant GTP-Sepharose at 4°C with an overlay of butanol to retard bacterial growth. Remove this butanol layer by aspiration and wash the gel with buffer prior to use of the gel. After use, the GTP-Sepharose gel can be recycled as described in Support Protocol 4.

ASSESSMENT OF INCORPORATION OF GTP INTO THE HYDRAZIDE GEL The following modification to Support Protocol 1 may be applied if there are any problems using the GTP-Sepharose medium prepared in Support Protocol 1 in the affinity purification steps (see Basic Protocol 1, steps 13 to 16). It is generally not necessary to resort to this procedure.

SUPPORT PROTOCOL 2

CAUTION: When working with radioactivity, take appropriate precautions to avoid contamination of the experimenter and the surroundings. Carry out the experiment and dispose of wastes in an appropriately designated area, following the guidelines provided by the local radiation safety officer (also see APPENDIX 2B). Additional Materials (also see Support Protocol 1) [8-14C]GTP (Amersham Pharmacia Biotech) 250:500:1 (v/v/w) Triton X-100/toluene/2,5-diphenyloxazole (PPO) Carry out Support Protocol 1 with the following modifications at the indicated steps. 4a. Add 2.7 µCi of [8-14C]GTP to the solution of unlabeled GTP before periodate oxidation. 7b. Determine radioactivity in a sample of the gel washings by scintillation counting in a mixture by scintillation counting in 250:500:1 Triton X-100/toluene/PPO. About 35% of the added GTP should be found to be tightly bound to the gel, corresponding to 0.6 ìmol of GTP per ml of settled gel (see Table 1.4.1).

Table 1.4.1 Recovery of [8-14C]GTP After Coupling to Sepharose 4B

Aqueous phase

Radioactivity (cpm)a,b

Total radioactivity (cpm)a,b

10 µM of initial 14C-GTP solution + 1 ml H2O 1 ml gel washings (from 475 ml) 10 µM of initial 14C-GTP solution + 1 ml gel washings

14760 5167 20100

3.77 × 106 2.44 × 106 —

a9 ml of 250:500:1 (v/v/w) Triton X-100:toluene:PPO were mixed with 1 ml of the aqueous phase and the

radioactivity determined in a liquid scintillation counter. bSince the cpm obtained with the 10 µl 14C-GTP solution + 1 ml gel washings equals the sum obtained for the

two solutions separately, there is no significant difference in the quenching for the two solutions and therefore it is unnecessary to convert the results to dpm.

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SUPPORT PROTOCOL 3

ACTIVATION OF SEPHAROSE 4B WITH CYANOGEN BROMIDE This protocol is given as an alternative to the purchase of preprepared cyanogen-bromideactivated Sepharose. Materials Sepharose 4B (Pharmacia Biotech) Cyanogen bromide Acetonitrile 5 M sodium hydroxide Sodium bicarbonate buffer: prepare 0.1 M NaHCO3 and adjust pH to 9.0 with NaOH CAUTION: Because cyanogen bromide is very toxic, it should be handled with extreme care. See APPENDIX 2A for general precautions. 1. Wash 10 ml Sepharose 4B with water and suspend in 40 ml water. 2. Stir this mixture slowly in an ice-bath in a fume-hood. Add 2.4 g cyanogen bromide dissolved in 1.7 ml acetonitrile and monitor the pH of the resultant mixture constantly, maintaining it at ≥pH 11 by the addition of 5 M sodium hydroxide. Use extreme caution as hydrocyanic acid may be formed in this step.

3. After hydrogen ion release has subsided (i.e., the pH ceases to fall), filter the gel on a sintered-glass funnel. Wash with at least 100 ml of ice-cold sodium bicarbonate and resuspend in 16 ml of that buffer. SUPPORT PROTOCOL 4

REUSE OF CHROMATOGRAPHY MEDIA Hydrazide-Sepharose (prepared as in Support Protocol 1, steps 1 to 3) Wash the resins after use with 500 ml of 2 M KCl, followed by 1 liter of water. Store at 4°C with an overlay of butanol, to retard bacterial growth. Remove the butanol layer by aspiration and equilibrate with affinity chromatography buffer (see recipe) immediately before use. GTP Sepharose (prepared as in Support Protocol 1, steps 1 to 7) Follow the same procedure as described for the hydrazide-Sepharose, above. Over a period of several months, the properties of the GTP-Sepharose change and the enzyme activity is eluted earlier in the gradient, probably as a result of instability of the bound GTP. The gel should therefore be discarded after 6 months. DEAE-Cellulose The manufacturers provide full instructions for recycling this material for reuse. After use, remove tightly bound material from the ion-exchange material by passing 2 column volumes of 2 M NaCl through the column. Equilibrate the column with the buffer to be used and then pass 1 column volume of the same buffer containing 0.02% sodium azide through the column. Before use, wash the column with azide-free buffer.

Purification of Glutamate Dehydrogenase from Liver and Brain

If a small portion at the top of the column becomes discolored as a result of tightly bound material, that portion may be removed and replaced by fresh DEAE-cellulose. Removal of the top few centimeters from the column may also be effective if the flow rate through the column becomes excessively slow. After removing the discolored top resin, transfer the remaining resin from the column to a beaker. Add the appropriate amount of fresh

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DEAE-cellulose from which the fines have been removed and which has been equilibrated with 20 mM sodium phosphate buffer, pH 7.4 (see Support Protocol 5), and repack the column. If the characteristics of the DEAE-cellulose change after protracted use, it may be regenerated by the following procedure. 1. Empty the contents of the column into a beaker. Stir with 15 volumes of 0.5 M HCl for 30 min. Stirring can be continued for up to, but no longer than, 2 hr if convenient.

2. Filter the DEAE-cellulose on a sintered-glass funnel and wash it with water until the pH of the effluent reaches 4. 3. Stir the DEAE-cellulose with 15 volumes of 0.5 N NaOH for 30 min. 4. Allow to settle, remove the supernatant by decantation, and repeat step 3. 5. Filter on a sintered glass funnel and wash with water until the pH of the effluent is close to neutrality. The DEAE-cellulose is then ready for re-equilibration and reuse, although it may be necessary to remove the fines (see Support Protocol 5). For longer-term storage, remove the DEAE-cellulose from the column and store it in a saturated solution of NaCl, in a tightly-sealed container. Sephadex G-25 Sephadex G-25 is supplied in dry form and must be swollen before use. Full instructions for swelling and recycling this material are provided in the literature from the manufacturers. EQUILIBRATION OF CHROMATOGRAPHIC MEDIA The chromatographic medium must never be allowed to dry. When using it in columns there should always be a layer of the appropriate buffer above the surface.

SUPPORT PROTOCOL 5

Equilibration of DEAE-Cellulose DEAE-cellulose (Whatman DE-52) may contain small amounts of relatively small-sized particles (“fines”) which can slow down the rate of flow through a column if not removed. The following steps are appropriate for the DEAE-cellulose chromatography performed in Basic Protocol 1 and the Alternate Protocol. For the DEAE-cellulose chromatography performed in Support Protocol 7 (i.e., step 15 of that protocol), the procedure is the same as that described below except that 1 M NH4HCO3 may be used for removing the fines. A total of 10 column volumes of 1 M NH4HCO3 should then be used to equilibrate the column, which is then washed with 8 liters of water. Materials DEAE-cellulose (Whatman DE-52) 200 mM and 20 mM sodium phosphate buffer, pH 7.4 (APPENDIX 2E) Chromatography column; 40 × 3–cm (length × diameter) Conductivity meter (e.g., Linton, type WPA CMD80) 1. Suspend the DEAE-cellulose resin in 5 volumes of 200 mM sodium phosphate buffer, pH 7.4. Allow the mixture to settle and remove the buffer and fines by decantation. Repeat this process at least three times, after which the supernatant, upon settling, should be clear.

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2. Pour a suspension of this material into a 40 × 3–cm chromatography column to produce a final bed volume of 30 × 3 cm. Allow 20 mM sodium phosphate buffer, pH 7.4, to flow through the column until the effluent has the same pH and conductivity as the in-flowing buffer. The column is now ready for use.

Equilibration of Dowex AG 1X2 Dowex AG 1X2 medium is used in Support Protocol 7. Suspend the Dowex AG 1X2 (200 to 400 mesh, Cl− form) in 3 M HCl and pour into a column of 4 cm diameter to give a settled bed volume of 10 cm. Wash the column before use with 1 liter of 3 M HCl, followed by exhaustive washing with at least 20 liters of water, until the pH of the effluent reaches neutrality. Equilibration of Sephadex G-25 Pass at least 2.5 column volumes of the appropriate buffer through the packed column before use. SUPPORT PROTOCOL 6

CONCENTRATION OF PROTEIN-CONTAINING SOLUTIONS Some of the purification procedures used in Basic Protocol 1 and the Alternate Protocol result in some degree of concentration. Precipitation with ammonium sulfate followed by resuspension of the pellet in a smaller volume, as well as ion-exchange chromatography, can also be effective concentration procedures. In contrast, however, dialysis and gel filtration tend to dilute samples. The procedures described below are specifically for concentration purposes. For additional information, see UNIT 4.4. It is important to note that in both of the procedures described below, inattention may result in the concentration proceeding until all the fluid has been removed. It is essential to monitor the progress of the procedure and stop when the desired volume has been reached, because attempts to recover enzyme activity from material that has been taken to dryness by either of these procedures will result in significant losses. Ultrafiltration The Amicon ultrafiltration cell uses pressure of nitrogen gas to force the solution through a membrane that retains molecules above a pre-determined size. Prepare the apparatus according to the manufacturer’s instructions using the ultrafiltration cell from Amicon either an Amicon Ultracel YM 30 or Ultracel PLTK membrane. Slowly stir the contents during the concentration process, which is carried out at 4°C. Continue until the required volume remains in the cell. Earlier work used Amicon Diaflo XM-50 membranes for this procedure, but these are no longer readily available. The Ultracel Amicon YM 30 or Ultracel PLTK membranes are appropriate substitutes.

Purification of Glutamate Dehydrogenase from Liver and Brain

Centriplus concentrators (Amicon) may be an alternative for concentrating the enzyme from volumes of less than 60 to 70 ml. They rely on centrifugation to force solvent through a membrane (e.g., Centriplus 50, MWCO 50 kDa). The sample containing enzyme is retained by the membrane. The authors have found this procedure to be successful with several other dehydrogenases.

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Osmotic Dehydration Although ultrafiltration is a highly effective procedure for concentrating relatively large volumes, it may be less effective and convenient with small volumes and high protein concentrations. Osmotic dialysis relies on the use of a high concentration of material outside a dialysis tube to draw water and small-molecule solutes from its contents by osmosis, leaving a more concentrated protein solution within the dialysis tubing. Either sucrose or polyethylene glycol (PEG; Mr = 15,000 to 20,000) may be used. However the purity of PEG can be variable and it is important to use preparations that are as pure as possible to avoid contamination of the enzyme solution from low-Mr impurities that may be inhibitory. Put the enzyme solution in a length of dialysis tubing (see APPENDIX 3B for preparation) that has been knotted at the lower end, and then tie a knot in the other end of the tubing. Surround the resulting tube with solid sucrose or PEG. This may be done by heaping the sucrose or PEG on a sheet of cooking foil, embedding the dialysis tubing in the sucrose, and then folding the foil to form a parcel around the sucrose or PEG-covered dialysis tube. Alternatively, the dialysis tubing may be buried in the material within a plastic box of appropriate dimensions. Leave at 4°C until the solution in the dialysis bag has reached the required volume; this will take several hours. Rinse the outside of the dialysis tube with water or buffer to remove adhering sucrose or PEG. This also dampens the dialysis membrane, which becomes brittle during the concentration procedure. Whereas with ordinary dialysis it is necessary to leave an empty space in the tubing to allow for the osmotic volume increase, this need not be done for osmotic dehydration where a decrease in volume occurs. In Basic Protocol 1 and the Alternate Protocol, this concentration procedure is followed by dialysis. In order to avoid too great a degree of dilution occurring at this stage, reknot the dialysis tube to confine the enzyme solution in a smaller volume of the tubing. DETERMINATION OF PROTEIN CONCENTRATION The first two methods presented under this heading are the Biuret and Bio-Rad protein assays. A spectrophotometer and 1-cm light-path cuvettes (either glass or plastic) will be required for each of these determinations. In the case of the third method presented below, determination of protein concentration by absorbance at 280 nm (A280), the cuvettes must be quartz or some other UV-transmitting material.

BASIC PROTOCOL 2

Other procedures for determining protein concentration may also be used, including the Lowry-Folin method, which is described in adequate detail in Dawson et al. (1989). UNIT 3.4 also provides additional information on determination of protein concentration. Biuret Protein Assay Materials Biuret reagent (see recipe). 1.5% (w/v) sodium deoxycholate (1.5 g sodium deoxycholate in 100 ml distilled H2O) 50 mg/ml bovine serum albumin (BSA), or alternative protein calibration standard 1. Prepare a series of standards in triplicate according to the scheme described in Table 1.4.2.

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Table 1.4.2

Preparation of Standard Curve for Biuret Protein Assay

mg protein

50 mg/ml BSA (µl)

1.5% (w/v) sodium deoxycholate (µl)

H2O (µl)

Biuret reagenta (ml)

0 0.25 0.5 0.75 1.0 1.5 2.0

0 5 10 15 20 30 40

100 100 100 100 100 100 100

900 895 890 885 880 870 860

4 4 4 4 4 4 4

aSee recipe in Reagents and Solutions.

2. For enzyme protein determination use 20 to 100 µl of appropriately diluted sample and prepare as described in Table 1.4.2. 3. Mix each sample and allow the reaction to proceed at room temperature for ∼30 min. 4. Measure absorbance at 540 nm using the samples containing no protein as blanks. 5. Prepare a standard curve by plotting absorbance at 540 nm against the protein concentration. 6. Use the standard curve to determine the protein concentration of the enzyme sample from its absorbance. Determine the absorbance of the sample against a blank containing the same medium. Dilute, if necessary, to ensure that the absorbance is <1.0. Bio-Rad Protein Assay Materials Bio-Rad Dye Reagent Concentrate 0.1 mg/ml bovine serum albumin (BSA), or alternative protein calibration standard (this can be conveniently prepared by diluting some of the stock solution prepared for the biuret assay; see above) Method 1. Prepare a series of standards in triplicate according to the scheme described in Table 1.4.3. 2. For enzyme protein determination use 20 to 100 µl of appropriately diluted sample and prepare as described in Table 1.4.3.

Purification of Glutamate Dehydrogenase from Liver and Brain

Table 1.4.3

Preparation of Standard Curve for Bio-Rad Protein Assay

µg protein

0.1 mg/ml BSA (µl)

H2O (µl)

Bio-Rad Dye Reagent Concentrate (µl)

0 2 5 10 15 20

0 20 50 100 150 200

800 780 750 700 650 600

200 200 200 200 200 200

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3. After mixing, leave for a period of 5 to 60 min, then determine the absorbance at 595 nm using the samples containing no protein as blanks. 4. Prepare a standard curve by plotting absorbance at 595 nm against the protein concentration. 5. Use the standard curve to determine the protein concentration of the enzyme sample from its absorbance. Determine the absorbance of the sample against a blank containing the same medium. Dilute, if necessary, to ensure that the absorbance is <1.0. Note that the Bio-Rad assay involves determination of the absorbance of 1 ml of mixture. The optical arrangements of some spectrophotometers may not permit this in standard 1-cm2 cross-section cuvettes. In such cases microcuvettes should be used. However, some older spectrophotometers may not focus the light beam to pass through the narrower path of a microcuvette. It is a good idea to check the instructions supplied with the spectrophotometer in regard to this. If all else fails, the volumes shown for this procedure can all be increased proportionally.

Absorbance at 280 nm Using quartz or other non-UV-absorbing cuvettes, determine the absorbance at 280 nm of the sample against a blank composed of the medium in which the sample is dissolved. A UV-Vis spectrophotometer is required. Use the rough approximation that a 1 mg/ml protein solution has an absorbance of 1.0 at this wavelength. ASSAY OF GLUTAMATE DEHYDROGENASE ACTIVITY Glutamate dehydrogenase catalyzes the reaction:

BASIC PROTOCOL 3

2-oxoglutarate + NH 3 + NADH + H + U L-glutamate + H 2 O + NAD +

The assay described here determines the decrease of absorbance at 340 nm as NADH is oxidized to NAD+ concomitant with the reductive amination of 2-oxoglutarate to L-glutamate. Materials Enzyme preparation (see appropriate steps of Basic Protocol 1 or Alternate Protocol) 0.4% (v/v) Triton X-100 in 50 mM sodium phosphate buffer, pH 7.4 100 mM potassium cyanide (KCN) 50 mM sodium phosphate buffer, pH 7.4 (APPENDIX 2E) 2.5 M ammonium sulfate in 50 mM sodium phosphate buffer, pH 7.4 4 mM NADH in 50 mM sodium phosphate buffer, pH 7.4 (prepared fresh on day of use) 0.25 M 2-oxoglutarate (α-ketoglutarate), in 50 mM sodium phosphate buffer, pH 7.4 Spectrophotometer with cuvette holder set at 37°C 1-cm path-length cuvettes (quartz or plastic with good light transmittance at 340 nm) NOTE: All GDH enzyme assays are performed at 37°C. In order to ensure rapid temperature equilibration, the assay buffer should be prewarmed to that temperature.

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Other hints on the performance and validation of enzyme assays can be found in Tipton (2002). 1. For crude enzyme preparations (see Basic Protocol 1, step 1): Incubate 50 µl of the enzyme solution with 2.68 ml of 0.4% Triton X-100 in 50 mM sodium phosphate buffer, pH 7.4, and 30 µl of 100 mM KCN (which will give a final KCN concentration of 1 mM in the complete assay) for 2 min at 37°C before proceeding to step 2. For assaying the enzyme at later steps of the purification, begin with step 2. Triton X-100 serves to disrupt particulate material, and KCN decreases any contaminating NADH oxidase activity. It has been shown that neither Triton X-100 nor KCN affect the activity of purified GDH at these concentrations. These assays are performed in phosphate buffer. Do not attempt to perform them in Tris buffer since the enzyme is unstable in Tris and either no activity or a rapid decrease in activity may be observed.

2. Add reagents to the cuvettes in the order listed, in a final volume of 3 ml: 2.71 ml 50 mM sodium phosphate buffer, pH 7.4 60 µl 2.5 M ammonium sulfate (final concentration 50 mM) 120 µl 4 mM NADH (final concentration 160 µM) 50 µl enzyme preparation. 3. Cover the cuvette with Parafilm, mix the contents by inversion and incubate in the spectrophotometer for 3 min. Initiate the reaction by the addition of 60 µl of 0.25 M 2-oxoglutarate (final concentration 5 mM), invert to mix and allow the reaction to proceed for 10 min in the spectrophotometer. 4. Determine the initial (linear) rate of decrease in absorbance as NADH is oxidized to NAD+, subtracting any blank rate occurring before the addition of the 2-oxoglutarate (see Critical Parameters). The initial absorbance resulting from the NADH present will be ∼1.0, although impurities in the enzyme solution may add to this. The assay has been found to proceed in a linear fashion until the absorbance has decreased by 0.15 absorbance units. In all cases it will be necessary to dilute the enzyme preparation in order to obtain a rate that is sufficiently slow to measure with accuracy. See Critical Parameters for a detailed discussion of dilution considerations.

5. Calculate the specific activity. Since the molar absorbance coefficient of NADH in a 1-cm path-length cuvette is 6220 M-1cm-1, the production of 1 ìmol of glutamate, or the reductive amination of 1 ìmol 2-oxoglutarate in an assay volume of 3.0 ml will give an absorbance change of 2.073. Thus, for a 1-cm path-length cuvette, the specific activity of the enzyme (ìmol product formed.per min per mg protein) will be related to the rate of decrease in absorbance per min (∆A/min), which will be given by:

specific activity =

∆A /min assay volume (ml) × 6.22 protein (mg)

It is most common to express enzyme activities in these terms. 1 unit (U) of enzyme activity is defined as the amount catalyzing the production of 1 ìmol of product (or the disappearance of 1 ìmol of substrate) in 1 min under stated conditions. Purification of Glutamate Dehydrogenase from Liver and Brain

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PURIFICATION OF RAT OR OX LIVER GDH INVOLVING AFFINITY PRECIPITATION WITH bis-NAD+

ALTERNATE PROTOCOL

Affinity precipitation of enzymes was developed by Mosbach and co-workers (see Flygare et al., 1983; Larsson et al., 1984). The bifunctional ligand bis-NAD+ (N2, N2′-adipodihydrazido-bis-(N6-carbonyl-methyl)NAD+) comprises two molecules of NAD+ joined by a spacer arm (see Fig. 1.4.5). This separates the NAD+ groups by a sufficient distance (∼1.7 nm) to allow them to bind to the coenzyme-binding sites of two different molecules of glutamate dehydrogenase. Mammalian GDH is a polymer, so extensive cross-linking of the enzyme molecules occurs and hence they precipitate from solution, as illustrated in Figure 1.4.3. Since there are many oligomeric dehydrogenases

+ glutamate dehydrogenase

bis-NAD+

Figure 1.4.3 A diagrammatic view of the cross-linking of glutamate dehydrogenase hexamers by bis-NAD+, which results in the formation of an insoluble lattice. The “locking-on” in the presence of glutarate is not shown. Further details of the process are given in the Alternate Protocol.

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in the cell, this process might be expected to be somewhat nonspecific, causing the precipitation of a number of enzymes containing binding sites for NAD+. However, the procedure can be made specific for the precipitation of a selected dehydrogenase by the use of a compound that is a competitive inhibitor of the enzyme with respect to its other substrate. Thus, in the case of GDH, glutarate, which is a competitive inhibitor towards glutamate, is used. This strengthens the binding between the bis-NAD+ and glutamate dehydrogenase, allowing it to be used at concentrations below those that would be necessary to precipitate other dehydrogenases.

ox liver (46 g)

rat liver (10g)

homogenize for 1 min in 10 volumes (w/v) of 4 mM sodium phosphate buffer, pH 7.4, containing 0.5 mM EDTA and 0.1 mM PMSF

Stage 1 (homogenate) add (NH4)2 SO4 (113 g/liter), stir for 20 min

centrifuge and discard pellet add (NH4)2 SO4 (188 g/liter), stir for 20 min centrifuge and discard pellet dialyze against 20 mM sodium phosphate buffer, pH 7.4

Stage 2 [dialyzed (NH4)2 SO4 pellet] apply to DEAE-cellulose column

elute with 20 to 200 mM sodium phosphate, pH 7.4, gradient and concentrate to ~10 ml

Stage 3 (DEAE eluate)

affinity precipitate with the appropriate amount determined from pilot precipitation experiment (Support Protocol 9) of bis-NAD+ in the presence of 79 mM glutaric acid and keep on ice overnight

centrifuge discard supernatant redissolve pellet by stirring for 6 hr in 1 ml of buffer containing 0.6 mM NADH

Stage 4 (purified enzyme)

dialyze against 20 mM sodium phosphate buffer, pH 7.4 purified glutamate dehydrogenase

Purification of Glutamate Dehydrogenase from Liver and Brain

Figure 1.4.4 Outline scheme for the purification of ox and rat liver glutamate dehydrogenase according to the Alternate Protocol. Full details of the steps involved are given in the text.

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This behavior, which was termed the “locking-on” effect by O’Carra (see O’Carra et al., 1996; O’Flaherty et al., 1999; Mulcahy and O’Flaherty, 2001), was originally developed for increasing the strength of enzyme binding to an immobilized ligand. It depends on the analog of the substrate that is oxidized by NAD+ binding to the enzyme⋅NAD+ binary complex and thus displacing the coenzyme-binding equilibrium by ternary complex formation: +

NAD glutarate + ZZZZZX Z GDH < NAD+ YZZZZZ ZZZZZZ X Z GDH YZZZZ Z Z Z GDH< NAD < glutarate

This “locking-on” behavior occurs only if the oxidizable substrate, or a competitive inhibitor that is an analog of the oxidizable substrate, binds to the enzyme⋅NAD+ binary complex and not to the free enzyme. The basic concept is that, although several enzymes bind NAD+, only the GDH⋅NAD+ complex binds glutarate. The majority of dehydrogenases have ordered sequential kinetic mechanisms in which the coenzyme binds before the second substrate. To date, the technique has been found to work well for the purification of yeast alcohol dehydrogenase and lactate dehydrogenase (see Irwin and Tipton, 1996), and the synthesis of the corresponding bis-ATP analog has extended the application of bis-coenzymes to kinases (Beattie et al., 1987). The precipitated dehydrogenase can then be dissolved by incubation in the presence of NADH, which effectively competes with bis-NAD+ for binding to the enzyme. The procedure described here is that of Graham et al. (1985), as modified by Irwin and Tipton (1996). The steps involved are summarized in Figure 1.4.4. Additional Materials (also see Basic Protocol 1) Bis-NAD+ (see Support Protocol 7) 0.7 M glutaric acid, adjusted to pH 7.0 with NaOH Additional reagents and equipment for pilot-scale precipitation (see Support Protocol 9) NOTE: Unless indicated otherwise all steps are performed at 0° to 4°C. 1. Homogenize the sample and perform ammonium sulfate precipitation and DEAEcellulose chromatography steps (see Basic Protocol 1, steps 1 to 12), concentrating the eluate from the DEAE-cellulose column to ∼10 ml at step 12a or b. Although it is possible to perform affinity precipitation with preparations that have not been subjected to chromatography on DEAE-cellulose, the yield is much lower and the precipitated material is not completely pure.

2. Dialyze the enzyme preparation overnight at 4°C against 200 ml of sodium phosphate buffer, pH 7.4, with at least one change of buffer. Carry out a pilot-scale precipitation as described in Support Protocol 9 to determine the optimal amount of bis-NAD+ to use. Add that amount of bis-NAD+ to the remaining dialyzed enzyme preparation and add 0.7 M glutaric acid (preadjusted to pH 7) to a final concentration of 79 mM glutarate. Keep the mixture on ice overnight.

3. Centrifuge 15 min at 10,000 × g, 4°C. Redissolve the pellet by stirring for 6 hr in 1 ml of 20 mM sodium phosphate buffer, pH 7.4, containing 0.6 mM NADH. Dialyze the redissolved pellet overnight (using an MWCO 12,000 dialysis membrane) against 200 ml of sodium phosphate buffer, pH 7.4. Add glycerol and store as described in Basic Protocol 1, step 18.

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SUPPORT PROTOCOL 7

SYNTHESIS OF N2, N2′-ADIPODIHYDRAZIDO-bis(N6-CARBONYLMETHYL)NAD+ (bis-NAD) This reagent is used in the Alternate Protocol. The steps involved are outlined in Figure 1.4.5. Materials Iodoacetic acid (fresh) 2 M and 1 M lithium hydroxide (LiOH) NAD+ (98%, free acid) 2 M and 6 M HCl 96% (v/v) ethanol 0.24 M NaHCO3 1 M NaOH Nitrogen source Sodium dithionite 2 M Tris (base) Acetaldehyde, redistilled Yeast alcohol dehydrogenase (crystalline, Sigma-Aldrich or Roche Diagnostics) 5 mM CaCl2, pH 2.7, and 50 mM CaCl2, pH 2.0 2 M Ca(OH)2 Adipic acid dihydrazide dichloride (Sigma-Aldrich) 0.5 M 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide hydrochloride (EDC; Sigma-Aldrich) 0.25, 1, and 2 M ammonium bicarbonate (NH4HCO3) Sintered-glass funnel (≥10 cm diameter) Rotary evaporator Vacuum desiccator 75°C water bath 4 × 10–cm Dowex AG 1X2 anion-exchange column (Support Protocol 5) Gradient mixer (e.g., UNIT 8.2) 30 × 2.5–cm chromatography column packed with Whatman DE-52 resin and equilibrated (see Support Protocol 5) with 1 M NH4HCO3, pH 8.0 Additional reagents and equipment for resolution of NAD+ derivatives (see Support Protocol 8) Synthesize N(1)-carboxymethyl-NAD+ 1. Dissolve 9 g fresh iodoacetic acid in ∼10 ml of water, neutralize the solution with 2 M LiOH, and add 3 g of NAD+. Adjust the pH to 6.5 with 2 M HCl (the total volume should be ∼30 ml) and leave the solution in darkness for 7 days at room temperature (∼20°C), or alternatively, at 37°C for 2 days. Check the pH daily (or every 4 to 6 hr at the higher temperature) and readjust to 6.5 with 2 M HCl as required. The progress of the reaction should also be followed by TLC and/or HPLC (see Support Protocol 8). The solid iodoacetic acid should be white; if it is discolored as a result of decomposition it should not be used. Solutions of iodoacetic acid should be prepared fresh.

2. When reaction is complete, adjust pH to 3.0 with 6 M HCl and add 2 volumes of 96% (v/v) ethanol to produce a milky, pink-tinged suspension. Add a further 10 volumes of cold 96% ethanol to complete the precipitation. Purification of Glutamate Dehydrogenase from Liver and Brain

The water/ethanol ratio is important; using a wet vessel can give rise to the formation of a brown, sticky substance which is water-soluble.

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3. Filter the crude N(1)-carboxymethyl-NAD+ on a sintered funnel, wash with ethanol and diethyl ether, and dry in a rotary evaporator at 35°C under vacuum or by lyphilization (average yield 2.9 g). Store the product at −20°C under vacuum. Carry out rearrangement of N(1)-carboxymethyl-NAD+ to N6-carboxymethyl-NAD+ 4. Dissolve the crude N(1)-carboxymethyl-NAD+ in 0.24 M NaHCO3 (90 ml), which gives a pale orange solution, and adjust the pH to 8.5 with 1 M NaOH. 5. Deoxygenate the solution by bubbling N2 gas through it for 2 min, add 1.5 g of sodium dithionite, and leave the solution in the dark until maximum reduction is achieved. Monitor progress of reduction by taking samples, diluting them 1:50 or 1:100, and measuring the increase in absorbance at 340 nm (A340). 6. Terminate the reaction by stirring vigorously for 10 min to oxygenate the solution and then bubble N2 gas through it for 2 min. Adjust the pH to 11.5 with 1 M NaOH and leave in a water bath at 75°C to allow the rearrangement from the N(1)- to the N6-substituted derivative to occur (Dimroth rearrangement; see Engel, 1975). Monitor the rearrangement by HPLC or TLC (see Support Protocol 8). 7. Cool the reaction mixture to room temperature and add 6 ml of 2 M Tris (base) and 1.5 ml of redistilled acetaldehyde. Adjust the pH to 7.5 with 1 M HCl and add 8 to 24 mg (2500 to 7500 U) of yeast alcohol dehydrogenase. 8. Monitor the reaction at 340 nm as in step 5. When a minimum A340 is reached, add 1 volume of 96% ethanol and pour the milky flocculent precipitate into 10 volumes of vigorously stirred 96% ethanol. Leave for 30 min (or overnight, if desired) and collect the precipitate by filtration. Store the crude N6-carboxymethyl-NAD+ at −20°C in a vacuum desiccator for up to a month (yield ∼2.6 g). Purify the crude N6-carboxymethyl-NAD+ 9. Dissolve the crude powder (2.6 g, from step 7) in 30 ml water, adjust the pH to 8.0 with 1 M LiOH, and apply this solution to a 2.5 × 30–cm Dowex AG 1X2 column. See Support Protocol 5 for details of packing and equilibrating the Dowex column.

10. After applying the coenzyme solution, wash the column with 0.5 liter of water, followed by 1 liter of 5 mM CaCl2, pH 2.7, until the pH of the effluent is 2.8. Apply a 2-liter linear gradient from 5 mM CaCl2, pH 2.7, to 50 mM CaCl2, pH 2.0. Collect 10 to 20 ml fractions and monitor the absorbance at 260 nm. The composition of the fractions can be monitored by TLC (see Support Protocol 8).

11. Pool the fractions containing the N6 derivative, adjust the pH to 7.0 with 2 M Ca(OH)2, and concentrate to 5 to 10 ml by rotary evaporation at 40°C. 12. Precipitate with 96% ethanol as in step 2, above, and lyophilize. This gives a pale yellow compound (yield 0.82 g, 25%); ε = 19,300 M−1cm−1 at 266 nm.

Synthesis of bis-NAD+ 13. Dissolve 0.82 g of purified N6-carboxymethyl-NAD+ and 105 mg of adipic acid dihydrazide dihydrochloride in 20 ml water, to produce a brownish solution. 14. Adjust the pH to 4.6 with 1 M HCl and then add 0.5 M EDC at 0°C in 15- to 100-µl aliquots over a period of 35 min. Monitor the pH and readjust it to 4.6 before each addition. 15. Add water (2 liters) to dilute the solution 100-fold, adjust the pH to 8.0 with 2 M NH4HCO3, and apply the solution to a 30 × 2.5–cm DEAE-cellulose (DE-52) column equilibrated with 1 M NH4HCO3, pH 8.0 (Support Protocol 5). Wash the column with

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water until the A260 is <0.1 and apply a 2-liter linear gradient from 0 to 0.25 M NH4HCO3, pH 8.0. 16. Monitor the Rf values of the fractions by TLC (Support Protocol 8). The yield from pure N6-carboxymethyl-NAD+ is ∼14%. ε= 42,800 M−1 cm−1 at 266 nm.

17. Concentrate the product-containing fractions to ∼3 to 5 ml by rotary evaporation at 35°C and store at –20°C. For longer-term storage (>7 to 10 days) the material may be lyophilized and stored in a vacuum desiccator over silica gel at –20°C.

NH2

NH2

N

N

N

N

R

NAD+

ICH2COO–

N

pH 6.5, 25°C, 5 to 7 days

N

+ N

CH2COO–

N N(1) carboxymethyl-NAD+

R

Sodium dithionite pH 8.5, 2 to 4 hr NHCH2 COO–

N N

NH2 Dimroth rearrangement

N

pH 11.5, 75°C, 2 hr

N

RH N 6-carboxymethyl-NADH

alcohol dehydrogenase, acetaldehyde, pH 7.5 NHCH2 COO–

N N R

+ N

N

CH2COO–

N

N RH N(1) carboxymethyl-NADH

CONH2 + N H

O–

OH

H

CH2O P O P OCH2 O O O O H OH OH H N O H H H H OH OH N 6-carboxymethyl-NAD+

R=

N

H2NOC(CH2)4 CONH2 + EDC pH 4.6, 0°C, 35 min 0 HN

N N R

CH2

0

0

C NH NH C (CH2)4 C NH NH C CH2 NH

N

N

N

N

N

bis-NAD+

EDC= CH3CH2-N Purification of Glutamate Dehydrogenase from Liver and Brain

0

N

R

C N

(CH2)3 NH(CH2)2 Cl-

1-ethyl-3-(3)-dimethylaminopropyl) carbodiimide hydrochloride

Figure 1.4.5 Outline of the steps involved in the synthesis of bis-NAD+. Full details of the steps involved are given in Support Protocol 7.

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RESOLUTION OF NAD+ DERIVATIVES

SUPPORT PROTOCOL 8

Thin-layer Chromatography (TLC) The techniques of TLC are not described in detail here since they are adequately described in many textbooks (e.g., Wilson and Walker, 2000; Hahn-Deinstrop, 2000). Two different TLC solvent systems have been used for monitoring the products of the synthesis of bis-NAD+: System A: 60:100:2 (w/v/v) (NH4)2SO4/0.1 M potassium phosphate, pH 6.8/1-propanol. System B: 5:3 (v/v) isobutyric acid/1 M aqueous NH3, saturated with disodium EDTA. Apply the samples as spots to TLC plates (aluminium backed, silica gel 60, fluorescent indicator F254, layer thickness 0.2 mm; Merck), dry, and develop by ascending chromatography in a TLC developing tank. After developing the plates and allowing them to dry, the spots can be visualized by their fluorescence under UV light (wavelength 254 nm). They appear purple on a green background. The Rf values of bis-NAD+ and the compounds involved as intermediates in its synthesis are shown in Table 1.4.4. High-Performance Liquid Chromatography (HPLC) Inject the samples onto the HPLC column (4 mm × 30-cm C18 reversed-phase; e.g., Waters µBondapak C18) and elute isocratically with 0.1 M KH2PO4, pH 6.0, containing 10% methanol, at a flow rate of 0.7 to 1.0 ml/min. The elution profile is determined by continuously monitoring the absorbance of the eluate at 259 to 266 nm using a UV detector flow cell. Good separation between NAD+ and N(1)-carboxymethyl-NAD+ can be achieved, and N6-carboxymethyl-NAD+ and bis-NAD+ can be well separated, but this system does not resolve N(1)-carboxymethyl-NAD+ and N6-carboxymethyl-NAD+ satisfactorily. Retention times for NAD+ derivatives are given in Table 1.4.4. The retention times are decreased if buffer containing 20% methanol is used. A gradient of 0% to 20% methanol may be used to separate the N(1)- and N6-substituted NAD+ derivatives. The advantage of HPLC over TLC is that it is easier to quantify the levels of any impurities present in the product.

Table 1.4.4

Some Properties of bis-NAD+ and its Precursors

Rfa (TLC)

Compound

Retention timeb Molar absorbance (M-1cm-1) Solvent Ac Solvent Bd (HPLC)

NAD+ N(1)-carboxymethyl-NAD+ N6-carboxymethyl NAD+ Bis- NAD+

0.41 0.31 0.22 0.09

0.44 0.27 0.22 0.05

4.6 min 3.7 min 3.6 min 6.9 min

16,900 (at 259 nm) 17,100 (at 259 nm) 19,300 (at 266 nm) 42,800 (at 266 nm)

aThe R values given for TLC of coenzyme derivatives are not exact. They may vary by up to ±0.05. This may result from f

slight changes in the solvent composition over time, possibly as a result of evaporation. bThe HPLC retention times were obtained for samples chromatographed on a reversed-phase µBondapak C-18 column

(4 mm × 30 cm) and eluted with 0.1 M KH2PO4 containing 10% methanol. cSolvent A: 60:100:2 (w/v/v) (NH ) SO ;/0.1 M potassium phosphate, pH 6.8/1-propanol. 42 4 d5:3 (v/v) isobutyric acid/1 M aqueous NH , saturated with disodium EDTA. 3

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SUPPORT PROTOCOL 9

PILOT-SCALE AFFINITY PRECIPITATION STUDY In order to obtain optimal results from the purification procedure described in the Alternate Protocol, it is necessary to perform a small-scale (pilot) study to determine the optimum concentrations of protein and bis-NAD+ to use (see Fig. 1.4.6). This will vary somewhat between different enzyme preparations, owing to differences in enzyme concentration and the presence of other contaminants that may bind the bis-coenzyme. The optimum ratio of NAD+ equivalents/enzyme subunit may vary with the preparation used; for example, with a preparation of beef liver enzyme that had a specific activity of 2.7 U/mg (see Basic Protocol 3), about half the activity was found to precipitate at a ratio of 2 NAD+ equivalents/GDH subunit. However, a preparation with the lower specific activity of 0.8 U/mg required ∼8 NAD+ equivalents/GDH subunit to precipitate half the enzyme activity. The precipitation yield also depends on the protein concentration, as shown, for example, in Figure 1.4.7. Materials Sample of the enzyme preparation (Alternate Protocol) Bis-NAD+ (see Support Protocol 7) 700 mM glutaric acid, pH 7.0 Additional reagents and equipment for assaying protein concentration (see Basic Protocol 2) and GDH activity (see Basic Protocol 3) 1. Take an aliquot of the enzyme-containing solution from the DEAE-cellulose purification step (see Alternate Protocol and Basic Protocol 1) and assay it for GDH activity (see Basic Protocol 3) and protein concentration (see Basic Protocol 2) to determine its specific activity.

Precipitation (%)

100

50

0 0

5

10

15

20

[bis-NAD+ ] (mol/mol GDH subunit) Purification of Glutamate Dehydrogenase from Liver and Brain

Figure 1.4.6 An example of a pilot-scale affinity precipitation of ox liver glutamate dehydrogenase purified according to steps 1 to 3 of the Alternate Protocol. The experimental details are described in Support Protocol 9. The precise behavior will vary between preparations. The affinity precipitation is expressed as a percentage of the activity remaining in the control sample.

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Precipitation (%)

100

50

0

0

0.05 0.1 0.2 0.3 0.4

1

5

10

[Protein] (mg/ml)

Figure 1.4.7 An example of the effects of protein concentration on the pilot-scale affinity precipitation of ox liver glutamate dehydrogenase by bis-NAD+. These results are from a sample experiment performed as described in Support Protocol 9 in which the enzyme-containing sample was diluted to the indicated protein concentration before precipitation with a constant amount of bis-NAD+.

2. Calculate the approximate concentration of enzyme, based on the specific activity and subunit relative molecular mass. Assuming each subunit to have a relative molecular mass of 56,700 and the pure enzyme to have a specific activity of 40 ìmol/min/mg protein, this corresponds to ∼2267 mol product/min/mol enzyme subunit. Thus:

mol of subunit =

activity (µmol/min ) 2267 × 10 6

3. Measure effects of bis-NAD+ concentration as follows: a. Add a 100-µl sample of the enzyme preparation to each of 10 microcentrifuge tubes. b. To each tube, add 12.7 µl of 700 mM glutaric acid, pH 7.0, and varying amounts of bis-NAD+ within the range 0 to 10 coenzyme equivalents/enzyme subunit. c. Also prepare sample containing no bis-NAD+ (to which water is added in place of the coenzyme derivative) as well as a control tube that contains no bis-NAD+ or glutarate, so that the percentage inhibition resulting from the presence of the glutarate can be calculated. The concentration of bis-NAD+ can be calculated from the absorbance at 266 nm and the molar absorbance coefficient given in Table 1.4.4. A 1 M solution of bis-NAD+ will give an absorbance, in a 1-cm light-path cuvette, of 42,800, and each mole of bis-NAD+ contains 2 NAD+ equivalents. Thus:

2( A266 ) = coenzyme equivalent concentration in mol/liter 42,800

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4. Allow the tubes to stand at 4°C for at least 30 min (or overnight, if required), then microcentrifuge 5 min at maximum speed (∼13,000 × g), 4°C. 5. Assay the GDH activity remaining in the supernatant (see Basic Protocol 3) of each sample and express it as a percentage of the activity remaining in the control sample. The maximum affinity precipitation has occurred in the tube with the minimal residual activity and thus gives the appropriate concentration of bis-coenzyme to add to obtain maximum affinity precipitation. These experiments should be performed at least in duplicate.

REAGENTS AND SOLUTIONS Use Milli-Q-purified water or equivalent for the preparation of all buffers. For common stock solutions, see APPENDIX 2E; for suppliers, see SUPPLIERS APPENDIX.

Affinity chromatography buffer 100 mM Tris acetate 1 mM KH2PO4 0.1 mM EDTA Adjust pH to 7.15 at 4°C Store at 0° to 4°C up to 1 week; check pH before use and adjust if necessary Biuret reagent Dissolve 1.5 g copper sulfate (CuSO4⋅5H2O) and 6.0 g sodium potassium tartrate (C4H4KNaO6⋅4H2O) in 500 ml distilled H2O. Add 300 ml of 10% (w/v) sodium hydroxide and dilute the mixture to 1 liter with distilled water. Homogenization buffer 4 mM sodium phosphate buffer, pH 7.4 (APPENDIX 2A) 0.5 mM EDTA Store with above components at 0° to 4°C up to 1 week; check pH before use and adjust if necessary 0.1 mM phenymethanesulfonyl fluoride (PMSF; add immediately before use as described below) PMSF is unstable in aqueous solution and must be added to the buffer immediately before use. This is done by adding a suitable volume of a 100 mM PMSF solution in acetone to a small quantity (approximately one-tenth of the final volume) of boiling buffer, and immediately mixing this solution with the remainder of the ice-cold buffer.

COMMENTARY Background Information

Purification of Glutamate Dehydrogenase from Liver and Brain

There are several factors that must be considered in the choice of a purification procedure. The major factors are how much enzyme is needed for the planned studies, how long the procedure will take, and how much it will cost. To these should be added the question of whether the purified enzyme is different from the material that was in the tissues to start with. As discussed below, GDH purified by some older procedures has been shown to have undergone limited proteolysis, which alters its kinetic and regulatory behavior. Clearly, such preparations can give misleading information about the behavior of the enzyme.

Several alternative procedures have been used to purify GDH from different mammalian sources. The procedures described in this unit are preferable to the earlier procedures that involved precipitation with ethanol or acetone and/or detergent extraction followed by fractional precipitation with ammonium sulfate and/or sodium sulfate (see e.g., Fahien et al., 1969). Although that procedure resulted in a crystalline preparation of the enzyme, it has been shown that limited proteolysis results in a loss of a tetrapeptide from the amino-terminal end of the protein (McCarthy et al., 1980). This modification, which results in a slightly faster mobility on SDS (0.1% w/v) PAGE in 13%

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polyacrylamide gels (McCarthy et al., 1980), significantly affects the kinetic behavior of the enzyme (McCarthy and Tipton, 1984) and its in teraction s with allosteric effectors (McCarthy and Tipton, 1985; Couée and Tipton, 1989a) and with antipsychotic drugs such as perphenazine (Couée and Tipton, 1990a,b). An obvious alternative to the hard work of purifying an enzyme is to buy it from a commercial source. Purified preparations of ox (beef) liver GDH have been available from several commercial sources: BoehringerMann heim (n ow Ro ch e Diagn ostics, Mannheim), Calbiochem, PL Biochemicals (now Pharmacia P-L Biochemicals), Sigma, and United States Biochemical (USB), although some of these sources no longer supply purified preparations of GDH. However, the authors have found the enzyme from all these sources to have suffered the same, or more, proteolytic modification as that suffered by the enzyme prepared by the earlier procedures discussed above (see McCarthy et al., 1980). Cloning and expression of GDH is an alternative source of enzyme (Shashidharan et al., 1994; Plaitakis et al., 2000). An expression system has the advantage that it permits site-directed mutagenesis studies (see Fang et al., 2002; Zaganas and Plaitakis, 2002). It is also, of course, useful in cases where there are rather small quantities of enzyme in the chosen source material. This is not the case with the major isoform of GDH (GLUD1) in liver, but it is the case for the so-called “nerve tissue–specific” form (GLUD2) which is present in much lower abundance in the brain (see Zaganas and Plaitakis, 2002). It is still necessary to purify such an expressed enzyme if one needs to work with a homogeneous preparation. In some cases, this can be facilitated by the insertion of a tag into the gene to be expressed. Such tags range from oligohistidine tails to another protein, such as glutathione-S-transferase. It is common not to remove a tag after purification, and indeed in some cases it is not easy to do so. The assumption that the presence of such a tag will not affect the behavior of the expressed enzyme may sometimes be true, but it is certainly not always so (see e.g., Halliwell et al., 2001; Hiser et al., 2001; Ledent et al., 1997). In the case of GDH, as already discussed, modification at the amino-terminal end results in alterations in its behavior, and the C-terminal region appears to be involved in its allosteric regulation (Peterson and Smith, 1999). This suggests that expression of the protein with a tag at either end would be

likely to produce artefactual results. Indeed, it is necessary to use conventional procedures, involving a GTP affinity column, to purify the enzyme after cloning and expression (see Fang et al., 2002; Zaganas and Plaitakis, 2002). To assess the purity of the protein, polyacrylamide gel electrophoresis (PAGE) in the presence and absence of sodium dodecyl sulfate (SDS) remains the accepted standard for assessing protein purity (UNITS 10.2-10.4). The hexameric glutamate dehydrogenase from ox and some other mammalian species forms higher aggregates at higher protein concentrations (see, e.g., McCarthy et al.,1981). Although this process is reversible, with a dissociation constant of ∼1 mg/ml (Cohen et al., 1976), it hampers attempts to determine the behavior on nondenaturing PAGE. However PAGE in the presence of SDS reveals that the enzyme prepared as described here contains only a few minor impurities (see McCarthy et al., 1980).

Critical Parameters and Troubleshooting

A critical aspect of protein purification procedures is to have everything that will be needed prepared in advance. For example, it is a recipe for disaster to finish one step and then find that the column needed for the next has not yet been equilibrated. Many enzymes are relatively unstable and susceptible to degradation by contaminating peptidases. Generally, to avoid losses of enzyme activity, the procedure should be completed as rapidly as possible. The essence of any robust purification procedure is that any minor problems with one step are corrected by the others. Thus, for example, a somewhat lower degree purification on the DEAE-cellulose column may be compensated for in the affinity-chromatographic step. Some problems that are common to many purification procedures are listed below. Failure to use the highest purity reagents. Analytical grade chemicals should be used for such procedures as preparing buffers and ammonium sulfate precipitation. Less pure materials may contain compounds that inhibit enzyme activity. Working at the wrong temperature. Most of the procedures involved should be executed at 0° to 4°C. This is most easily accomplished in a cold room, although cooling the material in an ice bucket is satisfactory for some steps. Performing those steps at higher temperatures will result in enzyme inactivation. Overzealous stirring. This can cause frothing of protein solutions, which results in some

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Purification of Glutamate Dehydrogenase from Liver and Brain

denaturation. Stirring speeds should be fast enough to ensure adequate mixing, but no faster; if frothing does occur the stirring is too fast. Note that some frothing does occur during steps 1 and 2 of Basic Protocol 1, but this does not result in significant losses of GDH activity, probably because of the high protein concentrations at those stages. Failure to equilibrate chromatographic columns. Equilibration can be judged to have occurred when the pH and conductivity of the out-flowing buffer is the same as that of the in-flowing equilibration buffer. To use a column before complete equilibration has occurred will usually result in poor resolution of the sample components. Using a chromatographic column that is too old. The degradation of GTP-Sepharose after ∼6 months of storage is discussed in Support Protocol 4. DEAE-cellulose is quite stable if stored under the conditions described in Support Protocol 4. However, with time, the accumulation of tightly-bound material may affect the performance of columns, in which case the recycling procedure outlined in Support Protocol 4 should be used, or, alternatively, fresh DEAE cellulose can be used. Failure to check that the pH of buffers is correct. The pH should be adjusted at the temperature at which it will be used. Some buffers contain additional components, such as EDTA, and the pH should be checked and, if necessary, adjusted after all ingredients have been added. Failure to desalt samples before chromatographic steps. The presence of high concentrations of salt may prevent compounds of interest from binding to the column. In all cases where the enzyme is meant to bind to the column, it is important to retain the material that passes unretarded through the column and to assay it for enzyme activity as soon as possible. If material that should have bound passes straight through the column, it may be possible to rescue it by further desalting and application to a freshly equilibrated column. If in doubt about effective desalting, check the conductivity. The flow rate through the column is very slow, or there is apparently no flow at all. Do not load the enzyme-containing material onto a column that is running more slowly than specified. Very slow flow rates may occur if the material in the column has become compacted. This can arise if the column is run at too high a pressure. Always follow the manufacturers’ instructions about the maximum hydrostatic pressure that can be applied, and do not exceed that value. The accumulation of denatured material at the top of

a column can also slow the flow rate. This may be rectified by removing some of the chromatographic material from the top of the column, which is often identifiable by discoloration, adding an equivalent amount of fresh material, and repacking the column (see Support Protocol 4). In the case of DE-52 DEAE-cellulose, failure to remove the “fines” before using the resin (see Support Protocol 5) can result in inadequate flow rates. Flow rates through columns that are flowing well are likely to slow considerably if the sample applied contains appreciable amounts of particulate material. If particulate material is present, it should be removed by centrifugation, or, where appropriate, filtration, before the sample is applied to the column. Air bubbles or cracks are present in columns. This can result in the applied material not coming into adequate contact with the chromatographic material. Air bubbles can occur all too easily in gel-filtration media (e.g., Sephadex and Sepharose, including GTP-Sepharose) unless the manufacturer’s instructions for packing the material into columns are heeded. Cracks can occur if the material in the column is allowed to dry out. If that occurs, the only solution is to remove the material from the column, rehydrate and reequilibrate it, and then repack the column. The following subsections discuss only those steps where difficulties may be encountered through causes other than those discussed above. Should the enzyme lose activity or the final product have a specific activity significantly lower than expected, comparison of the data for each step with those shown in Tables 1.4.5 and 1.4.6 for Basic Protocol 1, or Tables 1.4.7 and 1.4.8 for the Alternate Protocol, should indicate where the problem occurred. Basic Protocol 1: purification of GDH involving affinity chromatography on GTP-Sepharose Ammonium sulfate precipitation. Too rapid addition of solid ammonium sulfate can result in high local concentrations of the salt, causing excess precipitation of material that does not all redissolve. In the present procedure, homogenization between additions minimizes this problem. If the solid ammonium sulfate salt contains lumps, break these up using a pestle and mortar before using it. It is possible to perform the precipitation by continuously stirring the enzyme solution in a beaker and adding the ammonium sulfate to it. If this approach is used, the salt should be added very slowly, over a period of 1 to 2 hr, to minimize the above problem. In either proce-

1.4.28 Supplement 29

Current Protocols in Protein Science

Table 1.4.5 Protocol 1

Purification of Glutamate Dehydrogenase From Ox Brain According to Basic

Stage (see Fig. 1.4.1)

Volume (ml)

Total protein Total activity (mg) (µmol/min)

1500 1100 250 600 90

75,000a 23,000a 6200a 320b 12.5b

1 2 3 4 5

2280 1150 925 775 500

Specific activity Purification (µmol/min/mg) (x-fold) 0.03 0.05 0.15 2.4 40

1 2 5 80 1300

Yield (%) 100 50 41 33 22

aProtein concentration determined by the biuret procedure. bProtein concentration determined from the absorbance at 280 nm.

Table 1.4.6 Purification of Glutamate Dehydrogenase From Ox Liver According to Basic Protocol 1

Stage (see Fig. 1.4.1) 1 2 3 4 5

Volume (ml)

Total protein Total activity (mg) (µmol/min)

500 450 160 500 90

6000a 2500a 880a 150b 25b

1800 15000 1410 1200 1000

Specific activity Purification (µmol/min/mg) (x-fold) 0.3 0.6 1.6 8.0 40

1 2 5 25 120

Yield (%) 100 85 75 67 55

aProtein concentration determined by the biuret procedure. bProtein concentration determined from the absorbance at 280 nm.

Table 1.4.7 Protocol 1

Purification of Glutamate Dehydrogenase From Ox Liver According to Alternate

Stage (see Fig. 1.4.4)

Volume (ml)

Total protein Total activity (mg)a (µmol/min)

370 75 8.4 1.2

8580 2010 139 9.4

1 2 3 4

1920 1640 408 376

Specific activity Purification (µmol/min/mg) (x-fold) 0.2 0.3 2.9 40

1 4 13 180

Yield (%) 100 86 21 20

aProtein concentration determined by the Lowry method.

Table 1.4.8 Purification of Glutamate Dehydrogenase From Rat Liver According to Alternate Protocol 1

Stage (see Fig. 1.4.4) 1 2 3 4

Volume (ml)

Total protein Total activity (mg)a (µmol/min)

90 35 9.8 1.0

2720 980 130 9.4

760 505 195 190

aProtein concentration determined by the Lowry method.

Specific activity Purification (µmol/min/mg) (x-fold) 0.3 0.5 1.5 37

1 2 5 140

Yield (%) 100 67 26 25

Strategies of Protein Purification and Characterization

1.4.29 Current Protocols in Protein Science

Supplement 29

dure, it is important to continue to stir for 20 to 30 min after the last ammonium sulfate addition, to ensure that any overprecipitated material has a chance to redissolve and that any material that should precipitate at that salt concentration does so. The use of a homogenizer during this stage has proven to be effective, but it is not recommended for ammonium sulfate fractionation of purer material, since excessive frothing can result in denaturation. Addition of ammonium sulfate will result in a decrease in pH. For GDH purification, it is not necessary to adjust the pH to the starting value between each addition of ammonium sulfate, although such readjustment is necessary for many other enzymes. Dialysis and concentration. Provided that the dialysis tubing is pretreated as described in UNIT 4.4 and APPENDIX 3B and that it has been tested for leaks, there should be no difficulties with these procedures. It should be stressed again that an air-free space should be left at the top of the knotted dialysis tube, because volume increases during dialysis can otherwise cause the bag to rupture. The concentration procedures should not cause difficulties if the directions given in Support Protocol 6 are followed. Chromatography on DEAE cellulose. Common difficulties with this step have been discussed in the general points at the beginning of this section. Chromatography on GTP-Sepharose. If the GDH enzyme does not bind to the column and the troubleshooting points at the beginning of this section have been taken into account, the likely causes are either that the affinity resin has degraded (see Support Protocol 4) or that there was a problem with its preparation. In first case, the GTP-Sepharose should be replaced by fresh material. If there has been a problem with its preparation, a fresh batch should be prepared using the procedure described in Support Protocol 1 to check for effective GTP coupling. Choice of reaction buffer. As indicated in Basic Protocol 1, the enzyme is unstable in the Tris/acetate buffer and losses of activity can occur if the enzyme is left in that buffer for extended periods.

Purification of Glutamate Dehydrogenase from Liver and Brain

Basic Protocol 3: assay of glutamate dehydrogenase activity In all cases, it will be necessary to dilute the enzyme preparation in order to obtain a rate that is sufficiently slow to measure with accuracy. The amount of dilution will depend on the state of purification of the enzyme, its protein concentation, and also the scale expansion used on

the spectrophotometer. For example, the crude homogenate of liver GDH has a specific activity of 0.3 µmol/min/mg protein (see Table 1.4.6). Since the production of 1 µmol of glutamate in the assay will give an absorbance change of 2.073 (see Basic Protocol 3, annotation to step 5) , 1 mg of this material will give an absorbance decrease, in 1 min, of 0.3 × 2.073 ≈ 0.62. The protein concentration of this crude homogenate is ∼12 mg/ml (see Table 1.4.6); therefore, 50 µl of this would contain 0.6 mg protein, and would be expected to give an absorbance change of ∼0.373 absorbance units per min. This would mean that the reaction would be finished in <3 min, which would be too fast for accurate initial-rate determinations. See, for example, Tipton (2002) for a fuller treatment of enzyme assays and initial-rate determinations. Dilution of this material 20× with the assay buffer before adding 50 µl to the assay would give an absorbance change of 0.01865 absorbance units per min, which would mean that it would take a little over 8 min for the absorbance to fall by 0.15 absorbance units. Similar calculations from the values in Table 1.4.6 for the purified liver GDH show that a dilution factor of 600 would be necessary so that a 50-µl volume added to the assay would give a rate similar to that given by the crude enzyme. The appropriate dilution factors for the other stages of purification and for the brain enzyme can be estimated from the date given in Tables 1.4.6 and 1.4.5, respectively. It should be noted that these numbers are only a rough guide, since the degree of purification and protein concentration may vary for individual steps of the purification. The addition of smaller volumes of the enzyme preparation to the assay will reduce the degree of dilution necessary. Alternate Protocol: purification of GDH involving affinity precipitation with bis-NAD+ The initial steps in this procedure are the same as those of Basic Protocol 1. Affinity precipitation. A pilot-scale precipitation, as described in Support Protocol 9, should always be carried out to determine the optimum concentration of bis-NAD+ to use. Figure 1.4.6 shows an example of a concentration-precipitation relationship, but it should be remembered that the optimum bis-NAD+ concentration may vary with protein concentration. Failure to obtain affinity precipitation could result from: 1. A fault in the synthesis of the bifunctional ligand. Monitoring the success of each step in the preparatory procedure using the pro-

1.4.30 Supplement 29

Current Protocols in Protein Science

cedures outlined in Support Protocol 8 and the data in Table 1.4.2, should reveal any problems in this respect and where they occur. 2. Attempting to precipitate from a solution containing too low a concentration of GDH (see Fig. 1.4.7). Further concentration of the enzymecontaining sample should deal with this problem. Stability The enzyme, stored as described in Basic Protocol 1, is stable for at least 6 months without significant loss of activity or alteration of its mobility on a 13% polyacrylamide gel in the presence SDS. This is important in view of the relatively large amounts of purified GDH that can be prepared. However, changes can occur in its responses to allosteric effectors when the enzyme is stored in an atmosphere of air (Couée and Tipton, 1991). These changes, which may only become apparent after 2 to 3 months, are prevented by storing the enzyme solution under an atmosphere of nitrogen, and tubes containing stored GDH preparations should be routinely flushed with N2 each time they are opened. Safety All personnel involved should have received training in safe laboratory procedures and in the use of radioactive materials if Support Protocol 2 (involving radiolabeled GTP) is to be used. National and local regulations for the disposal of such materials as radiochemical waste and or-

Anticipated Results Basic Protocol 1: purification of ox brain or liver glutamate dehydrogenase involving affinity chromatography on GTP-Sepharose Tables 1.4.5 and 1.4.6 summarize the results obtained from typical purifications of the ox brain and liver enzymes using this procedure.

A280 nm ( ) conductivity ( ) GDH Activity ( )

6

8 4 6 2

GDH activity (µmol/min/ml)

Conductivity (mS)

10

ganic solvents should be followed. Several of the compounds used in these procedures are highly toxic and should be handled with great care. Toxicity information on these compounds is available from the manufacturers (e.g., Sigma-Aldrich at https://www.sigma-aldrich. com) and should be consulted in advance. For the use of animal materials, precautions against infectious and transmissible diseases are necessary. It is advisable that personnel be immunized against such diseases where appropriate. Although the ox tissue samples that the authors have used recently have been certified as being BSEfree, the initial steps of the purification are still performed under biohazard containment, protection and disposal conditions. It is essential that those steps indicated in Support Protocols 1 and 3 be performed under an adequately functioning, biohazard-containment level II fume hood. Safety glasses should be worn, and, in connection with the TLC procedure in Support Protocol 8, these should provide protection from UV light.

4

0 50

100 Fraction number

150

Figure 1.4.8 Chromatography of ox liver glutamate dehydrogenase at step 3 of Basic Protocol 1. A typical elution profile is shown. However the exact behavior may vary between preparations. The fraction size was ∼10 ml. In the left-hand y-axis label, mS stands for millisiemens.

Strategies of Protein Purification and Characterization

1.4.31 Current Protocols in Protein Science

Supplement 29

A280 nm ( ) conductivity ( ) GDH Activity ( )

12

10

2.0

8 30

A280

1.5

1.0

20

10

0.5

Conductivity (mS)

6

4

2

Glutamate dehydrogenase activity (µ mol/min/ml)

2.5

0 15

30 Fraction number

45

Figure 1.4.9 A typical elution profile of affinity chromatography of ox liver glutamate dehydrogenase on GTP-Sepharose at step 4 of Basic Protocol 1. The exact chromatographic behavior may vary between preparations. The fraction size was ∼10 ml. In the label for the inset scale, mS stands for millisiemens.

Examples of the elution profiles from DEAE-cellulose and GTP-Sepharose are shown in Figures 1.4.8 and 1.4.9.

Time Considerations

Day 3: Gel filtration into the GTPSepharose buffer, passage through the hydrazide gel column, and affinity chromatography on GTP-Sepharose. This schedule excludes the time taken to prepare the GTP-Sepharose. This should take 3 days or less, starting from CNBr-activated Sepharose. However much of this time is taken up by incubations, such as the 16- to 18-hr reaction of L-glutamic acid γ-methyl ester with the activated gel (see Support Protocol 1). The affinity resin, once made, can be used several times.

Basic Protocol 1: purification of ox brain or liver glutamate dehydrogenase involving affinity chromatography on GTP-Sepharose The entire purification procedure can easily be performed in 3 days. Day 1: Extraction and ammonium sulfate precipitation, followed by overnight dialysis. Day 2: DEAE-cellulose fractionation and concentration of appropriate column fractions.

Alternate Protocol: purification of rat or ox liver GDH involving affinity precipitation with bis-NAD+ This is a somewhat quicker procedure than Basic Protocol 1 because it avoids the final column chromatography steps. However, it still extends into several days. The first two of these are the same as those for Basic Protocol 1, and the third is for the pilot-scale precipitation study and the affinity precipitation itself. The preparation of bis-NAD+ can be completed in 4 days, provided that the reaction

Alternate Protocol: purification of rat or ox liver GDH involving affinity precipitation with bis-NAD+ Tables 1.4.7 and 1.4.8 summarize the results obtained from typical purifications of the ox and rat liver enzymes using this procedure. The time estimates given below do not include the time involved in pouring and equilibrating the chromatographic columns, which should be done before the purification procedure is started.

Purification of Glutamate Dehydrogenase from Liver and Brain

1.4.32 Supplement 29

Current Protocols in Protein Science

between iodoacetate and NAD+ is performed at 37°C (see Support Protocol 7). Once prepared, the bis-NAD+ may be used for several GDH preparations, as well as for the purification of some other dehydrogenases (see Irwin and Tipton, 1996)

Literature Cited

Beattie. R.E., Buchanan, M.,. and Tipton K.F. 1987. The synthesis of N2, N2-adipodihydrazido-bis(N-carboxymethyl-ATP) and its use in the purification of phosphofructokinase. Biochem. Soc. Trans. 15:1043-1044. Cho, S.W., Lee, J., and Choi, S.Y. 1995. Two soluble forms of glutamate dehydrogenase isoproteins from bovine brain. Eur. J. Biochem. 233:340-346. Cohen, R.J., Jedziniak, J.A., and Benedek, G.B. 1976. The functional relationship between polymerization and catalytic activity of beef liver glutamate dehydrogenase. II. Experiment. J. Mol. Biol. 108:179-199. Couée, I. and Tipton, K.F. 1989a. Activation of glutamate dehydrogenase by L-leucine. Biochim. Biophys. Acta 995:97-101. Couée, I. and Tipton, K.F. 1989b. The effects of phospholipids on the activation of glutamate dehydrogenase by L-leucine. Biochem. J. 261:921-925. Couée, I. and Tipton, K.F. 1990a. The inhibition of glutamate dehydrogenase by some antipsychotic drugs. Biochem. Pharmacol. 39:827-832. Couée, I. and Tipton, K.F. 1990b. Inhibition of ox brain glutamate dehydrogenase by perphenazine Biochem. Pharmacol. 39:1167-1173. Couée, I. and Tipton, K.F. 1991. The sulphydryl groups of ox brain and liver glutamate dehydrogenase preparations and the effects of oxidation on their inhibitor sensitivities.. Neurochem. Res. 16:773-780. Dawson, R.M.C., Elliott, D.C., Elliott ,W.H., and Jones, K.M. 1989. Data for Biochemical Research, 3rd ed. Clarendon Press, Oxford. Engel, J.D. 1975. Mechanism of the Dimroth rearrangement in adenine. Biochem. Biophys. Res. Commun. 64:581 - 585. Fahien, L.A., Strmecki, M., and Smith, S. 1969. Studies of gluconeogenic mitochondrial enzymes. I. A new method of preparing bovine liver glutamate dehydrogenase and effects of purification methods on properties of the enzyme. Arch. Biochem. Biophys. 130:449-455. Fang, J., Hsu, B.Y., MacMullen, C.M., Poncz, M., Smith, T.J., and Stanley, C.A. 2002. Expression, purification and characterization of human glutamate dehydrogenase (GDH) allosteric regulatory mutations. Biochem J. 363:81-87. Flygare, S., Griffin, T., Larsson, P.O., and Mosbach, K. 1983. Affinity precipitation of dehydrogenases. Anal. Biochem. 133:409-416. Godinot, C, Julliard, J.H., and Gautheron, D.C. 1974. A rapid and efficient new method of purification of glutamate dehydrogenase by affinity

chromatography on GTP-sepharose. Anal. Biochem. 61:264-270. Graham, L.D., Griffin, T.O., Beattie, R..E., McCarthy, A..D., and Tipton, K.F. 1985. Purification of liver glutamate dehydrogenase by affinity precipitation and studies on its denaturation. Biochim. Biophys. Acta 828:266-269. Hahn-Deinstrop, E. 2000. Applied Thin-Layer Chromatography: Best Practice and Avoidance of Mistakes. Wiley-VCH, Weinheim, Germany. Halliwell, C.M., Morgan, G., Ou, C.P., and Cass, A.E. 2001. Introduction of a (poly)histidine tag in L-lactate dehydrogenase produces a mixture of active and inactive molecules. Anal. Biochem. 295:257-261. Hiser, C., Mills, D.A., Schall, M., and FergusonMiller, S. 2001. C-terminal truncation and histidine-tagging of cytochrome c oxidase subunit II reveals the native processing site, shows involvement of the C-terminus in cytochrome c binding, and improves the assay for proton pumping. Biochemistry 40:1606-1615. Irwin, J.A., and Tipton, K.F. 1996. Affinity precipitation methods. In Methods in Molecular Biology 59: Protein Purification Protocols (S. Doonan, ed.) pp. 217-238. Humana Press, Totowa, N.J. Jackson, R.J., Wolcott, R.M., and Shiota, T. 1973. The preparation of a modified GTP-sepharose derivative and its use in the purification of dihydroneopterin triphosphate synthetase, the first enzyme in folate biosynthesis. Biochem Biophys. Res. Commun. 51:428-435. Larsson, P.O., Flygare, S., and Mosbach, K. 1984. Affinity precipitation of dehydrogenases. Methods Enzymol. 104:364-349. Ledent, P., Duez, C., Vanhove, M., Lejeune, A., Fonze, E., Charlier, P., Rhazi-Filali, F., Thamm, I., Guillaume, G., Samyn, B., Devreese, B., Van Beeumen, J., Lamotte-Brasseur, J., and Frere J.M. 1997. Unexpected influence of a C-terminal-fused His-tag on the processing of an enzyme and on the kinetic and folding parameters. FEBS Lett. 413:194-196. Lee, W.K., Shin, S., Cho, S.S., and Park, J.S. 1999. Purification and characterization of glutamate dehydrogenase as another isoprotein binding to the membrane of rough endoplasmic reticulum. J. Cell. Biochem. 76:244-253. McCarthy, A.D. and Tipton, K.F. 1984. The effects of magnesium ions on the interactions of ox brain and liver glutamate dehydrogenase with ATP and GTP. Biochem. J. 220:853-855. McCarthy, A.D. and Tipton, K.F. 1985. Ox liver glutamate dehydrogenase: Comparison of the kinetic properties of native and proteolysed preparations. Biochem. J. 230:95-99. McCarthy, A.D., Walker, J.M., and Tipton, K.F. 1980. Purification of glutamate dehydrogenase from ox brain and liver: Evidence that commercially available preparations of the enzyme have suffered proteolytic cleavage. Biochem. J. 191:605-611.

Strategies of Protein Purification and Characterization

1.4.33 Current Protocols in Protein Science

Supplement 29

McCarthy, A.D., Johnson, P., and Tipton, K.F. 1981. Sedimentation properties of native and proteolysed preparations of ox glutamate dehydrogenase. Biochem. J. 199:235-236. McDaniel, H.G. 1995. Comparison of the primary structure of nuclear and mitochondrial glutamate dehydrogenase from bovine liver. Arch. Biochem. Biophys. 319:316-321. Mulcahy, P. and O’Flaherty, M. 2001. Kinetic locking-on strategy and auxiliary tactics for bioaffinity purification of NAD(P)(+)-dependent dehydrogenases. Anal. Biochem. 299:1-18. O’Carra, P., Griffin, T., O’Flaherty, M., Kelly, N., and Mulcahy, P. 1996. Further studies on the bioaffinity chromatography of NAD(+)-dependent dehydrogenases using the locking-on effect. Biochim. Biophys. Acta 1297:235-243. O’Flaherty, M., O’Carra, P., McMahon, M., and Mulcahy, P. 1999. A “stripping” ligand tactic for use with the kinetic locking-on strategy: Its use in the resolution and bioaffinity chromatographic purification of NAD(+)-dependent dehydrogenases. Protein Expr. Purif. 16:424-431.

Purification of Glutamate Dehydrogenase from Liver and Brain

Approach (R. Eisenthal and M.J. Danson, eds.) pp. 1-47. Oxford University Press, Oxford, U.K. Tipton, K.F. and Couée, I. 1988. Glutamate dehydrogenase. In Glutamine and Glutamate in Mammals. (E. Kvamme, ed.) pp.81-100. CRC Press, Baton Roca, Fla. Wilson, K. and Walker, J. (eds.) 2000. Principles and Techniques of Practical Biochemistry, 5th ed.. Cambridge University Press, Cambridge, U.K. Yoon, H.Y., Hwang, S.H., Lee, E.Y., Kim, T.U., Cho, E.H., and Cho, S.W. 2001. Effects of ADP on different inhibitory properties of brain glutamate dehydrogenase isoproteins by perphenazine. Biochimie 83:907-913. Zaganas, I. and Plaitakis, A. 2002. A single amino acid substitution (Gly456Ala) in the vicinity of the GTP binding domain of human housekeeping glutamate dehydrogenase markedly attenuates GTP inhibition and abolishes the enzyme’s cooperative behavior. J. Biol. Chem. 277:2642226428.

Key References

Peterson, P.E. and Smith, T.J. 1999. The structure of bovine glutamate dehydrogenase provides insights into the mechanism of allostery. Structure Fold. Des. 15:769-782.

Glutamate dehydrogenase

Plaitakis, A. and Zaganas, I. 2001. Regulation of human glutamate dehydrogenases, implications for glutamate, ammonia and energy metabolism in brain. J. Neurosci. Res. 66:899-908.

There have been few comprehensive reviews of glutamate dehydrogenase in recent years. The references cited above cover some fundamental aspects.

Plaitakis, A., Metaxari, M., and Shashidharan, P. 2000. Nerve tissue-specific (GLUD2) and housekeeping (GLUD1) human glutamate dehydrogenases are regulated by distinct allosteric mechanisms: Implications for biologic function. J. Neurochem. 75:1862-1869.

Affinity chromatography

Preiss, T., Hall, A.G., and Lightowlers, R.N. 1993. Identification of bovine glutamate dehydrogenase as an RNA-binding protein. J. Biol. Chem. 268:24523-24526.

Affinity precipitation

Rajas, F. and Rousset, B. 1993. A membrane-bound form of glutamate dehydrogenase possesses an ATP-dependent high-affinity microtubule-binding activity. Biochem. J. 295:447-455.

Describes the theory underlying affinity precipitation and reviews its applications.

Shashidharan, P., Michaelides, T.M., Robakis, N.K., Kretsovali, A., Papamatheakis, J., and Plaitakis, A. 1994. Novel human glutamate dehydrogenase expressed in neural and testicular tissues and encoded by an X-linked intronless gene. J. Biol. Chem. 269:16971-16976

Contributed by Martha Motherway and Keith F. Tipton Department of Biochemistry Trinity College Dublin, Ireland

Shashidharan, P., Clarke, D.D., Ahmed, N., Moschonas, N., and Plaitakis, A. 1997. Nerve tissue-specific human glutamate dehydrogenase that is thermolabile and highly regulated by ADP. J. Neurochem. 68:1804-1811.

Alun D. McCarthy GlaxoSmithKline Middlesex, United Kingdom

Smith, T.J., Peterson, P.E., Schmidt, T., Fang, J., and Stanley, C.A. 2001. Structures of bovine glutamate dehydrogenase complexes elucidate the mechanism of purine regulation. J. Mol. Biol. 307:707-720.

Plaitakis, A. and Zaganas, I. 2001. See above. Tipton, K.F. and Couée, I. 1988. See above.

Matejtschuk, P. (ed.) 1997. Affinity Separations: A Practical Approach. Oxford University Press, U.K. A collection of chapters on the theory and practice of different aspects of affinity chromatography.

Irwin, J.A. and K.F. Tipton, K.F. 1995. Affinity precipitation: A novel approach to protein purification. Essays Biochem. 29:137-156.

Ivan Couée Centre National de la Recherche Scientifique Rennes, France Jane Irwin University College Dublin Dublin, Ireland

Tipton K.F. 2002. Principles of enzyme assay and kinetic studies. In Enzyme Assays: A Practical

1.4.34 Supplement 29

Current Protocols in Protein Science

Overview of the Physical State of Proteins Within Cells The word protein comes from the Greek word proteios, meaning primary. And, indeed, proteins are of primary importance in the study of cell function. It is difficult to imagine a cellular function not linked with proteins. Almost all biochemical catalysis is carried out by protein enzymes. Proteins participate in gene regulation, transcription, and translation. Intracellular filaments give shape to a cell while extracellular proteins hold cells together to form organs. Proteins transport other molecules, such as oxygen, to tissues. Antibody molecules contribute to host defense against infections. Protein hormones relay information between cells. Moreover, protein machines, such as actin-myosin complexes, can perform useful work, including cell movement. Thus, studying proteins is a prerequisite in understanding cell structure and function. The physical characterization of proteins began well over 150 years ago with Mulder’s characterization of the atomic composition of proteins. In the latter half of the nineteenth century Hoppe-Seyler (1864) crystallized he-

UNIT 1.5

moglobin and Kühn (1876) purified trypsin. A variety of physical methods have been developed over the years to increase convenience and precision in the characterization and isolation of proteins. These include ultracentrifugation, chromatography, electrophoresis, and others. In many instances our understanding of cell proteins parallels the introduction and use of new techniques to examine their structure and function.

PROTEIN CLASSIFICATIONS All proteins are constructed as a linear sequence(s) of various numbers and combinations of ∼20 α-amino acids joined by peptide bonds to form structures from thousands to millions of daltons in size. Proteins are the most complex and heterogeneous molecules found in cells, where they account for >50% of the dry weight of cells and ∼75% of tissues. Proteins can be classified into three broad groups: globular, fibrous, and transmembrane (Fig. 1.5.1; Table 1.5.1). Globular proteins are, by definition, globe-shaped, although in prac-

globular

fibrous

Figure 1.5.1 General classifications of proteins. In these schematic representations of globular, fibrous, and transmembrane proteins, hydrophobic regions are shaded. Note that the disposition of hydrophobic residues often reflects the protein class.

transmembrane

Strategies of Protein Purification and Characterization Contributed by Howard R. Petty Current Protocols in Protein Science (2002) 1.5.1-1.5.10 Copyright © 2002 by John Wiley & Sons, Inc.

1.5.1 Supplement 31

Table 1.5.1

Broad Classifications for Proteinsa

Type

Location/type

Examples

Globular

Intracellular

Hemoglobin, lactate dehydrogenase, cytochrome c Serum albumin, immunoglobulins, lysozyme

Extracellular Fibrous

Intracellular Extracellular

Transmembrane

Single pass Multipass

Intermediate filaments, tropomyosin, lamins Collagen, keratin, elastins Insulin receptor, glycophorin, HLAsb Glucose transporter, rhodopsin, acetylcholine receptor

aAdditional information regarding fibrous and transmembrane proteins can be found in Squire

and Vibert (1987) and Petty (1993). Information concerning globular proteins can be found in numerous books on proteins and enzymes such as Schultz and Schirmer (1979). bHuman histocompatibility leukocyte antigens.

tice they can be spherical or ellipsoidal. Globular proteins are generally soluble in aqueous environments. Examples of globular proteins are hemoglobin, serum albumin, and most enzymes. Fibrous proteins are elongated linear molecules that are generally insoluble in water and resist applied stresses and strains. Collagen is a physically tough molecule of connective tissue. Just as collagen gives strength to connective tissues, intermediate filaments linked to desmosomes give strength to cells in tissues. The third general class of proteins, transmembrane proteins, contain a hydrophobic sequence buried within the membrane; these proteins are discussed more fully below (see Membrane Proteins). These protein categories are not mutually exclusive. For example, the nominally fibrous intermediate filament proteins also have globular domains. Similarly, transmembrane proteins almost always possess globular domains. Thus, these definitions serve as a useful guide but should not be rigidly applied.

HYDROPATHY PATTERNS OFTEN REFLECT A PROTEIN’S CLASSIFICATION

Overview of the Physical State of Proteins Within Cells

A key physical feature of proteins is their hydropathy pattern (i.e., the distribution of hydrophobic and hydrophilic amino acid residues). Indeed, hydrophobic interactions provide the primary net free energy required for protein folding. Figure 1.5.1 illustrates the disposition of hydrophobic amino acids in proteins. In an intact globular protein, hydrophobic

amino acids are generally shielded from the aqueous environment by coalescing at the center of the molecule, with the more hydrophilic residues exposed at its surface. However, the linear arrangement of hydrophobic residues fluctuates in an apparently random fashion. The α helices within globular proteins may express a hydrophobic face oriented toward the center of the protein. (Within these helices hydrophobic residues are nonrandomly positioned every three or four amino acids to yield a hydrophobic face.) For coiled-coil α helix–containing fibrous proteins, such as tropomyosin and αkeratin, hydrophobic residues at periodic intervals allow close van der Waals contact of the chains and potentiate assembly as hydrophobic residues are removed from the aqueous environment (Schulz and Schirmer, 1979; Parry, 1987). Secondarily, regularly spaced charged groups can also contribute to the shape of fibrous proteins (Schulz and Schirmer, 1979; Parry, 1987). Transmembrane proteins provide a rather different physical arrangement of hydrophobic residues in which hydrophobic residues are collected primarily into a series of amino acids that is embedded within a cell membrane. One important means of analyzing the hydropathy of a sequenced protein is a hydropathy plot (Kyte and Doolittle, 1982). In this method, each amino acid residue is assigned a hydropathy value, an ad hoc measure that largely reflects its relative aqueous solubility; these values are plotted after being averaged. The successful interpretation of hydropathy plots

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depends on the parameters chosen for averaging. The parameters are the number of residues averaged (amino acid interval or “window”) and how many amino acids are skipped when calculating the next average (step size). Using this approach with a window of ∼10 residues, it is often possible to find the positions of hydrophobic residues coalescing near the interior of globular proteins. The method is particularly useful in predicting transmembrane domains of proteins, generally with a window of ∼20 amino acids. To detect the repetitious pattern of coiled-coil fibrous proteins, however, windows smaller than the repeat length would be required.

MEMBRANE PROTEINS In addition to their presence in the extracellular and intracellular milieus, proteins are also found in association with biological membranes. Proteins constitute one-half to threequarters of the dry weight of membranes. Membrane proteins perform a broad variety of functions including intermembrane and intercellular recognition, transmembrane signaling, most energy-harvesting processes, and biosynthesis in the endoplasmic reticulum (ER) and Golgi complex. Membrane proteins have been traditionally characterized as integral (or intrinsic) or peripheral (or extrinsic) on the basis of operational criteria. Peripheral membrane proteins are associated with membrane surfaces and can be dislodged from membranes using hypotonic or hypertonic solutions, pH changes, or chela-

Table 1.5.2

tion of divalent cations. Components of the erythrocyte membrane skeleton, for example, are peripheral membrane proteins. Although most peripheral proteins are removed by washing a sample with buffers, integral proteins cannot be removed by such treatments. To isolate integral membrane proteins, which are embedded within the lipid bilayer, one must use detergents that disrupt the bilayer and bind to the proteins, thus solubilizing them. In general, integral membrane proteins have a portion of their peptide sequence buried in the lipid bilayer whereas peripheral proteins do not. However, the discovery of glycosylphosphatidylinositol (GPI)-linked membrane proteins added to the ambiguity of the situation. GPI-linked proteins are globular proteins with no membrane-associated peptide sequence, yet they require harsh conditions for solubilization. As the technology for studying membrane proteins improved, it became necessary to develop a more precise vocabulary to describe membrane proteins. Transmembrane integral membrane proteins have at least one stretch of amino acids spanning a membrane. Membrane proteins are classified as type I, II, III, or IV depending on the nature of their biosynthesis and topology in membranes (Spiess, 1995; Table 1.5.2 and Fig. 1.5.2). The biosynthetic insertion of these proteins in membranes is, in turn, dependent on the presence or absence of a cleavable signal peptide, the relative positions of the hydrophobic transmembrane domain and positively charged topogenic signals, and/or

Definitions of Integral Transmembrane Proteins

Type

Definition

I

An N-terminal–cleavable signal peptide is removed at the luminal face yielding a luminal N terminal during biosynthesis. (Positive charges are found on C-terminal side of first long hydrophobic sequence after the signal peptide.) An N-terminal–uncleaved signal peptide leads to a cytoplasmic N terminus. (Positive charges are generally found on N-terminal side of first long hydrophobic sequence.) A long N-terminal hydrophobic sequence is followed by a sequence of positive charges. This leads to a luminal N terminus in the absence of a cleavable signal peptide. A short C terminus is present at the luminal side of membrane. A large N terminus is exposed at the cytoplasmic face.

II

III

IV

Examples LDL receptor, insulin receptor, glycophorin A, thrombin receptor

Transferrin receptor, sucrase/isomaltase, band 3

β-Adrenergic receptor, cytochrome P450

Synaptobrevin, UBC6

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transmembrane

nontransmembrane type I N

type II C

type III

type IV

N C

bilayer

exterior or lumen

cytosol

C

N

+ + +

C

+ + +

N

+ + +

C

N

Figure 1.5.2 Membrane proteins containing hydrophobic anchors. A nontransmembrane or monotopic membrane protein is anchored to the membrane via a hydrophobic amino acid sequence. Transmembrane proteins are classified as types I, II, III, and IV (Table 1.5.2). The first transmembrane segment of a multispanning membrane protein can be inserted as in type I, II, or III proteins. This segment functions as a start-transfer peptide. Subsequent transmembrane segments will function as stop-transfer and start-transfer sequences, resulting in a multispanning membrane topology.

Overview of the Physical State of Proteins Within Cells

the mechanism of nascent protein delivery to the ER. Type I membrane proteins are synthesized with an amino-terminal signal sequence that is inserted into the ER membrane. When the signal sequence is proteolytically removed in the ER lumen, a new luminal amino terminus is exposed. A series of positively charged residues at the C-terminal side of the first hydrophobic transmembrane domain following the signal sequence generally denotes the end of the first transmembrane domain (von Heijne and Gavel, 1988). Although a hydrophobic transmembrane domain followed by a positive sequence of amino acids is sufficient to act as a stop-transfer signal, this motif is not required for stoptransfer events and other, less well-understood regulatory mechanisms are also involved (Andrews and Johnson, 1996). Membrane proteins types I, II, and III are delivered to the ER membrane via a signal recognition particle (SRP)-dependent mechanism. In contrast to type I proteins, type II and III membrane proteins do not have a cleavable N-terminal signal sequence. Instead, they have an internal hydrophobic signal that acts as both a signal sequence for ER delivery and a transmembrane domain in the mature protein. Type II proteins have a cytoplasmic amino terminus and a luminal (or extracellular) carboxyl terminus. In this case a positively charged sequence

of amino acids at the N-terminal side of the first hydrophobic sequence causes the amino terminus to be retained at the cytoplasmic face of the ER membrane. Thus the internal uncleaved signal peptide becomes the transmembrane domain of the mature protein. Type III membrane proteins have the same overall topology as type I proteins, but they are inserted into membranes by a different mechanism. In type III proteins the first hydrophobic sequence of amino acids is immediately followed by a series of positively charged amino acids. Thus, the first hydrophobic sequence becomes the transmembrane domain of the protein, with the amino terminus at the luminal face of the membrane. Type IV membrane proteins are characterized by a large, cytoplasmically exposed amino-terminal domain and a short carboxylterminal domain facing the lumen. Importantly, these proteins are delivered to the ER by an unknown SRP-independent mechanism. In addition to the single-pass membrane proteins just described, integral membrane proteins can display zero, two, three, or more transmembrane domains. Some membrane proteins, such as cytochrome b5, have protein segments buried in the hydrophobic core of membranes but do not cross the membrane. Membrane proteins with multiple membranespanning domains are classified as type I, II, or

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III depending on the topogenic signals in the first transmembrane domain. For example, a multispan membrane protein with a cleavable signal sequence, luminal amino terminal, and a positively charged sequence following the first transmembrane domain from the amino terminal, such as the thrombin receptor, is a type I membrane protein. The remaining transmembrane domains are inserted into the bilayer depending on the orientation of the first transmembrane domain. Multispanning type II and III proteins are similarly defined according to the properties of their single-spanning counterparts. In addition to hydrophobic protein sequences acting as membrane anchors, membrane proteins may also carry bilayer-associated hydrophobic lipid components. These hydrophobic lipid anchors define three broad groups of lipid-modified proteins: fatty acylated, isoprenoid-linked, and GPI-linked (Fig. 1.5.3). Several cytosolic transmembrane proteins have been identified that contain a covalently attached hydrophobic fatty acyl residue. For example, fatty acids, including palmitic, palmitoleic, cis-vaccenic, and cyclopropylene-

hexadecanoic, are covalently linked to the amino terminus and the amino-terminal glycerylcysteine of E. coli lipoprotein. Moreover, palmitate- and myristate-labeled transmembrane proteins have been observed in eukaryotic cells (e.g., Schlesinger et al., 1980). In both isoprenoid-linked and GPI-linked proteins, globular proteins become membranebound due to the addition of a hydrophobic lipid moiety. Certain proteins containing conserved cysteine residues at or near the C-terminus are modified by prenylation, in which a farnesyl or geranylgeranyl isoprenoid tail is added (Zhang and Casey, 1996). This hydrophobic moiety promotes protein association with the cytoplasmic face of cell membranes. Notably, cytosolic G proteins and protein kinases that participate in signal transduction are prenylated. GPI-linked proteins are a major class of membrane proteins (Cardoso de Almeida, 1992; Englund, 1993). In contrast to isoprenoid-modified proteins, GPI-linked proteins are attached to the luminal or extracellular face of membranes via a glycosylphosphatidylinositol anchor of variable structure (e.g.,

EtN P

P exterior or lumen bilayer

P

cytosol

S

C O

fatty acylated

S GPI-linked

Cys-CH3

isoprenoid-linked

Figure 1.5.3 Membrane proteins containing lipid moieties. In the simplest case, fatty acids can be covalently attached to transmembrane proteins. Hydrophobic tails are also attached to proteins to form isoprenoid-linked proteins. A third class of lipid-attached proteins are the GPI-linked proteins. Hydrophobic regions are shaded.

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Fig. 1.5.3). Well over 100 GPI-linked proteins have been identified in cells, where they perform numerous functions including acting as enzymes and receptors. The ability of GPIlinked proteins, which possess no transmembrane or cytosolic sequences, to elicit transmembrane signals seems paradoxical. However, studies have suggested that interactions with other proteins, including transmembrane integrins (Petty et al., 1996), contribute to transmembrane signaling of these proteins. In addition, GPI-linked proteins can collect in microdomains called lipid rafts within cell membranes (Rietveld and Simons, 1998). Although GPI-linked proteins must collaborate with other membrane proteins to elicit signals, they do possess certain functional advantages. First, GPI-linked proteins (and isoprenoid-linked proteins as well) diffuse in membranes much faster than transmembrane proteins and thus relay information faster. Second, certain cells, such as leukocytes, can rapidly shed their GPIlinked proteins, thus altering their functional properties in seconds. Although the importance of lipid-linked membrane proteins has only recently been appreciated, the impact of these structures on our understanding of cell properties is growing rapidly.

ADDITIONAL FACTORS AFFECTING THE PHYSICAL HETEROGENEITY OF PROTEINS

Overview of the Physical State of Proteins Within Cells

Additional factors contributing to the physical-chemical heterogeneity of proteins are size, charge, chemical modifications, and assembly. A typical amino acid has a molecular mass of ∼110 Da, and a small protein has a molecular mass of a few thousand daltons (e.g., for insulin, Mr = 5733). Large proteins have molecular masses of several hundred thousand daltons. When proteins are assembled to form large multiprotein complexes such as ribosomes, molecular masses are well into the millions. The diameters of these structures range from 4 Å for an individual amino acid to ∼30 nm for a ribosome. Electrostatic charge is of major importance in protein structure and function. Charged proteins are more soluble than uncharged proteins. The large number of positive charges on histones allow them to bind DNA. The spatial arrangement of charges on cytochrome c allows it to bind the complementary charges of its oxidase and reductase, thereby orienting the proteins prior to electron transfer. Similarly, the arrangement of charges on the apoprotein and receptor for low-density lipoprotein (LDL) al-

lows for lock-and-key–like interactions (Petty, 1993). In addition to structural and binding considerations, electrostatic interactions play a regulatory role. For example, the phosphorylation and dephosphorylation of insulin receptors alter electrostatic interations between the active site and a regulatory loop of the kinase domain, thereby changing its three-dimensional shape (Hubbard et al., 1994). This changes the Vmax of the kinase, thus triggering intracellular signals. In addition to the types of physical heterogeneity listed above, >100 distinct chemical modifications of proteins have been observed. These include, for example, glycosylation, ubiquitin attachment, phosphorylation, acetylation, and hydroxylation (Table 1.5.3). Thus, proteins undergo extensive physical-chemical modification.

PROTEIN ASSEMBLIES Proteins can be assembled in a variety of states in both aqueous media and within membranes. Protein assembly into complex supramolecular structures plays vital roles in enzyme regulation, cell skeleton formation, and transmembrane signaling. Both covalent bonds and noncovalent bonds participate in protein assembly. One frequently encountered covalent mechanism of protein assembly is the formation of disulfide bonds. These covalent linkages often form during protein maturation. They can link two separate proteins together or two portions of the same protein. For example, the two chains of insulin molecules are held together by disulfides, as are the two chains of its membrane receptor. However, disulfide bond formation is mostly limited to oxidative environments such as the ER lumen and the exterior face of the cell surface. One of the best-known examples of noncovalent assembly is the formation of hemoglobin tetramers. Polymerization is another frequently encountered mechanism for protein assembly in cells. The globular protein actin polymerizes to form microfilaments in the absence of covalent bond formation. Intermediate filaments are formed by the polymerization of fibrous proteins. Under certain circumstances transmembrane proteins polymerize as well; bacteriorhodopsin, for example, forms two-dimensional pseudocrystals called purple membranes. Protein assemblies formed from various numbers of similar units are homodimers, homooligomers, and homopolymers. Assembly of protein structures from dissimilar subunits is more common than assem-

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Table 1.5.3

Common Physical-Chemical Modifications of Proteinsa

Modification

Example

Homodimerization Homooligomerization Homopolymerization Heterodimerization Heterooligomerization Heteropolymerization Proteolytic cleavage Prosthetic group addition Oxidation-reduction Glycosylation Phosphorylation

Transferrin receptor S. typhimurium glutamine synthetase Actin Integrins Histones, proteasomes Ribosomes Signal peptide cleavage in ER Heme addition to cytochromes and hemoglobin Disulfide bond formation in ER Glycoprotein maturation Regulation of protein function, such as the tyrosine kinase activity of insulin receptors Blockage of N-termini of certain membrane proteins Ubiquitin-dependent proteolysis via proteasomes, histones Proline hydroxylation on collagen Insulin receptors, E. coli lipoprotein G proteins Alkaline phosphatase, urokinase receptors

Acetylation Ubiquitination Hydroxylation Fatty acylation Isoprenylation GPI addition

aFor details, see Freedman and Hawkins (1980, 1985), Schlesinger et al. (1980), Englund (1993), and Zhang and

Casey (1996).

bly from identical subunits. For example, heterodimers are formed from the α and β chains of integrins within cell membranes. Complex heterooligomeric and heteropolymeric structures vary from relatively small structures such as histone octamers, which bind to DNA in the nucleus, to large particles such as ribosomes, found both in the cytosol and attached to nuclear and ER membranes. The signal recognition particle is a relatively small heterooligomeric structure, composed of one RNA subunit and six proteins, that potentiates the delivery of secretory and most membrane proteins to the ER membrane. Membrane-associated heterooligomeric structures have also been observed. One of the best examples of such structures is the components of the electron transport systems in chloroplasts and mitochondria (Petty, 1993). For example, the ubiquinone-cytochrome c reductase is composed of eleven different subunits. Thus, proteins can be assembled in a variety of manners within cells. Although some protein assemblies, such as intermediate filaments, are static structures, many are dynamic structures which provide functional flexibility. For example, microfilaments can rapidly assemble and disassemble. In addition to the physical changes in assembly state, compositional dynamics is also observed.

For example, interferon γ treatment alters the composition of proteasomes. Developmental changes in protein composition are also observed. As an example, fetal and newborn forms of a component of cytochrome c reductase are expressed in humans. Thus, protein assemblies can be characterized by both physical and compositional dynamics.

ALTERING THE SOLUBILITY OF PROTEINS: PROTEIN EXTRACTION The in vitro characterization of cellular proteins begins with their extraction from tissues or cells into a buffer. With the exception of globular secretory proteins, such as those found in plasma, proteins are generally not easily accessible for experimental manipulation. For example, many fibrous proteins are not soluble in aqueous buffers. Cellular proteins are entrapped within or on a cell and therefore must be extracted from the cell in a soluble form. A variety of methods including osmotic lysis, enzyme digestion, homogenization using a blender or mortar and pestle, and disruption by French press and sonication have been employed to disrupt cells. For a cytosolic protein such as hemoglobin, no further extraction from the sample is necessary. However, many important cellular proteins, such as those associated

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(CCl3COO−). Hydrophobic interactions are also reduced by exposure to organic solvents and low salt concentrations. Electrostatic interactions are reduced by high salt conditions; this decreases the Debye-Hückel screening length and coulombic attraction. To disrupt hydrogen bonds, high concentrations of urea or guanidine are often employed. More vigorous methods of sample denaturation using very low pH or harsh detergents such as sodium dodecyl sulfate are also used to diminish intermolecular contacts. Once proteins are extracted, their size can be characterized by ultracentrifugation on sucrose gradients (UNIT 4.2), gel filtration chromatography (UNIT 8.3), SDS-PAGE (UNIT 10.1), and other methods (Table 1.5.5). The charge characteristics of proteins can be assessed using isoelectric focusing and ion-exchange chromatography. Specific interactions, such as antigen-antibody and biotin-avidin interactions, can also be employed in the characterization and isolation of proteins. These are useful in immunoblotting (UNIT 10.10) and affinity chromatography methods (Chapter 9).

with membranes, cytoskeletal components, and DNA, remain insoluble. To further solubilize cell proteins, both nonionic (e.g., Triton X-100) and ionic (e.g., sodium dodecyl sulfate) detergents are often employed. Detergents are small amphipathic molecules that interact with both nonpolar and polar environments. Detergents disrupt membranes. They also bind to hydrophobic regions of proteins, such as their transmembrane domains, thereby replacing the unfavorable contacts between hydrophobic protein regions and water with the more favorable hydrophilic domains of the detergent. Thus, instead of the hydrophobic regions of the insoluble protein forming an aggregate in the bottom of a test tube, the protein becomes soluble and can be employed in most in vitro analyses. In addition to detergents, several other solubilization strategies are useful for the extraction and in vitro characterization of proteins (Table 1.5.4). Chaotropic agents enhance the transfer of nonpolar molecules to aqueous environments by their disrupting influence on water structure. Chaotropic agents are generally large molecular ions such as thiocyanate (SCN−), perchlorate (ClO4−), and trichloroacetate

Table 1.5.4

Physical Bases of Common Protein Extraction and/or Elution Methods

Physical property perturbed

Agents

Hydrogen bonds Ion pair interactions Hydrophobic interactions

Urea or guanidine⋅HCl, pH changes High salt, pH changes Detergents, chaotropic agents, organic solvents, low salt

Table 1.5.5 Physical Bases of Common Protein Characterization and Isolation Methods

Overview of the Physical State of Proteins Within Cells

Physical property

Method

References to other units

Solubility

Extraction with salts, detergents, and enzymes

Chapter 4, Racker (1985)

Size

Ultracentrifugation on sucrose gradients Gel filtration SDS-PAGE

UNIT 4.2 UNIT 8.3 UNIT 10.1

Charge

UNIT 10.2 Isoelectric focusing Ion-exchange chromatography UNIT 8.2

Biospecific interaction

Immunoblotting Immunoaffinity chromatography

Hydrophobicity

UNIT 10.10

Chapter 9

Hydrophobic chromatography UNIT 8.4 Reversed-phase HPLC UNIT 8.7

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LIMITATIONS OF THE IN VITRO MANIPULATION OF PROTEINS The very act of isolating proteins perturbs their physical environment. Although this is not often a major problem, a few cautionary notes should be made. The most primitive compartment of a cell, the cytosol, is a chemically reducing environment. Consequently, free sulfhydryl groups are observed in the cytosol; in fact, multiple cytosolic pathways help in preserving the proper redox conditions. On the other hand, the extracellular milieu and the luminal side of the ER are oxidative environments. The oxidizing condition within the ER is presumably due to the unidirectional transport of glutathione and cystine. Consequently, disulfides are frequently observed in the ER and extracellular environments. Thus, to prevent disulfide formation during manipulation, sulfhydryl blocking reagents such as iodoacetamide are included in extraction buffers. The cytosol is also a K+-rich and Ca2+-poor solution. These parameters should be considered in designing physiologically relevant experiments. The experimental manipulation of membrane proteins is decidedly more difficult. The exterior face exists in a high Na+ and Ca2+ solution that is oxidative; just the opposite is true for the cytoplasmic face. Since no appropriate solvent exists for such isolated proteins, experimental questions can be directed at properties associated with just one face of the molecule. A second limitation common to all in vitro studies of transmembrane proteins is that they must be solubilized using detergents. In addition to solubilizing a transmembrane protein, detergents can also bind to hydrophobic regions in the globular domain(s) of the protein, thus affecting the properties under study. One means of countering this problem is to test several detergents in the hope of finding one that retains the full biological activity of the purified protein. Protein solubilization can also lead to loss of physiologically relevant protein-protein interactions. This can occur by simple dilution or by disruption of noncovalent interactions among proteins. For example, hemoglobin exists as a supersaturated solution in vivo which cannot be duplicated in vitro. Furthermore, protein-protein associations are generally stronger in the restricted confines of membranes than after solubilization into a buffer. Thus, protein assemblies found in cells may disappear during solubilization. One means of countering these potential difficulties is to co-

valently cross-link protein assemblies prior to disruption and to solubilize proteins using mild detergents (e.g., Brij-58).

CONCLUSIONS Structural motifs, especially stretches of hydrophobic amino acids, contribute to the shape of a protein and its classification as globular, fibrous, or transmembrane. Proteins are heterogeneous at many different levels including physical attributes, covalent modifications, and supramolecular assembly. The physical properties of proteins are used to characterize and isolate these molecules. For example, the size of a protein is examined by sedimentation on sucrose gradients (UNIT 4.2), gel filtration (UNIT 8.3), and polyacrylamide gel electrophoresis (UNIT 10.1). Its charge is the key physical parameter in isoelectric focusing and ion-exchange chromatography. The units that follow contain detailed protocols describing the characterization of cellular proteins.

LITERATURE CITED Andrews, D.W. and Johnson, A.E. 1996. The translocon: More than a hole in the ER membrane? Trends Biochem. Sci. 21:365-369. Ausubel, F.M., Brent, R., Kingston, R.E., Moore, D.D., Seidman, J.G., Smith, J.A., and Struhl, K. (eds.) 1998. Current Protocols in Molecular Biology. John Wiley & Sons, New York. Cardoso de Almeida, M.L. 1992. GPI Membrane Anchors. Academic Press, New York. Englund, P.T. 1993. The structure and biosynthesis of glycosyl phosphatidylinositol protein anchors. Annu. Rev. Biochem. 62:121-138. Freedman, R.B. and Hawkins, H.C. 1980. The Enzymology of Post-Translational Modifications of Proteins, Vol. 1. Academic Press, New York. Freedman, R.B. and Hawkins, H.C. 1985. The Enzymology of Post-Translational Modifications of Proteins, Vol. 2. Academic Press, New York. Hoppe-Seyler, F. 1864. Über die chemischen und optischen Eigenschaften des Blutfarbstoffs. Virchows Arch. 29:233-235. Hubbard, S.R., Wei, L., Ellis, L., and Hendrickson, W.A. 1994. Crystal structure of the tyrosine kinase domain of the human insulin receptor. Nature 372:746-754. Kühn, W. 1876. Über das Verhalten verschiedner organisirter und sogenannter ungeformter Fermete. Über das Trypsin (Enzym des Pankreas) [Reprint, FEBS Lett. 62:E3-E7 (1976).]. Kyte, J. and Doolittle, R.F. 1982. A simple method for displaying the hydrophobic character of a protein. J. Mol. Biol. 157:105-132. Strategies of Protein Purification and Characterization

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Parry, D.A.D. 1987. Fibrous protein structure and sequence analysis. In Fibrous Protein Structure (J.M. Squire and P.J. Vibert, eds.) pp. 141-171. Academic Press, New York. Petty, H.R. 1993. Molecular Biology of Membranes: Structure and Function. Plenum, New York.

KEY REFERENCES Racker, 1985. See above. A wonderful little book on membrane protein manipulation which disproves the hypothesis that scientists can’t write.

Petty, H.R., Worth, R.G., and Todd, R.F. III. 2002. Interactions of integrins with their partner proteins in leukocyte membranes. Immunol. Res. 25:75-95.

Tanford, C. 1961. Physical Chemistry of Macromolecules. Academic Press, New York.

Racker, E. 1985. Reconstitutions of Transporters, Receptors, and Pathological States. Academic Press, New York.

Tanford, C. 1980. The Hydrophobic Effect. John Wiley & Sons, New York.

Rietveld, A. and Simons, K. 1998. The differential miscibility of lipids as the basis for the formation of functional membrane rafts. Biochim. Biophys. Acta. 1376:467-479. Schlesinger, M.J., Magee, A.I., and Schmidt, M.F.G. 1980. Fatty acylation of proteins in cultured cells. J. Biol. Chem. 255:10021-10024.

A rigorous introduction to the physical properties of proteins, which remains useful several decades later.

A very readable introduction to the hydrophobic effect.

INTERNET RESOURCES http://www.expasy.ch A user-friendly protein database including two-dimensional PAGE data and 3D protein structures.

Schultz, G.E. and Schirmer, R.M. 1979. Principles of Protein Structure. Springer-Verlag, New York.

ftp://ftp.pdb.bnl.gov/

Spiess, M. 1995. Heads or tails—what determines the orientation of proteins in the membrane. FEBS Lett. 369:76-79.

http://ww.ncbi.nlm.nih.gov

Contains protein crystallography data.

An important protein database.

Squire, J.M. and Vibert, P.J. (eds.) 1987. Fibrous Protein Structure. Academic Press, New York. von Heijne, G. and Gavel, Y. 1988. Topogenic signals in integral membrane proteins. Eur. J. Biochem. 174:671-678.

Contributed by Howard R. Petty Wayne State University Detroit, Michigan

Zhang, F.L. and Casey, P.J. 1996. Protein prenylation: Molecular mechanisms and functional consequences. Annu. Rev. Biochem. 65:241-269.

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CHAPTER 2 Computational Analysis INTRODUCTION

T

he development of the techniques of modern molecular biology, including PCR, cloning, and nucleotide sequencing, has revolutionized the practice of protein chemistry. Whereas the initial phase of detailed study of a new protein activity once involved chemical analysis of the amino acid sequence by Edman degradation of multiple fragments, it is more common today to establish a protein’s linear sequence from the nucleotide sequence of the gene that encodes it. While the number of new sequences reported based solely on classical protein sequence analysis has drastically declined, the molecular biology revolution has generated a rapidly expanding collection of new sequence data. The current efforts to sequence specific genomes, including the human genome, in their entirety will undoubtedly lead to further expansion of this information. Because linear sequence information is so readily available, computer methods for analyzing these data have also developed rapidly. This chapter will present methods for deriving important information on possible familial, structural, and functional relationships using only the linear sequence information as input.

As soon as any sequence information is obtained—for example, by running a newly isolated protein through an N-terminal gas-phase sequencer or by dideoxy sequencing a piece of cloned DNA—searches for similarity to other proteins can begin. If the new sequence can be identified as related to that of a known enzyme or gene from another organism, this provides excellent initial confirmation that the protein or gene under study is worthy of further investigations. Conversely, if a match is found to the sequence of a known protein that could only plausibly arise from sample contamination, the study can be halted immediately at considerable savings of time, energy, and resources. UNIT 2.1 describes procedures for searching sequence databases, provides theoretical background to aid in interpreting the resulting output, and discusses resources for further exploration of sequence relationships. Once substantial sequence information (particularly a whole gene sequence) is obtained, the most pressing question is what structure it is likely to have. A first step toward answering this question is the analysis of the corresponding amino acid sequence for hydrophobic and hydrophilic regions. Of particular interest are patterns or groupings of hydrophobic residues—typically transmembrane helices, leucine zippers, and amphipathic segments—which can indicate structural and even functional features of the protein. Hydropathy scales, indices designed to quantify the relative hydrophobicity of amino acid residues within a protein, and their application to protein sequences are reviewed in UNIT 2.2. More sophisticated forms of analysis can be used to predict overall protein secondary structure based on linear sequence. The procedures developed for this purpose utilize statistical data that has been compiled over the past 25 years as more and more three-dimensional protein structures have become available. Although these methods are not completely accurate at present, the success rate for predictions is now in the 60% to 80% range. This information is fully adequate to develop hypotheses, design experimental tests, and suggest relationships to known tertiary structures. The prediction of protein secondary structure and folding class are reviewed in UNIT 2.3. As well as providing Contributed by Ben M. Dunn Current Protocols in Protein Science (2000) 2.0.1-2.0.2 Copyright © 2000 by John Wiley & Sons, Inc.

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theoretical background and detailing the different predictive approaches, the discussion includes an analysis of the strengths and weaknesses of each approach with respect to different types of proteins (using human hemoglobin β, interleukin 1β, and profilin I as examples) and gives some guidance to obtaining the necessary software. To perform any method of computer analysis, especially comparisons with other known protein sequences, it is necessary to have facile methods for transfer and capture of a variety of files. Fortunately, standard methodology now exists to perform many of these functions. UNIT 2.4 provides detailed instructions for the initial steps of getting connected, finding information, and transferring data between computers around the world. Although each university or company will have their own local protocols and systems, and although individuals will need to obtain help from their local computer wizards, UNIT 2.4 is written to provide directions that are generally applicable to most systems. Specific recommendations for utilizing various forms of the BLAST programs for sequence searching and comparison are provided in UNIT 2.5. Protocols for submission of search commands and search sequence are provided in detail, and examples of search results are presented. The ultimate goal of any protein chemist should be to understand the relationship between the three-dimensional structure and the functional properties. Once a new sequence has been discovered, many scientists inevitably begin to speculate on the possible three-dimensional structure. To accomplish this, one must proceed through several steps. An important beginning is the search for proteins of related sequence, as one may find important clues through family relations. The appropriate databases that can be utilized for this purpose are collected in UNIT 2.6. There are several general methods to approach the prediction of structure: homology modeling, fold recognition, and ab initio prediction. The differences between these methods are described in UNIT 2.7. Comparative protein modeling can be extremely successful if there are structurally-defined proteins with a significant degree of sequence relatedness. The definitions and steps necessary for conducting comparative modeling using the SwissModel Web server are detailed in UNIT 2.8. Future updates to this chapter will present alternative methods to complete the sequence to structure algorithm. Ben M. Dunn

Introduction

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Current Protocols in Protein Science

Computational Methods for Protein Sequence Analysis Despite continued advances since the first applications of computers in protein sequence analysis in the late 1960s, computer-assisted biological sequence analysis is still undergoing rapid changes and improvements. With so much activity and growth in the scope and importance of genomic research, the need for efficient biological sequence analysis has become more urgent. Nevertheless, users of computational tools should be aware of the limitations of particular methods. Good results require the user to be able to select the right tools on the basis of some theoretical knowledge and to interpret all results with biological correctness in mind. This unit is presented as a guide to addressing the issue of what to do with a protein sequence once it is obtained. The practical approach depends on how far the investigation has progressed. If only a short part of the protein (e.g., ∼10 to 20 amino acids) has been determined, initial scans of databases to see if there are any significant matches with a known sequence are appropriate. If a complete protein sequence is in hand, it is time to start identifying regions of similarity that resemble functional sites of known proteins.

THEORETICAL BACKGROUND FOR PROTEIN SEQUENCE ANALYSIS Computer representations of protein sequences are of two basic types: artificial and natural. An example of an artificial or symbolic protein sequence is the common 24-letter residue code sequence (ABCDEFGHIKLMNPQRSTVWXYZ*) that represents each amino acid as suggested by the Nomenclature Committee of the International Union of Biochemistry (IUB). This code allows for ambiguities and unknown residue modifications. A natural protein sequence, on the other hand, is directly representative of the physical properties of the individual amino acids; for example, in a natural protein sequence the individual elements might be vectors representing the physical characteristics of the amino acid (Robson and Greaney, 1992; Rohde and Bork, 1993). Most molecular sequence analysis algorithms have been designed for the IUB 24-character amino acid representation. A sequence is an ordered list of elements. Contributed by George Michaels and Robert Garian Current Protocols in Protein Science (1995) 2.1.1-2.1.18 Copyright © 2000 by John Wiley & Sons, Inc.

Sequences can vary in length, composition, and pattern. The number of possible different protein sequences possible of a particular length is 20L, where L is the length. The patterns in such sequences are said to constitute the primary structure of the protein. In general, proteins have nonrandom sequences that are presumed to be related in the sense that all proteins have evolved from other proteins (Doolittle, 1981, 1986, 1989, 1990). Protein sequences are also subject to sequence convergence: different sequences may represent the same native structure of a protein. Some terms that deal with comparisons of protein sequence data are often used and misused in the literature. Similarity is generally used to describe two sequences or structures that resemble one another due to the order of identical residues. Homology means that similar sequences or structures resemble one another due to descent from a common evolutionary ancestor. It is important to realize that the sequence analysis tools used to search data bases detect character patterns that resemble portions of the query sequence. This means that they are finding similarities. Homologies are identified through the recognition of correlated structural and functional data in addition to similarity data. There are three basic types of sequence analyses: listings, traces, and alignments (Kruskal, 1983; Kruskal and Sankoff, 1983). Let a = (a1,..., am) and b = (b1,..., bn) be two sequences and let E be a set of elementary operations including insertion, deletion, and substitution. An indel is either an insertion or a deletion. A listing is a finite sequence of operations taken from E that transform a into b. A trace is a one-to-one correspondence of a subsequence of a and a subsequence of b. An alignment of a and b is a one-to-one correspondence such that each element of one sequence corresponds either to an element of the opposite sequence or to a null element indicating the presence of a gap. The best alignments are obtained when the lengths of a and b are similar. The correspondence between two nonnull elements is called an identity or a continuation if the two elements are identical and a substitution otherwise. Lines representing the correspondence between two sequences should not cross.

UNIT 2.1

Computational Analysis

2.1.1 CPPS

Computational Methods for Protein Sequence Analysis

In the comparison of two different alignments, it is not necessarily the one with the larger number of identities that is better. An alignment with fewer indels (<5%) might be preferable; therefore, some alignment methods set a limit to the number of indels allowed in a given alignment. Alignments are the most frequently used type of sequence analysis. There are two basic types of alignments: local and global. Local alignments are used to identify the most similar common subregions of two sequences. They are especially useful in identifying distantly related sequences. A global alignment, on the other hand, attempts a comparison of two sequences in their entirety and, therefore, often misses small subregions of similarity (Pizzi et al., 1991). Global alignments are most useful for comparing highly similar or homologous sequences that are close to one another in length. Two sequences are said to be homologous if it can be inferred on the basis of all available information that the sequences have a common ancestor. There are no degrees of homology. Divergent sequences are those that can be derived from the same sequence, usually by applying the operations in E. One way that divergent sequences are identified is by applying scoring matrices. There are several scoring matrices available based on allowable amino acid substitutions found in protein alignments of related proteins (see discussion of scoring matrices that follows). The most common matrices used are the protein alignment matrix (PAM; Dayhoff, 1978); the mutation data matrix (MDM; George, et al., 1990), which is a log-odds version of PAM; and BLOSUM (Henikoff and Henikoff, 1993), which incorporates the consideration of the conversation of function data between aligned proteins. It should be noted that the restriction of global alignments to operations in E would fail to align two sequences containing transpositions, in which segments of the protein sequence have been reordered within the sequence (Burks, 1990; States and Boguski, 1990). The similarity of two biological sequences is usually specified by either a similarity measure or a distance measure. Similarity measures typically compute the score for an alignment using an optimizing function that rewards identities and penalizes nonidentities and gaps. Distance measures are usually defined as functions that satisfy the axioms for a metric function:

Distance(a,b) = 0 if and only if a = b; Distance(a,b) = distance(b,a); Distance(a,b) ≤ distance(a,c) + distance(c,b).

Distance functions can be thought of as minimizing differences to find the best alignments, whereas similarity methods search for the best alignments by maximizing similarities. “All problems known to be solvable with distance methods can be solved with similarity methods” (Waterman, 1989). The metric approach appeals to the geometric intuition: sequences can be thought of as points in space, and any three distinct points a, b, and c form a triangle ABC that satisfies the rules above. The smaller the distance between two points, the greater the similarity of the sequences. There are many factors to be considered in protein sequence comparison (Hodgman, 1992). Amino acids are not equally abundant in protein sequences. Moreover, proteins fold into structures having well-conserved hydrophobic interior residues and poorly conserved exterior residues. Indeed, there is a descending order of residue conservation: active-site residues > crucial folding residues > internal packing residues > surface residues. Some residues have preference for position, secondary structure, and substitution; for example, active-site residues are often found in loops that are part of a supersecondary unit and may be partially buried. An identity of >25% implies similarity of function, whereas 18% to 25% identity may indicate similarity of structure or function. The latter range is within the “Twilight Zone” (15% to 25% identity) that, according to Doolittle, arises in the theory of protein evolution (Doolittle, 1986). Alignment methods usually have a specific region of applicability (Taylor, 1988). For example, pairwise alignment is most applicable at high levels of identity (∼70%); consensus methods (∼45%), template methods (∼25%), and structure prediction methods are applicable at even lower levels of identity. These ranges take into account the fact that even two unrelated or random sequences over 100 residues long may show identity in the range of 20%. Two complementary properties of sequence comparison methods are sensitivity and specificity (States and Boguski, 1990). Sensitivity is the ratio of false negatives to the sum of true positives and false negatives; specificity is the ratio of true positives to the sum of true positives and false positives. The goal of this unit is to provide some

2.1.2 Current Protocols in Protein Science

starting points for the analysis of protein sequence data. A theoretical review of principles is complemented by practical examples of computer methods for protein sequence analysis that were done using the GCG package of programs (Genetics Computer Group, 1994). There are many other commercial packages for IBM-compatible computers, Macintoshes, and UNIX workstations that have similar capabilities. There are also many public domain software packages available over the Internet that have identical or enhanced functionalities (see Table 2.1.1). When starting to assemble programs for protein sequence analysis, one is strongly advised to browse the Internet before purchasing. The CD-ROM version of Current Protocols in Protein Science also includes the Entrez and MACAW multiple sequence alignment programs from the National Center for Biotechnology Information (NCBI) at no additional charge. UNIT 2.2 describes the analysis of linear protein sequence with respect to hydrophobic/hydrophilic characterization that can suggest structural or functional segments. UNIT 2.3 presents methods for the prediction of secondary structural features of linear protein sequences.

MATRIX METHODS FOR SEQUENCE COMPARISON: DOT PLOTS Many computer-based sequence comparison methods rely on matrix methods (Fitch, 1966, 1969; Gibbs and McIntyre, 1970; McLauchlan, 1971, 1972; Maizel and Lenk, 1981; Argos, 1987; Landau et al., 1988, 1990). Pairwise comparison of two sequences can be represented in its simplest form by a matrix that is the logical product of the two sequences: if a corresponding pair of elements is identical, then the matrix element at the intersection of the row and column is marked true (dot); if the pair is different, then the matrix element is marked false (blank). The result is a visualization of the similarity of the two sequences. In dot-plot matrix displays, a run of identities will be seen as a continuous diagonal segment within the matrix. Discontinuities along diagonals with horizontal shifts to parallel lines are interpreted as indels and indicate the need for gaps in any alignment. If the two sequences being compared are identical, the matrix will reveal any internal repeats and the main diagonal will be filled in. Self-comparisons always result in symmetrical dot plots. Another commonly used dot-matrix method compares fixed-length segments (windows) of

the sequences instead of individual residues. See Staden (1994a-d) for a description of how to use SIP, a program based on dot-matrix plots. A palindrome is a subsequence that remains the same after being reversed. On a dot plot, palindromes are displayed as segments perpendicular to the main diagonal of the matrix. Palindromes are not usually relevant to protein sequences. In RNA and DNA sequences, a palindrome is defined as an inverted, self- co mp lementary region such as ACTGATATCAGT. More sophisticated comparisons have been defined on dot matrices that filter out isolated dots or small segments as noise. The stringency of the similarity determines the amount of data that will be filtered out before the matrix is displayed or undergoes further processing. In this case, the matrix elements are filled in with weighted numerical scores instead of simple identity values, and the result is plotted as a surface to visualize the regions of greatest similarity. Dot-matrix methods remain an active area of research (Streletc et al., 1991; Landes et al., 1993; Nedde and Ward, 1993). Dot plots are the simplest form of sequence comparison using a matrix display. These graphical methods allow quick observation of the level and extent of similarity between two sequences. For example, a comparison of horse and rat serum albumin protein sequences from the SWISS-PROT protein database is summarized in Figure 2.1.1. Dot-plot analysis is done in two phases: pairwise sequence comparison to generate a matrix of similarity values is followed by generation of the graphic display. The GCG package implements this process by using two programs—COMPARE and DOTPLOT—to generate the dot-matrix plot. COMPARE does a pairwise analysis to define positions of similarity between the two sequences. The parameters to be varied are (1) window size, or the number of residues to be compared, and (2) stringency, or the minimal number of matches per window to score a dot. DOTPLOT generates the graphic matrix display of the COMPARE analysis (Fig. 2.1.1).

SEQUENCE SIMILARITY SEARCHING Once a small portion of protein sequence data from an unknown sample has been generated, the immediate impulse is to ask what other proteins in the database does the unknown resemble, and how similar are the sequences. Several available tools can help answer these

Computational Analysis

2.1.3 Current Protocols in Protein Science

600

Albu_Horse.Sw ck: 307, 1 to 607

1, 1995 17:05

Points: 4,481 Stringency: 18.0

February

400

600

Window: 60

Density: 692.05

200

400

200

COMPARE

DOTPLOT of: albu_rat.pnt

0

0 Albu_Rat ck: 6,355, 1 to 608

Figure 2.1.1 Dot plot generated from comparison of rat and horse albumin protein sequences. The continuous diagonal line of identity traverses the middle of the matrix. Regions of similarity between the two sequences appear as lines parallel and offset to the line of identity. Axis label takes the form filename ck: checksum_value start_position to end_position.

questions. Most commercial sequence analysis packages contain programs for performing pairwise alignments of protein sequences. The most popular tools for searching databases, FASTA (FAST All) and BLAST (the Basic Local Alignment Search Tool), are included in commercial sequence analysis suites and are also freely available to be downloaded to a local computer to be used in a stand-alone manner or as network services.

BLAST Database Searching

Computational Methods for Protein Sequence Analysis

The program BLAST, which is available from the NCBI, conducts rapid searching of sequence databases (Altschul et al., 1990). The program searches for similarity between a query sequence and sequences in a nucleotide database (using BLASTN) or peptide database (using BLASTP). Two versions of the program are available: (1) a version that will run on a local workstation and (2) client software that connects a local computer via the Internet to a high-speed search engine located at the NCBI.

The latter accesses the most recent versions of sequence databases available. BLAST uses a word/dictionary approach to compare fixed-length “words” from the query sequence against the dictionary of words from the database searched. This program is an excellent choice for quickly screening for exact matches—for example, when the end of a new protein has been sequenced. A word of caution is, however, in order: BLAST can create enormous files, especially if the query sequence has repeated residues or is similar to a repeated motif in the databases. The BLAST output file contains (1) the sorted match statistics, probability measure of a random match and the number of matched regions; (2) the alignments generated for the query and the matched sequence region (shown in Fig. 2.1.2); and (3) a copy of the search parameters (not shown). The example in Figure 2.1.2 shows output from a BLAST search conducted using a sequence fragment from an unknown mammalian blood protein as a query against the nonredundant

2.1.4 Current Protocols in Protein Science

Similarity Table

Sequences producing High-scoring Segment Pairs: sp|P35747|ALBU_HORSE sp|P08835|ALBU_PIG gp|M36787|PIGALBA_1 gp|X17055|OASERALB_4 sp|P14639|ALBU_SHEEP sp|P02769|ALBU_BOVIN pir|A47391|A47391 gp|M73993|BOVALBUMIN_1 gp|V01222|RNALBU_4 sp|P02770|ALBU_RAT pir|S17599|S17599 pir|S29749|S29749 gp|A15293|A15293_1 gp|A00279|A00279_1 gp|A16010|A16010_1 gp|A03758|A03758_1 gp|M12523|HUMALBGC_1 gp|A09561|A09561_1 sp|P02768|ALBU_HUMAN pir|A93743|ABHUS

SERUM ALBUMIN PRECURSOR. >pir|S340.. SERUM ALBUMIN PRECURSOR (FRAGMENT).. albumin [Sus scrofa] Sheep mRNA for serum albumin. [Ovi.. SERUM ALBUMIN PRECURSOR. >pir|S069.. SERUM ALBUMIN PRECURSOR. >pir|A388.. serum albumin prepropeptide - rhes.. albumin [Bos taurus] Messenger RNA for rat preproalbumi.. SERUM ALBUMIN PRECURSOR. >pir|A938.. albumin - human serum albumin - dog Mature HSA [Homo sapiens] Human serum albumin [Unknown] synthetic mature HSA [Unknown] serum albumin [Homo sapiens] Human serum albumin (ALB) gene, co.. human serum albumin [Unknown] SERUM ALBUMIN PRECURSOR. >gp|M1252.. serum albumin precursor - human >g..

High Score 101 98 98 96 96 95 94 94 92 92 79 79 85 85 85 85 85 85 85 85

Smallest Sum Probability P(N) N 6.8e-08 1.8e-07 1.8e-07 3.4e-07 3.5e-07 4.8e-07 6.6e-07 6.6e-07 1.3e-06 1.3e-06 1.8e-06 4.0e-06 1.2e-05 1.2e-05 1.2e-05 1.2e-05 1.2e-05 1.2e-05 1.2e-05 1.2e-05

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Alignment >sp|P35747|ALBU_HORSE SERUM ALBUMIN PRECURSOR. >pir|S34053|ABHOS serum albumin precursor - horse >gp|X74045|ECSERALB_1 preproalbumin [Equus caballus] Length = 607 Score = 101 (47.0 bits), Expect = 6.8e-08, P = 6.8e-08 Identities = 22/26 (84%), Positives = 22/26 (84%) Query: Sbjct:

1 TIKSEIAHVFNDLGEPVFKGLVLVAF 26 T KSEIAH FNDLGE FKGLVLVAF 26 THKSEIAHRFNDLGEKHFKGLVLVAF 51

Figure 2.1.2 BLAST output table and alignment for an unknown mammalian blood protein. The Query is the sequence of interest and the sbjct is the sequence region found in the database that matches the query. Note that in searches of the nonredundant databases at the NCBI, matches are found between the test sequence and the translation of mRNAs in GenBank. Important similarities have small probability scores, i.e., P(N) < 10−3. This analysis suggests that the unknown is an albumin, but it is not identical to any known sequence.

protein sequence database maintained at the NCBI. This database contains a compilation of protein sequences found in all of the molecular sequence databases with duplicate sequences removed. Figure 2.1.2 presents a table of the top 20 highest similarity scoring sequences and the best alignment.

FASTA Database Searching FASTA conducts a Lipman and Pearson (1985) search for similarity between a query sequence and any FASTA-formatted database or group of sequences collected in a database by the user. FASTA is a program developed for fast searching using an algorithm that allows for the insertion of gaps during the alignment

phase. This approximates evolutionary insertions and deletions during divergence. However, the gaps are generated to maximize the number of aligned residues. FASTA does a good job of generating a global alignment for a sequence. Figure 2.1.3 and Figure 2.1.4 present results from a FASTA search of the SWISS-PROT database with a short sequence fragment from an unknown mammalian blood protein. The FASTA output contains (1) a histogram that shows the distribution of relevant word matches found between the query sequence and the database searched; (2) the match statistics; and (3) the alignments generated for the query sequence and the matched sequence region. There

Computational Analysis

2.1.5 Current Protocols in Protein Science

Score Init1 Initn (-) (+) < 2 533 533:================================================== 4 4 4:== 6 134 134:================================================== 8 222 222:================================================== 10 1964 1964:================================================== 12 5289 5289:================================================== 14 7609 7609:================================================== 16 9434 9434:================================================== 18 2834 2834:================================================== 20 4914 4914:================================================== 22 2686 2686:================================================== 24 1808 1808:================================================== 26 1241 1241:================================================== 28 721 721:================================================== 30 375 374:================================================== 32 261 259:================================================== 34 138 137:================================================== 36 57 57:============================= 38 27 27:============== 40 18 18:========= 42 4 5:==+ 44 6 7:===+ 46 0 0: 48 0 1:+ 50 3 4:== 52 1 1:= 54 1 1:= 56 2 2:= 58 0 0: 60 0 0: 62 0 0: 64 0 0: 66 0 0: 68 0 0: 70 0 0: 72 0 0: 74 0 0: 76 0 0: 78 0 0: 80 0 0: > 80 6 6:=== Mean score calculations exclude scores greater than 72 mean initn score: 16.3 (s.d. 5.20) mean init1 score: 16.3 (s.d. 5.19) 1838 scores better than 26 saved, joining threshold: 27

Figure 2.1.3 FASTA histogram from a search of the SWISS-PROT database for sequences similar to a fragment from an unknown mammalian blood protein. Numbers of windows at each init1 score are plotted. Note that there are six highly significant alignments, as well as several sequences (those with init1 scores in the 48 to 56 range) that represent protein sequences from more distantly related species.

Computational Methods for Protein Sequence Analysis

are three similarity scores presented in the output from FASTA: init1, the highest similarity score allowing conservative substitutions; initn, the highest score generated by linking nonoverlapping regions; and opt, the score for the best alignment presented. In practice the histogram would be used to determine how many sequences have highly significant levels of similarity. The table of scores allows one to decide quickly how many and which sequence alignments should be viewed.

ALIGNMENT METHOD CONSIDERATIONS Dynamic programming is a different matrix method that is used to find the optimal global alignment of two sequences (Needleman and Wünsch, 1970; Sellers, 1974; Pearson and Miller, 1992; Panjukov, 1993). Let a be a sequence of length m and let b be a sequence of length n. Let D be the working matrix. Then, given a predefined scoring function, the basic steps of the alignment algorithm (Doolittle, 1986) are as follows. Assign the scores for all exact matches to the corresponding matrix el-

2.1.6 Current Protocols in Protein Science

Alignment Score Table The best scores are: init1 initn opt Sw:Albu_Horse P35747 equus caballus (horse). serum album... 99 99 100 Sw:Albu_Rat P02770 rattus norvegicus (rat). serum albumi... 99 99 99 Sw:Albu_Pig P08835 sus scrofa (pig). serum albumin precu... 99 99 101 Sw:Albu_Bovin P02769 bos taurus (bovine). serum albumin ... 98 98 99 Sw:Albu_Sheep P14639 ovis aries (sheep). serum albumin p... 95 95 96 Sw:Albu_Human P02768 homo sapiens (human). serum albumin... 93 93 93 Sw:Albu_Chick P19121 gallus gallus (chicken). serum albu... 56 56 77 Sw:Albu_Mouse P07724 mus musculus (mouse). serum albumin... 55 55 55

Best Scoring Alignment SCORES

Init1: 99 Initn: 99 Opt: 84.6% identity in 26 aa overlap

100

10 20 TIKSEIAHVFNDLGEPVFKGLVLVAF | |||||| |||||| ||||||||| Albu_H MKWVTFVSLLFLFSSAYSRGVLRRDTHKSEIAHRFNDLGEKHFKGLVLVAFSQYLQQCPF 10 20 30 40 50 60 Pc12_P

Figure 2.1.4 Partial FASTA alignment table and best scoring alignment for the same search illustrated in Figure 2.1.3. The table shows the best alignment scores sorted by highest init1 score.

ements. Transform the matrix by beginning with element Dmn and adding its value (less any gap penalty) to Dm−1, n−1 and all the elements directly above it or to its left. Next, select the matrix element in column m − 2 and above row m − 1 that contains the largest score and repeat the summing procedure. Repeat the previous step until the entire matrix is transformed. Next, determine the path from the largest score in the matrix back toward the bottom right-hand corner. The path, with any gaps as indicated by off-diagonal moves, is then used to align a and b. This matching algorithm is used in most global alignment programs, which vary primarily in the sophistication of the scoring functions (similarity or distance). Note that this method assumes (incorrectly) that each residue has equal importance, and it ignores sequential constraints. Local alignments are usually found using some version of the Smith-Waterman method (1981), which applies when common regions are being sought in two sequences whose similarity is unknown or limited to a few regions, as is the case in database searches. The algorithm usually produces a single highest-scoring alignment with respect to the operations in E, although it has been successfully extended to produce the second-best and other alignments (Barton, 1990, 1993; Waterman and Eggert, 1991). In its simplest form (Barton, 1993), the local alignment method is as follows. Given two

sequences a and b, and a working (comparison) matrix H, the matrix elements of H are filled in by applying a scoring rule: the score assigned to a matrix element Hij is either zero or the maximum value of any of the three scores of its neighbors and predecessors that lie directly above, diagonally to the left, or directly to the left of Hij, adjusted for gaps or substitution of residues. The maximum element in H is found and traced back through the matrix to find the alignment.

SCORING MATRICES Scoring matrices (George et al., 1990; States and Boguski, 1990), such as the PAM, BLOSUM (Henikoff and Henikoff, 1993), and related MDM matrices (Dayhoff, 1978), are based on the compilation of frequencies of amino acid substitution in a set of homologous sequences. The data are used as an amino acid transition probability matrix. Increasing powers of the matrix correspond to increasing divergence. Lower powers are used for sequences that seem closely related and higher powers for those that are not so obviously related. Thus, PAM250 represents a matrix with 250 accepted protein mutations per 100 amino acids. The scoring matrix method has greatly improved the quality and sensitivity of alignments; however, it does have the deficiency that it ignores structure and the corresponding sequence constraints which are significant for predicting structure. Not all sequence comparison methods are

Computational Analysis

2.1.7 Current Protocols in Protein Science

Computational Methods for Protein Sequence Analysis

matrix methods. Programs such as FASTP and FASTN are representative of hash-coding methods (Dumas and Nunio, 1982; Wilbur and Lipman, 1983; Lipman and Pearson, 1985; Pearson, 1990, 1994). The methods are based on fast table lookups of subsequences of a target sequence using hash functions. Hash functions transform strings in numbers that serve as pointers into a table containing further information. Thus, a fixed-length subsequence (or k-tuple) of one sequence can quickly be checked for occurrence in another sequence after a hash table of k-tuples and their positions in the sequence is constructed for each sequence. FASTP focuses on regions having the highest density of identities and uses the PAM250 matrix. Readers are referred to Pearson (1994) for an introduction to using FASTA for comparing both DNA and protein sequences. The RDF2 and RSS programs for testing the statistical significance of FASTA alignments are also discussed. Other methods for sequence comparison include finite state machines that are capable of recognizing regular expressions, and various statistical approaches (States and Boguski, 1990; Tyler et al., 1991; Allison et al., 1992; Brendel et al., 1992). Regular expressions are formulas such as (a + b*)c, which represents the set of all strings that begin with the letter a or zero or more b’s and followed by a single c. Complicated patterns can be constructed and detected in sequences using the finite state method. Statistical approaches include the database-searching program BLAST (see above section on BLAST database searching), which approximates gapless alignments that optimize local similarity on the basis of maximal segment pairs. The BLAST strategy is to search for only those segment pairs that contain a word pair whose comparison score exceeds some threshold. It uses the PAM120 scoring matrix to allow for conservative substitutions of residues in a comparison. Statistical methods for sequence alignment (Karlin et al., 1988, 1989, 1991; Brendel, 1992; Pevzner, 1992; Mrazek and Kypr, 1993; Staden, 1994a-d) consider the statistical significance of a variety of protein sequence properties. Such properties include the relative composition of a sequence with respect to residue types; anomalous clusters, runs, and periodic variations of charge; internal repeats; local periodicities that might indicate regularity of structure; and the relative spacings of certain residue types. For a description of routines for

plotting hydrophobicity, charge, and hydrophobic moments using the program PIP, see Staden (1994a-d). In addition to the methods described above, interest in the development of theory and tools for linguistic (formal grammar) analysis of biological sequences is growing (Collado-Vides, 1991; Michaels et al., 1993; Searls, 1993). The next level of sequence analysis is the simultaneous comparison of more than two sequences, as described in the following section on Multiple Alignments.

MULTIPLE ALIGNMENTS Once a group of potentially homologous sequences has been identified, the next step could be to do a multiple alignment that compares the new sequence with the group of sequences determined to have highly significant similarities. There are several programs that will do multiple alignments. PILEUP from the GCG package does a good job with closely related sequences in an automated fashion. MACAW from NCBI, LINEUP from GCG, and DSCE (De Rijk and De Wachter, 1993) allow the investigator to generate an alignment manually. (The CD-ROM version of Current Protocols in Protein Science includes MACAW and Entrez from NCBI.) Figure 2.1.5 demonstrates a multiple alignment generated using PILEUP from five highscoring albumin sequences in the SWISSPROT database identified from the FASTA analysis (Fig. 2.1.4). PILEUP performs a cluster analysis based on pairwise comparisons of each sequence to be aligned. As well as the multiple alignment (Fig. 2.1.5), PILEUP produces a dendrogram (not shown) that displays the clustered relationships between the sequences. The dendrogram is not a phylogenetic tree, but a graphical representation of the clustered similarity statistics. After the multiple alignment is generated, one can begin to infer relationships between conserved regions and function or structure. The program Pro_Anal (Eroshkin et al., 1993) and the program for fast protein block searches (Fuchs, 1994) can help in this process. Programs like OBSTRUCT (Heringa et al., 1992) are useful in identifying clusters of protein sequences on the basis of structural and sequence similarity criteria. Kanaoka et al. (1989) have described a process of aligning protein sequences on the basis of hydrophobicity. Livingstone and Barton (1993) describe a strategy to generate multiple alignments for hierarchical analysis of residue conservation.

2.1.8 Current Protocols in Protein Science

Albu_Bovin.Sw Albu_Chick.Sw Albu_Horse.Sw Albu_Human.Sw Albu_Mouse.Sw

1 MKWVTFISLL MKWVTLISFI MKWVTFVSLL MKWVTFISLL EADKSEIADR

LLFSSAYSRG FLFSSATSRN FLFSSAYSRG FLFSSAYSRG YNDLGEIHFK

VFRRDTHKSE LQRFARDAEH VLRRDTHKSE VFRRDAHKSE CAIPNLRENY

IAHRFKDLGE KSEIAHRYND IAHRFNDLGE VAHRFKDLGE GELADCCTKQ

50 EQFKGLVLIA LKEETFKAVA KHFKGLVLVA ENFKALVLIA EPERNECFLQ

Albu_Bovin.Sw Albu_Chick.Sw Albu_Horse.Sw Albu_Human.Sw Albu_Mouse.Sw

51 FSQYLQQCPF MITFAQYLQR FSQYLQQCPF FAQYLQQCPF HKDDNPSLPP

DEHVKLVNEL CSYEGLSKLV EDHVKLVNEV EDHVKLVNEV FERPEAEAMC

TEFAKTCVAD KDVVDLAQKC TEFAKKCAAD TEFAKTCVAD TSFKENPTTF

ESHAGCEKSL VANEDAPECS ESAENCDKSL ESAENCDKSL MGHYLHEVAR

100 HTLFGDELCK KPLPSIILDE HTLFGDKLCT HTLFGDKLCT RHPYFYAPEL

Albu_Bovin.Sw Albu_Chick.Sw Albu_Horse.Sw Albu_Human.Sw Albu_Mouse.Sw

101 VASLRETYGD ICQVEKLRDS VATLRATYGE VATLRETYGE LYYAEQYNEI

MADCCEKQEP YGAMADCCSK LADCCEKQEP MADCCAKQEP LTQCCAEADK

ERNECFLSHK ADPERNECFL ERNECFLTHK ERNECFLQHK ESCLTPKLDG

DDSPDLPKLK SFKVSQPDFV DDHPNLPKLK DDNPNLPRLV VKEKALVSSV

150 PDPNTLCDEF QPYQRPASDV PEPDAQCAAF RPEVDVMCTA RQRMKCSSMQ

Albu_Bovin.Sw Albu_Chick.Sw Albu_Horse.Sw Albu_Human.Sw Albu_Mouse.Sw

151 KADEKKFWGK ICQEYQDNRV QEDPDKFLGK FHDNEETFLK KFGERAFKAW

YLYEIARRHP SFLGHFIYSV YLYEVARRHP KYLYEIARRH AVARLSQTFP

YFYAPELLYY ARRHPFLYAP YFYGPELLFH PYFYAPELLF NADFAEITKL

ANKYNGVFQD AILSFAVDFE AEEYKADFTE FAKRYKAAFT ATDLTKVNKE

200 CCQAEDKGAC HALQSCCKES CCPADDKLAC ECCQAADKAA CCHGDLLECA

Albu_Bovin.Sw Albu_Chick.Sw Albu_Horse.Sw Albu_Human.Sw Albu_Mouse.Sw

201 LLPKIETMRE DVGACLDTKE LIPKLDALKE CLLPKLDELR DDRAELAKYM

KVLASSARQR IVMREKAKGV RILLSSAKER DEGKASSAKQ CENQATISSK

LRCASIQKFG SVKQQYFCGI LKCSSFQNFG RLKCASLQKF LQTCCDKPLL

ERALKAWSVA LKQFGDRVFQ ERAVKAWSVA GERAFKAWAV KKAHCLSEVE

250 RLSQKFPKAE ARQLIYLSQK RLSQKFPKAD ARLSQRFPKA HDTMPADLPA

Albu_Bovin.Sw Albu_Chick.Sw Albu_Horse.Sw Albu_Human.Sw Albu_Mouse.Sw

251 FVEVTKLVTD YPKAPFSEVS FAEVSKIVTD EFAEVSKLVT IAADFVEDQE

LTKVHKECCH KFVHDSIGVH LTKVHKECCH DLTKVHTECC VCKNYAEAKD

GDLLECADDR KECCEGDMVE GDLLECADDR HGDLLECADD VFLGTFLYEY

ADLAKYICDN CMDDMARMMS ADLAKYICEH RADLAKYICE SRRHPDYSVS

300 QDTISSKLKE NLCSQQDVFS QDSISGKLKA NQDSISSKLK LLLRLAKKYE

Albu_Bovin.Sw Albu_Chick.Sw Albu_Horse.Sw Albu_Human.Sw Albu_Mouse.Sw

301 CCDKPLLEKS GKIKDCCEKP CCDKPLLQKS ECCEKPLLEK ATLEKCCAEA

HCIAEVEKDA IVERSQCIME HCIAEVKEDD SHCIAEVEND NPPACYGTVL

IPENLPPLTA AEFDEKPADL LPSDLPALAA EMPADLPSLA AEFQPLVEEP

DFAEDKDVCK PSLVEKYIED DFAEDKEICK ADFVESKDVC KNLVKTNCDL

350 NYQEAKDAFL KEVCKSFEAG HYKDAKDVFL KNYAEAKDVF YEKLGEYGFQ

Albu_Bovin.Sw Albu_Chick.Sw Albu_Horse.Sw Albu_Human.Sw Albu_Mouse.Sw

351 GSFLYEYSRR HDAFMAEFVY GTFLYEYSRR LGMFLYEYAR NAILVRYTQK

HPEYAVSVLL EYSRRHPEFS HPDYSVSLLL RHPDYSVVLL APQVSTPTLV

RLAKEYEATL IQLIMRIAKG RIAKTYEATL LRLAKTYETT EAARNLGRVG

EECCAKDDPH YESLLEKCCK EKCCAEADPP LEKCCAAADP TKCCTLPEDQ

400 ACYSTVFDKL TDNPAECYAN ACYRTVFDQF HECYAKVFDE RLPCVEDYLS

Figure 2.1.5 Multiple alignment of mammalian blood proteins using PILEUP. The first 400 residues of five high-scoring sequences identified from FASTA analysis (Fig. 2.1.4) are aligned.

Similarly, the program PIMA (Smith and Smith, 1992) uses an algorithm that takes advantage of secondary structure features to determine gap penalties during the multiple alignment process. Depiereux and Feytmans (1991) have used simultaneous multivariate multiple alignment methods to reveal a correspondence between physiochemical profiles and structurally conserved regions. The multiple alignment programs described above take advantage of pairwise comparison

algorithms and are subject to their limitations. Alignment algorithms that use a rigorous consideration of more than three sequences at once are computationally infeasible for most labs. Although some speed improvements have been obtained for the optimal alignment of three sequences simultaneously (Murata et al., 1985; Gotoh, 1986), new methods have to be developed for multiple sequence alignment. The earliest methods were sequence editors for interactive alignments. See Pearson and Miller

Computational Analysis

2.1.9 Current Protocols in Protein Science

Computational Methods for Protein Sequence Analysis

(1992) for a discussion of considerably improved dynamic programming algorithms for sequence analysis. Multiple sequence alignment tends to be order-sensitive. A sequence of pairwise comparisons may not lead to the best overall alignment for a given set of sequences because the sequences are not independent (States and Boguski, 1990)—that is, they are not necessarily equidistant from a common ancestor. The consensus sequence approach to multiple sequence alignment (Waterman, 1989, 1990; Waterman and Jones, 1990; Day and McMorris, 1993) is based on the construction of a single sequence that can be used to align all other given sequences; the sequence is determined by an iterative process of alignment followed by consensus sequence modification. A consensus residue can be stringently defined from a multiple alignment when the content of a column is represented by a single amino acid at that position. There are many multiple alignment algorithms—too many to describe here. A list of available sequence similarity and alignment software can be accessed via the Internet as described in the section below on Internet Resources. Once sequences have been aligned, the quality of the alignment should be assessed. It is clearly important to distinguish between chance similarities and true sequence similarities. One method for estimating the quality or statistical significance of an alignment (Doolittle, 1986) is to compare the alignment score with the mean score of a set of randomized sequences that are permutations of the original sequences and which preserve their length and relative composition. The further the alignment score is from the mean of the randomized set, the more confident we can be in the alignment. A possible objection to this data shuffling or permutation method is that the permuted sequences are not like real protein sequences (i.e., permuted sequences are unlikely to fold like real proteins). Other methods (States and Boguski, 1990) include the following: McLachlan’s (1972) “double matching probability” function, which is used to compute the frequency scores in infinite random sequences of the same composition as the given sequences; Reich and Meiske’s stochastic random sequence model for determining the significance of window scores in dot-matrix analysis (Reich and Meiske, 1987); and Argos’ significance tests (Argos and Vingron, 1990), based on actual protein sequences

instead of the randomized sequences used by McLachlan (1972). Despite efforts to determine the quality of alignments, it remains true that low statistical significance does not always imply a lack of biological significance (Collins and Coulson, 1990). Another important aspect of alignments is the accuracy of the original molecular sequence (States, 1992). Errors can be introduced in molecular sequence data in random or biased ways. The translation of nucleic acid sequences into amino acid sequences is very sensitive to indel errors: an error rate of 1% can lead to a frameshifting error in reading frames longer than 24 amino acids. Empirical estimates of sequence accuracy in current databases have revealed a rate of 3 to 5 errors per 1000 bases.

CLUSTER METHODS AND TREES Once a multiple alignment has been performed and regions of common similarity have been identified among the proteins, the question of evolutionary distance will arise. There are several programs for determining the level of divergence between a group of sequences. Some are available over the Internet via the World Wide Web (WWW; see also Internet Resources section). The programs DISTANCES and GROWTREE from the GCG package (Genetics Computer Group, 1994) calculate pairwise distance measures and plot a dendrogram of those measures. The programs depend on an accurate multiple alignment of the sequences involved. This means that the multiple alignments should be examined carefully and then edited as necessary to incorporate additional knowledge that might be available about the structure or function of the proteins. Figure 2.1.6 shows the table of pairwise phylogenetic distances calculated by DISTANCES for the top nine albumins identified from the FASTA analysis (Fig. 2.1.4), and the phylogram produced by GROWTREE is shown in Figure 2.1.7. The purpose of protein sequence comparison is to learn more about the structure, function, and evolution of proteins. It is known that different proteins and different parts of proteins change at characteristic but unequal rates in the course of evolution (Doolittle, 1986). Binding sites and catalytic units are conserved independently of their position, whereas surface regions change faster than buried regions. In addition, entire domains of a genome are subject to a number of editing operations such as domain joining, swapping, and reordering (States and Boguski, 1990). Thus, homologies

2.1.10 Current Protocols in Protein Science

Matrix 1, dimension: 9 Key for column and row indices: 1 2 3 4 5 6 7 8 9

Albu_Bovin.Sw Albu_Chick.Sw Albu_Horse.Sw Albu_Human.Sw Albu_Mouse.Sw Albu_Pig.Sw Albu_Ranca.Sw Albu_Rat.Sw Albu_Sheep.Sw

1 2 3 4 5 6 7 8 9 __________________________________________________________________________ .. | 1 | 0.00 999.99 31.75 192.01 999.99 999.99 999.99 237.70 8.19 | 2 | 0.00 999.99 999.99 999.99 999.99 999.99 999.99 999.99 | 3 | 0.00 190.56 999.99 999.99 999.99 222.61 29.05 | 4 | 0.00 999.99 999.99 999.99 32.75 193.48 | 5 | 0.00 999.99 999.99 999.99 999.99 | 6 | 0.00 999.99 999.99 999.99 | 7 | 0.00 999.99 999.99 | 8 | 0.00 237.70 | 9 | 0.00

Figure 2.1.6 Evolutionary distance table for mammalian blood proteins calculated using the GCG DISTANCES program. Corrections to the calculations were done using the Kimura protein method as described in the GCG manual (Genetics Computer Group, 1994). The matrix values represent the number of matches between each pair of sequences divided by its length (999.9 = identity). These data were used to generate the GROWTREE phylogram presented in Figure 2.1.7.

Growtree Phylogram of: Albu_Bovin.Distances, Tree Tree_ February 3, 1995 15:02 Albu_Ranca.Sw Albu_Pig.Sw Albu_Mouse.Sw Albu_Chick.Sw Albu_Rat.Sw Albu_Human.Sw Albu_Horse.Sw Albu_Sheep.Sw Albu_Bovin.Sw

Figure 2.1.7 GROWTREE phylogram of Albu_Bovin.Distances, Tree Tree_1. Results of graphical cluster analysis of the distance data presented in Figure 2.1.6 are shown. In this representation sequences with the least divergence have the shortest branches.

Computational Analysis

2.1.11 Current Protocols in Protein Science

might be found between entire proteins or just between certain parts of proteins.

IDENTIFICATION OF FUNCTIONAL SITES

Computational Methods for Protein Sequence Analysis

Sequence comparisons are used to test for homology and to find functional sites and motifs. The goal is to establish structure and function for parts of an unknown sequence using known homologous or analogous (i.e., similar, but not homologous) sequences as sources of additional information. The goal is to obtain answers to questions of what functions the protein has and what its structure is. There are several paths the analysis can take. One approach that will reveal much about potential functional sites in the protein is to look for motifs. Motifs are defined in three ways: as consensus patterns of amino acids that correspond to regions of proteins containing the common residues; as the excised alignment of several sequences that contain the motif for use as a database discriminator; or as a single representative sequence (States and Boguski, 1990; Parry-Smith and Atwood, 1992). Motif searches are usually done using a program that takes advantage of a dictionary of sequence patterns which have been identified with specific protein activities. Many programs will do this analysis, and most use the PROSITE database of motifs found through analysis of the SWISS-PROT protein database. Figure 2.1.8 shows an example using the GCG MOTIFS program to analyze an albumin sequence. Note that the output file contains an alignment of the motif found and an extensive annotation describing the motif and listing the associated bibliography. The PIPL program developed by Staden (1994a-d) searches libraries of sequences to find patterns, where a pattern is a set of motifs with variable spacing. Patterns are constructed using logical operators to form a search term that is then used to scan a database such as PROSITE (Bairoch, 1992). The MOTIFS program of the Genetics Computer Group (1994) uses PROSITE as a dictionary of motifs. Weight matrices are used in scanning for motifs and developing a motif pattern. The profile analysis method (Gribskov et al., 1988, 1990; Gribskov, 1994) also involves weight matrices and is related to the consensus method. It is used for sensitive similarity matching, for homology seeking, and for scanning databases for motifs. It measures the similarity between a given sequence, called the target, and a group of aligned sequences called

the probe. The alignment of the probe uses a modified MDM78 mutational matrix. The matrix values of the profile are computed columnwise as the weighted average of the score for each residue at that position in the aligned sequences and the residue represented by the column of the profile; a row of the profile is the weighted average of the rows in the MDM78 table corresponding to the aligned residues at that position. The result is the set of properties that tend to be conserved at particular positions along the sequence. For detailed information on using profile analysis software to scan for homologies and motifs, see Gribskov (1994). A relatively new homologous sequence retrieval program (using the BLOSUM250 matrix) is dFLASH, which is available by E-mail server (Rigoutsos and Califano, 1994). There are many ways to proceed in computational analysis of new sequences. Each will provide a wealth of information to guide further laboratory experimentation. The following procedures illustrate two approaches to protein sequence analysis and can be adapted as required. Hodgman (1992) suggests the following protocol for protein function determination: 1. Split sequences into overlapping 200- to 300-residue segments. 2. Use a program such as FASTA to search a protein database for sequences similar to those in the segments. 3. If a match having >25% identity over 80 residues is found, then the search has been successful. 4a. For weaker matches, use dot-matrix plots to verify similarity. 4b. Alternatively, search the database again with the matching sequence to find related sequences for alignment to detect conserved positions. 5. Conserved regions can be used to search further with PIPL (Staden, 1994a-d), PROFILESEARCH (GCG), or SCRUTINEER (EMBL). 6. Use any biological information about the conserved regions to determine an analogous function for the test protein. 7. If the procedure has not been successful, use other resources such as PROSITE. 8. Check that all results are biologically credible. 9. If all else fails, (a) look for repeats within the sequence itself: if there are only two or three, it usually indicates that the protein has a repeated functional domain; (b) examine the distribution of particular amino acids or classes

2.1.12 Current Protocols in Protein Science

Motifs Analysis Atp_Gtp_A

(A,G)x4GK(S,T) (G)x{4}GK(S) 425: HIVLS GESYSGKS TNARL

the motif from the Prosite database the pattern matched in this sequence the acutal region of sequence containing the motif

Attached annotation ***************************************** * ATP/GTP-binding site motif A (P-loop) * ***************************************** From sequence comparisons and crystallographic data analysis it has been shown [1,2,3,4,5] that an appreciable proportion of proteins that bind ATP or GTP share a number of more or less conserved sequence motifs. The best conserved of these motifs is a glycine-rich region, which probably forms a flexible loop between a betastrand and an alpha-helix. This loop interacts with one of the phosphate groups of the nucleotide. This sequence motif is generally referred to as the 'A' consensus sequence [1] or the 'P-loop' [5]. There are numerous ATP- or GTP-binding proteins in which the P-loop is found.We list below a number of protein families for which the relevance of the presence of such motif has been noted:

A list of proteins containing the motif has been omitted for brevity. Not all ATP- or GTP-binding proteins are picked-up by this motif. A number of proteins escape detection because the structure of their ATP-binding site is completely different from that of the P-loop. Examples of such proteins are the E1-E2 ATPases or the glycolytic kinases. In other ATP- or GTP-binding proteins the flexible loop exists in a slightly different form; this is the case for tubulins or protein kinases. A special mention must be reserved for adenylate kinase, in which there is a single deviation from the P-loop pattern: in the last position Gly is found instead of Ser or Thr. -Consensus pattern: [AG]-x(4)-G-K-[ST] -Sequences known to belong to this class detected by the pattern: a majority. -Other sequence(s) detected in SWISS-PROT: in addition to the proteins listed above, the 'A' motif is also found in a number of other proteins. Most of these proteins probably bind a nucleotide, but others are definitively not ATP- or GTP-binding (as for example chymotrypsin, or human ferritin light chain). -Expert(s) to contact by email: Koonin E.V. [email protected] -Last update: June 1994 / Text revised. [ 1] Walker J.E., Saraste M., Runswick M.J., Gay N.J. EMBO J. 1:945-951(1982). [ 2] Moller W., Amons R. FEBS Lett. 186:1-7(1985). [ 3] Fry D.C., Kuby S.A., Mildvan A.S. Proc. Natl. Acad. Sci. U.S.A. 83:907-911(1986). [ 4] Dever T.E., Glynias M.J., Merrick W.C.

Figure 2.1.8 MOTIFS analysis of an albumin sequence. This example shows results for a single motif found in the test protein. If the query sequence contains many motifs, the output can be quite extensive. Note that a long table of sequences which contain the motif and some of the references have been deleted due to space constraints.

for suggested functional sites; or (c) apply biological and biochemical knowledge to evaluate the composition of the sequence (e.g., regions rich in basic residues suggest the binding of nucleic acids, and acidic regions suggest generalized protein-protein interactions). Structure determination can begin with the ChouFasman (Chou and Fasman, 1974) or GarnierOsguthorpe-Robson algorithm (Garnier et al., 1978); both are discussed in UNIT 2.3. States and Boguski (1990) offer another scheme for the analysis of protein sequences:

1. Find all intrasequence repeats using dotmatrix methods, and, if repeats are found, split the sequence into pieces accordingly. 2. Use FASTA to find similar sequences or use PROFILESEARCH for a more sensitive search. 3. Compare the sequence to known motifs, for example, in the PROSITE database. 4. Search for hydrophobic segments using the Kyte-Doolittle (1982) method and for α helices and β strands using the hydrophobic moment (see UNITS 2.2 & 2.3).

Computational Analysis

2.1.13 Current Protocols in Protein Science

5. If step 1 or 2 was successful, then search for helices using the variation moment. 6. If step 1 or 2 was successful, calculate the hydrophobic correlation to estimate similarity. 7. Keeping in mind that the results will be only 55% to 65% accurate, compare the outputs of several secondary structure prediction methods with information from all available homologous sequences. 8. For antibodies, identify possible antigenic regions using hydrophilicity plots and antigenicity index plots. 9. Look for unusual features such as leucine zippers; this can be done with any program, such as PROSITE, that takes advantage of a dictionary of motifs. 10. Determine potential sites of cleavage by various chemicals; cleavage sites can be predicted by computer methods and verified experimentally.

entific and medical researchers. A few are presented here. For a listing of molecular sequence and related software, get the Catalogue of Molecular Biology from Genethon by connecting via ftp ftp.genethon.fr and entering cd pub/resig/catalogu then mget bio.catal*. Amos Bairoch’s bibliographic database on sequence analysis (seqanalref), the Listing of Molecular Biology Databases (LiMB), a directory of researchers who apply artificial intelligence (AI) techniques to problems in molecular biology (aimbdb), and many other valuable files can be found using anonymous FTP to ncbi.nlm.nih.gov. Bairoch’s lists of molecular biology e-mail, FTP, and BBO servers are also available using anonymous FTP: type ftp expasy.hcuge.ch, then cd databases/info, then mget serv_ema.txt serv_ftp.txt serv_bbo.txt. For a copy of the BIOSCI FAQ, a collection of Frequently Asked Questions, use ftp net.bio.net, then cd pub/BIOSCI/doc and get biosci.FAQ.

INTERNET RESOURCES

Computational Methods for Protein Sequence Analysis

The Internet has become an important resource in bioinformatics. There are new tools that make it easy to locate the latest software and databases. Since 1994 a new Internet-based network of hypertext tools that takes advantage of the World Wide Web (WWW) has emerged. To enter the WWW, you will need a network hypertext browser such as MOSAIC. Versions of MOSAIC, which was originally developed by the National Center for Supercomputer Applications (NCSA), are now freely available for most commonly used laboratory computers. If your local computer is connected to the Internet and your system has file transfer protocol (FTP) capability, connect to ftp.ncsa.uiuc.edu and log in under the username anonymous to retrieve a copy of MOSAIC for the computer you use most often. Once you have MOSAIC or another WWW browser running, we recommend connecting to the Australian National University’s Bioinformatics server (http://life.anu.edu.au) and selecting “Molecular biology” from the menu. Table 2.1.1 reproduces the list of resources available. Another current resource of hyperlinks is available through the author using MOSAIC or Netscape at the George Mason Comp uta tion al B iolo gy Resource (http://www.science.gmu.edu/~michaels/ comp_bio). As indicated by that list, there is a tremendous number of resources available on the Internet. In addition, there are many useful text and program files available by FTP that are of interest to molecular biologists and other sci-

LITERATURE CITED Allison, L., Wallace, C.S., and Yee, C.N. 1992. Finite-state models in the alignment of macromolecules. J. Mol. Evol. 35:77-89. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J. 1990. Basic local alignment search tool. J. Mol. Biol. 215:403-410. Argos, P. 1987. A sensitive procedure to compare amino acid sequences. J. Mol. Biol. 193:385396. Argos, P. and Vingron, M. 1990. Sensitivity comparison of protein amino acid sequences. Methods Enzymol. 183:352-365. Bairoch, A. 1992. PROSITE: A dictionary of protein sites and patterns. Nucl. Acids Res. 19:22412245. Barton, G.J. 1990. Protein multiple sequence alignment and flexible pattern matching. Methods Enzymol. 183:403-428. Barton, G.J. 1993. An efficient algorithm to locate all locally optimal alignments between two sequences allowing for gaps. CABIOS 9:729-734. Brendel, V. 1992. PROSET—A fast procedure to create nonredundant sets of protein sequences. Math. Comput. Modeling 16(6/7):37-43. Brendel, V., Bucher, P., Nourbaksh, I.R., Blaisdell, B.E., and Karlin, S. 1992. Methods and algorithms for statistical analysis of protein sequences. Proc. Natl. Acad. Sci. U.S.A. 89:20022006. Burks, C. 1990. The flow of nucleotide sequence data into data banks: Role and impact of largescale sequencing projects. In Computers and DNA, Santa Fe Institute (G. Bell and T. Marr, eds.) pp. 35-45. Addison-Wesley, Reading, Mass.

2.1.14 Current Protocols in Protein Science

Table 2.1.1

Australian National University (ANU) Molecular Biology Resources

Databases French WWW genomic service, including Caenorhabditis elegans Database BLAST Database Searches at NCBI CompoundKB database—981 metabolic intermediate compounds Codon usage tables (major species) & EMBL mirror DNA Data Bank of Japan EC enzyme database EMBL data (Heidelberg) ESTDB—Expressed Sequence Tag Database (TIGR) FASTA Database search program (Virginia) Genbank searches (Indiana) Genbank/Swiss-Prot/Protein/PIR (via NIH) GENETHON Human Genome Centre Mendelian Inheritance in Man (index) Metabolic intermediate compounds Microbial germplasm Miscellaneous, e.g. codon usage, profiles (Weizmann) PIR (Houston) Prosite (via NIH) Protein databank (Brookhaven) REF52 2D Gel Protein Database REBASE—Restrict. Enzyme Data Base (NEB) REBASE restriction enzymes Swiss-Prot: EMBNet (Heidelberg) and ExPASy (Geneva) SWISS-2DPAGE—Two-dimensional Polyacrylamide Gel Electrophoresis Database (Geneva) Various (ICGEB, Italy) Biologists’s Control Panel Bibliographies/Tutorials Biosequence Comparison Sequence analysis (search index) Biocomputing bibliography Biological Journals—current titles Molecular biology journals References to molecular biology algorithms Periodical references to journals in molecular biology Software ANU software (gopher link) SIMPLE34—Detection of sequence repetition in nucleic acid MELANIE software packages for 2D PAGE computer analysis (Geneva) NCBI repository

Sequence Analysis Tools (Trieste) IUBio Biology Software and Database Archive (Indiana) Quest II software for 2D gel proteins CODA—Conservation Options and Decision Analysis RAPDistance (gopher link) RAPDistance (WWW link) News groups BioNet—various biological topics The Scientist (biweekly news with a biotech slant) BIOSCI mailing lists and newsgroup archives Special topics The Australian National Genomic Information Service (ANGIS) Human Genome Mapping Project (UK) Artificial Intelligence in molecular biology Database of molecular biologists working in AI Agricultural Genome World Wide Web server A Caenorhabditis elegans DataBase (ACeDB) Other sites Australian National Genomic Information Service (ANGIS) CAMIS Centre for Advanced Medical Informatics (Stanford) Caenorhabditis Genetics Centre (CGC) Chlamydomonas Genetics (Duke) EMBNet ExPASy (Geneva) Harvard Biological Laboratories Human Genome Center at Lawrence Berkeley Laboratory Johns Hopkins University, BioInformatics National Center for Biotechnology Information (NCBI) QUEST Protein Database Center Ribosomal Database Project (Argon Natl. Laboratory) Molecular biology archives (list) NIH GenoBase server Othersites (gopher links) Yeast Genome Information Server (Stanford) WAIS—EC enzymes WAIS—Molecular Biology WAIS—indexes sorted alphabetically (ADFA)

Computational Analysis

2.1.15 Current Protocols in Protein Science

Chou, P.Y. and Fasman, G.D. 1974. Prediction of protein conformation. Biochemistry 13:222-244. Collado-Vides, J. 1991. The search for a grammatical theory of gene regulation is formally justified by showing the inadequacy of context-free grammars. CABIOS 7:321-326. Collins, J.F. and Coulson, A.F.W. 1990. Significance of protein sequence similarities. Methods Enzymol. 183:474-487. Day, W.H.E. and McMorris, F.R. 1993. A consensus program for molecular sequences. CABIOS 9:653-656. Dayhoff, M.O. 1978. Atlas of Protein Sequence and Structure. National Biomedical Research Foundation, Washington, D.C. Depiereux, E. and Feytmans, E. 1991. Simultaneous and multivariate alignment of protein sequences: Correspondence between physicochemical profiles and structurally conserved regions (SCR). Protein Eng. 4:603-613. De Rijk, P. and De Wachter, R. 1993. DCSE, an interactive tool for sequence alignment and secondary structure search. CABIOS 9:735-740. Doolittle, R.F. 1981. Similar amino acid sequences: Chance or common ancestry? Science 214:167339. Doolittle, R.F. 1986. Of URFs and ORFs: A Primer on How to Analyze Derived Amino Acid Sequences. University Science Books, Ann Arbor, Mich. Doolittle, R.F. 1989. Redundancies in protein sequences. In Prediction of Protein Structure and the Principles of Protein Conformation (G.D. Fasman, ed.) pp. 599-623. Plenum, New York.

Computational Methods for Protein Sequence Analysis

Doolittle, R.F. 1990. What we have learned and will learn from sequence databases. In Computers and DNA, Santa Fe Institute (G. Bell and T. Marr, eds.) pp. 21-31. Addison-Wesley, Reading, Mass. Dumas, J.P. and Nunio, J. 1982. Efficient algorithm for folding and comparing nucleic acid sequences. Nucl. Acids Res. 10:197-206. Eroshkin, A.M., Zhilkin, P.A., and Fomin, V.I. 1993. Algorithm and computer program: Pro_Anal for analysis of relationship between structure and activity in a family of proteins or peptides. CABIOS 9:491-497. Fitch, W.M. 1966. An improved method of testing for evolutionary homology. J. Mol. Biol. 16:916. Fitch, W.M. 1969. Locating gaps in amino acid sequences to optimize the homology between two proteins. Biochem. Genet. 3:99-108. Fuchs, R. 1994. Fast protein block searches. CABIOS 10:79-80. Garnier, J., Osguthorpe, D.J., and Robson, B. 1978. Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J. Mol. Biol. 120:97-120.

Genetics Computer Group. 1994. GCG Program Manual for the Wisconsin Package, Version 8, September 1994. Genetics Computer Group Inc., Madison, Wis. George, D., Hunt, L.T., and Barker, W.C. 1990. Mutation data matrix and its uses. Methods Enzymol. 183:333-351. Gibbs, A.J. and McIntyre, G.A. 1970. The diagram, a method for comparing sequences. J. Biochem. 16:1-11. Gotoh, O. 1986. Alignment of three biological sequences with an efficient traceback procedure. J. Theor. Biol. 121:327-337. Gribskov, M., Homyak, M., Edenfield, J., and Eisenberg, D. 1988. Profile scanning for three-dimensional structural patterns in protein sequences. CABIOS 4:61-66. Gribskov, M., Luethy, R., and Eisenberg, D. 1990. Profile analysis. Methods Enzymol. 183:146159. Gribskov, M. 1994. Profile analysis. Computer analysis of sequence data. Methods Mol. Biol. 24:247-266. Henikoff, S. and Henikoff, J.G. 1993. Proteins Struct. Funct. Genet. 17:49-61. Heringa, J., Sommerfeldt, H., Higgins, D.G., and Argos, P. 1992. OBSTRUCT: A program to obtain the largest cliques from a protein sequence set according to structural resolution and sequence similarity. CABIOS 8:599-600. Hodgman, T.C. 1992. Nucleic acid and protein sequence management. In Microcomputers in Biochemistry: A Practical Approach (C.F.A. Bryce, ed.) pp. 131-158. IRL Press, Oxford. Kanaoka, M., Kishimoto, F., Ueki, Y., and Umeyama, H. 1989. Alignment of protein sequences using the hydrophobic core scores. Protein Eng. 2:347-351. Karlin, S.P., Morris, M., Ghandour, G., and Leung M.-Y. 1988. Algorithms for identifying local molecular sequence features. CABIOS 4:41-51. Karlin, S.P., Ost, F., and Blaisdell, B.E. 1989. Patterns in DNA and amino acid sequences and their statistical significance. In Mathematical Methods for DNA Sequences (M.S. Waterman, ed.) pp. 133-157. CRC Press, Boca Raton, Fla. Karlin, S., Bucher, P., and Brendel, V. 1991. Statistical methods and insights for protein and DNA sequences. Annu. Rev. Biophys. Chem. 20:175203. Kruskal, J.B. 1983. An overview of sequence comparison. In Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison (D. Sankoff and J.B. Kruskal, eds.) pp. 1-44. Addison-Wesley, Reading, Mass. Kruskal, J.B. and Sankoff, D. 1983. An anthology of algorithms and concepts for sequence comparison. In Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison (D. Sankoff and J.B. Kruskal, eds.) pp. 265-310. Addison-Wesley, Reading, Mass.

2.1.16 Current Protocols in Protein Science

Kyte, J. and Doolittle, R.F. 1982. A simple method for displaying the hydrophobic character of a protein. J. Mol. Biol. 157:105-132. Landau, G.M., Vishkin, U., and Nussinov, R. 1988. Locating alignments with k differences for nucleotide and amino acid sequences. CABIOS 4:19-24. Landau, G.M., Vishkin, U., and Nussinov, R. 1990. Fast alignment of DNA and protein sequences. Methods Enzymol. 183:487-502.

Pearson, W.R. and Miller, W. 1992. Dynamic programming algorithms for biological sequence comparison. Methods Enzymol. 210:576-610. Pevzner, P.A. 1992. Statistical distance between texts and filtration methods in sequence comparison. CABIOS 8:121-127. Pizzi, E.M., Attimonelli, M., Liuni, S., Frontali, C., and Saccone, C. 1991. A simple method for global sequence comparison. Nucl. Acids Res. 20:131-136.

Landes, C., Henaut, A., and Risler, J.-L. 1993. Dotplot comparisons by multivariate analysis (DOCMA): A tool for classifying protein sequences. CABIOS 9:91-196. Lipman, D.J. and Pearson, W.R. 1985. Rapid and sensitive protein similarity searches. Science 227:1435-1441. Livingstone, C.D. and Barton, G.F. 1993. Protein sequence alignments: A strategy for the hierarchical analysis of residue conservation. CABIOS 9:745-756.

Reich, J.G. and Meiske, W. 1987. A simple statistical significance test of window scores in large dot matrices obtained from protein or nucleic acid sequences. Comput. Appl. Biosci. 3:25-30. Rigoutsos, I. and Califano, A. 1994. Searching in parallel for similar strings. IEEE Computatl. Sci. Eng. 60-75. Robson, B. and Greaney, P.J. 1992. Natural sequence code representations for compression and rapid searching of human-genome-style databases. CABIOS 8:283-289.

Maizel, J.V. and Lenk, R.P. 1981. Enhanced graphic matrix analysis of nucleic acids and protein sequences. Proc. Natl. Acad. Sci. U.S.A. 78:76657669. McLachlan, A.D. 1971. Test for comparing related amino acid sequences: Cytochrome c and cytochrome c-551. J. Mol. Biol. 61:409-424. McLachlan, A.D. 1972. Repeating sequences and gene duplication in proteins. J. Mol. Biol. 72:417-437. Michaels, G.S., Taylor, R., Hagstrom, R., Price, M., and Overbeek, R. 1993. Searching for genomic organizational motifs: Explorations of the E. coli chromosome. Comp. Chem. 17:209-217.

Rohde, K. and Bork, P. 1993. A fast, sensitive pattern-matching approach for protein sequences. CABIOS 9:183-189.

Mrazek, J. and Kypr, J. 1993. UNIREP: A microcomputer program to find unique and repetitive nucleotide sequences in genomes. CABIOS 9:355-360. Murata, M., Richardson, J.S., and Sussman, J.L. 1985. Simultaneous comparison of three protein sequences. Proc. Natl. Acad. Sci. U.S.A. 82:3073-3077. Nedde, D.N. and Ward, M.O. 1993. Visualizing relationships between nucleic acid sequences using correlation images. CABIOS 9:331-335. Needleman, S.B. and Wünsch, C.D. 1970. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48:443-453. Panjukov, V.V. 1993. Finding steady alignments: Similarity and distance. CABIOS 9:285-290. Parry-Smith, D.J. and Atwood, T.K. 1992. ADSP— A new package for computational sequence analysis. CABIOS 8:451-459. Pearson, W.R. 1990. Rapid and sensitive comparison with FASTP and FASTA. Methods Enzymol. 183:63-98. Pearson, W.R. 1994. Using the FASTA program to search protein and DNA sequence databases. Methods Mol. Biol. 24:365-389.

Searls, D. 1993. The computational linguistics of biological sequences. In Artificial Intelligence and Molecular Biology (L. Hunter, ed.) pp. 47120. MIT Press, Cambridge, Mass. Sellers, P.H. 1974. On the theory and computation of evolutionary distances. SIAM J. Appl. Math. 26:787-793. Smith, R.F. and Smith, T.F. 1992. Pattern-induced multisequence alignment (PIMA) algorithm employing secondary structure–dependent gap penalties for use in comparative protein modeling. Protein Eng. 5:35-41. Smith, T.F. and Waterman, M.S. 1981. Comparative biosequence metrics. J. Mol. Evol. 18:38-46. Staden, R. 1994a. Statistical and structural analysis of protein sequences. Methods Mol. Biol. 24:125-130. Staden, R. 1994b. Searching for motifs in protein sequences. Methods Mol. Biol. 24:131-139. Staden, R. 1994c. Using patterns to analyze protein sequences. Methods Mol. Biol. 24:141-154. Staden, R. 1994d. Comparing sequences. Methods Mol. Biol. 24:155-170. States, D.J. 1992. Molecular sequence accuracy: Analyzing imperfect data. Trends Genet. 8:5255. States, D.J. and Boguski, M.S. 1990. Sequence Analysis Primer. Stockton Press, New York. Streletc, V.B., Shindyalov, I.N., Kolchanov, N.A., and Lim, H.A. 1991. Fast, statistically based alignment of amino acid sequences on the base of diagonal fragments of dot matrices. CABIOS 8:529-534. Taylor, W.R. 1988. Pattern matching methods in protein sequence comparison and structure prediction. Protein Eng. 2:77-86. Computational Analysis

2.1.17 Current Protocols in Protein Science

Tyler, E., Horton, M.R., and Krause, P.R. 1991. A review of algorithms for molecular sequence comparison. Computers Biomed. Res. 24:72-96. Waterman, M.S. 1989. Sequence alignments. In Mathematical Methods for DNA Sequences (M.S. Waterman, ed.) pp. 53-90. CRC Press, Boca Raton, Fla. Waterman, M.S. 1990. Consensus patterns in sequences. In Mathematical Methods for DNA Sequences (M.S. Waterman, ed.) pp. 93-115. CRC Press, Boca Raton, Fla. Waterman, M.S. and Eggert, M. 1991. A new algorithm for best subsequence alignments with application to tRNA-rRNA comparisons. J. Mol. Biol. 197:723-728.

Waterman, M.S. and Jones, R. 1990. Consensus methods for DNA and protein sequence alignment. Methods Enzymol. 183:221-237. Wilbur, W.J. and Lipman, D.J. 1983. Rapid similarity searches of nucleic acid and protein data banks. Proc. Natl. Acad. Sci. U.S.A. 80:726-730.

Contributed by George Michaels and Robert Garian George Mason University Fairfax, Virginia

Computational Methods for Protein Sequence Analysis

2.1.18 Current Protocols in Protein Science

Hydrophobicity Profiles for Protein Sequence Analysis INTRODUCTION Seminal work by Anfinsen et al. (1961) showed that the three-dimensional structure of a protein is determined by its amino acid sequence. Thus, significant effort has been aimed at unraveling the complex relationship between sequence and structure. Although there is no immediate prospect of a general solution to the protein folding problem, stepwise empirical methods have been developed that analyze protein sequences and successfully provide predictive results concerning protein structure. The work of Kauzmann (1959) determined that hydrophobic interactions are a major force in protein folding. Accounting for the hydrophobicity of amino acids within a protein is thus the first step toward understanding protein folding (Tanford, 1980). Hydrophobicity studies have resulted in a wide range of hydropathy scales, indices that attempt to quantify the relative hydrophobicity of the amino acids. The actual hydrophobic content attributed to each amino acid is dependent on the methodology used to estimate it and may vary from scale to scale. Some scales are based solely on physicochemical measurements such as the free energy of side-chain transfer (Nozaki and Tanford, 1971; Wolfenden et al., 1981). Other scales are based on statistical analysis of threedimensional structures (Chothia, 1976; Rose et al., 1985). In addition, scales can be a combination of both procedures (Kyte and Doolittle, 1982). The general observation that hydrophobic residues tend to be buried in the interior of the protein whereas hydrophilic residues prefer to be exposed to solvent is predominant in all scales. Generating a hydropathy profile requires two things: (1) the primary structure and (2) the hydropathy scale. The primary structure can be obtained by translating the cDNA or by sequencing the protein. Hydropathy profiles for a protein are obtained by averaging or combining the hydrophobicity or hydrophilicity of an individual amino acid (taken from a particular hydropathy scale) with the values of several neighboring residues along the protein sequence, then plotting the local averages or sums against the amino acid sequence. In such hydrophobicity plots, regions of hydrophobicity and hydrophilicity appear as maxima and minima, respectively. A fundamental use of hy-

dropathy profiles, therefore, is predicting the overall hydropathy (hydrophobicity or hydrophilicity) of a protein from its amino acid sequence. The distribution of local hydropathy values can provide insight into the overall folding pattern of the protein (see Applications). Numerous procedures have been developed for calculating the hydropathy profile of a protein based on the free energy of side-chain transfer from water or vacuum to an organic solvent (Nozaki and Tanford, 1971; Tanford, 1980; Wolfenden et al., 1981; Fauchere and Pliska, 1983). The procedures differ primarily in the hydropathy scale used, and the different hydropathy scales have arisen, in part, to address specific applications. For example, the hydrophilicity scale of Hopp and Woods (1981) was developed to locate antigenic segments; the Goldman, Engleman, and Steitz (GES) scale (Engleman et al., 1986) was developed to identify transmembrane regions in a protein. Although individual scales were developed for different purposes or by alternative procedures, significant similarity exists in the resultant profiles. Most hydropathy plots show similar distributions of minima and maxima in the sequence profiles. In Figure 2.2.1, the result of applying the most widely used hydrophobicity scale, that of Kyte and Doolittle (1982), is compared to results of applying the scales of Hopp and Woods (1981) and Eisenberg (1984) to hen egg white lysozyme. The similarity in profiles is partially due to the fact that maxima and minima often reflect secondary structure elements; regions of hydrophobicity and hydrophilicity can be correlated with β sheets and β turns, respectively (Kuntz, 1972; Roy and Rose, 1980). Hydropathy profiles can be used to examine the surface features of proteins in order to generate hypotheses that can be confirmed experimentally. For example, such analyses received widespread use in the prediction of antigenic and other interaction sites on proteins. Furthermore, when used in conjunction with other secondary structure prediction algorithms (see UNIT 2.3), hydropathy profiles have been shown to correlate well with α-helical, turn, and β-sheet propensities. As such, they have proved to be invaluable for predicting protein secondary structure. The most extensive use of hydropathy profiles has been for

Contributed by Stanley R. Krystek, Jr., William J. Metzler, and Jiri Novotny Current Protocols in Protein Science (1995) 2.2.1-2.2.13 Copyright © 2000 by John Wiley & Sons, Inc.

UNIT 2.2

Computational Analysis

2.2.1 CPPS

20

Hydropathy value

10

0

–10

– 20

–30 0

50

100

150

Sequence position

Figure 2.2.1 Hydropathy profiles of hen egg white lysozyme. The three profiles were created with the Kyte and Doolittle, the Hopp and Woods, and the Eisenberg scales using a scanning window of 7 amino acids. The Hopp and Woods scale was modified as described in the text to match other hydropathy scales.

deducing transmembrane regions in membrane proteins. Previous reviews describe systematic evaluations of hydropathy procedures and provide criteria for selecting the most appropriate amino acid hydropathy scale (Engleman et al., 1986; Cornette et al., 1987; Banghan, 1988; Hopp, 1989; Rose and Dworkin, 1989). This unit describes the application of hydrophobicity plots to typical problems and provides suggested uses for a few selected scales.

METHODOLOGY Selecting Hydropathy Scales

Hydrophobicity Profiles for Protein Sequence Analysis

The first step in generating a hydropathy profile (or profiles) for an individual amino acid sequence is selecting the appropriate scale(s). Table 2.2.1 lists several widely used scales. This unit focuses primarily on four hydropathy scales: the Hopp and Woods, the Kyte and Doolittle, the Eisenberg, and the GES scales. Differences among the hydropathy indices for each amino acid in the four scales (Table 2.2.2) arise primarily from differences in the experimental data from which the scales were derived and from the methodologies by

which each scale was optimized. The hydrophilicity scale derived by Hopp and Woods is based on solubility values of hydrophobic and hydrophilic amino acids (Nozaki and Tanford, 1971; Levitt, 1976). In the Kyte and Doolittle and the Eisenberg scales, the hydropathy index is a measure of the relative affinity of an amino acid for hydrophobic phases. The GES scale is based on physicochemical considerations of helical structure and several energetic factors that reflect partitioning of an amino acid side chain between aqueous solution and a membrane bilayer. Both the Kyte and Doolittle and the Eisenberg scales are combinations of earlier scales. The Kyte and Doolittle scale combines values from the water-to-vapor transfers and the internal/external distributions of amino acid residues from known structures as determined by Chothia (1976). The Eisenberg scale is a consensus of the Wolfenden (Wolfenden et al., 1981), Chothia (Chothia, 1976), Janin (Janin, 1979), and von Heijne (von Heijne and Blomberg, 1979) scales and is designed to minimize effects of the outlying values observed for some amino acids in previous scales.

2.2.2 Current Protocols in Protein Science

Table 2.2.1

Hydropathy Scales

Reference scale

Literature

Derivationa

Bigelow Tanford Chothia Levitt Janin von Heijne and Blomberg Roy and Rose Hopp and Woods Wolfenden Argos Fraga Kyte and Doolittle Fauchere and Pliska Eisenberg Hopp Rose GES Parker, Guo, and Hodges Esposti et al.

Bigelow, 1967 Nozaki and Tanford, 1971 Chothia, 1976 Levitt, 1976 Janin, 1979 von Heijne and Blomberg, 1979 Roy and Rose, 1980 Hopp and Woods, 1981 Wolfenden et al., 1981 Argos et al., 1982 Fraga, 1982 Kyte and Doolittle, 1982 Fauchere and Pliska, 1983 Eisenberg, 1984 Hopp, 1984 Rose et al., 1985 Engleman et al., 1986 Parker et al., 1986 Esposti et al., 1990

Experimental Experimental Theoretical Experimental Theoretical Theoretical Experimental Experimental Experimental Theoretical Theoretical Combination Experimental Combination Theoretical Theoretical Theoretical Experimental Theoretical

aExperimental, largely original data from physicochemical experiments; theoretical, derivation based on

analysis of structural data; combination, consensus of multiple scales.

There are several other interesting scales that deserve mention. The first hydropathy scale for membrane proteins was developed and revised by von Heijne (von Heijne and Blomberg, 1979) and corresponds rather closely to the GES scale described above (Engleman et al., 1986). A hydrophilicity scale by Parker, Guo, and Hodges (1986) provides new hydrophilicity values that are determined from HPLC retention times of model synthetic peptides. Another important set of scales includes the statistical scales developed by Argos and co-workers for prediction of membraneburied helices (Argos et al., 1982; Rao and Argos, 1986). These scales are derived from the statistical preference of specific amino acid residues to form the integral regions in transmembrane proteins, and they have been shown to correlate with highly resolved crystal structures much better than the physicochemical hydropathy scales (Esposti et al., 1990). Because all hydropathy scales are conceptually similar, the strategy of applying them to a given problem should be analogous regardless of the particular scale chosen. Moreover, although there may always be debates over which of the scales (methods) will prove to be best, experience has shown that application of

several different hydropathy schemes and alternative predictive rules will tend to build a consensus picture of protein hydrophobicity. For this unit the values of the Hopp and Woods and GES scales are multiplied by −1 so that these scales comply with others having peak maxima corresponding to hydrophobicity and peak minima corresponding to hydrophilicity (the scale values were not modified in Table 2.2.2). The Hopp and Woods and GES plots shown are therefore inverse plots.

Applying the Scanning Window Averaging Technique After the appropriate scale(s) has been selected, the next stage is generating the hydropathy profile for a protein. To create the profile, the numerical values derived from the hydropathy scales for each residue in the protein are repetitively averaged over the length of protein sequence. This procedure is commonly referred to as the scanning window averaging technique (Hopp and Woods, 1981; Kyte and Doolittle, 1982). In practice, the hydropathy is obtained by summing the individual hydropathy values (hydrophobicities or hydrophilicities) for a given number of contiguous amino acids along the protein chain. The sum of hy-

Computational Analysis

2.2.3 Current Protocols in Protein Science

Table 2.2.2

Hydrophobicity Indices for Selected Scales

Amino acid

H & Wa

K & Db

Eisenbergc GESd

Neutral, hydrophobic, aliphatic −0.4 Gly 0.0 −0.5 Ala 1.8 −1.5 Val 4.2 −1.8 Ile 4.5 −1.8 Leu 3.8 −1.3 Met 1.9

0.16 0.25 0.54 0.73 0.53 0.26

−1.0 −1.6 −2.6 −3.1 −2.8 −3.4

Neutral, hydrophobic, aromatic −2.5 Phe 2.8 −2.3 −1.3 Tyr −3.4 −0.9 Trp

0.61 0.02 0.37

−3.7 0.7 −1.9

Neutral, hydrophilic Ser 0.3 −0.4 Thr Asn 0.2 Gln 0.2

−0.8 −0.7 −3.5 −3.5

−0.26 −0.18 −0.64 −0.69

−0.6 −1.2 4.8 4.1

Acidic, hydrophilic Asp 3.0 Glu 3.0

−3.5 −3.5

−0.72 −0.62

9.2 8.2

Basic, hydrophilic −0.5 His Lys 3.0 Arg 3.0

−3.2 −3.9 −4.5

−0.40 −1.1 −1.8

3.0 8.8 12.3

Thiol-containing −1.0 Cys

2.5

0.04

−2.0

−1.6

−0.07

0.2

Imino acid Pro

0.0

aHopp and Woods, 1981. bKyte and Doolittle, 1982. cEisenberg, 1984. dEngleman et al., 1986.

Hydrophobicity Profiles for Protein Sequence Analysis

dropathy values is calculated progressively through the sequence, with each amino acid being the start of a new sum (Fig. 2.2.2). The resultant sums (or averages) are plotted versus the sequence position at the position of the central (or first) amino acid in the segment being summed. The choice of the center versus the first residue has no effect on the calculated profiles but shifts the sequence axis by half the window size. We prefer to plot versus the center position as it seems more intuitive. Similarly, the choice of summing or averaging has no effect on the profile but modifies the relative values plotted on the y-axis. We use sums for the profiles presented in this unit. This approach

displays the general hydropathic character of a protein. The ability to discern structurally interpretable features in the calculated hydropathy profile is critically dependent on the width of the scanning window, that is, the number of amino acids included in the summation. Figure 2.2.3 shows the effect of altering the size of the scanning window on hydropathy profiles generated for lysozyme. In Figure 2.2.3A, where the window size is set to 1 amino acid, the resultant hydropathy profile is featureless, providing essentially no structural information. On introducing a window size of 7 amino acids, well-resolved peaks appear in the hydropathy

2.2.4 Current Protocols in Protein Science

10 20 protein sequence . . . RTYFCDEQASQDWLVNAR . . . sliding window (size of 7) average hydropathy . . . 4 5 6 7 8 9 10 11 12 13 14 . . . value mapped to center residue of window

Figure 2.2.2 Schematic depicting the scanning window averaging technique.

profile (Fig. 2.2.3B). Increasing the window size to 11 amino acids (Fig. 2.2.3C) results in a smoothing or broadening of observed peaks in the profile, and with window sizes of 15 or 19 amino acids (Fig. 2.2.3D,E), the profiles again have few well-resolved features. From the family of profiles in Figure 2.2.3, it is readily apparent that window sizes exceeding 10 amino acids result in a loss of local information. This decrease in local information leads to increased errors in predicting hydrophobic regions in globular proteins (Kyte and Doolittle, 1982). In contrast, significant correlations do exist between hydropathy profiles generated with window sizes of 5 to 9 amino acids and known structural elements (Roy and Rose, 1980; Kyte and Doolittle, 1982; Rao and Argos, 1986; Cornette et al., 1987). In selecting the appropriate window size for a particular problem, the length of the structural property being investigated should be used as a guide. For example, transmembrane helices are roughly 20 residues in length, but the helices of globular proteins tend to be much shorter. One might therefore opt for a larger window size if searching for transmembrane helices, whereas a length of 7 or 9 amino acids is more appropriate for predicting surface sites or secondary structure. In cases of uncertainty, analysis of profiles generated with various window sizes will afford the most reliable results.

Threshold or Cutoff Values To facilitate the analysis of hydropathy profiles, it is convenient to define a threshold value, that is, the amplitude above which the hydropathy value must remain for a particular amino acid to be considered part of a contiguous region. Although somewhat arbitrary, the actual choice of threshold value for a particular profile is often derived empirically or from

experience. For example, if one were interested in analyzing the sequences of known membrane proteins for transmembrane helices, then an appropriate threshold value might be one which delineates peaks in the hydropathy profile to approximately 20 amino acids (see section on Predicting Transmembrane Regions in Proteins). As with selecting scanning window size, analyzing profiles with different threshold values should yield more reliable results until experience is gained.

APPLICATIONS Predicting Interaction Sites Hydropathy profiles have found widespread use in investigations aimed at elucidating the antigenic structure and surface features of proteins for which there are no three-dimensional structures (Hopp and Woods, 1981; Krystek et al., 1985a,b). Antigenic sites tend to occur on protein surfaces, where predominantly hydrophilic residues are both exposed to solvent and accessible for interaction with antibody. One would therefore anticipate good correlation between antigenic sites and regions of high hydrophilicity. A test of this hypothesis carried out by Tanaka et al. (1985) reported a success rate of 56%. Figure 2.2.4 depicts the Hopp and Woods profiles for myoglobin generated with a window size of 7 amino acids. Regions of hydrophilicity are detected as peak minima in the profile. The lines at the bottom of Figure 2.2.4 indicate the locations of five major antigenic sites determined experimentally (Atassi, 1984). All of the antigenic sites are associated with regions of high hydrophilicity. In addition, there are two regions of high hydrophilicity (peaks at positions 40 to 45 and 78 to 84) that are not associated with antigenicity. It is plau-

Computational Analysis

2.2.5 Current Protocols in Protein Science

A

10 5 0 –5 –10 20

B

10 0 –10 –20 –30 –40

C Hydrophobicity value

20

D

10 0 –10 –20 –30 –40 20 10 0 –10 –20 –30 –40

E

20 10 0 –10 –20 –30 –40 0

50

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Sequence position

Hydrophobicity Profiles for Protein Sequence Analysis

Figure 2.2.3 Effect of altering the scanning window size on the resolution of the hydropathy profile of lysozyme. All profiles were created with the Kyte and Doolittle scale. Window sizes were set to (A) 1, (B) 7, (C) 11, (D) 15, and (E) 19 amino acids. Note that the scale in A is reduced to show the profile more clearly.

2.2.6 Current Protocols in Protein Science

15

Hydrophilicity value

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Figure 2.2.4 Hopp and Woods profile for sperm whale myoglobin using a scanning window of 7 amino acids. Lines below the profile indicate the locations of five experimentally determined antigenic sites (Atassi, 1984).

sible that these correspond to antigenic sites that have yet to be identified experimentally, or they may point to an alternate, functionally significant property of myoglobin. Whatever the case, these results serve to emphasize the point that hydropathy profiles should be used only as a guide for predicting antigenicity. Additional features of protein surfaces can be highlighted by hydropathy profiles. As an example, consider human hemoglobin, whose three-dimensional structure is known (Fermi, 1975). Hemoglobin is found as a tetramer in its native state, being composed of two α- and two β-subunits. In the native protein the β- and α-subunits are folded against one another, forming contact regions that more closely resemble the interior of a globular protein. Because these contact regions are hydrophobic, one might examine hydropathy profiles for regions of hydrophobicity as a means of identifying potential subunit interacting surfaces. The Hopp and Woods profile for human hemoglobin is shown in Figure 2.2.5. The regions of contact in the β-subunit of human hemoglobin (Yoshioka and Atassi, 1986) are indicated as bars above the profile. The two largest hydrophobic peaks correspond to regions of the β-subunit in contact with each of

the α-subunits. Analogous analysis of hydropathy profiles has been successfully used to identify the subunit interacting surfaces for the α-subunits of glycoprotein hormones lutropin and follitropin (Krystek et al., 1991, 1992). Human hemoglobin also provides a demonstration of the dangers of using hydropathy profiles for analyzing antigenic sites of proteins that possess significant quaternary structure. In Figure 2.2.5 the lines below the profile indicate the regions of five major continuous antigenic determinants. Although two sites correlate with regions of hydrophilicity, the others do not. In such cases the amino acid sequence alone is not sufficient for predictability. This may be due in part to the unique nature of proteins composed of multiple subunits.

Correlating Profiles with Secondary Structure An increasingly important application of hydropathy profiles is their use in conjunction with secondary structure prediction tools (see UNIT 2.3). Figure 2.2.6 shows the hydropathy profiles for interleukin 1β (IL-1β) generated with the Eisenberg scale (Fig. 2.2.6A) and the Kyte and Doolittle scale (Fig. 2.2.6B), using a scan window of 7 amino acids. The solid bars

Computational Analysis

2.2.7 Current Protocols in Protein Science

15

Hydrophilicity value

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–10 0

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Figure 2.2.5 Hopp and Woods profile for human hemoglobin β-subunit using a scanning window size of 7 amino acids. Bars above the profile mark the locations of β-subunit surfaces in contact with α-subunits (Yoshioka and Atassi, 1986). Lines below the profile indicate the locations of antigenic sites.

Hydrophobicity Profiles for Protein Sequence Analysis

above the profiles indicate the locations of β strands found in the crystal structure of this all-β protein (Finzel et al., 1989). Although there is a difference in scale magnitude between the Kyte and Doolittle and Eisenberg methods, both profiles show exceptionally good correlation of hydrophobicity (peak maxima) with the location of β strands. The correlation of hydrophobicity with β-sheet structural elements is a direct reflection of the fact that sheets are most often located in the hydrophobic interior of a protein; thus, they comprise residues most compatible with the hydrophobic environment. There is also good correlation between hydrophilicity (peak minima) and the location of turns in proteins. For interleukin 1β essentially all of the turns identified in the structure correspond to minima. Figure 2.2.7 shows the hydropathy profiles for myoglobin, a protein composed solely of α helices. The larger α helices appear to correlate with peak maxima. Like β sheets, helices are frequently found in regions of hydrophobicity in the hydrophobic core of proteins. Because of the interspersed nature of hydrophilic and hydrophobic residues of amphipathic helices, however, hydropathy profiles are less useful in

predicting the location of these helices. Moreover, as both helices and sheets result in peak maxima, the hydropathy profile is not sufficient to discriminate between them. Therefore, correlations of secondary structure with protein hydropathic character are most successful when used in conjunction with protein secondary structure prediction algorithms (see UNIT 2.3).

Predicting Transmembrane Regions in Proteins Perhaps the most popular use of hydropathy profiles is predicting transmembrane regions in proteins. In addition to hydropathy scales, there are several membrane preference scales. Such scales have been developed independently from the hydropathy scales and are based on statistical distributions of each amino acid in membrane regions of proteins. The statistical scales have been previously compared to the hydropathy scales (Esposti et al., 1990); in general, they are equally efficient at predicting the location of the transmembrane and amphipathic helices. Examining the structure of known membrane proteins has enabled the predictive rules

2.2.8 Current Protocols in Protein Science

6

A

4 2 0 –2

B

Hydropathy value

–4 –6 –8 20 10 0 –10 –20 –30 0

50

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Sequence position

Figure 2.2.6 Hydropathy profiles for interleukin 1β using a scanning window size of 7 amino acids. Bars above the profile indicate locations of the β strands, and lines below the profile mark the locations of turns (Finzel et al., 1989). (A) Profile generated using the Eisenberg scale. (B) Profile generated using the Kyte and Doolittle scale.

for the various scales to be optimized. In addition, estimates of the hydrophobic thickness of the bilayer have provided guides to the minimum number of helical amino acids necessary to span the bilayer: the minimum number is estimated to be 20 (Engleman et al., 1986). This greatly facilitates the prediction of putative transmembrane helices by adding an additional criterion to the analysis of hydropathy profiles for transmembrane regions, namely, maxima in the profiles must span ∼20 residues. Figure 2.2.8A shows the Kyte and Doolittle profile for bacteriorhodopsin, a membrane protein whose structure is known (Henderson et al., 1990). For Kyte and Doolittle profiles, the best resolution is seen for window scans of 7 or 9 amino acids and threshold values of 0.7 × window size. For a window scan of 7, the

threshold value is 4.9. There are seven major peaks with threshold values greater than 4.9 that span at least 20 amino acids (indicated by brackets above the profile in Fig. 2.2.8A). Comparison of the predicted transmembrane regions with the location of the actual helices identified in the structure (bars above the profile) indicates that the prediction is quite good. Similar agreement is seen when the method of Engleman et al. (1986) is used to predict the transmembrane regions for bacteriorhodopsin (Fig. 2.2.8B). Although the location of the helices can be predicted reliably, neither the Kyte and Doolittle nor the GES method is able to predict accurately the boundaries of the transmembrane regions. Fortunately, visual inspection of the sequence, as well as general features of the

Computational Analysis

2.2.9 Current Protocols in Protein Science

30

Hydrophobicity value

20

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Figure 2.2.7 Kyte and Doolittle hydropathy profile for sperm whale myoglobin generated with a scanning window size of 7 amino acids. Bars above the profile mark the locations of the helices in myoglobin.

Hydrophobicity Profiles for Protein Sequence Analysis

transmembrane helices, can frequently be used to delineate transmembrane regions and the interfacial residues at each end of the helices. Some general features common to transmembrane regions to consider are as follows. (1) Approximately 75% of the amino acids in transmembrane regions are hydrophobic (Leu, Ile, Val, Met, Phe, Trp, Tyr, Cys, Ala, Pro, and Gly). (2) Proline residues are found in ∼20% of the transmembrane helices but in only 3% of globular proteins (Barlow and Thornton, 1988). (3) Aromatic residues are clustered near the interface of the transmembrane helix and bulk water. Positional preferences for amino acids in transmembrane regions have been identified for single-spanning membrane proteins (Landolt-Marticorena et al., 1993) and can serve as a further guide for delineating the ends of transmembrane regions. It can be expected that these preferences will be similar for polytopic membrane proteins. The positional preferences define a motif with the following characteristics: an extracellular terminal flanking region (Asp, Ser and Pro); an extracellular-interfacial region (Trp); a transmembrane domain composed primarily of hydrophobic residues (Leu, Ile, Val, Met, Phe, Trp, Cys, Ala, Pro, and Gly); an

intracellular-interfacial region (Tyr, Trp, and Phe); and an intracellular terminal flanking region (Lys and Arg). Application of these sequence analysis rules for transmembrane proteins greatly improves the results of using hydropathy profiles for predicting transmembrane regions.

CONCLUSIONS 1. Virtually all hydropathy scales are able to reveal structural information in an equivalent manner. Selection of the appropriate hydropathy scale is therefore a practical and/or personal decision. 2. Window scanning procedures should use an appropriate window size for the structural element being examined: 7 or 9 amino acids for predicting surface sites or secondary structure and 19 amino acids for predicting membranespanning regions. 3. Identification of interaction sites can be accomplished using peak minima (hydrophilicity). 4. Correlation of protein secondary structure with hydropathy profiles may be useful for protein structure prediction methodology (see UNIT 2.3).

2.2.10 Current Protocols in Protein Science

A

30

Hydrophobicity value

20

10

0

–10

Free energy of transfer to water

B

–20 50 40 30 20 10 0 –10 –20 –30 –40 –50 0

50

100

150

200

250

300

Sequence position

Figure 2.2.8 Hydropathy profiles for bacteriorhodopsin. Bars above the profiles mark the locations of the helices. (A) Kyte and Doolittle profile generated using a scanning window size of 7 amino acids. Brackets indicate the helices predicted on the basis of the profile alone. (B) GES profile generated using a scanning window size of 19 amino acids.

5. Prediction of transmembrane regions of integral membrane proteins can be made using a variety of scales and protocols; analysis of the hydropathy profiles should be augmented with the applicable sequence analysis rules.

rithms can also easily be encoded into programming languages or spreadsheets and interfaced with numerous graphical software packages for plotting the hydropathy profiles (if graphics are not included in the particular prediction program). These are also discussed in UNIT 2.1.

ACCESSIBILITY OF SOFTWARE Most commercial personal computer or computational packages (Genetics Computer Group, 1994; Biosym Technologies, 1991; Tripos Associates, 1993) contain algorithms for hydropathy profile generation along with other secondary structure prediction tools (see Table 2.3.6). In addition, the authors of each scale or protocol have published analysis programs (Table 2.2.1), of which many are available from public domain sources on the Internet. Algo-

LITERATURE CITED Anfinsen, C.B., Haber, E., Sela, M., and White, F.H., Jr. 1961. The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain. Proc. Natl. Acad. Sci. U.S.A. 47:1309-1314. Argos, P., Rao, J.K.M., and Hargrave, P.A. 1982. Structural prediction of membrane-bound proteins. Eur. J. Biochem. 128:565-575. Atassi, M.Z. 1984. Antigenic structure of proteins. Eur. J. Biochem. 145:1-20.

Computational Analysis

2.2.11 Current Protocols in Protein Science

Banghan, J.A. 1988. Data-sieving hydrophobicity plots. Anal. Biochem. 174:142-145. Barlow, D.J. and Thornton, J. 1988. Alpha helices in proteins. J. Mol. Biol. 210:601-619. Bigelow, C.C. 1967. On the average hydrophobicity of proteins and the relation between it and protein structure. J. Theor. Biol. 16:187-211. Biosym Technologies. 1991. Insight II/Homology Modules. Biosym Technologies, San Diego. Chothia, C. 1976. The nature of the accessible and buried surfaces of proteins. J. Mol. Biol. 105:114. Cornette, J.L., Cease, K.B., Margalit, H., Spouge, J.L., Berzofsky, J.A., and DeLisa, C. 1987. Hydrophobicity scales and computational techniques for detecting amphipathic structures in proteins. J. Mol. Biol. 195:659-685. Eisenberg, D. 1984. Three-dimensional structure of membrane and surface proteins. Annu. Rev. Biochem. 53:595-623. Engleman, D.M., Steitz, T.A., and Goldman, A. 1986. Identifying nonpolar transbilayer helices in amino acid sequences of membrane proteins. Annu. Rev. Biophys. Chem. 15:321-353. Esposti, M.D., Crimi, M., and Venturoli, G. 1990. A critical evaluation of the hydropathy profile in membrane proteins. Eur. J. Biochem. 190:207219. Fauchere, J.L. and Pliska, V. 1983. Hydrophobic parameters P of amino acid side chains from the partitioning of N-acetyl-amino acid amides. Eur. J. Med. Chem. 18:369-375. Fermi, G. 1975. Three-dimensional Fourier synthesis of human deoxyhaemoglobin at 2.5 angstroms resolution, refinement of the atomic model. J. Mol. Biol. 97:237-256. Finzel, B.C., Clancy, L.L., Holland, D.R., Muchmore, S.W., Watenpaugh, K.D., and Einspahr, H.M. 1989. Crystal structure of recombinant human interleukin-1β at 2.0 angstroms resolution. J. Mol. Biol. 209:779-791. Fraga, S. 1982. Theoretical prediction of protein antigenic determinants from amino acid sequences. Can. J. Chem. 60:2606-2610. Genetics Computer Group. 1994. GCG Program Manual for the Wisconsin Package. Genetics Computer Group, Inc., Madison, Wis. Henderson, R., Baldwin, J.M., Ceska, T.A., Zemlin, F., Beckmann, E., and Downing, K.H. 1990. Model for the structure of bacteriorhodopsin based on high resolution electron cryomicroscopy. J. Mol. Biol. 213:899-929. Hopp, T.P. 1984. Protein antigen conformation: Folding patterns and predictive algorithms; selection of antigenic and immunogenic peptides. Ann. Sclavo 2:47-60.

Hydrophobicity Profiles for Protein Sequence Analysis

Hopp, T.P. 1989. Use of hydrophilicity plotting procedures to identify protein antigenic segments and other interaction sites. Methods Enzymol. 178:571-585.

Hopp, T.P. and Woods, K.R. 1981. Prediction of protein antigenic determinants from amino acid sequences. Proc. Natl. Acad. Sci. U.S.A. 78:3824-3828. Janin, J. 1979. Surface and inside volumes in globular proteins. Nature 277:491-492. Kauzmann, W. 1959. Some factors in the interpretation of protein denaturation. Adv. Prot. Chem. 14:1-63. Krystek, S.R., Jr., Dias, J.A., Reichert, L.E., and Andersen, T.T. 1985a. Prediction of antigenic sites in follicle stimulating hormones: Difference profiles enhance antigenicity prediction methods. Endocrinology 117:1125-1130. Krystek, S.R., Jr., Reichert, L.E., and Andersen, T.T. 1985b. Analysis of computer generated hydropathy profiles for human glycoprotein and lactogenic hormones. Endocrinology 117:11101117. Krystek, S.R., Jr., Dias, J.A., and Andersen, T.T. 1991. Identification of subunit contact sites on the α-subunit of lutropin. Biochemistry 30:18581864. Krystek, S.R., Jr., Dias, J.A., and Andersen, T.T. 1992. Identification of a subunit contact site on the α-subunit of follitropin. Pept. Res. 25:165168. Kuntz, I.D. 1972. Protein folding. J. Am. Chem. Soc. 94:4009-4012. Kyte, J. and Doolittle, R.F. 1982. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157:105-132. Landolt-Marticorena, C., Williams, K.A., Deber, C.M., and Reithmeier, A.F. 1993. Nonrandom distribution of amino acids in the transmembrane segments of human type I single span membrane proteins. J. Mol. Biol. 229:602-608. Levitt, M. 1976. A simplified representation of protein conformations for rapid simulation of protein folding. J. Mol. Biol. 104:59-107. Nozaki, Y. and Tanford, C. 1971. The solubility of amino acids and two glycine peptides in aqueous ethanol and dioxane solutions. J. Biol. Chem. 246:2211-2217. Parker, J.M.R., Guo, D., and Hodges, R.S. 1986. New hydrophobicity scale derived from highperformance liquid chromatography peptide retention data: Correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry 25:5425-5432. Rao, J.K.M. and Argos, P. 1986. A conformational preference parameter to predict helices in integral membrane proteins. Biochim. Biophys. Acta 869:197-214. Rose, G.D. and Dworkin, J.E. (1989). The hydrophobicity profile. In Prediction of Protein Structure and the Principles of Protein Conformation (G.D. Fasman, ed.) pp. 625-633. Plenum, New York. Rose, G.D., Geselowitz, A.R., Lesser, G.J., Lee, R.H., and Zehfus, M.H. 1985. Hydrophobicity of amino acid residues in globular proteins. Science 229:834-838.

2.2.12 Current Protocols in Protein Science

Roy, S. and Rose, G.D. 1980. Hydrophobic basis of packing in globular proteins. Proc. Natl. Acad. Sci. U.S.A. 8:4643-4647. Tanaka, T., Slamon, D.J., and Cline, M.J. 1985. Efficient generation of antibodies to oncoproteins by using synthetic peptide antigens. Proc. Natl. Acad. Sci. U.S.A. 82:3400-3404. Tanford, C. 1980. The Hydrophobic Effect. John Wiley & Sons, New York. von Heijne, G. and Blomberg, C. 1979. Transmembrane translocation of proteins. The direct transfer model. Eur. J. Biochem. 97:175-181. Tripos Associates. 1993. Biopolymer/Composer Module. Tripos Associates, Inc., St. Louis, Mo. Wolfenden, R., Andersson, L., Cullis, P.M., and Southgate, C.C. 1981. Affinities of amino acid side chains for solvent water. Biochemistry 20:849-855.

Yoshioka, N. and Atassi, M.Z. 1986. Subunit interacting surfaces of human haemoglobin. Biochem. J. 234:457-461.

KEY REFERENCES Engleman et al., 1986. See above. Provides applications of hydropathy profiles to transmembrane region prediction. Kyte and Doolittle, 1982. See above. Describes the fundamental development and application of hydropathy profiles.

Contributed by Stanley R. Krystek, Jr., William J. Metzler, and Jiri Novotny Bristol-Myers Squibb Pharmaceutical Research Institute Princeton, New Jersey

Computational Analysis

2.2.13 Current Protocols in Protein Science

Protein Secondary Structure Prediction It is widely accepted that knowledge of the structure of a protein can provide important information regarding its function and mechanism of action. However, determining the threedimensional structure of a protein, whether by X-ray crystallography (Wyckoff et al., 1985; Blundell and Johnson, 1976) or nuclear magnetic resonance (NMR) spectroscopy (Oppenheimer and James, 1989; Wuthrich, 1986), remains a formidable task, as it requires relatively large amounts of pure protein (generally greater than milligram quantities). Thus, the structures of many proteins will remain out of reach. At the same time, continual advances in molecular biology provide protein sequence information (primary structures) at a pace that far exceeds the speed with which higher-order protein structures can be determined, thus making the need for methods to derive structural information even more pressing. This unit describes procedures developed for predicting protein structure from the amino acid sequence. On the basis of function and location in the cell, proteins can be loosely divided into three groups: globular, structural, and membrane. To date, the majority of protein structural data has been derived from studies of globular (soluble) proteins because of the ease of chemical handling of globular proteins. Thus, a wide disparity has developed in the number of three-dimensional structures that have been experimentally determined in the three protein groups. As a result, current secondary structure prediction schemes apply almost solely to globular proteins. In the native structures of globular proteins, the hydrophobic residues are generally buried in the interior and the hydrophilic residues are exposed to the surrounding solvent (Kauzmann, 1959; Fisher, 1964; Dill, 1990). Disulfide bonds are often present and stabilize the structure. Sections of amino acid sequence rich in hydrophobic residues can be correlated with secondary structural elements such as α helices and β strands (Roy and Rose, 1980). Those rich in polar residues correspond to surface loops and β turns (Kuntz, 1972; UNIT 2.2). These secondary structural elements (Pauling and Corey, 1951; Pauling et al., 1951) are characterized by hydrogen bonding between the amino and carboxyl groups of the polypeptide backbone (Schulz and Schirmer, 1979). Several reviews of protein structure exist, and readers are referred to books by Schulz and

Schirmer (1979) and Branden and Tooze (1991) for thorough discussions of protein structure. Protein secondary structure elements associate to form compact domains. Levitt and Chothia (1976) and Richardson (1981) have classified protein domains according to the types of arrangements of secondary structures that form them. Illustrations for the four classes, referred to as all-α, all-β, α/β, and α + β, are available (Richardson, 1981; Lesk, 1991). This unit focuses on examining secondary structure predictions using specific examples from different protein classes; methods of predicting folding class are also discussed. This unit is divided into four sections. The first section is an overview and brief history of structure prediction schemes. The second section describes four distinct prediction schemes, with emphasis on their differences. In the third part each prediction scheme is used to evaluate three proteins that have different folding patterns. The final section is a comparison of the prediction results and suggestions for secondary structure prediction.

OVERVIEW OF PREDICTION SCHEMES Early Predictive Schemes Two of the earliest secondary structure prediction methods were developed by Guzzo (1965) and Prothero (1966). By analyzing a database of protein structures and corresponding sequences, they generated a set of rules for predicting structure. Because of the limited number of structures available at the time, their prediction methods were restricted to helices, the most abundant conformation in the structures available. Soon after, Schiffer and Edmunson (1967) developed the helical wheel method, a pictorial tool that is still widely used. The helical wheel is a circular plot that represents the view one obtains when looking down the helix axis (Fig. 2.3.1). The “spokes” of the wheel are precisely positioned on the basis of the Corey-Pauling α-helical geometry, having 3.6 residues per turn, which results in an angular shift of two neighboring residues of 100° (Pauling et al., 1951). A helical wheel is constructed by placing successive amino acids at the spokes of the wheel. Helical wheels have been particularly valuable for identifying proteins and peptides that contain amphipathic helices. In an amphipathic

Contributed by Stanley R. Krystek, Jr., William J. Metzler, and Jiri Novotny Current Protocols in Protein Science (1995) 2.3.1-2.3.20 Copyright © 2000 by John Wiley & Sons, Inc.

UNIT 2.3

Computational Analysis

2.3.1 CPPS

T

K

11

7

P

A 4

14 G

A

15

3

T 10

8

17

1

I

L

6

V

G

12 G

5

13 L

2 I

9 L

16

V

L

Figure 2.3.1 Helical wheel for a portion of the amphipathic helix of melittin. Residues in bold type form a hydrophobic side. Note that this is not a perfect amphipathic helix as the polar face includes Ala-4, Ala-15, and Val-8. This helix also contains several glycine residues, which are not commonly found in helices (Table 2.3.1).

Protein Secondary Structure Prediction

helix, hydrophobic residues will cluster to one side of the wheel and polar residues will be found on the opposite side. Figure 2.3.1 shows a helical wheel for the amphipathic helix of melittin. Helical wheels have often been used for analyzing helical dimerization proteins such as leucine zippers (Krystek et al., 1991; O’Shea et al., 1992), for designing helical multimers (Harbury et al., 1993; Kamtekar et al., 1993; Lovejoy et al., 1993), and for predicting and analyzing membrane proteins which contain helices that span the lipid bilayer (Baldwin, 1993). Other early predictive schemes include those developed by Dunhill (1968) and Lim (1974). Dunhill’s “helical net” is a pictorial method that is similar to the helical wheel and is still used in protein design and helix prediction (DeGrado, 1988; Goodman and Kim, 1991). Lim’s method relies on an extensive set of rules derived from the physicochemical properties of amino acids in conjunction with the analysis of a structural database. The large number of detailed rules, however, makes the Lim method unwieldy. When several methods were compared in a blind test organized by Schulz et al. (1974), the methods of Ptitsyn and Finkelstein (1970) and Chou and Fasman (1974a,b) performed better than others (Burgess et al., 1971; Lewis et al., 1971; Robson and Pain, 1971; Nagano, 1973).

Statistical Prediction Schemes The statistical methods developed by Chou and Fasman (1974a,b) and by Garnier et al. (1978) are the most prominent of the class of predictive schemes based on statistical amino acid preference rules. These secondary structure prediction schemes tacitly assume that only local sequence interactions influence the formation of local structure. This is almost certainly an oversimplification. Significant interactions occur between protein secondary structural elements (tertiary structure); these interactions may involve hydrophobic and/or electrostatic interactions between structural units. Therefore, the success and accuracy of prediction schemes that rely on empirical algorithms are necessarily limited and are unlikely to exceed a level of 70% to 75%. Reports of secondary structure prediction accuracy levels of ∼70%, however, are not uncommon using these methods (Fasman, 1985).

Other Predictive Schemes Two additional methodologies for structure prediction not based on statistics of amino acid occurrence have evolved. Of the neural network methods (Quinn and Sejnowski, 1988; Rost and Sander, 1993a,b; Kneller et al., 1990), that of Rost and Sander claims a level of predictive accuracy exceeding 70% for all proteins tested and is publicly available through the

2.3.2 Current Protocols in Protein Science

Internet computer network (for details, see discussion of Applying the PHD Scheme and Table 2.3.6 therein). The method of Stultz et al. (1993) is based on state-space modeling with a set of predefined structural class models.

Selecting Prediction Tools Secondary structure prediction proceeds as follows. First, it should be confirmed that the three-dimensional structure has not yet been determined. This can be done by searching known X-ray and NMR structures in the Protein Data Bank (Brookhaven, 1994) for an amino acid sequence identical to that of interest. Next, identification of proteins with significantly similar sequences whose structures are already known should be attempted. Numerous software packages are available for performing similarity searches (see UNIT 2.1). If proteins of similar sequence (>20% identity) are found, the Protein Data Bank should again be searched to determine whether the structures of any of those proteins are known. If so, then comparative homology modeling (Greer, 1990; Bajorath et al., 1993) can be used to develop a three-dimensional model of the desired sequence. Only if there are no proteins in the structural database of sufficient similarity should protein secondary prediction schemes be used. It should be pointed out, however, that proteins may have the same fold without a detectable sequence similarity (Kabsch and Sander, 1984, 1985). In the future, sequence threading through known three-dimensional structures will be helpful (Jones et al., 1992; Sippl and Weitckus, 1992). It is recommended that protein secondary structure prediction be performed in conjunction with other sequence analysis approaches, such as hydrophobicity profiling, flexibility analysis, surface exposure experiments, charge distribution mapping, and motif searches (UNIT 2.2; Fasman, 1989b; Genetics Computer Group, 1994). For investigators using methods to predict protein secondary structure, at least two questions should be addressed: (1) which predictive scheme(s) should be used, and (2) what are the advantages of one method over others? This unit attempts to find answers to these questions by studying three proteins in detail: human hemoglobin β-subunit, human interleukin 1β (IL-1β), and human profilin I (see Application of Secondary Prediction Methods and Fig 2.3.2).

METHODS FOR SECONDARY STRUCTURE PREDICTION The Chou and Fasman Method The most popular secondary structure prediction scheme is the statistical method of Chou and Fasman (1974a). Using a database of 15 protein structures determined by X-ray crystallography, Chou and Fasman tabulated the number of occurrences of a given amino acid in α helix, β strand, or coil. From the tabulation, a set of conformational preference parameters for each amino acid type was calculated. The preference parameters were based on three functions: (1) the relative frequency of a specific amino acid type within each protein, (2) the occurrence of that amino acid in a given type of secondary structure, and (3) the fraction of amino acid residues occurring in each type of secondary structure. With the conformational preference parameters in hand, the authors then derived a set of empirical rules for predicting protein secondary structure (Chou and Fasman, 1974b). Chou and Fasman later published an extension of their analysis using a larger set of X-ray structures, totaling 29 proteins and over 4700 amino acids. In addition to refining the values for α helices and β strands, they included a conformational preference parameter for β turns as well (Chou and Fasman, 1977, 1978a,b). For β turns, a positional preference for residues was identified; for example, proline was found to occur more frequently in position 2 of the turn than in positions 1, 3, or 4. A further refined set of conformational preference parameters for helical and sheet residues was later derived from an expanded data set of 64 protein structures (Chou, 1989). These parameters are similar to those based on the 29 protein structures (differences are noted in Chou, 1989). An interesting addition to secondary structure prediction using the conformational preference parameters is the prediction of protein class. Chou presented a computerized algorithm that assigns a structural class to proteins on the basis of amino acid composition; however, these predictions have not been widely used. In the initial studies, Chou and Fasman used the conformational preference parameters to describe amino acids as being formers, breakers, or indifferent for helical and strand regions (Chou and Fasman, 1974a). Their analysis showed that residues with the highest prefer-

Computational Analysis

2.3.3 Current Protocols in Protein Science

A T α, β

HB

B T α, β

HB

C T α, β

HB Residue number Figure 2.3.2 Secondary structure prediction using the method of Chou and Fasman as implemented by Novotny and Auffray (1984). Predicted secondary structure for (A) human hemoglobin β-subunit, (B) interleukin 1β, and (C) human profilin I. Below the amino acid sequence are given the turn prediction (T), the α helix (thin line) and β strand prediction (thick line), the location of positive (upward spikes) or negative charges (downward spikes) as derived specifically from amino acid side chains, and a hydrophobicity profile (HB).

Protein Secondary Structure Prediction

ence for helices resided near the helix center, whereas residues with low helix preference could be found clustered at the helix termini. For helix and strand, residues were independently classified as strong formers, formers, weak formers, indifferent, breakers, and strong breakers. Table 2.3.1 contains a list of the conformational preference parameters and refined conformational assignments derived from the analysis of 64 proteins (Chou, 1989). These

values are combined with the turn preferences shown in Table 2.3.2, or values from the 29protein study (Chou and Fasman, 1978a,b; Prevelige and Fasman, 1989), to carry out sequence prediction as described below. An important contribution to helix prediction strategies is the recognition that helix start and stop signals in the amino acid sequence grammar are quite distinct (Presta and Rose, 1988; Richardson and Richardson, 1988). The

2.3.4 Current Protocols in Protein Science

Table 2.3.1 β Stranda

Conformational Preference and Assignments for α Helix and

α helix

β strand

Residue

Pα

Assignmentb

Glu Ala Met Leu Lys His Gln Phe Asp Trp Arg Ile Val Cys Thr Asn Tyr Ser Gly Pro

1.44 1.39 1.32 1.30 1.21 1.12 1.12 1.11 1.06 1.03 1.00 0.99 0.97 0.95 0.78 0.78 0.73 0.72 0.63 0.55

Hα Hα Hα Hα hα hα hα hα hα Iα Iα iα iα iα iα iα bα bα Bα Bα

Residue

Pβ

Assignmentc

Val Ile Thr Tyr Trp Phe Leu Cys Met Gln Ser Arg Gly His Ala Lys Asp Asn Pro Glu

1.64 1.57 1.33 1.31 1.24 1.23 1.17 1.07 1.01 1.00 0.94 0.94 0.87 0.83 0.79 0.73 0.66 0.66 0.62 0.51

Hβ Hβ hβ hβ hβ hβ hβ hβ Iβ Iβ iβ iβ iβ iβ iβ bβ bβ bβ Bβ Bβ

aValues taken from Chou, 1989. bHα, strong helix former; hα, helix former; Iα, weak helix former; iα, indifferent; bα, helix

breaker; Bα, strong helix breaker. cHβ, strong β-strand former; hβ, β-strand former; Iβ, weak β-strand former; iβ, indifferent; bβ,

β-strand breaker; Bβ, strong β-strand breaker

α-helical structures in proteins are commonly stabilized by reciprocal hydrogen bond formation between the unpaired main-chain amino and carboxyl groups near the helix termini and the side chains of amino acids flanking the termini (Harper and Rose, 1993). The helix-terminal residues are referred to as the N-cap and C-cap. Because of the unique geometry of proline, this amino acid is frequently found at the N-cap + 1 position and has been referred to as being an α-helix initiator (Richardson and Richardson, 1988). Residues that typically occupy the N-cap and C-cap positions of helices are listed in Table 2.3.3.

The Garnier, Osguthorpe, and Robson Method The Garnier, Osguthorpe, and Robson (GOR) method (Garnier et al., 1978; Garnier and Robson, 1989) is a widely available secondary structure prediction method that has been incorporated into several computer packages,

such as that from Genetics Computer Group (GCG). Although the GOR method incorporates essentially all the elements of the ChouFasman statistics, it also uses information theory to reach the final decision. A detailed description of the GOR method can be found elsewhere (Garnier and Robson, 1989). In principle, the GOR method assigns the conformation of a given residue by considering the contribution of residues relatively distant to it in the sequence. On the basis of studies showing the interdependence of residue conformations up to eight amino acids away, the GOR method assigns single-residue information values to each amino acid along the polypeptide chain. The predicted conformational state for an amino acid is the state with the highest positive information value. An extension of this procedure, the GORIII method, has been developed to use pair information instead of directional information. The GORIII method extracts both single-residue information values

Computational Analysis

2.3.5 Current Protocols in Protein Science

Table 2.3.2

Conformational Preference and Assignments for β Turnsa

Positional preferences for β turnsb

Residue Preference fi Asn Gly Pro Asp Ser Cys Tyr Lys Gln Thr Trp Arg His Glu Ala Met Phe Leu Val Ile

1.56 1.56 1.52 1.46 1.43 1.19 1.14 1.01 0.98 0.96 0.96 0.95 0.95 0.74 0.66 0.60 0.60 0.59 0.50 0.47

Asn Cys Asp His Ser Pro Gly Thr Tyr Trp Gln Arg Met Val Leu Ala Phe Glu Lys Ile

0.161 0.149 0.147 0.140 0.120 0.102 0.102 0.086 0.082 0.077 0.074 0.070 0.068 0.062 0.061 0.060 0.059 0.056 0.055 0.043

fi+2

fi+1 Pro Ser Lys Asp Thr Arg Gln Gly Asn Met Ala Tyr Glu Cys Val His Phe Ile Leu Trp

0.301 0.139 0.115 0.110 0.108 0.106 0.098 0.085 0.083 0.082 0.076 0.065 0.060 0.053 0.048 0.047 0.041 0.034 0.025 0.013

Asn Gly Asp Ser Cys Tyr Arg His Glu Lys Thr Phe Trp Gln Leu Ala Pro Val Met Ile

0.191 0.190 0.179 0.125 0.117 0.114 0.099 0.093 0.077 0.072 0.065 0.065 0.064 0.037 0.036 0.035 0.034 0.028 0.014 0.013

fi+3 Trp Gly Cys Tyr Ser Gln Lys Asn Arg Asp Thr Leu Pro Phe Glu Ala Ile Met His Val

0.167 0.152 0.128 0.125 0.106 0.098 0.095 0.091 0.085 0.081 0.079 0.070 0.068 0.065 0.064 0.058 0.056 0.055 0.054 0.053

aValues taken from Chou and Fasman (1977, 1978a,b). bThe positional preference f is defined as the relative occurence of a specific amino acid found in position i of a β turn. i

Table 2.3.3 Amino Acid Positional Preferences in α Helicesa

Effect

Residuesb

Amino-terminal stabilizing (N-cap)

Gly Ser, Thr Asp, Glu Asn, Gln His Gly Asn His(+), Lys, Arg His(+), Lys, Arg Val, Leu, Ile, Met Ser Asp Val, Leu, Ile, Met

Carboxyl-terminal stabilizing (C-cap)

Amino-terminal destabilizing Carboxyl-terminal destabilizing

aAmino acid preferences were compiled from Fersht and Serrone (1993),

Presta and Rose (1988), and Richardson and Richardson (1988).

Protein Secondary Structure Prediction

bResidues listed in order of increasing magnitude of the effect.

2.3.6 Current Protocols in Protein Science

and pair information values for each possible pair and for amino acids within eight amino acids of a central residue. The predictive accuracy of the GOR methods is similar to that of the Chou and Fasman method (Garnier et al., 1978). Thus, the GOR methods may serve as a check of the results obtained with the Chou and Fasman method.

The EMBL Profile Neural Network Method An important feature of the EMBL Profile Neural Network (PHD) prediction scheme is the use of multiple sequence alignments (Rost and Sander, 1993a,b). Previous studies have shown that homologous proteins (generally with at least 20% to 30% sequence identity but often less) frequently have identical three-dimensional folds (tertiary structure). Thus, multiple sequence alignments are grouped by structural family and used as input. By considering many aligned sequences, the algorithm makes use of more information about structure than is available from just the individual protein sequence for which the secondary structure is to be predicted. To determine which protein sequences are to be used in the prediction, the PHD method uses a weighted dynamic programming method, MaxHom (Sander and Schneider, 1991), in searching a database of sequences. A family profile for the input sequence is generated and the amino acid frequencies at each alignment position are calculated for use in the prediction. Results have shown that the multiple sequence alignments improve secondary structure prediction >6% compared to singlesequence predictions (Rost and Sander, 1993a,b). The PHD method uses a reference network or neural network that has been trained and tested on a database of nonhomologous protein structures. In a typical database there is an uneven distribution of secondary structure types, such that there are many more examples of turns than of helices or sheets (Rost and Sander, 1993a,b). Because of the uneven distribution, empirically based predictions of loops are more accurate than those of helices, which in turn are more accurate than those of sheets. The neural network approach of the PHD method eliminates this bias: Rost and Sander developed a network that is trained with each type of secondary structure in equal proportion, rather than the proportions present in any database of protein structures. This results in a more balanced prediction.

The network system for structure prediction consists of three layers. In the first layer, secondary structure is predicted on the basis of the multiple aligned sequences. The results are passed to the second layer, which collectively considers secondary structure elements that are adjacent in the protein sequence, thereby allowing nearest neighbor structure to influence the structure of a particular region of the protein. The third layer of the network averages the results of the independently trained networks. The output from the third layer is determined by a “winner take all” prediction of the secondary structure at a given sequence position (Rost and Sander, 1993a,b).

The PSA Method The Protein Sequence Analysis (PSA) method (Stultz et al., 1993) computes the probability that a given amino acid belongs to a particular secondary structure element when there are no homologous protein sequences or structural data. To do this, mathematical models were developed for 15 single-domain protein superclasses or macroclasses. These superclasses, patterned after the protein class categorization of Richardson (1981), are groups of proteins with similar secondary and tertiary structures. The mathematical models represent constraints on the patterns of secondary structure elements for a given protein class. To perform structure prediction, the PSA method uses the amino acid sequence to calculate the probability that a given residue is contained in each of the modeled secondary structure elements. Also calculated is the probability that the submitted sequence is a member of one of the predefined superclasses. Thus, in addition to evaluating the secondary structure prediction, the PSA method provides the bonus of the potential tertiary structural information contained in the predicted structural superclass.

The Helical Wheel The helical wheel is a plot of the amino acid residues around a potentially helical segment (see Fig. 2.3.1 and discussion in Early Predictive Schemes). The method was developed to find helices with a hydrophobic face buried away from a polar solvent, with the graphical representation showing the clustering of polar and/or nonpolar residues toward one face of a helix. In its original use the helical wheel facilitated the identification of potential helical segments in protein sequences (Schiffer and Edmundson, 1967), but its applicability has since been expanded to include designing proteins (e.g.,

Computational Analysis

2.3.7 Current Protocols in Protein Science

leucine zipper proteins; Harbury et al., 1993; Kamtekar et al., 1993; Lovejoy et al., 1993) and studying transmembrane proteins (e.g., G-protein-coupled receptors; Baldwin, 1993).

APPLICATION OF SECONDARY STRUCTURE PREDICTION METHODS Applying the Chou and Fasman Method The wide use of the Chou and Fasman method is due in part to the simplicity of application, ease of understanding, and potential interactive nature of the analysis. It also ranks favorably in accuracy of prediction relative to other methods. Application of the method entails the following steps. 1. Conformational preference parameters for each type of secondary structure are locally smoothed by averaging techniques (see UNIT 2.2 for an example of the scanning window averaging method) in order to calculate α-helix, β-sheet, and turn probabilities along the polypeptide backbone. 2. The α-helix, β-strand, and turn probabilities are plotted versus sequence position for interpretation. Basically, the conformation is assigned according to the highest curve (Fig. 2.3.2, Fig. 2.3.3, and Fig. 2.3.4). 3. On the basis of conformational preference parameters, amino acids are assigned as formers, breakers, or indifferent for α-helical and β-sheet regions (Fig. 2.3.3A). 4. Program options usually include full predictions that are based on the original Chou and Fasman rules (Table 2.3.4). Predictions can also be made by the user in an interactive environment by combining the Chou and Fasman rules with sequence analysis using other empirical methods such as hydrophobicity profiling, flexibility prediction, charged residue mapping, and surface probability calculation (Fig. 2.3.3 and Fig. 2.3.4; see also Comparison of Prediction Schemes). We find that an interactive approach allows for the best interpretation of the prediction results (Fasman, 1989a; Schulz, 1988). Several other correlations can be used in this analysis and are listed in Table 2.3.5. Many programs can also generate sketches of predicted peptide structures (Fig. 2.3.5).

Applying the GOR Scheme Protein Secondary Structure Prediction

The GOR scheme is coded into many commercial and academic protein structure analysis packages. This method provides estimated

probabilities that a given residue is in a particular conformational state, and it predicts the conformation of an amino acid without any input from the user other than the sequence of interest. There are two outputs from the GOR method. The first is a plot of the turns, α helices, and β strands as predicted from the method. The second is a summary of the full protein structure prediction similar to that produced by the Chou and Fasman method. Unlike the Chou and Fasman method, assignment of each amino acid to a structural element is made automatically. Because many prediction schemes can be printed in the same output format, it is possible to print results of Chou and Fasman, GOR, and other structural predictions together for a more interactive analysis of the sequence (Fig. 2.3.4). Use of a combination of structural prediction schemes and the secondary structure correlations presented in Table 2.3.5 should increase the accuracy of secondary structure prediction.

Applying the PHD Scheme Access to the PHD scheme is available only through electronic mail or the WWW (World Wide Web). The Internet address for this service is given in Table 2.3.6. To perform sequence analysis with the PHD scheme, one merely needs to supply the sequence to the EMBL Predictprotein server. Additionally, one can submit prealigned sequences or a defined set of homolog sequences to be used in the predictive scheme. If a single sequence is submitted, the scheme will search the sequence database, and, if homologs are found, the sequence prediction will be performed on the multiple sequence alignments (Fig. 2.3.6). The output from the scheme consists of several pages describing the network and analysis, including the list of any homologous proteins along with the sequence alignment used in the analysis, the definition of accessibility, and a reliability index for the accuracy of prediction (Fig. 2.3.7). This method provides prediction of solvent accessibility as well as secondary structure. A comparison of this analysis to others described in this unit is described in the section on Comparison of Prediction Schemes.

Applying the PSA Scheme The PSA scheme, like the PHD scheme, has been established on the Internet (Table 2.3.6). The electronic mail server, called Protein Sequence Analysis (PSA) System, accepts as input the amino acid sequence of a protein. After sequence analysis the server returns four

2.3.8 Current Protocols in Protein Science

A

PEPPLOT

0 A G W N A Y I D N L M A D G

50 T C Q D A A I V G Y K D S P S V W A A V P G K T F

V N I T P A E V G V L V G K D R S S F Y V N G L

100 T L G G Q K C S V I R D S L L Q D G E F S M D L

R T K S T G G A P T F N V T V T K T D K T L V L L

M G K E G V H G G L I N K K C Y E M A S H L R R

S Q Y

Basic Acidic

1.5

HPhobic Hphilic Beta Forming Beta Breaking

Beta Chou & Fasman

1.0 Alpha 0.5 Alpha Forming 15 Alpha Breaking Beta Alpha 0 Beta Alpha 5

NH2 End

15 COOH End 0 Turn Hydrophobic Moment

0

1

Beta Alpha

0

3

Goldman et al. Kyte-Doolittle

0

HPhobic HPhilic

–3

0

50

100

B

Alpha Alpha Beta Beta Alpha Beta Pos Res Stat Ave Stat Ave NH2 COOH NH2 COOH Turn ------------------------------------------------------------------ .. 1 A 1.42 0.94 0.83 0.96 0.43 0.17 0.29 0.56 0.30 2 G 0.57 0.94 0.75 0.96 0.73 0.04 2.33 0.42 0.15 3 W 1.08 0.97 1.37 1.14 0.57 0.25 0.47 0.76 0.28 4 N 0.67 0.97 0.89 1.20 1.08 0.78 0.75 1.41 0.78 5 A 1.42 1.05 0.83 1.11 0.51 1.25 0.16 1.75 0.04 6 Y 0.69 0.86 1.47 1.13 0.25 0.31 1.24 1.27 0.45 7 I 1.08 0.99 1.60 1.08 0.58 0.08 2.44 0.52 0.63 8 D 1.01 1.09 0.54 0.95 2.19 0.12 0.56 1.08 0.24 9 N 0.67 1.19 0.89 1.02 1.98 0.95 0.09 2.40 0.03 10 L 1.21 1.27 1.30 0.93 0.82 3.18 0.04 2.33 0.14 11 M 1.45 1.11 1.05 0.79 0.31 1.52 0.31 0.63 1.41 12 A 1.42 0.96 0.83 0.83 0.84 0.17 2.28 0.22 0.99 13 D 1.01 0.78 0.54 0.92 2.45 0.13 1.21 0.25 1.04 14 G 0.57 0.80 0.75 1.06 3.18 0.13 1.15 0.46 1.26 15 T 0.83 0.91 1.19 1.01 1.79 0.29 0.45 0.39 0.14 16 C 0.70 1.06 1.19 0.92 0.53 0.19 2.91 0.18 1.52 17 Q 1.11 1.24 1.10 0.83 0.27 0.13 2.86 0.10 0.17 18 D 1.01 1.23 0.54 0.95 0.15 0.40 1.29 0.25 0.22 19 A 1.42 1.25 0.83 1.24 0.12 0.71 0.30 0.76 0.03 20 A 1.42 1.03 0.83 1.22 0.14 3.45 0.11 2.52 0.09 21 I 1.08 0.85 1.60 1.38 0.75 0.78 0.15 1.95 0.49 22 V 1.06 0.87 1.70 1.17 1.03 0.56 0.18 4.19 0.57 23 G 0.57 0.86 0.75 0.88 4.24 0.87 0.31 3.57 0.39 24 Y 0.69 0.91 1.47 0.88 1.08 1.11 0.49 4.63 1.79 25 K 1.16 0.88 0.74 0.65 1.39 0.50 1.50 0.74 0.51

HPhob –0.56 –0.33 –0.08 0.22 0.22 –0.02 0.04 –0.18 –0.18 –0.18 –0.40 –0.41 –0.11 0.74 0 .74 0 .68 –0.03 –0.03 0.27 –0.11 –0.40 –0.43 –1.00 –0.76 –0.41

Figure 2.3.3 Secondary structure prediction for human profilin I using the method of Chou and Fasman as implemented by GCG:PepPlot algorithm. (A) Predicted secondary structure and (B) numerical output for residues 1 through 25.

output files to the requester: a text file summarizing the tertiary class probabilities and most probable superclass; a file containing plots of the class and superclass probabilities; a file containing the secondary structure probability distributions displayed on a contour plot; and a file containing the secondary structure probability distributions on x-y plots. The secondary structure and protein class are easily deter-

mined from the combination of plots (Fig. 2.3.8) and can be compared with those suggested by other structural prediction methods.

COMPARISON OF PREDICTION SCHEMES The secondary structures of hemoglobin (an all-α protein), interleukin 1β (an all-β protein), and profilin (an α/β protein) were predicted

Computational Analysis

2.3.9 Current Protocols in Protein Science

A

PLOTSTRUCTURE

50

100

50

100

5.0 KD Hydrophilicity –5.0 10.0 Surface Prob. 0.0 1.2 Flexibility 0.8 1.7 Jameson-Wolf (Antigenic Index) –1.7 CF Turns CF Alpha Helices CF Beta Sheets GOR Turns GOR Alpha Helices GOR Beta Sheets Glycosyl Sites

B

PEPTIDESTRUCTURE Hydrophilicity (Kyte-Doolittle) averaged over a window of: 7 Surface Probability according to Emini Chain Flexibility according to Karplus-Schulz Secondary Structure according to Chou-Fasman Secondary Structure according to Garnier-Osguthorpe-Robson Antigenicity Index according to Jameson-Wolf Pos 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

Protein Secondary Structure Prediction

AA A G W N A Y I D N L M A D G T C Q D A A I V G Y K D S P S V W A A V P

GlycoS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

HyPhil

0.750 0.240 0.417 –0.286 0.471 0.914 0.243 –0.529 –0.529 –0.214 0.486 0.086 -0.771 0.271 1.043 1.043 0.286 –0.414 –1.114 –0.700 –1.014 –0.957 –0.200 0.171 1.043 1.757 1.100 1.043 0.229 –0.529 –1.243 –1.243 –1.300 –0.143 –0.171

SurfPr

FlexPr

0.633 0.500 0.613 0.426 0.718 1.099 0.563 0.552 0.356 0.848 0.502 0.451 0.293 0.513 0.848 0.513 0.523 0.254 0.352 0.201 0.189 0.374 0.618 1.181 2.460 3.331 1.578 0.830 0.502 0.378 0.182 0.210 0.279 0.531 0.759

1.000 1.000 1.000 1.000 0.921 0.927 0.946 0.962 0.959 0.958 0.960 0.983 1.016 1.039 1.049 1.036 1.018 0.994 0.963 0.938 0.921 0.927 0.949 0.989 1.030 1.056 1.068 1.042 1.000 0.951 0.908 0.906 0.929 0.975 1.029

CF-Pred GORPred . . . . . . h h h h h h t t t t T T B B B B B B T T . T T B B B B B T

. . . . . B B B B B T T T T T B B B B B B B B T T . . . . B B B B B .

AI-Ind .. 0.750 0.450 0.450 –0.150 0.300 0.750 0.300 –0.600 –0.600 –0.300 0.700 0.700 0.150 1.050 1.350 0.950 0.850 –0.200 –0.600 –0.600 –0.600 –0.600 –0.300 0.850 1.700 1.300 0.900 1.150 0.850 –0.600 –0.600 –0.600 –0.600 –0.300 0.250

Figure 2.3.4 Secondary structure prediction for human profilin I using the methods of Chou and Fasman and GOR as implemented by GCG: PeptideStructure algorithm and plotted using PlotStructure utility. (A) Predicted secondary structure and (B) numerical output for residues 1 through 35.

2.3.10 Current Protocols in Protein Science

with the Chou and Fasman, GOR, PHD, and PSA methods (Fig. 2.3.9). Analysis of the Chou and Fasman profiles was enhanced by displaying smoothed hydrophobicity profiles and consulting the amino acid positional preferences of Table 2.3.3. An example of the complete

analysis of profilin is presented in Figure 2.3.3, Figure 2.3.4, and Figure 2.3.5 (or Fig. 2.3.2C). The GOR predictions were made with the GCG commercial package (Fig. 2.3.4). The PHD and PSA predictions were obtained from the electronic mail response to a structure query; ex-

Abbreviated Chou and Fasman Prediction Rulesa

Table 2.3.4

Feature

Rule

Helix

Helix is initiated by a cluster of four helix-promoting residues out of six amino acids along the polypeptide sequence. Helical segment is extended in both directions until sets of tetrapeptide breakers are reached. Prolines can occur only within the first three residues of the N-terminal end. A segment length of six or more amino acids with α probability greater than β probability is predicted as helical. A cluster of three β formers or a cluster of three β formers out of five amino acids along the polypeptide sequence will initiate β-strand formation. The β strand is extended in both directions until terminated by a set of tetrapeptide breakers. A segment of three or more amino acids with β probability greater than α probability is predicted as β strand. Turn probability is calculated as peaks of probability that are greater than α or β probability. The β turns are also identified by Pt > 0.75 × 10−4, where Pt = fi × fi+1 × fi+2 × fi+3. For a region containing overlapping α helix and β strand, the prediction would be for helix if the α probabilities are greater than β probabilities, and strand if β probabilities exceed α probabilities.

Strand

Turns

Overlap

aFor the complete set of prediction rules, see Chou and Fasman (1974a,b, 1978a,b).

Table 2.3.5

Secondary Structure Correlations

Structure

Characteristics

β strands

Often correspond to peaks of hydrophobicity β-branched amino acids (Val, Ile, Thr) are typical Positively charged residues are relatively abundant Negatively charged residues (Glu) virtually never occur Average residue length is 7, resulting in short and acute peaks in hydrophobicity plots (see UNIT 5.2) β bulges may occur in the edge strands. Their signature is small amino acids and glycines (e.g., Gly-Xxx-Gly sequence in immunoglobulin V fold, strand G). Hydrophobicity peaks are longer, with a fine structure (teeth). Ideally, fine structure shows n + 4 periodicity. Glu, Ala, Leu, are typical Amino acid preferences exist at the N-cap and C-cap positions (see Table 2.3.3) Prolines can occur in the first turn of helices in globular proteins, and most frequently occupy the N-cap + 1 position Prolines, which “kink” the helices, are overrepresented in transmembrane helices Glycines frequently occur at the C-cap position Hydrophobic residues often occupy positions N-cap + 4 and C-cap − 4 Helices may be completely buried and not amphipathic

α helices

Computational Analysis

2.3.11 Current Protocols in Protein Science

PLOTSTRUCTURE NH2

50 0

100 0

COOH

Figure 2.3.5 Sketch of the human profilin secondary structure as predicted in Figure 2.3.3 and Figure 2.3.4 by Chou-Fasman method.

Protein Secondary Structure Prediction

amples of the output for the profilin secondary structure prediction are provided in Figure 2.3.6, Figure 2.3.7, and Figure 2.3.8. Figure 2.3.9A summarizes the results of secondary structure prediction for the β subunit of human hemoglobin. Immediately below the amino acid sequence is the location of the eight helices found in the three-dimensional structure of hemoglobin (Brookhaven Protein Data Bank entry 2hhb; Fermi, 1975). The Chou and Fasman scheme predicts most of the helical residues in helices 1, 2, 3, 4, 6, 7, and 8. Helices 6 and 7 are predicted to be longer than they actually are, and helices 2 and 3 are not distinguished as independent helices. A turn is predicted in place of the fifth helix. The GOR method predicts most of the residues for helices 1, 2, 5, 6, and 8, but it predicts a turn for helix 3 as well as a turn and a strand for helix 7. The PHD program found over 400 sequence homologs of hemoglobin-β, with one of the sequences being the submitted sequence. The PHD prediction identified a homolog with a known three-dimensional structure in the protein database and also suggested structure prediction by homology modeling. The prediction by PHD did quite well, accounting for seven of the eight helices and missing only the small helix 4. The PSA procedure suggested the α/β class as the most probable. Most of the helical residues in helices 2, 3, 5, 6, and 8 and part of helix 7 were correctly predicted. Helix 1 was missed by the PSA method, however, and the

C- and N-terminal residues of helices 2 and 3, respectively, were predicted to be in sheet structure. Figure 2.3.9B summarizes the results of secondary structure prediction for interleukin 1β. IL-1β contains 12 β strands connected by intervening turns (Brookhaven Protein Data Bank entry 1i1b; Finzel et al., 1989). The Chou and Fasman method predicted most of the residues in 8 of the 12 strands in the β sheet (strands 1, 2, 4, 5, 6, 8, 11, and 12). Helical regions were predicted for β strands 3 (with the adjacent turn) and 7. The Chou and Fasman method correctly predicted several of the turns. The GOR method correctly predicted 4 of the 12 β strands, but it predicted helical regions for β strands 3 (with the adjacent turn), 5, 6, and 8. Interestingly, both the Chou and Fasman and GOR methods predict helix for the third strand. This may be due to the negatively charged glutamate, which is generally considered a strand breaker (see Table 2.3.1). For IL-1β, the PHD output listed the 10 sequence homologs that were used in the structure prediction and also identified a known three-dimensional structure in the protein database. The PHD method correctly predicted most of the residues in the 12 β strands; however, a deficiency in the method is that it does not predict the location of possible turns. The PSA method did not successfully predict either the class or the secondary structure of IL-1b: the method pre-

2.3.12 Current Protocols in Protein Science

Table 2.3.6

Commercial Packages for Sequence Analysis

Software

Platforma

Vendor or Internet locationb

Sybyl/Biopolymerc InsightII/Homologyc GCG MacVector Geneworks PC/Gene DNASTAR

SGId SGId VAX Macintosh Macintosh IBM-compatible IBM-compatible or Macintosh

Tripos Associates Biosym Technologies Genetics Computer Group International Biotechnologies IntelliGenetics/Betagen IntelliGenetics/Betagen DNASTAR

Internet locations for sequence analysis PHD methode

PSA methode

EMBL Predictprotein Server [email protected] [email protected] http://www.embl-heidelberg.de/predict protein/predict protein.html Protein Sequence Analysis Server [email protected]

aSoftware may run on other platforms in addition to these listed here. For a comprehensive list, contact

the appropriate vendor. bFor suppliers addresses, see SUPPLIERS APPENDIX. cBiopolymer and Homology are optional modules that can be purchased to run with Sybyl and InsightII, respectively. dSilicon Graphics Inc. ePHD and PSA methods are currently accessible only through the Internet; thus, results can be accessed by any platform.

dicted the protein fold class to be all-α rather than all-β, and therefore incorrectly predicted α structure rather than β structure. It is noteworthy that several of the predicted α helices correspond to the location of β strands, namely, those of β strands 2, 3, 4, 5, 7, 9, 11, and 12. Further testing is required to determine whether there are any inherent problems with the predictions of all-β proteins by the PSA method. Figure 2.3.9C summarizes the results of secondary structure prediction for human profilin, an α/β protein whose high-resolution structure (Metzler et al., 1993, 1995) was not publicly known at the time of testing. Profilin is composed of a central seven-stranded, antiparallel β sheet and four helices (Fig. 2.3.10). Seven turns that connect the helical and sheet structural elements in the structure have been identified. Figure 2.3.2C, Figure 2.3.3A, and 2.3.4A show typical Chou and Fasman profiles as determined with the programs of Novotny (Novotny and Auffray, 1984), PepPlot (GCG, 1994), and Plotstructure (GCG, 1994). Figure 2.3.3B shows the numerical output from the modified Chou and Fasman prediction pro-

grams (PepPlot), and Figure 2.3.4B shows the automated prediction by the Peptidestructure program using the Chou and Fasman and the GOR methods. A schematic representation of the predicted protein secondary structure is shown in Figure 2.3.5. The Chou and Fasman method predicted six of the seven β strands and the two terminal helices of profilin (Fig. 2.3.8C). This method also predicted correct locations of several of the turns located between the strands. In comparison, the GOR method performed significantly worse, predicting only four of seven β strands and neither of the terminal helices. The PHD method identified six sequence homologs of profilin (Fig. 2.3.6) that it used in the secondary structure prediction (Fig. 2.3.6). The method correctly predicted all seven β strands and the two terminal helices. It did, however, predict additional β strands for the interior helices but did not predict the locations of any turns (owing to a deficiency in the method, as stated previously). The PSA method predicted the profilin sequence to be a member of the all-β protein fold class (Fig. 2.3.8). The method did

Computational Analysis

2.3.13 Current Protocols in Protein Science

## PROTEINS : EMBL/SWISSPROT identifier and alignment statistics NR. ID STRID %IDE %WSIM IFIR ILAS JFIR JLAS LALI NGAP PROTEIN 1 : pro1_human 1.00 1.00 1 139 1 139 139 0 PROFILIN I. 2 : prof_mouse 0.96 0.97 1 139 1 139 139 0 PROFILIN. 3 : prof_bovin 0.95 0.97 1 139 1 139 139 0 PROFILIN. 4 : pro2_human 0.62 0.71 1 139 1 139 139 0 PROFILIN II. 5 : prof_varv 0.32 0.48 1 137 2 131 130 2 PUTATIVE PROFILIN. 6 : prof_vaccv 0.31 0.47 1 137 2 131 130 2 PUTATIVE PROFILIN. ## ALIGNMENTS 1 6 SeqNo PDBNo AA STRUCTURE BP1 .4....:....5....:....6....:....7 1 A U 0 2 G U 0 3 W U 0 4 N U 0 5 A U 0 6 Y U 0 7 I U 0 8 D U 0 9 N U 0 10 L U 0 11 M U 0 12 A U 0 13 D U 0 14 G U 0 15 T U 0 16 C U 0 17 Q U 0 18 D U 0 19 A U 0 20 A U 0 21 I U 0 22 V U 0 23 G U 0 24 Y U 0 25 K U 0

BP2 ACC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

NOCC 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

VAR 0 30 0 35 50 41 5 15 29 21 53 56 24 33 51 47 24 6 0 0 0 0 24 0 27

LGAP LSEQ2 ACCNUM 0

139

P07737

0

139

P10924

0

139

P02584

0

139

P35080

7

133

P33828

7

133

P20844

....:....1....:....2....:....3....:... AAAAAA GGGGEE WWWWWW NNNQHH AAASKK YYYYII IIIVII DDDDEE NSNNDD LLLLII MMMMSS AAACKK DDDDNN GGGGNN TTTCNK CCCCFF QQQQEE DDDEDD AAAAAA AAAAAA IIIIII VVVVVV GGGGDD YYYYYY KKKCKK

Figure 2.3.6 Sequence homologs and a portion of the sequence alignment statistics used in secondary structure prediction for human profilin I with PHD, the EMBL neural network method. Secondary structure prediction given in Figure 2.3.7. For description of column labels see Rost and Sander (1993a,b) and EMBL Predictprotein Server (address given in Table 2.3.6).

Protein Secondary Structure Prediction

correctly predict the location of five of the β strands, but it predicted additional β strands for the amino and carboxyl termini and the second interior helices. The PSA method correctly predicted several of the turns located between the β strands, as well. In summary, both the PHD and the interactive Chou and Fasman method performed well, correctly predicting most secondary structural elements (see Table 2.3.7). The interactive Chou and Fasman method generally outperformed the GOR method. However, it must be

pointed out that in predicting the secondary structure with the Chou and Fasman method, the correlations contained in Table 2.3.3 and Table 2.3.5 and the Chou and Fasman rules contained in Table 2.3.4 facilitated both locating helix ends and discriminating between helix and strand when ambiguity existed. In contrast, a strict interpretation of the GOR results was used. It is also possible to interpret the GOR results interactively; this may equalize the predictive power of the two methods. In testing the PHD method, we were unable

2.3.14 Current Protocols in Protein Science

Table 2.3.7 Prediction

Summary of Secondary Structure

Structure Hemoglobin Interleukin 1β Profilin

Method (%)a C-F

GOR

PHD

PSA

78 53 60

62 41 44

86 67 87

60 — 48

aNumber of amino acids correctly predicted, divided by the total

number of amino acids known to be in helices, sheets, or turns. No value is provided for the PSA analysis of interleukin 1β because the class predicted was all-α.

protein:

2a20

length

139

....,....1....,....2....,....3....,....4....,....5....,....6 AA |AGWNAYIDNLMADGTCQDAAIVGYKDSPSVWAAVPGKTFVNITPAEVGVLVGKDRSSFYV| PHD sec | HHHHHHHHH EEEEE EEEEEE EEEEE EEEEEEEE EEEE| Rel sec |965679988733797356089964258635986257448665481777999726874787| detail: prH prE prL subset: SUB

sec sec sec sec

ACCESSIBILITY 3st: P_3 acc 10st: PHD acc Rel acc subset: SUB acc

|026778888763100210000000000110000000000000001000000000000000 |000000010000001211489876421156887521268777304788989752116788 |972110001136897567410013568622012368631222684111000147883111 |LLHHHHHHHH..LLL.LL.EEEE..LLL.EEEE.LL..EEEE.L.EEEEEEE.LLL.EEE |

|eebeebbebbbeeeebebbbbbbbee eebbbbbbeebbbeb be bbbbbeee eebbb| |980670060007777060000000775770000007700070507400000777576000 |842231810404525012897740440647597304515146213084247355141324 |ee....b..b.ee.e...bbbbb.ee.eebbbb..ee.b.eb....bb.bb.ee.e...b|

....,....7....,....8....,....9....,....10...,....11...,....1 AA |NGLTLGGQKCSVIRDSLLQDGEFSMDLRTKSTGGAPTFNVTVTKTDKTLVLLMGKEGVHG| PHD sec |EEEEE EEEEEEEEE EEEEEE EEEEEE EEEEEE | Rel sec |324627717999975224589315997625799988313999975525888882678767| detail: prH prE prL subset: SUB

sec sec sec sec

ACCESSIBILITY 3st: P_3 acc 10st: PHD acc Rel acc subset: SUB acc

|000000000000000111000011000001000000000000001121100000011100 |656751148999986542200346887741100011345899982126888884100011 |342247851000012336788542001156898988653000016741000005788877 |...E.LL.EEEEEEE...LLL..EEEEE.LLLLLLL...EEEEELL.EEEEEE.LLLLLL |

|bbbebbeeebbbb bbbbeebebbbeb beeeeebbbbebbbbebbebbbbbbbeeebee| |000600776000050000770700060507779700006000060070000000776077 |201100442808311640440542725137454511011738111055997684450044 |......ee.b.b...bb.ee.eb.b.b..eeeee.....b.b....ebbbbbbbee..ee |

2...,....13...,....14...,....15...,....16...,....17...,....1 AA |GLINKKCYEMASHLRRSQY| PHD sec | HHHHHHHHHHHHHH | Rel sec |8424899999999972589 detail: prH prE prL subset: SUB

sec sec sec sec

|0146889999999985200 |0210000000000000000 |8632100000000014689 |L...HHHHHHHHHHH.LLL|

ACCESSIBILITY 3st: P_3 acc 10st: PHD acc Rel acc subset: SUB acc

|eebeeeb ebbeeb eeee| |7606770570076057789 |4131359142931413469 |e....eb.e.b..b..eee|

Figure 2.3.7 Secondary structure prediction for human profilin I with PHD, the EMBL neural network method. Sequence homologs and a portion of the sequence alignment used in the analysis are given in Figure 2.3.6.

Computational Analysis

2.3.15 Current Protocols in Protein Science

Superclass Probabilities for Sequence p using vpp6.m, 15-Dec-94 (10:50:50)

Probability

1 0.8 0.6 0.4 0.2 0 alpha-beta

alpha

irregular

beta

Probability of being in a strand for Sequence p (psp2.m), 15-Dec-94 (10:53:40)

1 0.5 0 Probability of being in a turn for Sequence p (psp2.m), 15-Dec-94 (10:53:40)

Probability

1 0.5 0 Probability of being in a helix for Sequence p (psp2.m), 15-Dec-94 (10:53:39)

1 0.5 0 0

20

40

60

80

100

120

130

Residue position

Figure 2.3.8 Class and secondary structure predictions for human profilin I with the PSA method.

to identify any protein whose structure is included in the Brookhaven Protein Data Bank for which there are no sequence homologs in sequence databases. If such a sequence and structure were available, the comparison of the predictive methods might have been less biased. However, this point highlights an advantage of the PHD method. Sequence homologs with known three-dimensional structures often help achieve an accurate prediction of secondary structure. The availability of homologous amino acid sequences imparts a significant predictive advantage to the PHD method in many cases.

Protein Secondary Structure Prediction

analysis (which requires interpretation of the output data) and PHD neural network predictions (which are automatic) is recommended as the most accurate and preferable procedure. As three-dimensional protein structural databanks grow, the future of protein structure prediction may lie with methods that “thread” the polypeptide sequence onto known motifs and evaluate the resulting model by pseudoenergetic analysis (e.g., PROSA; Sippl and Weitckus, 1992). Such programs currently exist, and successful fold identifications are being reported. However, the probability of a successful sequence-structure match is currently 10% (Sippl and Weitckus, 1992).

RECOMMENDATIONS AND THE FUTURE

CONCLUSIONS

On the basis of the examples presented here and our experience in general, a combination of the Chou and Fasman interactive sequence

1. Empirically derived rules can aid sequence prediction using the GOR and Chou and Fasman statistical methods. Summaries of

2.3.16 Current Protocols in Protein Science

A

1 Hem. Structure C-F GOR PHD PSA Hem. Structure

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VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLA

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HLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH

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Il-1b. Structure C-F GOR PHD PSA

APRVRSLNCTLRDSQQKSLVMSGPYELKALHLQGQDMEQQVVFSMSFVQGEESNEKIPVALGLKEKNLYLSCVLKDD

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KPTLQLESVDPKNYPKKKMEKRFVFNKIEINNKLEFESSAQFPNWYISTSQAENMPVFLGGTKGGQDITDFTMQFVSS

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AGWNAYIDNLMADGTCQDAAIVGYKDSPSVWAAVPGKTFVNITPAEVGVLVGKDRSSFYVNGLTLGG

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QKCSVIRDSLLQDGEFSMDLRTKSTGGAPTFNVTVTKTDKTLVLLMGKEGVHGGLINKKCYEMASHLRRSQY

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Figure 2.3.9 Summary of the secondary structure predicted for (A) human hemoglobin β-subunit, (B) human interleukin 1β, and (C) human profilin I. Each panel lists, from top to bottom, the amino acid sequence, the secondary structure determined experimentally, and the secondary structure predicted by each of the following: C-F, the Chou and Fasman method; GOR, the GOR method; PHD, the EMBL neural network method; and PSA, the state-space method. The location of helices (solid bars), turns (open bars), and sheet structure (hatched bars) are indicated.

these correlations (rules) are contained in Tables 2.3.4 and 2.3.5. 2. Methods that are open to interpretation do not guarantee the same result when applied by different users. 3. The PHD and PSA methods allow no input from the user, other than the sequence of interest, and thus are the easiest to use. For the other methods described here, the user can interactively participate in the prediction process. 4. If sequence homologs and, in particular, three-dimensional data are available, the PHD method gives reliable secondary structure predictions.

5. The PSA method was found to be less accurate than the other predictive schemes tested.

ACCESSIBILITY OF SOFTWARE Several prediction schemes have been incorporated into commercial packages (e.g., GCG) that also contain algorithms for additional sequence analysis. Other packages have interactive graphics modules that combine sequence analysis with molecular displays (Tripos and Biosym); however, these packages do not easily incorporate additional user input. There have been numerous reports in the literature describing computerized implementation of various

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2.3.17 Current Protocols in Protein Science

Figure 2.3.10 Schematic representation of the three-dimensional structure of human profilin I (Metzler et al., 1995) as displayed by Molscript (Kraulis, 1991). A seven-stranded antiparallel β sheet bisects the molecule. The N- and C-terminal α helices are on the left-hand side, and the two interior α helices are on the right-hand side in the view shown.

prediction schemes (Fasman, 1989b). Several of these programs have been published in a variety of programming languages. There are also several sites available on the Internet for sequence analysis. A listing of some of the commercial and Internet programs is contained in Table 2.3.6.

Literature Cited Bajorath, J., Stenkamp, R., and Aruffo, A. 1993. Knowledge-based model building of proteins: Concepts and examples. Protein Sci. 2:17981810. Baldwin, J.M. 1993. The probable arrangement of helixes in G protein–coupled receptors. EMBO J. 12:1693-1703. Blundell, T.L. and Johnson, L.N. (eds.) 1976. Protein Crystallography. Academic Press, London.

Protein Secondary Structure Prediction

acid residues and prediction of backbone topography in proteins. Isr. J. Chem. 12:239-286. Chou, P.Y. 1989. Prediction of protein structural class from amino acid compositions. In Prediction of Protein Structure and the Principles of Protein Conformation (G.D. Fasman, ed.) pp. 549-586. Plenum, New York. Chou, P.Y. and Fasman, G.D. 1974a. Conformational parameters for amino acids in helical, beta sheet and random coil regions calculated from proteins. Biochemistry 13:211-222. Chou, P.Y. and Fasman, G.D. 1974b. Prediction of protein conformation. Biochemistry 13:223-245. Chou, P.Y. and Fasman, G.D. 1977. β-turns in proteins. J. Mol. Biol. 115:135-175. Chou, P.Y. and Fasman, G.D. 1978a. Empirical predictions of protein conformation. Annu. Rev. Biochem. 47:251-276.

Branden, C. and Tooze, J. 1991. Introduction to Protein Structure. Garland Publishing, New York.

Chou, P.Y. and Fasman, G.D. 1978b. Prediction of the secondary structure of proteins from their amino acid sequence. Adv. Enzymol. 47:45-148.

Brookhaven. 1994. Protein Data Bank. Brookhaven National Laboratory, Upton, N.Y.

DeGrado, W.F. 1988. Design of peptides and proteins. Adv. Protein Chem. 39:51-124.

Burgess, A.W., Ponnuswamy, P.K., and Scheraga, H.A. 1971. Analysis of conformations of amino

Dill, K.A. 1990. Dominant forces in protein folding. Biochemistry 29:7133-7155.

2.3.18 Current Protocols in Protein Science

Dunhill, P. 1968. The use of helical net diagrams to represent protein structures. Biophys. J. 8:865875.

Jones, D.T., Taylor, W.R., and Thornton, J.M. 1992. A new approach to protein fold recognition. Nature 358:86-89.

Fasman, G.D. 1985. A critique of the utility of the prediction of protein secondary structure. J. Biosci. 8:15-23.

Kabsch, W. and Sander, C. 1984. On the use of sequence homologies to predict different conformations. Proc. Natl. Acad. Sci. U.S.A. 81:10751078.

Fasman, G.D. 1989a. The development of the prediction of protein structure. In Prediction of Protein Structure and the Principles of Protein Conformation (G.D. Fasman, ed.) pp. 193-316. Plenum, New York. Fasman, G.D. (ed.) 1989b. Prediction of Protein Structure and the Principles of Protein Conformation. Plenum, New York. Fermi, G. 1975. Three-dimensional Fourier synthesis of human deoxyhaemoglobin at 2.5 angstroms resolution, refinement of the atomic model. J. Mol. Biol. 97:237-256. Fersht, A.R. and Serrone, L. 1993. Principles of protein stability derived from protein engineering experiments. Curr. Opin. Struct. Biol. 3:7583. Finzel, B.C., Clancy, L.L., Holland, D.R., Muchmore, S.W., Watenpaugh, K.D., and Einspahr, H.M. 1989. Crystal structure of recombinant human interleukin-1β at 2.0 angstroms resolution. J. Mol. Biol. 209:779.

Kabsch, W. and Sander, C. 1985. Identical pentapeptides with different backbones. Nature 317:207. Kamtekar, S., Schiffer, J.M., Xiong, H., Babik, J.M., and Hecht, M.H. 1993. Protein design by binary patterning of polar and non-polar amino acids. Science 262:1680-1685. Kauzmann, W. 1959. Some factors in the interpretation of protein denaturation. Adv. Protein Chem. 14:1-63. Kneller, D.G., Cohen, F.E., and Largridge, R. 1990. Improvements in protein secondary structure prediction by an enhanced neural network. J. Mol. Biol. 214:171-182. Kraulis, P.J. 1991. MOLSCRIPT: A program to produce detailed and schematic plots of protein structures. J. Appl. Cryst. 24:946-950. Krystek, Jr., S.R., Bruccoleri, R.E., and Novotny, J. 1991. Stabilities of leucine zipper dimers estimated by an empirical force energy method. Int. J. Pept. Protein Res. 38:229-236.

Fisher, M.F. 1964. A limiting law relating the size and shape of protein molecules to their composition. Proc. Natl. Acad. Sci. U.S.A. 51:1285-1291.

Kuntz, I.D. 1972. Protein folding. J. Am. Chem. Soc. 94:4009-4012.

Garnier, J. and Robson, B. 1989. The GOR method for predicting secondary structure in proteins. In Prediction of Protein Structure and the Principles of Protein Conformation (G.D. Fasman, ed.) pp. 417-466. Plenum, New York.

Levitt, M. and Chothia, C. 1976. Structural patterns in globular proteins. Nature 261:552-558.

Garnier, J., Osguthorpe, D.J., and Robson, B. 1978. Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J. Mol. Biol. 120:97-120. Genetics Computer Group. 1994. (GCG) Wisconsin Sequence Analysis Package Program Manual. Genetics Computer Group, Inc., Madison, Wis. Goodman, E.M. and Kim, P.S. 1991. Periodicity of amide proton exchange rates in a coiled-coil leucine zipper peptide. Biochemistry 30:1161511620. Greer, J. 1990. Comparative modeling: Application to the family of the mammalian serine proteases. Proteins Struct. Funct. Genet. 7:317-334. Guzzo, A.V. 1965. The influence of amino acid sequence on protein structure. Biophys. J. 5:809822. Harbury, P.B., Zhang, T., Kim, P.S., and Alber, T. 1993. A switch between two-, three-, and fourstranded coiled coils in GCN4 leucine zipper mutants. Science 262:1401-1407. Harper, E.T. and Rose, G.D. 1993. Helix stop signals in proteins and peptides: The capping box. Biochemistry 32:7605-7609.

Lesk, A.M. 1991. Protein architecture. IRL Press, Oxford.

Lewis, P.N., Momany, F.A., and Scheraga, H.A. 1971. Folding of polypeptide chains in proteins: A proposed method of folding. Proc. Natl. Acad. Sci. U.S.A. 68:2293-2297. Lim, V.I. 1974. Algorithms for prediction of α helices and β-structural regions in globular proteins. J. Mol. Biol. 88:873-894. Lovejoy, B., Choe, S., Cascio, D., McRorie, D.K., DeGrado, W.F., and Eisenberg, D. 1993. Crystal structure of a synthetic triple-stranded α-helical bundle. Science 259:1288-1293. Metzler, W.J., Constantine, K., Friedrichs, M.S., Bell, A., Ernst, E., Lavoie, T.B., and Mueller, L. 1993. Characterization of the three-dimensional structure of human profilin: 1H, 13C and 15N assignments and global folding pattern. Biochemistry 32:13818-13829. Metzler, W.J., Farmer, B.T., II, and Mueller, L. 1995. Refined solution structure of human profilin I. Protein Sci. 4:450-459. Nagano, K. 1973. Logical analysis of the mechanism of protein folding. I. Prediction of helices, loops and β-structures from primary structure. J. Mol. Biol. 75:401-420.

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Novotny, J. and Auffray, C. 1984. A program for prediction of protein secondary structure from nucleotide sequence data: Application to histocompatability antigens. Nucl. Acids Res. 12:243255. Oppenheimer, N.J. and James, T.L. (eds.) 1989. Nuclear magnetic resonance (Part B). Methods Enzymol. Volume 177.

Roy, S. and Rose, G.D. 1980. Hydrophobic basis of packing in globular proteins. Proc. Natl. Acad. Sci. U.S.A. 8:4643-4647. Sander, C. and Schneider, R. 1991. Database of homology-derived structures and the structural meaning of sequence alignment. Proteins Struct. Funct. Genet. 9:56-68.

O’Shea, E.K., Rutkowski, R., and Kim, P.S. 1992. Mechanism of specificity in the Fos-Jun oncoprotein heterodimer. Cell 68:699-708.

Schiffer, M. and Edmundson, A.B. 1967. The use of helical wheels to represent the structures of proteins and to identify segments with helical potential. Biophys. J. 7:121.

Pauling, L. and Corey, R.B. 1951. The pleated sheet, a new layer of configuration of polypeptide chains. Proc. Natl. Acad. Sci. U.S.A. 37:251-256.

Schulz, G.E. 1988. A critical evaluation of methods for prediction of protein secondary structures. Annu. Rev. Biophys. Chem. 17:1-21.

Pauling, L., Corey, R.B., and Branson, H.R. 1951. The structure of proteins: Two hydrogen-bonded helical configurations of the polypeptide chain. Proc. Natl. Acad. Sci. U.S.A. 37:205-211.

Schulz, G.E., Barry, C.D., Friedman, J., Chou, P.Y., Fasman, G.D., Finkelstein, A.V., Lim, V.I., Ptitsyn, O.B., Kabat, E.A., Wu, T.T., Levitt, M., Robson, B., and Nagano, K. 1974. Comparison of predicted and experimentally determined secondary structure of adenyl kinase. Nature 250:140-142.

Presta, L.G. and Rose, G.D. 1988. Helix signal in proteins. Science 240:1632-1641. Prevelige, P., Jr. and Fasman, G.D. 1989. Chou-Fasman prediction of the secondary structure of proteins. In Prediction of Protein Structure and the Principles of Protein Conformation (G.D. Fasman, ed.) pp. 391-416. Plenum, New York. Prothero, J.W. 1966. Correlation between the distribution of amino acids in alpha helices. Biophys. J. 6:367-370. Ptitsyn, O.B. and Finkelstein, A.V. 1970. Biofizika 15:757-768. Quinn, N. and Sejnowski, T.J. 1988. Predicting the secondary structure of globular proteins using neural network models. J. Mol. Biol. 202:865884. Richardson, J.S. 1981. The anatomy and taxonomy of protein structure. Adv. Protein Chem. 34:167339. Richardson, J.S. and Richardson, D.C. 1988. Amino acid preferences for specific locations at the ends of α-helices. Science 240:1648-1652. Robson, B. and Pain, R.H. 1971. Analyses of the code relating sequence to conformation in proteins: Possible implication for the mechanism of formation of helical regions. J. Mol. Biol. 58:237-259. Rost, B. and Sander, C. 1993a. Improved prediction of protein secondary structure by use of sequence profiles and neural networks. Proc. Natl. Acad. Sci. U.S.A. 90:7558-7562. Rost, B. and Sander, C. 1993b. Prediction of protein structure at better than 70% accuracy. J. Mol. Biol. 232:584-599.

Schulz, G.E. and Schirmer, R.H. 1979. Principles of Protein Structure. Springer-Verlag, New York and Heidelberg. Sippl, M.J. and Weitckus, S. 1992. Detection of native-like models for amino acid sequences of unknown three-dimensional structure in a data base of known protein conformations. Proteins Struct. Funct. Genet. 13:258-271. Stultz, C.M., White, J.V., and Smith, T.F. 1993. Structural analysis based on state-space modeling. Protein Sci. 2:305-314. Wuthrich, K. 1986. NMR of Proteins and Nucleic Acids. John Wiley & Sons, New York. Wyckoff, H.W., Hirs, C.H.W., and Timasheff, S.N. (eds.) 1985. Diffraction methods for biological molecules (Parts A and B). Methods Enzymol. Volumes 114 and 115.

Key Reference Fasman, 1989b. See above. Provides extensive descriptions and a historical perspective describing methods for protein structure prediction.

Contributed by Stanley R. Krystek, Jr., William J. Metzler, and Jiri Novotny Bristol-Myers Squibb Pharmaceutical Research Institute Princeton, New Jersey

Protein Secondary Structure Prediction

2.3.20 Current Protocols in Protein Science

Internet Basics

UNIT 2.4

With the explosion of sequence and structural information available to researchers, the field of bioinformatics is playing an increasingly large role in the study of fundamental biomedical problems. The challenge facing computational biologists will be to aid in gene discovery and in the design of molecular modeling, site-directed mutagenesis, and experiments of other types that can potentially reveal previously unknown relationships with respect to the structure and function of genes and proteins. This challenge becomes particularly daunting in light of the vast amount of data that has been produced by the Human Genome Project and other systematic sequencing efforts to date. Before embarking on any practical discussion of computational methods in solving biological problems, it is necessary to lay the common groundwork that will enable users to both access and implement the algorithms and tools discussed in this book. We begin with a review of the Internet and its terminology, also discussing major classes of Internet protocols, without becoming overly engaged in the engineering minutiae underlying these protocols. A more in-depth treatment on the inner workings of these protocols may be found in a number of well-written reference books intended for the lay audience (Krol and Klopfenstein, 1996; Rankin, 1996; Kennedy, 1999). This unit will also discuss matters of connectivity, ranging from simple modem connections to digital subscriber lines (DSL). Finally, we will address one of the most common problems that has arisen with the proliferation of Web pages throughout the world—i.e., finding useful information on the World Wide Web. INTERNET BASICS Despite the impression that the Internet is a single entity, it is actually a network of networks, composed of interconnected local and regional networks in more than 100 countries. While work on remote communications began in the early 1960s, the true origins of the Internet lie with a research project on networking at the Advanced Research Projects Agency of the U.S. Department of Defense in 1969 named ARPANET. The original ARPANET connected four nodes on the West Coast, with the immediate goal of being able to transmit information on defense-related research between laboratories. A number of different network projects subsequently surfaced, with the next landmark developments coming over ten years later. In 1981, BITNET (“Because It’s Time”) was introduced, providing point-to-point connections between universities for the transfer of electronic mail and files. In 1982, ARPA introduced the Transmission Control Protocol (TCP) and Internet Protocol (IP); TCP/IP allowed different networks to be connected to and communicate with one another, creating the system that is in place today. A number of references chronicle the development of the Internet and communications protocols in detail (Quarterman, 1990; Froehlich and Kent, 1991; Krol and Klopfenstein, 1996). Most users, however, are content to leave the details of how the Internet works to their systems administrators; the relevant fact to most is that it does work. Once the machines on a network are connected to one another, there needs to be some way to unambiguously specify a single computer so that messages and files find their intended recipient. To accomplish this, all machines directly connected to the Internet have an IP number. IP numbers are unique, identifying one and only one machine. The IP number is made up of four numbers separated by periods; for example, the IP number for the main file server at the National Center for Biotechnology Information (NCBI) at the National Institutes of Health (NIH) is 130.14.25.1. The numbers themselves represent, Contributed by Andreas D. Baxevanis and B.F. Francis Ouellette Current Protocols in Protein Science (2000) 2.4.1-2.4.16 Copyright © 2000 by John Wiley & Sons, Inc.

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from left to right, the domain (130.14 for the NIH), the subnet (.25 for the National Library of Medicine at NIH), and the machine itself (.1). While the use of IP numbers aids the computers in directing data, they are obviously very difficult for users to remember. Therefore, an IP number often has a fully-qualified domain name (FQDN) associated with it, which is dynamically translated in the background by a domain name server. Going back to the NCBI example, instead of using 130.14.25.1 to access the NCBI computer, a user could instead use ncbi.nlm.nih.gov and achieve the same result. Reading from left to right, the IP number goes from least to most specific, while the FQDN equivalent goes from most specific to least. The name of any given computer can then be thought of as taking the general form computer.domain, with the top-level domain (the portion coming after the last period in the FQDN) falling into one of the six broad categories shown in Table 2.4.1. Outside the United States, the top-level domain names may be replaced with a two-letter code specifying the country where the machine is located (e.g., .ca for Canada and .uk for the United Kingdom). In an effort to anticipate the needs of Internet users in the future, as well as to try to erase the arbitrary line between top-level domain names based on country, the now-dissolved International Ad Hoc Committee (IAHC) was charged with developing a new framework of generic top-level domains (gTLD). The new, recommended gTLDs were set forth in a document entitled The Generic Top Level Domain Memorandum of Understanding (gTLD-MOU); these gTLDs are overseen by a number of governing bodies and are also shown in Table 2.4.1. The most concrete measure of the size (and, thereby, the success) of the Internet lies in actually counting the number of machines physically connected to it. The Internet Table 2.4.1

Top-Level Domain Names

Top-level domain names Inside U.S. .com .edu .gov .mil .net .org

Commercial site Educational site Government site Military site Gateway or network host Private (usually not-for-profit) organizations

Examples of top-level domain names used outside the United States Canadian site .ca Academic site in the United Kingdom .ac.uk Commercial site in the United Kingdom .co.uk Generic top-level domains proposed by IAHC Firms or businesses .firm Businesses offering goods to purchase (stores) .shop Entities emphasizing activities relating to the World Wide Web .web Cultural and entertainment organizations .arts Recreational organizations .rec Information sources .info Personal names (e.g., yourlastname.nom) .nom

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Software Consortium conducts an Internet Domain Survey twice each year to count these machines, otherwise known as hosts. In performing this survey, ISC considers not only how many hostnames have been assigned, but how many of those are actually in use; a hostname might be issued, but the requestor may be holding the name in abeyance for future use. To test for this, a representative sample of host machines are sent a probe (a “ping”), with a signal being sent back to the originating machine if the host was indeed found. The rate of growth of the number of hosts has been phenomenal; from a paltry 213 hosts in August 1981, the Internet has in excess of 60 million hosts now. The doubling time for the number of hosts is on the order of 18 months. Most of this growth has come from the commercial sector, capitalizing on the growing popularity of multimedia platforms for advertising and communications such as the World Wide Web. CONNECTING TO THE INTERNET Of course, before being able to use all of the resources that the Internet has to offer, one needs to actually make a physical connection between one’s own computer and “the information superhighway.” For purposes of this discussion, the elements of this connection have been separated into two discrete parts: the actual, physical connection (meaning the “wire” running from one’s computer to the Internet backbone) and the service provider, who handles issues of routing and content once connected. Keep in mind that, in practice, these are not necessarily treated as two separate parts—for instance, one’s service provider may also be the same company that will run cables or fibers right into one’s home or office. Copper Wires, Coaxial Cables, and Fiber Optics Traditionally, users who were attempting to connect to the Internet away from the office have had one and only one option—a modem, which uses the existing copper twisted-pair cables carrying telephone signals to transmit data. Data transfer rates using modems are relatively slow, allowing for data transmission in the range of 28.8 to 56 kilobits per second (Kbps). The problem with using conventional copper wire to transmit data lies not in the copper wire itself, but in the switches that are found along the way that route information to their intended destinations. These switches were designed for the efficient and effective transfer of voice data, but were never intended to handle the high-speed transmission of data. While most people still use modems from their homes, a number of new technologies are already in place and will become more and more prevalent for accessing the Internet away from hard-wired Ethernet networks. The maximum speeds at which each of the services that are discussed below can operate are shown in Figure 2.4.1. The first of these “new solutions” is the integrated services digital network, or ISDN. While the advent of ISDN was originally heralded as the way to bring the Internet into the home in a speed-efficient manner, it required that special wiring be brought into the home. It also required that users be within a fixed distance from a central office, on the order of 20,000 feet or less. The cost of running this special, dedicated wiring, along with a per-minute pricing structure, effectively placed ISDN out of reach of most individuals. While ISDN is still available in many areas, this type of service is quickly being supplanted by more cost-effective alternatives. In looking at alternatives that did not require new wiring, cable television providers began to look at ways in which the coaxial cable already running into a substantial number of households could be used to also transmit data. Cable companies are able to use bandwidth that is not being used to transmit television signals (effectively, unused channels) to push Computational Analysis

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Telephone modem 0.056 Cellular wireless 0.128 ISDN 0.128 0.4 Satellite T1 1.544 Cable modem ADSL Ethernet 0

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Figure 2.4.1 Performance of various types of Internet connections, by maximum throughput. The numbers indicated in the graph refer to peak performance; often the actual performance of any given method may be on the order of one-half slower, depending on configurations and system conditions.

data into the home at very high speeds, up to 4.0 megabits per second (Mbps). The actual computer is connected to this network through a cable modem, which uses an Ethernet connection to the computer and a coaxial cable to the wall. Homes in a given area all share a single cable, in a wiring scheme very similar to that by which individual computers are connected via the Ethernet in an office or laboratory setting. While this branching arrangement can serve to connect a large number of locations, there is one major disadvantage—as more and more homes connect through their cable modems, service effectively slows down as more signal attempts to pass through any given node. One way of circumventing this problem is the installation of more switching equipment and reducing the size of a given “neighborhood.” Since the local telephone companies were the primary ISDN providers, they quickly turned their attention to ways in which the existing, conventional copper wire already in the home could be used to transmit data at high speed. The solution here is the digital subscriber line, or DSL. By using new, dedicated switches that are designed for rapid data transfer, DSL providers can circumvent the old voice switches that slowed down transfer speeds. Depending on the user’s distance from the central office and whether a particular neighborhood has been wired for DSL service, speeds are on the order of 0.8 to 7.1 Mbps. The data transfers do not interfere with voice signals, and users can use the telephone while connected to the Internet; the signals are “split” by a special modem that passes the data signals to the computer and a microfilter that passes voice signals to the handset. There is a special type of DSL called asynchronous DSL, or ADSL. This is the variety of DSL service that is becoming more and more prevalent. Most home users download much more information than they send out, so systems are engineered to provide super-fast transmission in the “in” direction, with transmissions in the “out” direction being 5 to 10 times slower. Using this approach maximizes the amount of bandwidth that can be used without necessitating new wiring. One of the advantages of ADSL over cable is that ADSL subscribers effectively have a direct line to the central office, meaning that they do not have to compete with their neighbors for bandwidth. This, of course, comes at a price; at the time of this writing, ADSL connectivity options were on the order of twice as expensive as cable Internet. Internet Basics

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Some of the newer technologies involve wireless connections to the Internet. These include using one’s own cell phone or a special cell phone service (such as Ricochet) to upload and download information. These cellular providers can provide speeds on the order of 28.8 to 128 Kbps, depending on the density of cellular towers in the service area. Fixed-point wireless services can be substantially faster, since the cellular phone does not have to “find” the closest tower at any given time. Along these same lines, satellite providers are also coming on-line. These providers allow for data download directly to a satellite dish with a southern exposure, with uploads occuring through traditional telephone lines. While the satellite option has the potential to be amongst the fastest of the options discussed, current operating speeds are only on the order of 400 Kbps. Content Providers versus ISPs Once an appropriately fast and price-effective connectivity solution is found, users will then need to actually connect to some sort of service that will enable them to traverse the Internet space. The two major categories in this respect are on-line services or Internet service providers (ISPs). On-line services such as America Online (AOL) and CompuServe offer a large number of interactive digital services, including information retrieval, electronic mail (e-mail), bulletin boards, and “chat rooms” where users who are on line at the same time can converse with each other about any number of subjects. While the on-line services now provide access to the World Wide Web (see discussion of The World Wide Web), most of the features and services available through these systems reside within a proprietary, closed network; once a connection is made between the user’s computer and the on-line service, accessing the special features, or content, of these systems does not require ever leaving the on-line system’s host computer. Specialized content can range from access to on-line travel reservation systems to encyclopedias that are constantly being updated—items that would not be available to anyone unless they subscribed to that particular on-line service. Internet service providers, or ISPs, take the opposite tack. Instead of focusing on providing content, the ISPs provide the tools necessary for users to send and receive e-mail, upload and download files, and navigate around the World Wide Web to find information at remote locations. The major advantage of ISPs is connection speed; ISPs often provide faster connections than the on-line services. Most ISPs charge a monthly fee for unlimited use. The line between on-line services and ISPs has already begun to blur. AOL’s now monthly flat fee pricing structure allows users to obtain all of the proprietary content found on AOL as well as all of the Internet tools available through ISPs, often at the same cost as a simple ISP connection. The extensive AOL network puts access to AOL as close as a local phone call in most of the United States, providing access to e-mail no matter where the user is located—a feature that small, local ISPs cannot match. Not to be outdone, many of the major national ISP providers now also provide content through the concept of portals. Portals are Web pages that can be customized to the needs of the individual user and which serve as a jumping-off point to other sources of news or entertainment on the Net. In addition, many national firms such as Mindspring are able to match AOL’s ease of connectivity on the road, and both ISPs and online providers are becoming more and more generous in providing users the capacity to publish their own Web pages. Developments such as this, coupled with the move of local telephone and cable companies into providing Internet access through new, faster fiber optic networks, foretell major changes in how people will access the Net in the future, changes that should favor the end-user both in price and performance.

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ELECTRONIC MAIL Most people are introduced to the Internet through the use of electronic mail (e-mail). The use of e-mail has become practically indispensable in many settings owing to its convenience as a medium for sending, receiving, and replying to messages. Its advantages are many: It is much quicker than postal, or “snail mail.” Messages tend to be much clearer and more to the point than is the case in typical telephone or face-to-face conversations. Recipients have more flexibility in deciding whether a response needs to be sent immediately, relatively soon, or at all, giving individuals more control over workflow. It provides a convenient method by which messages can be filed or stored. There is little or no cost involved in sending an e-mail message. While these and other advantages have pushed e-mail to the forefront of interpersonal communication in both industry and the academic community, users should be aware of several major disadvantages. First is the issue of security. As mail travels towards its recipient, it may pass through a number of remote nodes. The message could be intercepted and read at any one of those nodes by someone with high-level access, such as a systems administrator. Second is the issue of privacy. In industrial settings, e-mail is often considered to be an asset of the company for use only in official communication and, as such, is subject to monitoring by supervisors. The opposite is often true in academic, quasi-academic, or research settings; for example, National Institutes of Health policy encourages personal use of e-mail within the bounds of certain published guidelines. The key words here are “published guidelines”; no matter what the setting, users of e-mail systems should always be informed as to their organization’s policy regarding the appropriate use and confidentiality of e-mail so that they may use the tool properly and effectively. An excellent, basic guide to the effective use of e-mail is highly recommended (Rankin, 1996). Sending E-Mail E-mail addresses take the general form [email protected], where user is the name of the individual user and computer.domain specifies the actual computer that the e-mail account is located on. Like a postal letter, an e-mail message is comprised of an envelope or header, showing the e-mail addresses of the sender and recipient, a line indicating the subject of the e-mail, and information about how the e-mail message actually travelled from the sender to the recipient. The header is followed by the actual message, or body, analogous to what would go inside the postal envelope. Figure 2.4.2 illustrates all the components of an e-mail message. E-mail programs vary widely, depending on both the platform and the needs of the users. Usually the characteristics of the local area network (LAN) dictate what types of mail programs can be used, and the decision is often left to systems administrators rather than individual users. Among the most widely used e-mail packages with a graphical user interface are Eudora for the Macintosh and both Netscape Messengerand Microsoft Exchange for Macintosh, Windows, and UNIX platforms. Text-based e-mail programs, which are accessed by logging into a UNIX-based account, include Elm and Pine.

Internet Basics

Bulk E-Mail As with postal mail, there has been an upsurge in “spam” or “junk e-mail,” where companies compile bulk lists of e-mail addresses for use in commercial promotions. Since most of these lists are compiled from on-line registration forms and similar sources, the

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Header Body

From: [email protected] (PredictProtein) To: [email protected] Subject: PredictProtein

Delivery details (Envelope)

Received: from dodo.cpmc.columbia.edu (dodo.cpmc.columbia.edu [156.111.190.78]) by members.aol.com (8.9.3/8.9.3) with ESMTP id RAA13177 for <[email protected]>; Sun, 2 Jan 2000 17:55:22 -0500 (EST) Received: (from phd@localhost) by dodo.cpmc.columbia.edu (980427.SGI.8.8.8/980728.SGI.AUTOCF) id RAA90300 for [email protected]; Sun, 2 Jan 2000 17:51:20 -0500 (EST) Date: Sun, 2 Jan 2000 17:51:20 -0500 (EST) Message-ID: <[email protected]>

Sender, Recipient, and Subject

PredictProtein Help PHDsec, PHDacc, PHDhtm, PHDtopology, TOPITS, MaxHom, EvalSec Burkhard Rost Table of Contents for PP help 1. Introduction 1. What is it? 2. How does it work? 3. How to use it?



Figure 2.4.2 Anatomy of an e-mail message, with relevant components indicated. This message is an automated reply to a request for help file for the PredictProtein E-mail server.

best defense for remaining off of these bulk e-mail lists is to be selective as to whom e-mail addresses are provided. Most newsgroups keep their mailing lists confidential; if in doubt and this is a concern, one should ask. E-mail Servers Most often, e-mail is thought of as a way to simply send messages, whether it be to one recipient or many. It is also possible to use e-mail as a mechanism for making biological predictions or retrieving records from biological databases. Users can send e-mail messages in a predefined format, defining the action to be performed for remote computers known as servers; the servers will then perform the desired operation and e-mail back the results. While this method is not interactive (in that the user cannot adjust parameters or have control over the execution of the method in real time), it does place the responsibility of hardware maintenance and software upgrades on the persons maintaining the server, allowing users to concentrate on their results instead of on programming. For most servers, sending the message help to the server e-mail address will return a detailed set of instructions for using that server, including the way in which queries need to be formatted. Aliases and Newsgroups In the example in Fig. 2.4.2, the e-mail message is being sent to a single recipient. One of the strengths of e-mail is that a single piece of e-mail can be sent to a large number of people. The primary mechanism for doing this is through aliases; a user can define a group of people within their mail program and give the group a special name, or alias. Instead of using the individual e-mail addresses for all of the people in the group, the user can just send the e-mail to the alias name, and the mail program will handle broadcasting the message to each person in that group. Setting up alias names is a tremendous time-saver even for small groups; it also ensures that all members of a given group actually receive all e-mail messages intended for the group.

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The second mechanism for broadcasting messages is through newsgroups. This model works slightly differently in that the list of e-mail addresses is compiled and maintained on a remote computer through subscriptions, much like magazine subscriptions. For example, the BIOSCI newsgroups are amongst the most highly-trafficked, offering a forum for discussion or the exchange of ideas in a wide variety of biological subject areas. To begin receiving the messages posted to the automated sequencing discussion group within BIOSCI, a user would send a message to [email protected] with the wording subscribe autoseq in the body of the message. The user would then receive all future postings to that group and be able to participate in the discussions. If a user wished to be removed from the group, a message would be sent to the same address, but this time, the body of the message would read unsubscribe autoseq. For more information on BIOSCI, including a complete list of discussion groups, an e-mail message can be sent to [email protected]; in this case the subject line should be left blank and the words info faq typed in the body of the message. The BIOSCI server will then return a copy of the Frequently Asked Questions (FAQ) in response, with detailed information on each newsgroup overseen by BIOSCI. It is also possible to participate in newsgroups without having each and every piece of e-mail flood into one’s private mailbox. Instead, interested participants can use newsreading software, such as NewsWatcher for the Macintosh, which provides access to the individual messages making up a discussion. The major advantage is that the user can pick and choose which messages to read by scanning the subject lines; the remainder can be discarded by a single operation. NewsWatcher is an example of what is known as a client-server application—the client software (here, NewsWatcher) runs on a client computer (a Macintosh), which in turn interacts with a machine at a remote location (the server). Client-server architecture is interactive in nature, with a direct connection being made between the client and server machines. Once NewsWatcher is started, the user is presented with a list of newsgroups available to them (Fig. 2.4.3); this list will vary, depending on the user’s location, as systems administrators have the discretion to allow or block certain groups at a given site. From the rear-most window in the figure, the user double-clicks on the newsgroup of interest (here, bionet.genome.arabidopsis), which spawns the window shown in the center. At the top of the center window is the current unread message count, and any message within the list can be read by double-clicking on that particular line. This, in turn, spawns the last window (in the foreground), showing the actual message. If a user decides not to read any of the messages, or is done reading individual messages, the balance of the messages within the newsgroup (center) window can be deleted by first choosing Select All from the File menu, then selecting Mark Read from the News menu. Once the newsgroup window is closed, the unread message count is reset to zero. Every time NewsWatcher is restarted, it will automatically poll the news server for new messages that have been created since the last session. As with most of the tools that will be discussed in this unit, news-reading capability is built into Web browsers such as Netscape Navigator and Microsoft Internet Explorer. FILE TRANSFER PROTOCOL

Internet Basics

Despite the many advantages afforded by e-mail in transmitting messages, experienced e-mail users have no doubt experienced frustration in trying to transmit files (attachments) along with an e-mail message. The mere fact that a file can be attached to an e-mail message and sent does not mean that the recipient will be able to detach, decode, and actually use the attached file. While more cross-platform e-mail packages such as

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Figure 2.4.3 Using NewsWatcher to read postings to newsgroups. The list of newsgroups that the user has subscribed to is shown in the Subscribed List window (left). The list of new postings for the highlighted newsgroup (bionet.genome.arabidopsis) is shown in the center window. The window in the foreground shows the contents of the posting selected from the center window.

Microsoft Exchange are being developed, the use of different e-mail packages by people at different locations means that sending files via e-mail is not an effective, foolproof method, at least in the short term. One solution to this problem is through the use of a file transfer protocol (or FTP). The workings of FTP are quite simple—a connection is made between a user’s computer (the client) and a remote server, and that connection remains in place for the duration of the FTP session. File transfers are very fast, at rates on the order of 5 to 10 kilobytes per second, with speeds varying with time of day, distance between the client and server machines, and overall traffic on the network. In the ordinary case, making an FTP connection and transferring files requires that a user have an account on the remote server. However, there are many files and programs that the academic community makes freely available, and access to those files does not require having an account on each and every machine where these programs are stored. Instead, connections are made using a system called anonymous FTP. Under this system, the user connects to the remote machine, and instead of entering a username/password pair, types anonymous as the username and enters an e-mail address in place of a password. Providing one’s e-mail address allows the server’s systems administrator to compile

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$ ftp ftp.bio.indiana.edu Connected to magpie.bio.indiana.edu. 220 iubio.bio.indiana.edu FTP server ready. Name: anonymous 331 Guest login ok, send your complete e-mail address as password. Password: ******** 230-

Welcome to IUBio archive!

230230-

This is a user-supported archive for biology software and data.

230230-

See the file Archive.Doc for details of this archive.

230230-

See IUBio Bio-Mirror archive of large data sets at

230230-

ftp to iubio.bio.indiana.edu, user: iubio, password: iubio This includes GenBank, EMBL and DDBJ and other biosequence data.

230230-

Report problems, uploads and other matters via e-mail to

230-

[email protected].

230230 Guest login ok, access restrictions apply. Remote system type is UNIX. Using binary mode to transfer files. ftp> cd /molbio/align/clustal 250 CWD command successful. ftp> get clustalw1.75.unix.tar.Z local: clustalw1.75.unix.tar.Z remote: clustalw1.75.unix.tar.Z 200 PORT command successful. 150 Opening BINARY mode data connection for clustalw1.75.unix.tar.Z (230379 bytes). 226 Transfer complete. 230379 bytes received in 0.45 seconds (500.75 Kbytes/s) ftp> quit 221-You have transferred 230379 bytes in 1 files. 221-Total traffic for this session was 231859 bytes in 1 transfers. 221-Thank you for using the FTP service on iubio.bio.indiana.edu. 221 Goodbye.

Figure 2.4.4 Using UNIX FTP to download a file. An anonymous FTP session is established with the molecular biology FTP server at the University of Indiana to download the ClustalW alignment program. The user inputs are shown in boldface.

access statistics which may, in turn, be of use to those actually providing the public files or programs. An example of an anonymous FTP session using UNIX is shown in Figure 2.4.4. Although FTP occurs within the UNIX environment, Macintosh and PC users can employ programs that utilize graphical user interfaces (GUI, pronounced “gooey”) to navigate through the UNIX directories on the FTP server. Users need not have any knowledge of UNIX commands to download files; they can instead rely on pop-up menus and the ability Internet Basics

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Figure 2.4.5 Using Fetch to download a file. An anonymous FTP session is established with the molecular biology FTP server at the University of Indiana (top) to download the ClustalW alignment program (bottom). Notice the difference between this GUI-based program and the UNIX equivalent illustrated in Figure 2.4.4.

to point-and-click their way through the UNIX file structure. The most popular FTP program on the Macintosh platform for FTP sessions is Fetch. A sample Fetch window is shown in Figure 2.4.5 to illustrate the difference between using a GUI-based FTP program and the equivalent UNIX FTP in Figure 2.4.4. In the figure, notice that the Automatic radio button (near the bottom of the second window under the Get File button) is selected, meaning that Fetch will determine the appropriate type of file transfer to perform. This may be manually overridden by selecting either Text or Binary, depending on the nature of the file being transferred. As a rule, text files should be transferred as Text, programs or executables as Binary, and graphic format files such as PICT and TIFF files as Raw Data. Computational Analysis

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THE WORLD WIDE WEB While FTP is of tremendous use in the transfer of files from one computer to another, it does suffer from some limitations. When working with FTP, once a user enters a particular directory, they can only see the names of the directories or files. In order to actually view what is within the files, it is necessary to physically download them onto one’s own computer. This inherent drawback led to the development of a number of distributed document delivery systems (DDDS), interactive client-server applications that allowed information to be viewed without having to perform a download. The first generation of DDDS development led to programs like Gopher, which allowed plain text to be viewed directly through a client-server application. From this evolved the most widely known and widely used DDDS, namely the World Wide Web. The Web is an outgrowth of research performed at the European Nuclear Research Council (CERN) in 1989 that was aimed at sharing research data between several locations. That work led to a medium through which text, images, sounds, and videos could be delivered to users on demand, anywhere in the world. Navigation on the World Wide Web Navigation on the Web does not require advance knowledge of the location of the information being sought. Instead, users can navigate by clicking on specific text, buttons, or pictures. These clickable items are collectively known as hyperlinks. Once one of these hyperlinks is clicked, the user is taken to another Web location, which could be at the same site or halfway around the world. Each document displayed on the Web is called a Web page, and all of the related Web pages on a particular server are collectively called a Web site. Navigation strictly through the use of hyperlinks has been nicknamed “Web surfing.” Users can take a more direct approach to finding information by entering a specific address. One of the strengths of the Web is that the programs used to view Web pages (appropriately termed browsers) can be used to visit Gopher and FTP sites as well, somewhat obviating the need for separate Gopher or FTP applications. As such, a unified naming convention was introduced to indicate to the browser program both the location of the remote site and, more importantly, the type of information at that remote location so that the browser could properly display the data. This standard-form address is known as a uniform resource locator (URL), and takes the general form protocol://computer. domain, where protocol specifies the type of site and computer.domain specifies the location (Table 2.4.2). The http used for the protocol in World Wide Web URLs stands for hypertext transfer protocol, the method used in transferring Web files from the host computer to the client.

Table 2.4.2 Uniform Resource Locator (URL) Format for Each Type of Transfer Protocol

Site

URL format (example)

General form FTP site

protocol://computer.domain ftp://ftp.ncbi.nlm.nih.gov

Gopher site Web site

gopher://gopher.iubio.indiana.edu http://www.nhgri.nih.gov

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Browsers Browsers, which are used to look at Web pages, are client-server applications that connect to a remote site, download the requested information at that site, display the information on the user’s monitor, then disconnect from the remote host. The information retrieved from the remote host is in a platform-independent format called hypertext markup language (HTML). HTML code is strictly text-based, and any associated graphics or sound for that document exist as separate files in a common format. For example, images may be stored and transferred in GIF format, developed by CompuServe for the quick and efficient transfer of graphics; while GIF format is most commonly used for graphics, other formats such as JPEG and BMP may also be used. Because of this, a browser can display any Web page on any type of computer, whether it be a Macintosh, IBM-compatible, Linux, or UNIX machine. The text is usually displayed first, then the remaining elements are placed on the page as they are downloaded. With minor exceptions, a given Web page will look the same when the same browser is used on any of the above platforms. The two major players in the area of browser software are Netscape, with their Communicator product, and Microsoft, with Internet Explorer. As with many other areas where multiple software products are available, the choice between Netscape and Internet Explorer comes down to one of personal preference. While the computer literati will debate the fine points of difference between these two packages, for the average user, both packages perform equally well and offer the same types of features, adequately addressing the Web-browser needs of most users. It is worth mentioning that, while the Web is by definition a visually based medium, it is also possible to travel through Web space and view documents without the associated graphics. For users limited to line-by-line terminals, a browser called Lynx is available. Developed at the University of Kansas, Lynx allows users to use their keyboard arrow keys to highlight and select hyperlinks, using their return key the same way that Netscape and Internet Explorer users would click their mouse. Internet versus Intranet The World Wide Web is normally thought of as a way to communicate with people at a distance, but the same infrastructure can be used to connect people within an organization. Such intranets provide an easily accessible repository of relevant information, capitalizing on the simplicity of the Web interface. It also provides another channel for broadcast or confidential communication within the organization. Having an intranet is of particular value when members of an organization are physically separated, whether it be in different buildings or different cities. Intranets are protected in such a way that people who are not on the organization’s network are prohibited from accessing the internal Web pages; additional protections through the use of passwords are also common. Finding Information on the World Wide Web Most people find information on the Web the old fashioned way—by word of mouth either by using lists such as Table 2.4.3 or by simply following hyperlinks put in place by Web authors. Continuously clicking from page to page can be a highly ineffective way of finding information, especially when the information sought is of a very focused nature. One way of finding interesting and relevant Web sites is to consult virtual libraries, which are curated lists of Web resources arranged by subject. Virtual libraries of special interest to biologists include the WWW Virtual Library, maintained by Keith Robison at Harvard and the EBI BioCatalog, based at the European Bioinformatics Institutes. The URLs for these sites can be found in Table 2.4.3.

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Table 2.4.3

World Wide Web Sites of Interest

Site

URL

Domain names gTLD-MOU Internet Software Consortium

http://www.gtld-mou.org http://www.isc.org

Electronic mail and newsgroups BIOSCI Newsgroups http://www.bio.net/docs/biosci.FAQ.html Eudora http://www.eudora.com Microsoft Exchange http://www.microsoft.com/exchange/ NewsWatcher ftp://ftp.acns.nwu.edu/pub/newswatcher/ File Transfer Protocol Fetch 3.0/Mac FTP Voyager

http://www.dartmouth.edu/pages/softdev/fetch.html http://ftpvoyager.deerfield.com

Internet access America Online AT&T Bell Atlantic Bell Canada CompuServe MCI Ricochet Telus

http://www.aol.com http://www.att.com/worldnet http://www.bellatlantic.net http://www.bell.ca http://www.compuserve.com http://www.mci.com http://www.ricochet.net http://telus.com

Virtual libraries EBI BioCatalog Amos’ WWW Links Page NAR Database Collection WWW Virtual Library

http://www.ebi.ac.uk/biocat/biocat.html http://www.expasy.ch/alinks.html http://www3.oup.co.uk/nar/Volume_28/Issue_01/introduction/ http://mcb.harvard.edu/BioLinks.html

World Wide Web browsers Internet Explorer http://www.microsoft.com/insider/ie5/default.htm Lynx ftp://ftp2.cc.ukans.edu/pub/lynx Netscape Navigator http://home.netscape.com World Wide Web search engines AltaVista http://www.altavista.com Excite http://www.excite.com HotBot http://hotbot.lycos.com Infoseek http://infoseek.go.com Lycos http://www.lycos.com Northern Light http://www.northernlight.com World Wide Web meta-search engines MetaCrawler http://www.metacrawler.com Savvy Search http://www.savvysearch.com

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Table 2.4.4 Number of Hits Returned for Four Defined Search Queries on Some of the More Popular Search and Meta-Search Engines

Search term genetic mapping human genome positional cloning prostate cancer

Search engine

Meta-search engine

HotBot

Excite

Infoseek

Lycos

478 13,231 279 14,044

1,040 34,760 735 53,940

4,326 15,980 1,143 24,376

9,395 19,536 666 33,538

MetaCrawler SavvySearch 62 42 40 60

58 54 52 57

It is also possible to directly search the Web by using search engines such as Alta Vista and Excite, among others. A search engine is simply a specialized program that can perform full-text or keyword searches on databases that catalog Web content. The result of a search is a hyperlinked list of Web sites fitting the search criteria from which the user can visit any or all of the found sites. However, search engines use slightly different methods in compiling their databases. One variation is the attempt to capture most or all of the text of every Web page that the search engine is able to find and catalog (“Web crawling”). Another technique is to catalog only the title of each Web page rather than its entire text. A third is to consider words that must appear next to each other or only relatively close to one another. Because of these differences in search-engine algorithms, the results returned by issuing the same query to a number of different search engines can produce wildly different results (Table 2.4.4). It may also be noted from Table 2.4.4 that most of the numbers are exceedingly large, reflecting the overall size of the World Wide Web. Unless a particular search engine ranks its results by relevance (for example, by scoring words in a title higher than words in the body of the Web page), the results obtained may not be particularly useful. It is also necessary to keep in mind that, depending on the indexing scheme that the search engine is using, the found pages may actually no longer exist, leading the user to the dreaded “404 Not Found” error. Compounding this problem is the issue of coverage—the number of Web pages that any given search engine is actually able to survey and analyze. A comprehensive study by Lawrence and Giles (1998) indicates that the coverage provided by any of the search engines studied is both small and highly variable. For example, the HotBot engine produced 57.5% coverage of what was estimated to be the size of the “indexable Web,” while Lycos had only 4.41% coverage, a full order of magnitude less than HotBot. The most important conclusion from this study was the extent of coverage increased as the number of search engines are increased and the results from those individual searches are combined. Combining the results obtained from the six search engines examined in this study produced coverage approaching 100%. To address this point, a new class of search engines called meta-search engines have been developed. These programs will take the user’s query and poll anywhere from five to ten of the “traditional” search engines. The meta-search engine will then collect the results, filter out duplicates, and return a single, annotated list to the user. One big advantage is that the meta-search engines take relevance statistics into account, returning much smaller lists of results. Even though the hit list is substantially smaller, it is much more likely to contain sites that directly address the original query. Since the programs must poll a number of different search engines, searches conducted this way obviously take longer to perform, but the higher degree of confidence in the compiled results for a given query outweighs the extra few minutes (sometimes only seconds) of search time. Reliable and easy-to-use meta-search engines include SavvySearch and MetaCrawler (see Table 2.4.3).

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LITERATURE CITED Froehlich, F. and Kent, A. 1991. ARPANET, the Defense Data Network, and Internet. In Encyclopedia of Communications. Marcel Dekker, New York. Kennedy, A.J. 1999. The Internet: Rough Guide 2000. Rough Guides, London. Krol, E. and Klopfenstein, B.C. 1996. The Whole Internet User’s Guide and Catalog. O’Reilly and Associates, Sebastopol, Calif. Lawrence, S. and Giles, C.L. 1998. Searching the World Wide Web. Science 280:98-100. Rankin, B. 1996. Dr. Bob’s Painless Guide to the Internet and Amazing Things You Can Do with E-mail. No Starch Press, San Francisco. Quarterman, J. 1990. The Matrix: Computer Networks and Conferencing Systems Worldwide. Digital Press, Bedford, Mass.

Contributed by Andreas D. Baxevanis National Human Genome Research Institute, NIH Bethesda, Maryland B.F. Francis Ouellette Centre for Molecular Medicine and Therapeutics University of British Columbia Vancouver, British Columbia

Internet Basics

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Sequence Similarity Searching Using the BLAST Family of Programs

UNIT 2.5

Database sequence similarity searching is carried out thousands of times each day by researchers worldwide. Scientists in traditional laboratories use search results, for example, to infer the functions of newly discovered cDNAs, predict new members of gene families, and explore evolutionary relationships between sequences. In turn, they populate sequence databases with biochemically and/or genetically characterized sequences. With the advent of whole-genome sequencing, a new breed of scientist is now using these characterized sequences to predict the location and function of coding and regulatory regions in large segments of genomic DNA. Their contribution to the sequence databases is the submission, at a rapid pace, of generally uncharacterized mRNA and genomic DNA sequences. These data can then be used by the first group of scientists to enhance the understanding of their sequences. Both types of sequencing efforts have greatly increased the size and quality of the sequence databases in recent years. Thus, sequence similarity searching has become a valuable research tool for all molecular biologists (Altschul et al., 1994; Schuler, 1998; also see UNIT 2.1). Over the years, a number of algorithms have been implemented that allow searching of sequence databases. The most useful of these tools should share the following characteristics: (1) Speed. Because today’s databases are so large, the programs must be fast in order to process megabases of sequence in seconds. (2) Sensitivity. The programs must report all potentially interesting similarities. (3) Rigorous statistics. The programs must provide a way to evaluate the significance of the results. (4) Ease of use. Scientists with no formal training in sequence-analysis algorithms should understand how to use the programs and interpret the results. Advanced users should have the option to tailor the programs to their needs. (5) Access to up-to-date databases. The doubling time of GenBank is currently ∼16 months. It is important to search the most recent version of the database. The BLAST (Basic Local Alignment Search Tool) family of sequence similarity search programs satisfies the above criteria. In short, users input either a nucleotide or amino acid query sequence, and search a nucleotide or amino acid sequence database. The program returns a list of the sequence “hits,” alignments to the query sequence, and statistical values. This unit describes how to choose an appropriate BLAST program and database, perform the search, and interpret the results. ACCESSING BLAST PROGRAMS AND DOCUMENTATION The National Center for Biotechnology Information (NCBI) currently supports two versions of BLAST free of charge: BLAST 2.0 (gapped BLAST) and Position-Specific Iterated BLAST (PSI-BLAST). BLAST 2.0 is the standard version of BLAST, which allows a user to search a sequence database with a nucleotide or protein sequence of interest. BLAST 2.0 places gaps into the query and target sequences so that separate areas of similarity between the two sequences can be returned as one hit. PSI-BLAST is an iterative BLAST search, which is optimized for finding distantly related sequences. Other BLAST programs are also available from the NCBI Web page. “BLAST 2 sequences” uses the BLAST search engine to produce an alignment of two sequences entered by the user. On the Specialized BLAST pages, researchers can use the BLAST engine to search sequences that are not in GenBank. At present, these databases include unfinished microbial genomes, P. falciparum (the human malaria parasite), and tentative Contributed by Tyra G. Wolfsberg and Thomas L. Madden Current Protocols in Protein Science (1999) 2.5.1-2.5.29 Copyright © 1999 by John Wiley & Sons, Inc.

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human consensus (THC) sequences from The Institute for Genomic Research (TIGR). The content of these pages is, however, subject to change. The easiest and most popular way to access the BLAST suite of programs is through the NCBI World Wide Web site, at http://www.ncbi.nlm.nih.gov/BLAST/. All versions of BLAST are accessible from this site, and can be used to query all sequence databases available at the NCBI. Documentation, which includes an overview of BLAST, BLAST frequently asked questions (FAQs), a “What’s New” page, the BLAST manual, and a list of references, is also available here. For users who want to run BLAST against private local databases or downloaded copies of NCBI databases, the NCBI offers a stand-alone version of the BLAST program. BLAST binaries and documentation are provided for the latest versions of IRIX, Solaris, DEC OSF1, and Win32 systems. BLAST 2.0 executables may be found on the NCBI anonymous FTP server at ftp://ncbi.nlm.nih.gov/blast/executables/. BLAST can also be run as a client-server program, in which the user installs client software on a local machine that communicates across the network with a server at NCBI. This setup is useful for researchers who run large numbers of searches on NCBI databases, because they can automate the process to run on their local computer. The BLAST client may be found on the NCBI anonymous FTP server at ftp://ncbi.nlm.nih.gov/blast/network/netblast. The NCBI BLAST e-mail server is the best option for people without convenient access to the Web. A similarity search can be performed by sending a properly formatted e-mail message containing the nucleotide or protein query sequence to [email protected]. The query sequence is compared against a specified database and the results are returned in an e-mail message. For more information on formulating e-mail BLAST searches, please send a message consisting of the word HELP to the same address, [email protected]. This unit concentrates on the BLAST searches which can be performed from the NCBI Web site. The other implementations are mainly for advanced users of BLAST, or those with special needs. For any of these services, questions should be directed to [email protected]. INTRODUCTION TO BLAST Basic Versus Advanced BLAST Searches BLAST 2.0 is available in a basic or advanced version. In both versions, the user can select the type of BLAST program and the database to be searched, and choose whether to filter the query sequence to mask low-complexity regions (see below). The advanced version allows the user to change parameters as well. For most researchers, the Basic version, which uses the default parameters, is adequate. For a discussion of BLAST parameters, see Appendix A at the end of this unit. BLAST Programs Five types of the BLAST program have been developed to support sequence similarity searching using a variety of nucleotide and protein sequence queries and databases. These programs are listed and described in Table 2.5.1. The type of BLAST search to be carried out is dependent on the type of information that is desired. Sequence Similarity Searching Using BLAST

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Table 2.5.1

BLAST Search Programs

Program

Query sequence Database sequence Comments

BLASTP

Protein

Protein

BLASTN

Nucleotide (both strands)

Nucleotide

BLASTX

Nucleotide (six-frame translation)

Protein

TBLASTN Protein

Nucleotide (six-frame translation)

TBLASTX Nucleotide (six- Nucleotide (sixframe translation) frame translation)

Can be run in standard mode or in a more sensitive iterative mode (PSI-BLAST), which uses the previous search results to build a profile for subsequent rounds of similarity searching. Parameters optimized for speed, not sensitivity; not intended for finding distantly related coding sequences. Automatically checks complementary strand of query. Very useful for preliminary data containing potential frameshift errors (ESTs, HTGs, and other “single-pass” sequences). Essential for searching protein queries against EST database. Often useful for finding undocumented open reading frames or frameshift errors in database sequences. Should be used only if BLASTN and BLASTX produce no results. Restricted for search against EST, STS, HTGS, GSS, and Alu databases.

NCBI Databases One frequent mistake in sequence similarity searching is failure to search an up-to-date database. The NCBI produces GenBank (Benson et al., 1998; ftp://ncbi.nlm.nih.gov/genbank/gbrel.txt) and updates it daily. It also shares data on a daily basis with the DNA Data Bank of Japan (DDBJ; Tateno et al., 1998) and the European Molecular Biology Laboratory (EMBL; Stoesser et al., 1998). A search of the NCBI databases using the BLAST Web page, client, or e-mail server guarantees access to the most recent database. The NCBI supports a number of databases for sequence similarity searching. These databases are subject to change, and a current list and description are available on the NCBI Web site at http://www.ncbi.nlm.nih.gov/BLAST/blast_databases.html. This section describes some of the commonly used NCBI databases. Examples demonstrating the utility of various BLAST programs and NCBI databases are given in the next section. Peptide sequence databases for BLASTP and BLASTX nr. The nr (nonredundant) database is the most comprehensive. GenBank, DDBJ, and EMBL are nucleotide sequence databases. If any nucleotide sequence in any of the databases is annotated with a coding sequence (CDS), this CDS appears in nr. nr also contains protein sequences obtained from PDB (sequences associated with 3-dimensional structures in the Brookhaven Protein Data Bank), Swiss-Prot (a curated database of protein sequences; Bairoch and Apweiler, 1998), PIR (Protein Identification Resource, a comprehensive collection of protein sequences; Barker et al., 1998), and PRF (Protein Research Foundation). Although nr may contain multiple copies of similar sequences, identical sequences are merged into one entry. To be merged, two sequences must have identical lengths and every residue at every position must be the same. There are nr databases for both peptide and nucleotide sequences. The peptide database is automatically selected for BLASTP and BLASTX searches.

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month. The month database receives its sequences from the same sources as nr, but contains only those sequences released within the last 30 days. All sequences in month are also present in nr. Alu. The Alu database contains six-frame translations of representative Alu repeats from all Alu subfamilies (Claverie and Makalowski, 1994). If a query sequence containing an Alu repeat is used in a BLAST search of nr or month, many of the resulting high-scoring hits will also contain Alu sequences. It may be useful, especially with a genomic sequence query, to perform a search of the Alu database to identify the location of any Alu repeats that might produce high-scoring and potentially misleading hits in queries of other databases. Nucleotide sequence databases for BLASTN, TBLASTN, and TBLASTX nr. The nr (nonredundant) database contains all nucleotide sequences present in GenBank, EMBL, and DDBJ. It also contains nucleotide sequences obtained from PDB (sequences associated with 3-dimensional structures in the Brookhaven Protein Data Bank). nr comprises only sequences that are normally well annotated, so it does not contain expressed sequence tag (EST), sequence-tagged site (STS), genome survey sequence (GSS), or high-throughput genomic (HTG) sequences. Although nr may contain multiple copies of similar sequences, identical sequences are merged into one entry. To be merged, two sequences must have identical lengths and every nucleotide at every position must be the same. month. The month database contains all nucleotide sequences present in GenBank, EMBL, DDBJ, and PDB that were released within the last 30 days. Unlike nr, it also contains EST, STS, GSS, and HTG sequences released within the last month. EST. EST accesses a nonredundant copy of all ESTs present in GenBank, EMBL, and DDBJ (Boguski et al., 1993). ESTs are short sequences, a few hundred nucleotides in length, which are derived by partial, single-pass sequencing of inserts of randomly selected cDNA clones (Adams et al., 1991). Since the number of ESTs is increasing rapidly, it is an important database to search for novel cDNAs. As of August, 1998, ∼70% of the sequences in GenBank were ESTs; of these, 61% were from human, 20% from mouse. STS. STS contains a nonredundant copy of all STSs present in GenBank, EMBL, and DDBJ. An STS is a short unique genomic sequence that is used as a sequence landmark for genomic mapping efforts (Olson et al., 1989). As of August, 1998, 83% of the sequences in the STS database were from human.

Sequence Similarity Searching Using BLAST

HTGS. HTGS contains “unfinished” DNA sequences generated by the high-throughput sequencing centers (Ouellette and Boguski, 1997). A typical HTG record might consist of all the first-pass sequence data generated from a single cosmid, BAC, YAC, or P1 clone. The record is composed of two or more sequence fragments that have a total length of ≥2 kb and contain one or more gaps. The sequences are normally updated by the sequencing centers as more data become available. A single accession number is assigned to this collection of sequences. The accession number does not change as the record is updated, and only the most recent version of the record remains in GenBank. Phase 1 HTG sequences are unordered, unoriented contigs with gaps. Phase 2 HTG sequences are ordered, oriented contigs with or without gaps. All HTG records contain a prominent warning that the sequence data is unfinished and may contain errors. When a record is considered finished, it becomes a Phase 3 HTG and is moved to the nr database with the same accession number. HTGS is a valuable source of new genomic sequences not yet in nr.

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GSS. GSS includes short, single-pass genomic data identified by various means (Smith et al., 1994). Many of the sequences have been mapped. As of August, 1998, 80% of the sequences in GSS were from human, 14% from Arabidopsis thaliana. Alu.The Alu database contains representative Alu repeats from all Alu subfamilies (Claverie and Makalowski, 1994). If a query sequence containing an Alu repeat is used in a BLAST search of the above nucleotide databases, many of the resulting high-scoring hits will also contain Alu sequences. It may be useful, especially with a genomic sequence query, to perform a search of the Alu database to identify the location of any Alu repeats that might produce high-scoring and potentially misleading hits in queries of other databases. Vector. The Vector database contains nucleotide sequences of a number of standard cloning vectors. New sequences should be screened against the Vector database to assure that they do not contain any vector contamination. Mito. The Mito database contains representative mitochondrial sequences from many families. Nuclear-derived sequences may be screened against the Mito database to assure that they do not contain any mitochondrial contamination. Formatting the Query Sequence Users of the BLAST Web page can initiate a search either by entering the sequence itself, or, if the sequence is already in the sequence database, by entering the accession number or gi (see Appendix B at the end of this unit). The preferred format for entering new sequences is the so-called FASTA format; however, if the sequence is not in FASTA format or the sequence is interspersed with numbers and spaces, BLAST will still accept the query. A sequence in FASTA format begins with a single-line description, followed by lines of sequence data. The description line is distinguished from the sequence data by a > symbol (greater than) in the first column. An example sequence in FASTA format is: >aaseq Human choroideremia protein MADNLPTEFDVVIIGTGLPESILAAACSRSGQRVLHIDSRSYYGGNWASFSFSGLLSWLKEYQQNNDIGE ESTVVWQDLIHETEEAITLRKKDETIQHTEAFPYASQDMEDNVEEIGALQKNPSLGVSNTFTEVLDSALP EESQLSYFNSDEMPAKHTQKSDTEISLEVTDVEESVEKEKYCGDKTCMHTVSDKDGDKDESKSTVEDKAD EPIRNRITYSQIVKEGRRFNIDLVSKLLYSQGLLIDLLIKSDVSRYVEFKNVTRILAFREGKVEQVPCSR ADVFNSKELTMVEKRMLMKFLTFCLEYEQHPDEYQAFRQCSFSEYLKTKKLTPNLQHFVLHSIAMTSESS CTTIDGLNATKNFLQCLGRFGNTPFLFPLYGQGEIPQGFCRMCAVFGGIYCLRHKVQCFVVDKESGRCKA IIDHFGQRINAKYFIVEDSYLSEETCSNVQYKQISRAVLITDQSILKTDLDQQTSILIVPPAEPGACAVR VTELCSSTMTCMKDTYLVHLTCSSSKTAREDLESVVKKLFTPYTETEINEEELTKPRLLWALYFNMRDSS GISRSSYNGLPSNVYVCSGPDCGLGNEHAVKQAETLFQEIFPTEEFCPPPPNPEDIIFDGDDKQPEAPGT NNVVMAKLESSEESKNLESPEKHLQN

Alternatively, the user can enter the database accession number or gi. The above sequence is in fact already in the database with a gi identifier of 116365 and a Swiss-Prot accession number of P26374. Either of these sequence identifiers can be entered on the BLAST page. The type of identifier (for nucleotide or protein sequence) must match the type of query sequence used in that search (Ostell and Kans, 1998; Ouellette, 1998). Sequences in manuscripts are often referred to by their GenBank accession number; however, this accession number refers to a nucleotide (not protein) sequence, and cannot be used to initiate a BLASTP or TBLASTN search, both of which require a protein sequence query. The identifier of the protein sequence encoded by a nucleotide accession number can be obtained by searching the NCBI’s Entrez nucleotide database, at http://www.ncbi.nlm. nih.gov/Entrez. Computational Analysis

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Filtering Sequences Both nucleotide and protein sequences may contain regions of low complexity, i.e., regions with homopolymeric tracts, short-period repeats, or segments enriched in one or only a few residues. Such low-complexity regions commonly give spuriously high BLAST scores that reflect compositional bias rather than significant position-by-position alignment (Altschul et al., 1994). For example, two protein sequences that contain low-complexity regions rich in the same amino acids may produce high-scoring alignments in those regions even though other parts of the proteins are entirely dissimilar. Because these alignments do not reflect a common ancestry, no functional inference is justified despite their high statistical significance. Filtering the query sequence (that is, replacing the repeated sequence with strings of n for nucleotide sequence or X for protein sequence) can eliminate potentially confounding matches, such as hits to low-complexity, proline-rich regions or poly(A) tails present in the database. By default, all searches performed through the NCBI BLAST Web page automatically filter the query sequence, as do the BLAST clients, e-mail server, and stand-alone programs. However, filtering can be turned off, even on the Basic BLAST page. Filtered sequence is represented in the final BLAST report as a string of n or X (e.g., nnnnnnnnnn or XXXXXXXXX). BLASTN queries are filtered with DUST (R.L. Tatusov and D.J. Lipman, pers. comm.). Other BLAST queries use SEG (Wootton and Federhen, 1993, 1996). Viewing the BLAST Results The NCBI BLAST Web page can return results in one of three ways. The default is to display the results in the browser window from which the user initiated the search. The document will have hypertext links that make it easier to analyze the results. BLAST queries are processed in the order in which they are received, except that less computationally intensive jobs (e.g., BLASTN, BLASTP, and smaller databases such as month) are given priority. In the middle of the afternoon, the BLAST server may be busy. Thus, it is sometimes more efficient to receive the BLAST results by e-mail. E-mail results are sent either as plain text or in HTML format. The HTML-formatted results must be opened in a Web browser, and the resulting document contains hypertext links. EXAMPLES OF BLAST SEARCHES In this section, interpretation of BLAST results is explained using examples for BLASTP, BLASTX, TBLASTN, BLASTN, and PSI-BLAST. An example is not shown for TBLASTX, as it is a tool of last resort and not useful for the majority of users. The first example, for BLASTP, contains general information, and should be read by all BLAST users. GenBank increases in size by thousands of sequences every week; thus, results obtained from running the same searches again will differ from those shown in the examples. BLASTP The BLASTP program compares a protein query to a protein database. A typical search is shown in Figure 2.5.1. The Swiss-Prot database has been selected and the program has been changed to BLASTP. A query in FASTA format has been entered in the input box. The query is the human choroideremia protein, implicated in hereditary blindness (Seabra et al., 1993). Sequence Similarity Searching Using BLAST

The top of the results page for this search is shown in Figure 2.5.2. It begins with some header information about the type of program (BLASTP), the version (2.0.5), and a release

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Figure 2.5.1 Submitting a BLASTP search using the NCBI’s World Wide Web interface.

Figure 2.5.2 Example of the top portion of a BLASTP report.

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Figure 2.5.3 Example of the graphical view of a BLASTP report. The bars are color coded by the strength of the database match. The strongest matches (those with a bit score >200) are red, followed by pink (bit score 80 to 200), green (50 to 80), blue (40 to 50), and black (<40).

date. The version and release date will change as the program is updated. Also listed are a reference, the query definition line, and a summary of the database used. Figure 2.5.3 presents a graphical overview of the results. The database hits are shown aligned to the query, which is the numbered bar at the top. The next three bars show high-scoring database matches that align to the query sequence throughout its length. The next nine bars show lower-scoring matches that align to two regions of the query—one from residue 3 or 4 of the query up to about residue 60, and the other from about residues 220 to 500. For these nine bars the solid segments (on the left and right) indicate aligned regions; the cross-hatched segment indicates that the two alignments (i.e., the solid portions) are from the same database sequence, but that there is no alignment where the bar is cross-hatched. If there is no cross-hatching between two or more bars in the same row (i.e., the thirteenth row), this denotes different alignments with unrelated database sequences. These bars are presented on the same line to conserve space in the graphical view. Moving the cursor over a bar in the graphic (“mousing over”) causes the identifier and definition line of the database match to be shown in the window. If the results of a BLAST search are sent by e-mail, this graphical view is not included.

Sequence Similarity Searching Using BLAST

The hit list produced by the BLASTP search is shown in Figure 2.5.4. Each line of the hit list is composed of four fields. The first field contains the database designation, accession number, and locus name for the matched sequence, separated by vertical bars (see Appendix B at the end of this unit for more information). The second field contains a brief textual description of the sequence, the definition line. The content of the definition line varies between and within databases, but usually includes information on the organism from which the sequence was derived, the type of sequence (e.g., mRNA or DNA), and some information about function or phenotype. The definition line is often truncated in the hit list to keep the display compact, but is fully displayed in association with the local alignments (see below). The third field contains the alignment score in bits. Higher scoring hits are found at the top of the list. In very general terms, this score is calculated from a formula that takes into account the alignment of similar or identical

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Figure 2.5.4 Example of the hit list from a BLASTP report.

residues, as well as any gaps that must be introduced in order to align the sequences. A key element in this calculation is the “substitution matrix,” which assigns a score for aligning any possible pair of residues. The BLOSUM-62 matrix is the default for BLAST, and it works well for most searches. The fourth field contains the Expect (E) value, which provides an estimate of statistical significance. E values reflect how many times one expects to see such a score occur by chance. A statistician would consider an E value <0.05 to be significant. However, an inspection of the alignments (see below) is required to determine biological significance. The maximum number of hits displayed in the hit list is set at a default value of 500. This number can be changed on the advanced BLAST page with the descriptions option. For the first hit in the list, the database designation is sp (for Swiss-Prot), the accession number is P26374, the locus name is RAE2_HUMAN, the definition line begins RAB PROTEINS, the score is 1223, and the E value is 0.0. The higher the score and the lower the E value, the more statistically significant is the hit. Note that the first thirteen hits all have very low E values (<10−14) and are either RAB proteins or GDP dissociation inhibitors. The next eighteen database matches have much higher E values (≥0.35), meaning that roughly one match with that score would be expected by chance. Furthermore, the definition lines no longer show the same consistency. Given the combination of inconsistent definition lines and high E values, caution should be used in assuming an evolutionary relationship between the query and the eighteen lower-scoring database hits. It would be necessary to examine the alignments in order to make a final determination. The pairwise alignments between the query and the database hits are displayed below the hit list. Figure 2.5.5 shows one sequence alignment between the query sequence and a

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Figure 2.5.5 Example of a BLASTP alignment.

Sequence Similarity Searching Using BLAST

database hit; other alignments are omitted for brevity. The alignment is preceded by the sequence identifier, the full definition line, and the length, in amino acids, of the database sequence. Next comes the score of the match in bits (the raw score is in parentheses), the Expect value (E value) of the hit, the number of identical residues in the alignment (identities), the number of conservative substitutions (positives) according to the scoring system (e.g., BLOSUM-62), and, if applicable, the number of gaps in the alignment. Finally the actual alignment is shown, with the query on top and the database match labeled as subject (Sbjct). Since both the query and the match are amino acid sequences, the numbers at left and right refer to the position in the amino acid sequence. The center line between the two sequences indicates the similarities between the sequences. If the residue is identical between the query and the subject, the residue itself is shown; conservative substitutions, as judged by the substitution matrix, are indicated with a + sign. One or more dashes (−) within a sequence indicates insertions or deletions. Query residues masked for low complexity are replaced by Xs (see fourth and eleventh blocks, Fig. 2.5.5). By default, a maximum of 500 pairwise alignments are displayed. This number can be changed on the advanced BLAST page with the alignments option.

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Results generated on the NCBI BLAST Web page contain text hyperlinks that assist the user in navigating around the page. Clicking on a colored bar in the graphical view (Fig. 2.5.3) moves the user to the sequence alignment (Fig. 2.5.5). The database sequence identifiers, visible both in the one-line descriptions of the database matches (Fig. 2.5.4) and in the sequence alignment (Fig. 2.5.5), link to the Entrez record that describes the sequence. Entrez may provide more information about the sequence, including links to relevant publication abstracts in PubMed. The score (to the right of the one-line description; Fig. 2.5.4) provides a link within the page to the alignment between that hit and the query. BLASTX BLASTX searches involve a six-frame translation of a nucleotide sequence queried against a protein database (Gish and States, 1993). Researchers most often use a BLASTX search if they have sequenced a DNA molecule and do not know the reading frame of the coding sequence, the beginning or end of the open reading frame, or the function of the encoded protein. It is also useful for analyzing preliminary sequence data containing potential sequencing or frameshift errors, such as ESTs, HTGs, or GSSs. A BLASTX search of the nr protein database can quickly highlight any similarities between all the open reading frames in a nucleotide sequence and any characterized proteins. In the example illustrated here, the mRNA sequence encoding human ataxia telangiectasia (ATM; accession number U33841; Lavin and Shiloh, 1997) was queried against the nr protein database. The output is similar to that from BLASTP. At the top (not shown) is information about the search, including the query, database, and type of BLAST program used. Next is the graphical overview representing the alignment of the database hits to the query sequence (Fig. 2.5.6), followed by a list of the hits, including their definitions, scores, and E values (Fig. 2.5.7 shows the top portion of the list). Pairwise alignments between the query and hit sequences follow (selected alignments are shown in Fig. 2.5.8). The top-scoring seven hits, which have scores >2600, are to other orthologs of the ATM gene (Fig. 2.5.7). Most of these hits align with the query sequence over its entire length (in Fig. 2.5.6, the first five bars span the entire length of the query sequence; note also the longer bars in the sixth and seventh lines). The 3′ end of the ATM mRNA (carboxy terminal end of the resulting protein translation), aligns with the catalytic domain of a number of phosphatidylinositol 3-kinases or similar proteins—such as the yeast TEL1 gene (a phosphatidylinositol kinase involved in controlling telomere length), the yeast ESR1 gene (a putative phosphatidylinositol kinase required for mitotic cell growth, DNA repair, and meiotic recombination), and the yeast TOR1 protein (a phosphatidylinositol kinase required for cell cycle progression). The alignment with these proteins starts in different places depending on the protein; one group starts around nucleotide 4700 of ATM (lines 9, 10, and 15 in Fig. 2.5.6), and other groups start around nucleotide 5800 (lines 17 to 19), 6800 (lines 7, 8, and 12 to 14), and 8000 (lines 20 to 60). Such similarities have provided insight into the role of ATM in response to DNA damage and cell cycle control (Lavin and Shiloh, 1997). The ATM gene also shares similarity with viral capsid proteins around nucleotides 2500 and 6000 (short bars on lines 6 to 14). However, these hits have low scores and high E values (not shown) and are not likely to be significant. The hits to the orthologs of ATM reveal the location of the coding sequence within the query nucleotide sequence. In a BLASTX output, position in the query sequence (the top line of a pairwise alignment) is described as a nucleotide position, while the position in the database hit (the bottom line of the alignment) is described as an amino acid position (Fig. 2.5.8). For example, amino acids 1 to 3066 of the mouse ortholog of ATM, gi 1469394, align with the translation of nucleotide positions 190 through 9357 of the query

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Figure 2.5.6 Example of the graphical view of a BLASTX report.

sequence. The exact location of the ATM coding sequence cannot be easily determined from the hits to the yeast sequences, as the similarity does not extend throughout the length of the protein (Fig. 2.5.8, bottom). However, the hits to the yeast sequences do provide confirmation about the phase of the reading frame, and possibly the location of specific domains conserved between humans and yeast. TBLASTN

Sequence Similarity Searching Using BLAST

TBLASTN searches involve a protein sequence queried against the six-frame translation of a nucleotide sequence database. TBLASTN searches are especially useful for finding novel similarities to a protein sequence in the open reading frames from uncharacterized, sometimes lower-quality nucleotide sequences, such as those found in EST, STS, HTGS, and GSS databases. The translations of these sequences are not present in nr because the sequences are not annotated. The most common use of TBLASTN is to search for ESTs that are similar to a protein of interest. These ESTs might represent orthologs of the protein from a different species, or just additional members of a gene family.

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Figure 2.5.7 Example of the hit list from a BLASTX report.

The following example shows a TBLASTN search of HTGS, a database that contains unfinished and uncharacterized genomic sequences generated by genome sequencing centers. Since HTGs are genomic and often contain introns, the results can be more difficult to interpret than those from an EST search. However, HTGs can also provide insight into the chromosomal organization of genes. In this case, the six-frame translation of HTGS is searched with the amino acid sequence of the disintegrin-like domain of mouse ADAM 1 (Wolfsberg et al., 1995). TBLASTN output is similar to that of BLASTP and BLASTX. Figure 2.5.9 shows the graphical overview of the alignment, Figure 2.5.10 shows the list of database hits, and Figure 2.5.11

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Figure 2.5.8 Example of selected BLASTX alignments.

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Figure 2.5.9 Example of the graphical view of a TBLASTN report.

Figure 2.5.10 Example of the hit list from a TBLASTN report.

shows selected pairwise alignments between the query sequence and the hits. For TBLASTN searches, the top line (Query) of the alignment contains protein sequence, and the location refers to amino acids. The bottom line (Sbjct) of the alignment contains a translated nucleotide sequence, and the location refers to the nucleotide location. Disintegrin-like domains contain a characteristic pattern of cysteines, so it is usually easy to identify the true hits by checking for conservation in the number of cysteine residues, and the spacing between them. The first hit, a 627-kb C. elegans clone, aligns with the

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Figure 2.5.11 Example of a TBLASTN alignment.

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Figure 2.5.12 Example of the graphical view of a BLASTN report.

query sequence in two regions. Both alignments encompass the full length of the query sequence, and both contain a characteristic alignment of cysteine residues. The first region (the first aligned block) comprises nucleotides 322651-322911 of the C. elegans clone. The second region consists of the second and third alignment blocks. The first 55 amino acids of the disintegrin domain align with nucleotides 102880-103107 of the clone, and the last 25 amino acids align with nucleotides 103155-103232 of the clone. Thus, this C. elegans clone appears to encode two distinct disintegrin-like domains, and these two sequences are likely linked in the C. elegans genome. The region between nucleotides 103107-103155 of the clone may contain an intron. The second database hit is to a shorter C. elegans clone that, from the sequence of its encoded disintegrin domain, appears to contain some of the same sequence as the first C. elegans clone. The third database hit is to a human clone. As amino acids 1 to 16 of the query aligns with nucleotides 53178 to 53225 of the hit, and amino acids 24 to 49 align with nucleotides 53474 to 53551, the human sequence may contain an intron between nucleotides 53225 and 53474. There is a likely second intron between nucleotides 53551 and 57012. BLASTN Figure 2.5.12 presents the graphical overview of a BLASTN search of accession number U93237 against the human EST database. U93237 is the 9.2-kb genomic sequence of the human menin (MEN1) gene for multiple endocrine neoplasia type I. This example demonstrates the power of a BLAST search to confirm exons in a genomic sequence, as well as the danger of accepting results without further investigation. The matches on the

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Figure 2.5.13 Example of the hit list from a BLASTN report.

right half of the diagram (base pairs 4000 to 9000) confirm the locations of exons 2 to 10 of the MEN1 gene (Chandrasekharappa et al., 1997; Zhang and Madden, 1997). The large number of matches on the left side of the figure (base pairs 500 to 1000 and 3000 to 3600) appear at first glance to be interesting matches, but are actually caused by the presence of Alu elements in the query and EST sequences. One can determine this by checking the definition lines of the matches in this region, as many of these ESTs are annotated as being similar to Alu elements. It is important to remember, though, that the definition line summaries (e.g., Fig. 2.5.13) contain truncated definition lines, and that it is necessary to look at the full definition line (provided in the alignment section, Fig. 2.5.14) to see the Alu annotation. One can also perform a BLASTN search of U93237 against the Alu database. Fig. 2.5.13 shows the top portion of the list of database hits for the BLASTN search of U93237 against human ESTs. Fig. 2.5.14 presents one alignment of the query sequence with an EST containing an Alu repeat. Vertical bars connect bases that are identical in the query and subject. Since both the query and the subject are nucleotide sequences, the numbers shown refer to nucleotide sequence locations. Nucleotides in the query sequence that have been masked for low complexity are replaced by the letter n (e.g., nnnnnnn in the third block). BLASTN checks the plus and minus strand of the query against the plus strand of the database sequence. The alignment shown here presents the plus strand of the query to the minus strand of the database sequence, which is equivalent to showing the minus strand of the query against the plus strand of the database sequence.

Sequence Similarity Searching Using BLAST

BLASTN is optimized for speed, not sensitivity. The algorithm uses a simple system to assess the alignments; matches are given a positive score, mismatches a negative score. BLASTN should only be used when a direct comparison of nucleotide sequences is desired. BLASTN results should not be used to make any predictions about the functions of the encoded proteins. Generally, searches that involve protein comparisons are more sensitive, owing to the larger alphabet (twenty residues versus four nucleotides) and the

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Figure 2.5.14 Example of a BLASTN alignment.

degeneracy of the genetic code (two codons, with different third bases often coding for the same protein). The more sophisticated scoring system used with protein comparisons also takes into account similarities between different residues. PSI-BLAST A major new feature of the BLAST version 2.0 is the iterative search. Position-specific iterated BLAST (PSI-BLAST) first performs a normal BLASTP run, but then internally computes a profile using the most significant hits (Altschul et al., 1997). A profile may be understood as a table that lists the probability of finding a residue at each position in a conserved protein domain. Conserved regions between the query and the most-significant matches are used to compute the profile. These regions are presumably important to the function of the protein. The profile is then used to perform another search (or iteration), at which point the new hits are also added into the profile. The cycle may then be repeated, with a new profile computed from the results of the last iteration. Each PSI-BLAST iteration takes into account the most significant sequences found in the previous round (i.e., those below the threshold for inclusion in the profile). The algorithm is more sensitive than BLASTP, and more likely to lead to the discovery of distantly related sequences. At present, such iterative searching is only available with the BLASTP program. The PSI-BLAST output is different from the other programs. Figure 2.5.15 shows the one-line summaries from the first PSI-BLAST iteration (i.e., the first search using the profile) of a mouse adenosine deaminase (Swiss-Prot accession P03958) against the nr database. The one-line summaries are divided into two groups, those with an E value better (lower) than that used to build the profile (Fig. 2.5.15; the default value is 0.001), and those with a worse E value (not shown). Matches better than the threshold, which will be included in the profile, are checked. The user may check (or uncheck) matches, causing them to be included (or excluded) from the profile used in the next iteration. Matches found on a previous iteration are marked with a green ball. New sequences are

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Figure 2.5.15 Example of the hit list from a PSI-BLAST report.

marked with a yellow “new”. The page also alerts the user when convergence is achieved (i.e., no new sequences below the threshold for inclusion in the profile were found in that iteration). In the hit list in Figure 2.5.15, the first iteration of PSI-BLAST finds many adenosine deaminase sequences (marked with a green ball) found in the previous (standard) BLASTP search. In addition, PSI-BLAST identifies a number of AMP deaminase sequences (marked with “new”). Indeed, there is a well-known homology between adenosine deaminase and AMP deaminase (Chang et al., 1991; Holm and Sander, 1997) that would not normally be found by BLASTP, but is found by PSI-BLAST. Sequence Similarity Searching Using BLAST

If one wants to report the E value for a hit found with a PSI-BLAST search, one should use the E value calculated during the first iteration that the match is below the threshold

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for inclusion in the profile (i.e., when the hit is marked with a “new” flag). However, if the hit is found in the first BLASTP run, then the reported E value should be from this run. In either case, in subsequent iterations, the hit itself is used to compute the profile, and the reported E value is not representative of the true value. SEARCHING STRATEGIES Filtering for Low-Complexity Regions Low-complexity regions tend to result in misleading database search results. The residue frequencies within such regions differ dramatically from the database as a whole, disrupting the statistics used by BLAST. This can produce alignments that are based purely on compositional bias rather than a significant position-by-position alignment. By default, low-complexity sequences are filtered out of query sequences and replaced by strings of n (nucleotide) or X (protein). A BLASTP search of a proline-rich, DNA-directed RNA polymerase (Swiss-Prot accession P11414) against the nr database illustrates the differences in output resulting from a filtered or unfiltered query sequence. Twenty four database matches with an E value better than 10 are found if filtering is enabled; 4873 are found if filtering is disabled. The top-scoring database matches for the unfiltered search are shown in Figure 2.5.16; high-scoring matches that are not found in a filtered search are marked. Note that the definition lines of most of the new matches indicate that the sequences are proline-rich, like the query, but do not provide information about the function of the protein. Other hits to RNA polymerases, found near the top of the list in a filtered search, are buried much deeper in the list of matches. The match to “sp|P14248,” apparently a good match based on the definition line, does not appear in a filtered BLAST search. An examination of the alignment (not shown) indicates that the match is based on solely on proline-rich regions. Reporting BLAST Results The most reliable indicator of the importance of a BLAST alignment is the Expect (E) value (the number of chance database matches one expects to see at the same score). This number takes into account the length of the query and size of the database as well as the scoring system. Neither the bit nor the raw score is a reliable indicator, as their significance cannot be judged independently of the information used to calculate the E value. Normal intuition fails when one is faced with databases of the current size (e.g., the EST database contains ∼700 million base pairs at this time), as chance alignments are likely and it is impossible for a user to know at what score matches become significant. Statistical significance also varies from database to database (e.g., a hit with a certain score may be statistically significant in a search of the relatively small month database, but not in a search of nr), so that reporting a certain score without the context of the database size can be misleading. The percentage identity is also a poor indicator of statistical significance for the same reasons; additionally, there is really no way to estimate an E value or even a score from a percentage identity, making it impossible to even guess whether a hit could be due to chance or not. Analyzing mRNA Sequences 1. Identify the open reading frame to obtain the protein sequence. Perform a BLASTX search of the cDNA sequence against the nr protein database. Any resulting hits may identify the correct reading frame for the protein sequence and provide information about its function. Computational Analysis

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Figure 2.5.16 Example of a hit list from a BLASTP report in which the query sequence was not filtered. Black squares, added manually by the authors, indicate hits that would not appear if the query had been filtered.

2. Determine whether the protein sequence is similar to that of any previously identified proteins. Perform a BLASTP and a PSI-BLAST (iterative) search against the nr protein database using the protein sequence as a query. 3. Determine whether the protein sequence is similar to the translation products of any uncharacterized DNA sequences whose translations are not in nr. Perform a TBLASTN search of the EST, STS, GSS, and HTGS databases. Sequence Similarity Searching Using BLAST

4. After the initial search, perform a monthly search of the month nucleotide and protein sequence databases to provide information about newly added sequences. A BLASTP

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search of the month protein database will reveal newly characterized protein sequences. A TBLASTN search of the month nucleotide database will reveal new open reading frames in nr, EST, HTGS, STS, and GSS. PSI-BLAST searches of nr should also be conducted occasionally, as new sequences may be added to the profile that will, in turn, help identify more distantly related sequences. 5. If the protein sequence contains multiple functional domains, it may be useful to perform the searches with each of these domains individually. Analyzing Genomic DNA Sequences 1. Identify a potential transcribed segment. Perform a BLASTN search of the nucleotide sequence against the DNA sequences in the EST database to determine if any mRNAs are derived from the genomic sequence. EST hits will highlight the location of exons. A BLASTN search against nr may identify transcripts or previously characterized genomic segments. 2. Identify potential open reading frames. Perform a BLASTX search against the nr protein database. Any resulting hits may identify the correct reading frame of a protein sequence and provide information about its function. If no results are found with the previous methods, a TBLASTX search (translated nucleotide query versus a translated nucleotide database) against EST, GSS, HTGS, and STS may identify other open reading frames. 3. After the initial search, a monthly search of the month nucleotide (with BLASTN) and protein (with BLASTX) sequence databases will provide information about newly added sequences. 4. Any potential transcribed sequences can be analyzed as per the above instructions for mRNA sequences. Searching Short Sequences Short sequences can only produce short alignments. Such short alignments need to be relatively strong (i.e., have a high percentage of matching residues) to rise above the background noise. Short, but strong, alignments are more easily detected using a matrix with a higher relative entropy than that of the default BLOSUM-62 (Altschul, 1991). Relative entropy is basically the average information available per position to distinguish the alignment from chance. The BLOSUM series of matrices does not include any that are suitable for the shortest queries, so it is recommended to use the PAM matrices instead (Dayhoff et al., 1978; Schwartz and Dayhoff, 1978). These matrices may be chosen from a menu on the Advanced BLAST page. Suggested matrices for different query lengths are shown in Table 2.5.2 (S.F. Altschul, pers. comm.). Table 2.5.2 Substitution Matrices for Short Sequences

Query length (amino acids)

Matrix

<35 35-50 50-85 >85

PAM-30 PAM-70 BLOSUM-80 BLOSUM-62 Computational Analysis

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SEQUENCE ALIGNMENT ALGORITHMS There are two types of pairwise sequence alignments, global and local. Global strategies attempt to align along the entire length of both sequences, while local alignments focus on sequence similarity over shorter regions. Historically, local alignments have been very successful at identifying similarities between proteins. Subsequences of similarity, surrounded by unrelated sequences, can identify, for example, active sites that are conserved between proteins or exons in genomic sequence. Smith and Waterman (1981) were the first to develop a local alignment algorithm. However, such rigorous alignment algorithms are often quite slow and impractical for searching current databases on a routine basis. Heuristic algorithms implement certain shortcuts to allow a comparison against an entire database within a reasonable time. They are not exhaustive searches in the sense that they do not explore possibilities that seem unlikely to be interesting. Some programs allow the user to adjust parameters that increase the sensitivity of the search (i.e., explore more possibilities), but this must be balanced against the slower speed of the search. Studies have shown that heuristic algorithms, with default settings, approach the sensitivity of exhaustive searches (Altschul et al., 1997). BLAST (Altschul et al., 1990, 1997) and FASTA (Pearson, 1990; Pearson and Lipman, 1988) are two popular heuristic programs. The rest of this discussion will focus on the newest version (2.0) of BLAST. BLAST calculates a statistical significance for every alignment it produces. Significance is expressed in terms of the E value, which is the number of matches with a given score (the calculation of the score is described below) that one would expect by chance. The E value depends on the size of the database and the length of the query, as increasing either of these parameters increases the number of chance matches. Calculation of the E value is performed with Karlin-Altschul statistics (Karlin and Altschul, 1990, 1993). The raw score of an alignment is calculated with a scoring system, which is a table of residue substitution scores and penalties for the existence and extension of a gap. For normal protein/protein comparisons, a matrix (i.e., table of residue substitution scores) provides a score for each possible alignment of two amino acids. For example, L aligned with I gets a positive score, while L aligned with E gets a negative score. BLOSUM-62 is the default matrix, and experimentation has shown that it is among the best for detecting weak protein similarities (Henikoff and Henikoff, 1992). PSI-BLAST uses a table that lists the frequency of finding a certain residue at each position in a conserved protein domain. This table is calculated “on the fly” for each iteration from the best matches to the database. BLASTN uses a simple scheme where matches have a positive score and mismatches have a negative score. Gaps are opened or extended when one sequence is longer than another over a certain region. BLAST charges the score −a for the existence of a gap and −b for each letter in a gap, so that a gap of length k letters is penalized −(a + bk). Gap costs of this form are known as affine gap costs. These gap costs may be changed by the user. The scoring system determines the raw score of an alignment. A bit score can be calculated from the raw score using the following formula: Sbit = [λ × Sraw − ln(K)]/ln(2)

Sequence Similarity Searching Using BLAST

where λ and K are Karlin-Altschul parameters that depend on the scoring system. The statistical significance corresponding to the resulting bit score is independent of the scoring system used. This allows one to compare the bit scores obtained from two different BLAST runs, each performed using different matrices or gap extension and existence values. Nevertheless, it is more meaningful to compare the results of different BLAST

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runs using their E values rather than their bit scores, as the E value takes into account the size of the database. A word-based approach is used to find matches in BLAST. An ungapped search, with BLASTN, may be used to demonstrate the concept of a word. First a list is made of all the subsequences in the nucleic acid query sequence that are of a certain length (called the word size), and the positions are recorded. The database is then scanned, looking for words that are in the list. If a match is found between a query and a database word, the alignment is extended (without allowing gaps) until the corresponding score drops a certain amount below the maximum found, at which point that maximum score (and the corresponding alignment) is used. The aligned region is a local alignment. With BLASTN, only exact matches are extended and the default word size is 11 bp. The procedure is more complicated for the other programs (BLASTP, BLASTX, TBLASTN, and TBLASTX). Exact word matches are not required and the default word size is three residues. A match between a query word and a database word is based on the scoring matrix, which specifies whether two residues result in a positive, negative, or neutral match. If three residues in a query word are compared with three residues in a database sequence, and the score is above a certain threshold, this word warrants further consideration. The default behavior in BLAST version 2.0 is that two words (on the query) must match with two words (on the database sequence) within a window of 40 residues before the match is extended, as described for BLASTN. The effect of looking for two word matches is to eliminate random matches, increasing the speed of BLAST by a factor of three. Ungapped alignments are used as a starting point for the gapped alignments that BLAST produces. The eleven highest-scoring letters are determined and the center letter is used as the start of the gapped alignment. The alignment is then extended, with gaps allowed. This extension is not exhaustive; if the score drops by more than a certain amount from the maximum found so far, that maximum (and the corresponding alignment) is used (Altschul et al., 1997; Zhang et al., 1998). The default BLAST parameters are tuned for a balance of speed and sensitivity. It is possible that significant matches may be missed, especially if small databases are searched. In that case, it may be advisable to use a shorter word size for BLASTN or a lower-threshold word for the other programs. It is important to remember that BLAST calculates similarity. Homology is an evolutionary relationship that must be proved by further analysis. LITERATURE CITED Adams, M.D., Kelley, J.M., Gocayne, J.D., Dubnick, M., Polymeropoulos, M.H., Xiao, H., Merril, C.R., Wu, A., Olde, B., Moreno, R.F., et al. 1991. Complementary DNA sequencing: Expressed sequence tags and human genome project. Science 252:1651-1656. Altschul, S.F. 1991. Amino acid substitution matrices from an information theoretic perspective. J. Mol. Biol. 219:555-565. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J. 1990. Basic local alignment search tool. J. Mol. Biol. 215:403-410. Altschul, S.F., Boguski, M.S., Gish, W., and Wootton, J.C. 1994. Issues in searching molecular sequence databases. Nature Genet. 6:119-129. Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. 1997. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucl. Acids Res. 25:3389-3402. Bairoch, A. and Apweiler, R. 1998. The SWISS-PROT protein sequence data bank and its supplement TrEMBL in 1998. Nucl. Acids Res. 26:38-42.

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Barker, W.C., Garavelli, J.S., Haft, D.H., Hunt, L.T., Marzec, C.R., Orcutt, B.C., Srinivasarao, G.Y., Yeh, L.S.L., Ledley, R.S., Mewes, H.W., Pfeiffer, F., and Tsugita, A. 1998. The PIR-International Protein Sequence Database. Nucl. Acids Res. 26:27-32. Benson, D.A., Boguski, M.S., Lipman, D.J., Ostell, J., and Ouellette, B.F. 1998. GenBank. Nucl. Acids Res. 26:1-7. Boguski, M.S., Lowe, T.M., and Tolstoshev, C.M. 1993. dbEST—database for “expressed sequence tags”. Nature Genet. 4:332-333. Chandrasekharappa, S.C., Guru, S.C., Manickam, P., Olufemi, S.E., Collins, F.S., Emmert-Buck, M.R., Debelenko, L.V., Zhuang, Z., Lubensky, I.A., Liotta, L.A., et al. 1997. Positional cloning of the gene for multiple endocrine neoplasia-type 1. Science 276:404-407. Chang, Z.Y., Nygaard, P., Chinault, A.C., and Kellems, R.E. 1991. Deduced amino acid sequence of Escherichia coli adenosine deaminase reveals evolutionarily conserved amino acid residues: Implications for catalytic function. Biochemistry 30:2273-2280. Claverie, J.M. and Makalowski, W. 1994. Alu alert. Nature 371:752. Dayhoff, M.O., Schwartz, R.M., and Orcutt, B.C. 1978. A model of evolutionary change in proteins. In Atlas of Protein Sequence and Structure, Vol. 5, suppl. 3. (M.O. Dayhoff, ed.) pp. 345-352. Natl. Biomed. Res. Found., Washington, D.C. Gish, W. and States, D.J. 1993. Identification of protein coding regions by database similarity search. Nature Genet. 3:266-272. Henikoff, S. and Henikoff, J.G. 1992. Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. U.S.A. 89:10915-10919. Holm, L. and Sander, C. 1997. An evolutionary treasure: Unification of a broad set of amidohydrolases related to urease. Proteins 28:72-82. Karlin, S. and Altschul, S.F. 1990. Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes. Proc. Natl. Acad. Sci. U.S.A. 87:2264-2268. Karlin, S. and Altschul, S.F. 1993. Applications and statistics for multiple high-scoring segments in molecular sequences. Proc. Natl. Acad. Sci. U.S.A. 90:5873-5877. Lavin, M.F. and Shiloh, Y. 1997. The genetic defect in ataxia-telangiectasia. Annu. Rev. Immunol. 15:177-202. Olson, M., Hood, L., Cantor, C., and Botstein, D. 1989. A common language for physical mapping of the human genome. Science 245:1434-1435. Ostell, J.M. and Kans, J.A. 1998. The NCBI data model. In Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins (A.D. Baxevanis and B.F.F. Ouellette, eds.) pp. 121-144. John Wiley & Sons, New York. Ouellette, B.F.F. 1998. The GenBank sequence database. In Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins (A.D. Baxevanis and B.F.F. Ouellette, eds.) pp. 16-45. John Wiley & Sons, New York. Ouellette, B.F. and Boguski, M.S. 1997. Database divisions and homology search files: A guide for the perplexed. Genome Res. 7:952-955. Pearson, W.R. 1990. Rapid and sensitive sequence comparison with FASTP and FASTA. Methods Enzymol. 183:63-98. Pearson, W.R. and Lipman, D.J. 1988. Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. U.S.A. 85:2444-2448. Schuler, G.D. 1998. Sequence alignment and database searching. In Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins (A.D. Baxevanis and B.F.F. Ouellette, eds.) pp. 145-171. John Wiley & Sons, New York. Schwartz, R.M. and Dayhoff, M.O. 1978. Matrices for detecting distant relationships. In Atlas of Protein Sequence and Structure, Vol. 5, suppl. 3. (M.O. Dayhoff, ed.) pp. 353-358. Natl. Biomed. Res. Found., Washington, D.C. Seabra, M.C., Brown, M.S., and Goldstein, J.L. 1993. Retinal degeneration in choroideremia: Deficiency of rab geranylgeranyl transferase. Science 259:377-381. Smith, T.F. and Waterman, M.S. 1981. Identification of common molecular subsequences. J. Mol. Biol. 147:195-197. Smith, M.W., Holmsen, A.L., Wei, Y.H., Peterson, M., and Evans, G.A. 1994. Genomic sequence sampling: A strategy for high resolution sequence-based physical mapping of complex genomes. Nature Genet. 7:40-47. Sequence Similarity Searching Using BLAST

Stoesser, G., Moseley, M.A., Sleep, J., McGowran, M., Garcia-Pastor, M., and Sterk, P. 1998. The EMBL nucleotide sequence database. Nucl. Acids Res. 26:8-15.

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Tateno, Y., Fukami-Kobayashi, K., Miyazaki, S., Sugawara, H., and Gojobori, T. 1998. DNA Data Bank of Japan at work on genome sequence data. Nucl. Acids Res. 26:16-20. Wolfsberg, T.G., Straight, P.D., Gerena, R.L., Huovila, A.P., Primakoff, P., Myles, D.G., and White, J.M. 1995. ADAM, a widely distributed and developmentally regulated gene family encoding membrane proteins with a disintegrin and metalloprotease domain. Dev. Biol. 169:378-383. Wootton, J.C. and Federhen, S. 1993. Statistics of local complexity in amino acid sequences and sequence databases. Comput. Chem. 17:149-163. Wootton, J.C. and Federhen, S. 1996. Analysis of compositionally biased regions in sequence databases. Methods Enzymol. 266:554-571. Zhang, J. and Madden, T.L. 1997. PowerBLAST: A new network BLAST application for interactive or automated sequence analysis and annotation. Genome Res. 7:649-656. Zhang, Z., Berman, P., and Miller, W. 1998. Alignments without low-scoring regions. J. Comput. Biol. 5:197-210.

Contributed by Tyra G. Wolfsberg and Thomas L. Madden National Center for Biotechnology Information National Library of Medicine, NIH Bethesda, Maryland

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APPENDIX A: BLAST PARAMETERS The Advanced BLAST page offers the possibility to change a number of BLAST parameters. The most important options supported are listed below. Descriptions (pull-down menu) restricts the number of one-line descriptions to the number specified. The default is 500. Alignments (pull-down menu) restricts the number of database hits for which alignments are shown. Several alignments may be associated with one database sequence, so the number of alignments may actually be larger than this number. The default is 500. Expect value (pull-down menu; also known as the E value) is the statistical significance threshold for reporting matches. The default value is 10, such that ten alignments are expected to be found merely by chance, according to the stochastic model of Karlin and Altschul (1990). Lower E thresholds are more stringent, leading to fewer chance matches being reported. Fractional values are acceptable. Filtering (pull-down menu) masks off segments of the query sequence that have low compositional complexity, as determined by the SEG program (Wootton and Federhen, 1993, 1996) or, for BLASTN, by the DUST program (R.L. Tatusov and D.J. Lipman, pers. comm.). Filtering is only applied to the query sequence (or its translation products), not to database sequences. Default filtering is DUST for BLASTN, SEG for other programs. Genetic code (pull-down menu) selects the genetic code to be used for a BLASTX translation of the query. The default setting is the universal code. The choice of code is determined by the source of the query sequence (e.g., species, mitochondrial). Graphical overview (checkbox) selects whether an overview of the database sequences aligned to the query sequence will be shown. The score of an alignment is indicated by one of five different colors, which divides the range of scores into five groups. If more than one alignment is displayed for a database sequence, the two alignments are connected by a cross-hatched bar. Mousing over a match causes the definition and score to be shown in the window at the top; clicking on a bar takes the user to the associated alignments. The overview is on by default. Matrix (menu) allows one to change the matrix from the default of BLOSUM-62. This is useful for searches against short sequences (see Searching Short Sequences). It is also possible to change the gap existence and extension penalties, but the defaults for a given matrix are recommended. Organism (pull-down menu) limits the search by organism. The menu provides a list of the most common organisms; others may be entered in a text box.

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APPENDIX B: SEQUENCE IDENTIFIER SYNTAX The syntax of sequence header lines used by the NCBI BLAST server depends on the database from which each sequence was obtained. Table 2.5.3 lists the identifiers for the databases from which the sequences were derived. For example, an identifier might be gb|M73307|AGMA13GT, where the gb tag indicates that the identifier refers to a GenBank sequence, M73307 is its GenBank accession number, and AGMA13GT is its GenBank locus. NCBI assigns gi identifiers for all sequences contained within NCBI’s sequence databases (Ostell and Kans, 1998). The gi identifier provides a uniform and stable naming convention whereby a specific sequence is assigned its unique gi identifier. If a nucleotide or protein sequence changes, a new gi identifier is assigned, even if the accession number of the record remains unchanged. Thus, gi identifiers provide a mechanism for identifying the exact sequence that was used or retrieved in a given search. For searches of the nr protein database where the sequences are derived from conceptual translations of sequences from the nucleotide databases the gi syntax is gi|gi_identifier. An example would be gi|451623 (U04987) env [Simian immunodeficiency..., where 451623 is the gi identifier and U04987 is the accession number of the nucleotide sequence from which it was derived. Users may select the -gi option for BLAST output, which will produce a header line with the gi identifier concatenated with the database identifier of the database from which it was derived. For example, gi|176485|gb|M73307|AGMA13GT would be used for a match from a nucleotide database, and gi|129295|sp|P01013|OVAX_CHICK for a protein database. The gnl (general) identifier allows databases not listed in Table 2.5.3 to be identified with the same syntax. An example here is the PID identifier gnl|PID|e1632. PID stands for Protein-ID, and the e in e1632 indicates that this ID was issued by EMBL. As mentioned above, use of the -gi option produces the NCBI gi (in addition to the PID), which users can also use to retrieve sequences of interest. Table 2.5.3

Identifier Syntax for Sequence Databases

Database

Identifier syntax

GenBank EMBL Data Library DDBJ (DNA Data Bank of Japan) NBRF PIR Protein Research Foundation Swiss-Prot Brookhaven Protein Data Bank Patents GenInfo Backbone Id General database identifier

gb|accession|locus emb|accession|locus dbj|accession|locus pir||entry prf||name sp|accession|entry name pdb|entry|chain pat|country|number bbs|number gnl|database|identifier

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Protein Databases on the Internet

UNIT 2.6

Protein databases have become a crucial part of modern biology. Huge amounts of data for protein structures, functions, and particularly sequences are being generated. These data cannot be handled without using computer databases. Searching databases is often the first step in the study of a new protein. Without the prior knowledge obtained from such searches, known information about the protein could be missed, or an experiment could be repeated unnecessarily. Comparison between proteins and protein classification provide information about the relationship between proteins within a genome or across different species, and hence offer much more information than can be obtained by studying only an isolated protein. In this sense, protein comparison through databases allows one to view life as a forest instead of individual trees. In addition, secondary databases derived from experimental databases are also widely available. These databases reorganize and annotate the data or provide predictions. The use of multiple databases often helps researchers understand evolution, structure, and function of a protein. Protein databases are especially powered by the Internet. Unlike traditional media, such as the CD-ROM, the Internet allows databases to be easily maintained and frequently updated with minimum cost. Researchers with limited resources can afford to set up their own databases and disseminate their data quickly. Notably, many small databases on specific types of proteins, such as the EF-Hand Calcium-Binding Proteins Data Library (http://structbio.vanderbilt.edu/cabp_database/) and O-GlycBase (http://www.cbs.dtu. dk/databases/OGLYCBASE/), became widely available. Users worldwide can easily access the most up-to-date version through a user-friendly interface. Most protein databases have interactive search engines so that users can specify their needs and obtain the related information interactively. Many protein databases also allow submitters to deposit data interactively, and allow database servers to check the format of the data and provide immediate feedback. Although some protein databases are widely known, they are far from being fully utilized in the protein science community. This unit provides a starting point for readers to explore the potential of protein databases on the Internet. Databases for different aspects of proteins are discussed, with the focus on sequence, structure, and family. The strengths and weaknesses of the databases will be addressed. For Web addresses of the databases discussed in this unit, see Internet Resources and Table 2.6.1. From hundreds of on-line protein databases, several major databases are discussed as examples to illustrate their features and how they can be used effectively. Most other protein databases can be explored in a similar way. PROTEIN SEQUENCE DATABASES Thanks to the Human Genome Project and other sequencing efforts, new sequences have been generated at a prodigious rate. A large number of DNA sequences, which are equivalent to hundreds of new proteins, are determined each day for the human genome alone. It is expected that 10,000 genomes will be sequenced in the next decade. These sequences provide a rich information source and the core of the revolutionary movement toward “large-scale biology.” Various databases contain protein sequences with different focuses. SwissProt (Bairoch and Apweiler, 1999) provides more annotations than any other sequence database, with a minimal level of redundancy, through human input or integration with other databases. However, the labor-intensive enhancement process prevents many sequences from being added to SwissProt quickly. As a supplement of SwissProt, the TrEMBL database Contributed by Dong Xu and Ying Xu Current Protocols in Protein Science (2003) 2.6.1-2.6.15 Copyright © 2003 by John Wiley & Sons, Inc.

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Table 2.6.1

Web Addresses and Sizes of Selected Protein Databases

Web site

Sizea

DDBJ DAtA GeneCards Genome Channel KEGG nr OWL PEDANT PIR PRF STACK SwissProt SYSTERS TIGR Gene Indices TrEMBL UniGene Structure:

http://www.ddbj.nig.ac.jp/ http://luggagefast.stanford.edu/group/arabprotein/ http://bioinfo.weizmann.ac.il/cards/ http://compbio.ornl.gov/channel/ http://www.genome.ad.jp/kegg/ http://www.ncbi.nlm.nih.gov/BLAST/ http://www.bioinf.man.ac.uk/dbbrowser/OWL/ http://pedant.mips.biochem.mpg.de http://pir.georgetown.edu http://www.genome.ad.jp/htbin/www_bfind?prf http://www.sanbi.ac.za/Dbases.html http://www.expasy.ch/sprot/sprot-top.html http://systers.molgen.mpg.de/ http://www.tigr.org/tdb/tgi/ http://www.expasy.ch/sprot/sprot-top.html http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=unigene

25,149,821 sequences 25,846 sequences 14,519 genes NA 433,051 genes 129,6341 sequences 279,796 sequences NA 1,173,204 sequences 216,752 sequences NA 123,192 sequences 290,811 sequences NA 829,760 sequences NA

3Dee BioMagResBank Decoys ’R’ Us EBI-MSD Enzyme Structures GRASS Image Library LPFC MolMovDB PDB PDBsum PRESAGE STING WPDB Sequence family:

http://www.compbio.dundee.ac.uk/3Dee/ http://www.bmrb.wisc.edu http://dd.stanford.edu/ http://msd.ebi.ac.uk http://www.biochem.ucl.ac.uk/bsm/enzymes/ http://trantor.bioc.columbia.edu/GRASS/ http://www.imb-jena.de/IMAGE.html http://www-smi.stanford.edu/projects/helix/LPFC/ http://bioinfo.mbb.yale.edu/MolMovDB/ http://www.rcsb.org/pdb/ http://www.biochem.ucl.ac.uk/bsm/pdbsum/ http://presage.berkeley.edu/ http://trantor.bioc.columbia.edu/STING/ http://www.sdsc.edu/pb/wpdb/

NA 2494 structures NA NA 10208 structures NA NA NA NA 20,473 structures 21,361 structures NA NA >6000 structures

BLOCKS COG DOMO InterPro Pfam PRINTS ProClass ProDom PROSITE SBASE VIDA Structure family:

http://www.blocks.fhcrc.org/ http://www.ncbi.nlm.nih.gov/COG/ http://www.infobiogen.fr/services/domo/ http://www.ebi.ac.uk/interpro/ http://www.sanger.ac.uk/Pfam/ http://www.bioinf.man.ac.uk/dbbrowser/PRINTS/ http://pir.georgetown.edu/gfserver/proclass.html http://protein.toulouse.inra.fr/prodom.html http://www.expasy.ch/prosite/ http://hydra.icgeb.trieste.it/~kristian/SBASE/ http://www.biochem.ucl.ac.uk/bsm/virus_database/VIDA.html

8656 blocks 104,101 sequences 83,054 sequences 6383 families 5193 families 1750 sites 155,868 sequences 481,952 sequences 1614 sites 338,655 sequences NA

CATH CE

http://www.biochem.ucl.ac.uk/bsm/cath/ http://cl.sdsc.edu/ce.html

36,480 domains NA

Database Sequence:

continued

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Table 2.6.1

Web Addresses and Sizes of Selected Protein Databases, continued

Database

Web site

Sizea

CL Dali Domain Dictionary FSSP HOMSTRAD HSSP PartsList SCOP SLoop Function family:

http://cl.sdsc.edu/cl1.html http://columba.ebi.ac.uk:8765/holm/ddd2.cgi

NA 771 domain classes

http://www2.ebi.ac.uk/dali/fssp/ http://www-cryst.bioc.cam.ac.uk/~homstrad/ http://swift.embl-heidelberg.de/hssp/ http://bioinfo.mbb.yale.edu/align/ http://scop.mrc-lmb.cam.ac.uk/scop/ http://www-cryst.bioc.cam.ac.uk/~sloop/

3242 families 1033 families NA NA 17406 structures NA VAST

http://rose.man.poznan.pl/aars/index.html http://ir2lcb.cnrs-mrs.fr/ABCdb/ http://www.eez.csic.es/arac-xyls/ http://wwwmgs.bionet.nsc.ru/mgs/gnw/aspd/ http://condor.bcm.tmc.edu/ermb/bcgd/ http://www.chemie.uni-marburg.de/~csdbase/ http://www.helicase.net/dexhd/dbhome.htm

NA NA NA NA NA NA NA

http://structbio.vanderbilt.edu/cabp_database/ http://ecocyc.org/ http://us.expasy.org/enzyme/ http://geneontology.org/ http://www.gpcr.org/7tm/ http://www.biosci.ki.se/groups/tbu/homeo.html http://merops.sanger.ac.uk/ http://wehih.wehi.edu.au/mhcpep/ http://npd.hgu.mrc.ac.uk/

NA NA 4173 enzymes NA NA NA NA 13,000 peptides 1227 proteins

http://www.cbs.dtu.dk/databases/OGLYCBASE/ http://www-lecb.ncifcrf.gov/PDD/ http://www.biochem.ucl.ac.uk/bsm/PROCAT/PROCAT.html http://pkr.sdsc.edu/html/index.shtml

242 glycoproteins NA NA 7588 clusters

http://www.mbio.ncsu.edu/RNaseP/home.html http://www-wit.mcs.anl.gov/sentra/ http://66.93.129.133/transporter/wb/index2.html http://condor.bcm.tmc.edu/oncogene.html http://wit.mcs.anl.gov/WIT2/ http://www.stanford.edu/~rnusse/wntwindow.html

NA NA NA >300 genes NA NA

http://binddb.org http://dip.doe-mbi.ucla.edu http://biodata.mshri.on.ca/grid/ http://daisy.bio.nagoya-u.ac.jp/golab/hetpdbnavi.html http://surya.bic.nus.edu.sg/mpid/

15,141 interactions 22,229 interactions 13,819 interactions NA 90 complexes

AARSDB ABCdb AraC/XylS database ASPD Breast Cancer Gene CSDBase DExH/D Family Database EF-hand CaBP EcoCyc ENZYME Gene Ontology (GO) GPCRDB (receptors) Homeobox Page MEROPS (peptidase) MHCPEP (peptides) Nuclear Protein Database (NPD) O-GlycBase PDD PROCAT Protein Kinase Resource (PKR) RNase P Sentra TransportDB Tumor Gene WIT2 Wnt gene Homepage Binding: BIND DIP GRID Het-PDB Navi MHC-Peptide Interaction Database

continued

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Table 2.6.1

Web Addresses and Sizes of Selected Protein Databases, continued

Database

Web site

Sizea

Protein-Protein Interface ReLiBase Energetics: ProTherm References:

http://www-lmmb.ncifcrf.gov:80/~tsai/

NA

http://relibase.rutgers.edu/

11,938 proteins

http://www.rtc.riken.go.jp/jouhou/Protherm/protherm.html

13046 entries

http://www.infobiogen.fr/services/dbcat/

511 databases

http://www.ncbi.nlm.nih.gov/PubMed/ http://www.expasy.ch/seqanalref/

>12,000,000 citations NA

http://www.rtc.riken.go.jp/jouhou/3dinsight/3dinsight.html http://www.ncbi.nlm.nih.gov/Entrez/ http://srs.ebi.ac.uk

NA NA 264 databases

DBCAT (Catalog of Databases) MEDLINE SeqAnalRef Combined: 3DinSight Entrez SRS

aNA, size not available at time of printing. The data are as of March, 2003.

(Bairoch and Apweiler, 1999) contains all the translations of EMBL nucleotide sequence entries (Rodriguez-Tom et al., 1996) that have not been integrated into SwissProt. Another protein sequence database, the Protein Identification Resource (PIR; Barker et al., 1999), attempts to build a complete and nonredundant database from a number of protein and nucleic acid sequence databases. Identical and highly similar sequences from the same species are merged into a single entry. Each entry in PIR provides bibliographic and annotated information for the protein. The nr protein database is used for BLAST searching (Altschul et al., 1997), which is described in UNIT 2.5 of this book. It includes entries from the nonredundant GenBank (Benson et al., 1999) translations, SwissProt, PIR, Protein Research Foundation (PRF) in Japan, and the Protein Data Bank (PDB). Only entries with absolutely identical sequences are merged. Most of the sequence databases have a sequence search tool and cross-references to entries of other protein and gene databases. Many sequence databases, such as SwissProt and PIR, also provide text searching using, for instance, protein names or key words. To study a new protein, the authors recommend first performing a sequence search using BLAST in nr if the protein sequence is available. The search often gives entry names in the protein databases included in nr. Even when the protein is not found in nr, it is likely that a homologous protein will be hit, which can often lead to some useful information, such as the function of the query protein. If the sequence of the query protein is unavailable, doing a text search in SwissProt or PIR usually identifies the protein. SwissProt is probably the place to obtain the most information about a protein if it can be found in SwissProt. However, some additional information may be found by checking other sequence databases. For example, PIR provides some useful information on protein family classification for each entry, and the Kyoto Encyclopedia of Genes and Genomes (KEGG; Ogata et al., 1999) annotates some gene entries with information about metabolic and regulatory pathways. If the query protein is not available in nr, one may find a gene model (predicted protein sequence) by searching genetic sequence databases that contain predicted genes from nucleotide sequences (e.g., the Genome Channel; Mural et al., 1999). Although predicted sequences generated by computational gene-finding tools may contain errors, such databases cover a large number of proteins and are often reliable enough to provide useful information. Protein Databases on the Internet

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SwissProt, as a curated protein sequence database, offers a wide range of annotations, covering areas such as function, domain parsing, post-translational modifications, and variants. SwissProt can be accessed at http://www.expasy.ch/sprot/sprot-top.html (home server, Switzerland), http://us.expasy.org/sprot/sprot-top.html (USA), http://ca.expasy. org/sprot/sprot-top.html (Canada), http://cn.expasy.org/sprot/sprot-top.html (China), http://kr.expasy.org/sprot/sprot-top.html (Korea), and http://tw.expasy.org/sprot/sprot-top. html (Taiwan). SwissProt is integrated into many other databases, such as SRS (see SRS below). Human vitronectin is used here as an example for searching protein sequence databases. To locate the SwissProt entry for this protein, one can search either the entry name (VTNC_HUMAN) or the accession number (P04004) obtained from a BLAST search. Alternatively, one can use the full-text search at the SwissProt Web page to search by protein name (human vitronectin) or key words (e.g., serum spreading, as vitronectin is also called serum spreading factor s-protein). A combination of several entries can be used in a search. The entry name in SwissProt has the general format X_Y, where X is a mnemonic code of up to four characters indicating the protein name (in this case, VTNC), and Y is a mnemonic species identification code of up to five characters for the biological source of the protein. Some codes used for Y are full English names, e.g., HORSE, HUMAN, MAIZE, MOUSE, PIG, RAT, SHEEP, YEAST (baker’s yeast, Saccharomyces cerevisiae), and WHEAT. Some are abbreviations, including BOVIN (bovine), CHICK (chicken), ECOLI (Escherichia coli), PEA (garden pea, Pisum sativum), RABIT (rabbit), SOYBN (soybean, Glycine max), and TOBAC (common tobacco, Nicotina tabacum). An entry name may have several accession numbers if they have been merged. An accession number is always conserved from release to release, and therefore allows unambiguous citation. Each entry contains the following items shown in table format in the NiceProt View layout: (1) general information about the entry, (2) name and origin of the protein, (3) references providing the protein sequence, (4) comments (e.g., annotated functions, subunits, similar proteins), (5) cross-references (links to other databases), (6) key words, (7) features, and (8) sequence information. The text in the comments entry provides a function annotation for the protein (e.g., “Vitronectin is a cell adhesion and spreading factor found in serum and tissues. Vitronectins interact with glycosaminoglycans and proteoglycans...”). Cross-references lists the annotations of the protein by other databases, such as GeneCards (Rebhan et al., 1998) and ProDom (Corpet et al., 1999). GeneCards, a database of human genes, shows chromosomal location and the involvement of the protein in certain diseases (if applicable). ProDom contains protein domain families by automated sequence comparisons. Clicking the link to ProDom from SwissProt leads to a nice graphic view for domain parsing, as shown in Figure 2.6.1 for vitronectin.

0

100

200

300

400

500

100

200

300

400

500

VTNC_HUMAN

0

Figure 2.6.1 Domain parsing of human vitronectin by ProDom in SwissProt.

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Various research results are given under features. Some of the features items for VTNC_HUMAN are as follows: SIGNAL CHAIN CHAIN PEPTIDE DOMAIN DOMAIN SITE SITE MOD_RES CARBOHYD DISULFID BINDING CONFLICT

1 20 399 20 150 288 64 398 75 86 293 362 50

19 398 478 63 287 478 66 399 75 86 430 395 50

V65 SUBUNIT. V10 SUBUNIT. SOMATOMEDIN B. HEMOPEXIN-LIKE 1. HEMOPEXIN-LIKE 2. CELL ATTACHMENT SITE. CLEAVAGE. SULFATATION.

HEPARIN. C -> N (IN REF. 5).

where SIGNAL represents the extent of a signal sequence (prepeptide), MOD_RES indicates a post-translationally modified residue (sulfatation, in this case), CARBOHYD shows the glycosylation site, DISULFID means that a disulfide bond exists between the two indicated residues (293 and 430), and CONFLICT shows that different papers report differing sequences. PROTEIN STRUCTURAL DATABASES Searching structure databases is becoming more and more popular in molecular biology. The three-dimensional structures of proteins not only define their biological functions, but also hold a key in rational drug design. Traditionally, protein structures were solved at a low-throughput mode. However, recent advances in new technologies, such as synchrotron radiation sources and high-resolution nuclear magnetic resonance (NMR), accelerate the rate of protein structure determination substantially. There is an overwhelming consensus in the structural biology community that protein structures can be solved en masse (an effort called structural genomics), in a similar fashion as for determining DNA sequences, and that impact of this approach can be compared with that of the Human Genome Project (see Chapter 17). The only international repository for the processing and distribution of protein structures is the PDB (Bernstein et al., 1977). The structures in the PDB were determined experimentally by X-ray crystallography (∼86%) and NMR (∼14%). Theoretical models have been removed from PDB, effective July 2, 2002, based on the new PDB policy. The PDB also contains some structures of chemical ligands and nucleotides. Each PDB entry is represented by a four-character identifier (PDB ID), where the first character is always a number from 0 to 9 (e.g., 1cau, 256b). The PDB can be accessed through the home server (http://www.rcsb.org/pdb/ or http://www.pdb.org in the USA) or through one of many mirror sites from around the world, as listed at the home server. Some of the mirror sites are as follows: Rutgers University, Piscataway, NJ, United States: http://rutgers.rcsb.org/ NIST, Gaithersburg, MD, United States: http://nist.rcsb.org/ Cambridge Crystallographic Data Centre, United Kingdom: http://pdb.ccdc.cam.ac.uk/ National University of Singapore, Singapore: http://pdb.bic.nus.edu.sg/ Osaka University, Japan: http://pdb.protein.osaka-u.ac.jp/ Universidade Federal de Minas Gerais, Brazil: http://www.pdb.ufmg.br/ Max Delbrück Center for Molecular Medicine, Germany: http://www.pdb.mdc-berlin.de/ Protein Databases on the Internet

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The PDB offers three search methods: search by PDB ID, by SearchLite, and by SearchFields. SearchLite is a simple key-word search that uses, for instance, protein name or author’s name. A search using SearchFields, as an advanced search engine, allows a user to specify features of the protein, such as Enzyme Commission (EC) number, name of binding ligand, range of protein size, range of resolution in the X-ray structure, and secondary structure content. The PDB stores structural information in two formats: the PDB file format (Bernstein et al., 1977) and the macromolecular crystallographic information file (mmCIF) format (Bourne et al., 1997). The PDB file format is still the dominant format used in the protein community. It contains three parts: annotations, coordinates, and connectivities. The connectivity part, which shows chemical connectivities between atoms, is optional. It is listed at the end of the PDB file, beginning the line with the key word CONECT. The coordinate part uses each line for a three-dimensional coordinate of an atom, starting from ATOM (for standard amino acids) or HETATM (for nonstandard groups). The following shows an example of the PDB file format: HEADER

OXIDOREDUCTASE

COMPND

GLYCOLATE OXIDASE (E.C.1.1.3.1)

(OXYGEN(A))

14-JUN-89

1GOX

1GOX

3

1GOX

4

... ATOM

232

N

ALA

29

54.035

4.332

19.352

1.00

23.93

1GOX

374

ATOM

233

CA

ALA

29

52.992

65.356 19.569

1.00

24.74

1GOX

375

ATOM

234

C

ALA

29

53.519

66.762 19.309

1.00

25.43

1GOX

376

ATOM

235

O

ALA

29

54.648

67.179 19.655

1.00

25.66

1GOX

377

ATOM

236

C

BALA 29

52.433

65.340 20.993

1.00

24.54

1GOX

378

HETATM

3165

O

HOH 658

62.480

62.480 0.000

0.50

65.79N

1GOX

3170

CONECT

2837 2838

1GOX

3171

... 2854

Each line shows the atom serial number, atom type, residue type, chain identifier (in case of multi-chain structure), residue serial number, orthogonal coordinates (three values), occupancy, temperature factor, and segment identifier. The annotation part of the PDB file format contains dozens of possible record types, including: HEADER (name of protein and release date), COMPND (molecular contents of the entry), SOURCE (biological source), AUTHOR (list of contributors), SSBOND (disulfide bonds), SLTBRG (salt bridges), SITE (groups comprising important sites), HET (nonstandard groups or residues [heterogens]), MODRES (modifications to standard residues), SEQRES (primary sequence of backbone residues), HELIX (helical substructures), SHEET (sheet substructures), and REMARK (other information and comments). The PDB allows a user to view a molecule structure interactively through a Virtual Reality Modeling Language (VRML) viewer, RasMol (Sayle and Milner-White, 1995), Chime, or QuickPDB (a Java applet for viewing sequence and structure) when the browser is configured to support these free rendering tools. The PDB provides related information about the protein, such as secondary structure assignment and geometry. Each PDB entry also links to a wide range of annotations from secondary databases, including (1) summary and display databases such as Graphical Representation and Analysis of Structure Server (GRASS; Nayal et al., 1999), Image Library (Shnel, 1996), Molecular Modelling Database (MMDB; Marchler-Bauer et al., 1999) in Entrez, PDBsum (Laskowski et al., 1997), and Sequence to and within Graphics (STING); (2) domain partition information from 3Dee (Siddiqui and Barton, 1995); (3) the MEDLINE bibliography; (4) structure quality assessment in PDBREPORT from WHAT IF (Vriend, 1990); (5) protein movements recorded in Database of Macromolecular Movement (MolMovDB; Gerstein and Krebs,

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1998); (6) structure families (CATH, CE, FSSP, SCOP, and VAST, as discussed later in this unit); and (7) geometry analyses of the protein, such as CSU Contacts of Structural Units (Sobolev et al., 1999) and castP Identification of Protein Pockets & Cavities (Liang et al., 1998). Several structure databases that are not linked by the PDB can also provide useful information. WPDB (Shindyalov and Bourne, 1995) can be used to visualize and analyze a PDB entry from Microsoft Windows. BioMagResBank (University of Wisconsin, 1999) is a repository for NMR spectroscopy data on proteins, peptides, and nucleic acids. Particularly, it provides partial NMR data (e.g., chemical shifts) before the full structure is solved. PROTEIN FAMILY DATABASES Introduction Proteins can be classified according to their evolutionary, structural, or functional relationships. A protein in the context of its family is much more informative than the single protein itself. For example, residues conserved across the family often indicate special functional roles. Two proteins classified in the same functional family may suggest that they share similar structures, even when their sequences do not have significant similarity. There is no unique way to classify proteins into families. Boundaries between different families may be subjective. The choice of classification system depends in part on the problem; in general, the authors suggest looking into classification systems from different databases and comparing them. Three types of classification methods are widely adopted, based upon the similarity of sequence, structure, or function. Sequence-based methods are applicable to any proteins whose sequences are known, while structure-based methods are limited to the proteins of known structures, and function-based methods depend on the functions of proteins being annotated. Sequence- and structure-based classifications can be automated and are scalable to high-throughput data, whereas function-based classification is typically carried out manually. Structure- and function-based methods are more reliable, while sequence-based methods may result in a false positive result when sequence similarity is weak (i.e., two proteins are classified into one family by chance rather than by any biological significance). In addition, since protein structure and function are better conserved than sequence, two proteins having similar structures or similar functions may not be identified through sequence-based methods. Databases for Sequence-Based Protein Families Sequence-based protein families are classified according to a profile derived from a multiple-sequence alignment. The profile can be shown across a long domain (typically 100 residues or more) or can be revealed in short sequence motifs. Classification methods based on profiles across long domains tend to be more reliable but less sensitive than those based on short sequence motifs.

Protein Databases on the Internet

Several sequence-based methods focus more on profiles across long domains, including Pfam (Bateman et al., 1999), ProDom (Corpet et al., 1999), SBASE (Murvai et al., 1999), and Clusters of Orthologous Group (COG; Tatusov et al., 1997). These methods differ in the techniques used to construct families. Pfam builds multiple-sequence alignments of many common protein domains using hidden Markov models. The ProDom protein domain database consists of homologous domains based on recursive PSI-BLAST searches (UNIT 2.5). SBASE is organized through BLAST neighbors and is grouped by standard protein names that designate various functional and structural domains of protein sequences. COG aims toward finding ancient conserved domains by delineating families of orthologs across a wide phylogenetic range.

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The following shows an example of Pfam for the GRIP domain (accession number PF01465). Pfam lists some useful information for the entry as follows: The GRIP (golgin-97, RanBp2alpha, Imh1p and p230/golgin245) domain is found in many large coiled-coil proteins. It has been shown to be sufficient for targeting to the Golgi. The GRIP domain contains a completely conserved tyrosine residue. The references of the above annotation are also given. In addition, Pfam gives the alignment between the family members: 015045/1511-1558

SAANLEYLKNVLLQFIFLKPG--SERERLLPVINTMLQLSPEEKGKLAAV

O15045

YNP9_CAEEL/633-681

NEKNMEYLKNVFVQFLKPESVP-AERDQLVIVLQRVLHLSPKEVEILKAA

P34562

Q06704/864-909

KNEKIAYIKNVLLGFLEHKE----QRNQLLPVISMLLQLDSTDEKRLVMS

Q06704

Q92805/691-737

REINFEYLKHVVLKFMSCRES---EAFHLIKAVSVLLNFSQEEENMLKET

Q92805

O42657/703-748

MLIDKEYTRNILFQFLEQRD----RRPEIVNLLSILLDLSEEQKQKLLSV

O42657

O70365/1161-1205

EPTEFEYLRKVMFEYMMGR-----ETKTMAKVITTVLKFPDDQAQKILER

070365

Q21071/692-741D

DPAEAEYLRNVLYRYMTNRESLGKESVTLARVIGTVARFDESQMKNVISS

Q21071

Q18013/574-623

STSEIDYLRNIFTQFLHSMGSPNAASKAILKAMGSVLKVPMAEMKIIDKK

Q18013

The alignment shows accession numbers and the range of each sequence. One can identify some features of the family through this pattern (i.e., from particularly conserved residues at specific alignment positions). Some methods are based on “fingerprints” of small conserved motifs in sequences, as with PROSITE (Hofmann et al., 1999), PRINTS (Attwood et al., 1999), and BLOCKS (Heniko et al., 1999). In protein sequence families, some regions have been better conserved than others during evolution. These regions are generally important for the function of a protein or for the maintenance of its three-dimensional structure, and hence are suitable for fingerprinting. The fingerprints can be used to assign a newly sequenced protein to a specific family. Fingerprints are derived from gapped alignments in PROSITE and PRINTS, but are derived from ungapped alignments (corresponding to the highly conserved regions in proteins) in BLOCKS. A fingerprint in PRINTS may contain several motifs from PROSITE, and thus may be more flexible and powerful than a single PROSITE motif. Therefore, PRINTS can provide a useful adjunct to PROSITE. It should be noted that some functionally unrelated proteins may be classified together due to chance matches in short motifs. Other sequence-based protein family databases consist of multiple sources. The ProClass database (Wu et al., 1999) is a nonredundant protein database organized according to family relationships as defined collectively by PROSITE patterns and PIR superfamilies. The MEGACLASS server (States et al., 1993) provides classifications by different methods, including Pfam, BLOCKS, PRINTS, ProDom, and SBASE. The MOTIF search engine at http://motif.genome.ad.jp/ includes PROSITE, BLOCKS, ProDom, and PRINTS. Databases for Structure-Based Protein Families The hierarchical relationship among proteins can be clearly revealed in structures through structure-structure comparison. Structure families often provide more information on the relationship between proteins than what sequence families can offer, particularly when two proteins share a similar structure but no significant sequence identity. Figure 2.6.2 shows an example of a structure-structure alignment between two proteins. Sometimes, sequence similarity between two proteins exists but is not strong enough to produce an unambiguous alignment. In this case, the alignment between two structures can generate Computational Analysis

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Figure 2.6.2 Structure superposition between glycolate oxidase (1gox, in black) and inosine monophosphate dehydrogenase (1ak5, in gray). This figure was made using MOLSCRIPT (Kraulis, 1991).

better alignment in terms of biological significance, and thus may pinpoint the active sites more accurately. Different structure-structure comparison methods yield different structure families. CATH (Class, Architecture, Topology and Homologous superfamily; Orengo et al., 1997) is a hierarchical classification of protein domain structures. CE (Combinatorial Extension of the optimal path; Shindyalov and Bourne, 1998) provides structural neighbors of the PDB entries with structure-structure alignments and three-dimensional superpositions. FSSP (Fold classification based on Structure-Structure alignment of Proteins; Holm and Sander, 1996) features a protein family tree and a domain dictionary, in addition to whole-chain-based classification, sequence neighbors, and multiple structure alignments. SCOP (Structural Classification of Proteins; Murzin et al., 1995) uses augmented manual classification, class, fold, superfamily, and family classification. VAST (Vector Alignment Search Tool; Gibrat et al., 1996) contains representative structure alignments and threedimensional superpositions. Among these five databases, SCOP provides more functionrelated information. However, due to the manual work involved, SCOP is not updated as frequently as the others (as of June, 2003, it was last updated for the PDB release on March 1, 2003), whereas FSSP and CATH follow the PDB updates closely. SCOP is used here as an example to show the features of structure-based families. SCOP can be accessed through its home server in the UK (http://scop.mrc-lmb.cam.ac.uk/scop/). It is also widely mirrored around the world, including: http://pdb.wehi.edu.au/scop/ (Australia) http://mdl.ipc.pku.edu.cn/scop/ (China) http://www.cdfd.org.in:5555/scop/ (India) http://pdb.weizmann.ac.il/scop/ (Israel) http://loki.polito.it/scop/ (Italy) http://scop.protres.ru/ (Russia) http://scop.bic.nus.edu.sg/ (Singapore) http://scop.life.nthu.edu.tw/ (Taiwan) http://scop.berkeley.edu/ (USA) Protein Databases on the Internet

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SCOP describes the hierarchical relationship among proteins through the major levels of (homologous) family, superfamily, and fold. Proteins are clustered together into a (homologous) family if they have significant sequence similarity. Different families that have low sequence similarity but whose structural and functional features suggest a common evolutionary origin are placed together in a superfamily. Different superfamilies are categorized into a fold if they have the same major secondary structures in the same arrangement and with the same topological connections (the peripheral elements of secondary structure and turn regions may differ in size and conformation). Two superfamilies in the same fold may not have a common evolutionary origin. Their structural similarities may arise from the physics and chemistry of proteins favoring certain packing arrangements and chain topologies (Murzin et al., 1995). Figure 2.6.3 shows the SCOP interface using an example of protein 1gox in the PDB. Databases for Function-Based Protein Families There are various protein functional families classified from different perspectives. The ENZYME data bank (Bairoch, 1993) contains the following data for each enzyme: EC number, recommended name, alternative names, catalytic activity, cofactors, pointers to the SwissProt entry, and pointers to any disease associated with a deficiency of the enzyme. PROCAT is a database of three-dimensional enzyme active site templates (Wallace et al., 1996). PDD (Protein Disease Database; Lemkin et al., 1995; Merril et al., 1995) correlates diseases with proteins observable in serum, urine, and other common human body fluids based on biomedical literature. There are also a growing number of databases dedicated to special types of proteins, such as antibodies, G-protein-coupled receptors, HIV proteases, glycoproteins, and RNases, as shown in Table 2.6.1. OTHER DATABASES Protein Binding Databases Protein binding includes protein-substrate docking and protein-protein association. ReLiBase (Hendlich, 1998) is a database system for analyzing receptor-ligand complexes in the PDB. DIP (Database of Interacting Proteins) records protein pairs that are known to bind with each other. The information in DIP may provide information related to signaling pathways, multiple interactions, and complex systems. Protein Energetics Databases There are few databases for protein energetics, due to the low-throughput nature of the data source. One useful energetics database can be found in ProTherm (Thermodynamic Database for Proteins and Mutants; Gromiha et al., 1999). It contains thermodynamic data on mutations, including Gibbs free energy, enthalpy, heat capacity, and transition temperature. These data are important for understanding the structure and stability of proteins. Bibliographic Databases Searching for protein information through traditional bibliographic databases, such as MEDLINE or Grateful Med, can be rewarding. In addition, some bibliographic reference databases dedicated to proteins may provide certain information more directly. For example, SeqAnalRef stores papers dealing with sequence analysis.

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Figure 2.6.3 An example of the SCOP interface when searching the structure of 1gox in the PDB.

COMBINED DATABASES Introduction By integrating different types of protein databases together, a database of databases (or a data warehouse) can be built. Such combined databases not only serve as “one-stop shopping,” but also provide cross-references between entries in different databases. Two combined databases, Entrez and SRS, have been very successful. Entrez Entrez (Schuler et al., 1996) is a combined database consisting of literature, protein sequence and structure, nucleotide sequence, and taxonomy. Different types of information are interconnected through the grouping of sequences/structures and references by computed similarity scores. Entrez can be used through a variety of media, including CD-ROM, a custom Graphical Interface client, a World Wide Web browser, a command line browser (CLEVER), and the National Center for Biotechnology Information’s (NCBI’s) toolkit written in C. SRS SRS (Sequence Retrieval System; Etzold et al., 1996) is the most comprehensive database for molecular biology. The home server at http://srs.ebi.ac.uk supports 264 biological databases (as of March, 2003), including almost all the major protein/genetic databases. As an indexing system, it provides fast access to different databases through searches by sequence or by key words from various data fields. SRS also builds indices using cross-references between databases. An entry from one database can be linked to other databases that contain the entry. However, it should be noted that the contents of SRS may Protein Databases on the Internet

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lag behind the other databases in updating (i.e., some new entries in the original databases may not be included in SRS). SUMMARY This unit reviews several major protein databases on the Internet, and shows what kind of information users can expect from protein databases. Although all technical procedures cannot be described here, most of the protein databases are easy to use and provide detailed on-line manuals so that even users with little computer skill can learn them quickly. Protein databases may not always be easily accessible or usable through the Internet. Sometimes a database server may be down or the Internet connection may be interrupted. Some structures or image files are very large (several megabytes), and the download time may be long. It can be helpful to use a mirror site of the database at a close location in order to accelerate the access speed. For a frequent user, it may be worthwhile to install the database on a local machine. On the other hand, it must be kept in mind that a mirror site or a local copy may contain an older version of the database than the one on the home server. It is important to assess the quality of the data. There are three types of data in protein databases. (1) Experimental data are generally very reliable. However, some entries may contain errors (e.g., some protein sequences) or may be based on low-resolution data (e.g., some protein structures determined by NMR). (2) Annotation data uses computational techniques on experimental data, for example, secondary structure assignment and domain partition in structure. These data depend on the quality of the experimental data and the computational methods used. Different methods may yield different results. (3) Prediction data includes, for example, sequence domain parsing and three-dimensional structure prediction. No matter how good the method, the results are still predictions and should be subjected to experimental verification. In addition, different methods typically give different predictions. In summary, caution is needed when using the data from databases to draw a conclusion. It is worthwhile to check the same type of data from different databases and compare them. It is sometimes necessary to use additional computational tools (e.g., tools to assess the quality of a structure) for further analysis. LITERATURE CITED Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. 1997. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucl. Acids Res. 25:3389-3402. Attwood, T.K., Flower, D.R., Lewis, A.P., Mabey, J.E., Morgan, S.R., Scordis, P., Selley, J., and Wright, W. 1999. PRINTS prepares for the new millennium. Nucl. Acids Res. 27:220-225. Bairoch, A. 1993. The ENZYME data bank. Nucl. Acids Res. 21:3155-3156. Bairoch, A. and Apweiler, R. 1999. The SwissProt protein sequence data bank and its supplement TrEMBL in 1999. Nucl. Acids Res. 27:49-54. Barker, W.C., Garavelli, J.S., McGarvey, P.B., Marzec, C.R., Orcutt, B.C., Srinivasarao, G.Y., Yeh, L.L., Ledley, R.S., Mewes, H., Pfeiffer, F., Tsugita, A., and Wu, C. 1999. The PIR-international protein sequence database. Nucl. Acids Res. 27:39-42. Bateman, A., Birney, E., Durbin, R., Eddy, S.R., Finn, F.D., and Sonnhammer, E.L.L. 1999. Pfam 3.1: 1313 multiple alignments match the majority of proteins. Nucl. Acids Res. 27:260-262. Benson, D.A., Boguski, M.S., Lipman, D.J., Ostell, J., Ouellette, B.F., Rapp, B.A., and Wheeler, D.L. 1999. Genbank. Nucl. Acids Res. 27:12-17. Bernstein, F.C., Koetzle, T.F., Williams, G.J.B., Meyer, E.F., Brice, M.D., Rodgers, J.R., Kennard, O., Shimanouchi, T., and Tasumi, M. 1977. The protein data bank: A computer based archival file for macromolecular structures. J. Mol. Biol. 112:535-542. Bourne, P., Berman, H., Watenpaugh, K., Westbrook, J., and Fitzgerald, P. 1997. The macromolecular crystallographic information file (mmCIF). Methods Enzymol. 277:571-590.

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Corpet, F., Gouzy, J., and Kahn, D. 1999. Recent improvements of the ProDom database of protein domain families. Nucl. Acids Res. 27:263-267. Etzold, T., Ulyanov, A., and Argos, P. 1996. SRS: Information retrieval system for molecular biology data banks. Methods Enzymol. 266:114-128. Gerstein, M. and Krebs, W. 1998. A database of macromolecular motions. Nucl. Acids Res. 26:4280-4290. Gibrat, J.F., Madej, T., and Bryant, S.H. 1996. Surprising similarities in structure comparison. Curr. Opinion Struct. Biol. 6:377-385. Gromiha, M.M., An, J., Kono, H., Oobatake, M., Uedaira, H., and Sarai, A. 1999. Protherm: Thermodynamic database for proteins and mutants. Nucl. Acids Res. 27:286-288. Hendlich, M. 1998. Databases for protein-ligand complexes. Acta Crystallogr., Sect. D 1:1178-1182. Heniko, J.G., Heniko, S., and Pietrokovski, S. 1999. New features of the blocks database servers. Nucl. Acids Res. 27:226-228. Hofmann, K., Bucher, P., Falquet, L., and Bairoch, A. 1999. The PROSITE database, its status in 1999. Nucl. Acids Res. 27:215-219. Holm, L. and Sander, C. 1996. Mapping the protein universe. Science 273:595-602. Kraulis, P. 1991. MOLSCRIPT—a program to produce both detailed and schematic plots of protein structures. J. Appl. Crystallogr. 24:946-950. Laskowski, R.A., Hutchinson, E.G., Michie, A.D., Wallace, A.C., Jones, M.L., and Thornton, J.M. 1997. PDBsum: A web-based database of summaries and analyses of all PDB structures. Trends Biochem. Sci. 22:488-490. Lemkin, P.F., Orr, G.A., Goldstein, M.P., Creed, G.J., Myrick, J.E., and Merril, C.R. 1995. The protein disease database of human body fluids: I. Computer methods and data issues. Appl. Theor. Electrophor. 5:55-72. Liang, J., Edelsbrunner, H., and Woodward, C. 1998. Anatomy of protein pockets and cavities: Measurement of binding site geometry and implications for ligand design. Protein Science 7:1884-1897. Marchler-Bauer, A., Addess, K.J., Chappey, C., Geer, L., Madej, T., Matsuo, Y., Wang, Y., and Bryant, S.H. 1999. MMDB: Entrez’s 3D structure database. Nucl. Acids Res. 27:240-243. Merril, C.R., Goldstein, M.P., Myrick, J.E., Creed, G.J., and Lemkin, P.F. 1995. The protein disease database of human body fluids: I. Rationale for the development of this database. Appl. Theor. Electrophor. 5:49-54. Mural, R.J., Parang, M., Shah, M., Snoddy, J., and Uberbacher, E.C. 1999. The Genome Channel: A browser to a uniform first-pass annotation of genomic DNA. Trends Genet. 15:38-39. Murvai, J., Vlahovicek, K., Barta, E., Szepesvari, C., Acatrinei, C., and Pongor, S. 1999. The SBASE protein domain library, release 6.0: A collection of annotated protein sequence segments. Nucl. Acids Res. 27:257-259. Murzin, A.G., Brenner, S.E., Hubbard, T., and Chothia, C. 1995. SCOP: A structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol. 247:536-540. Nayal, M., Hitz, B.C., and Honig, B. 1999. GRASS: A server for the graphical representation and analysis of structures. Protein Sci. 8:676-679. Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., and Kanehisa, M. 1999. KEGG: Kyoto encyclopedia of genes and genomes. Nucl. Acids Res. 27:29-34. Orengo, C.A., Michie, A.D., Jones, D.T., Swindells, M.B., and Thornton, J.M. 1997. CATH—a hierarchic classification of protein domain structures. Structure 5:1093-1108. Rebhan, M., Chalifa-Caspi, V., Prilusky, J., and Lancet, D. 1998. GeneCards: A novel functional genomics compendium with automated data mining and query reformulation support. Bioinformatics 14:656-664. Rodriguez-Tom, P., Stoehr, P.J., Cameron, G.N., and Flores, T.P. 1996. The European Bioinformatics Institute (EBI) databases. Nucl. Acids Res. 24:6-13. Sayle, R.A. and Milner-White, E.J. 1995. RASMOL: Biomolecular graphics for all. Trends Biochem. Sci. 20:374-376. Schuler, G.D., Epstein, J.A., Ohkawa, H., and Kans, J.A. 1996. Entrez: Molecular biology database and retrieval system. Methods Enzymol. 266:141-162. Shindyalov, I.N. and Bourne, P.E. 1995. WPDB: A PC-based tool for analyzing protein structure. J. Appl. Crystallogr. 28:847-852. Shindyalov, I.N. and Bourne, P.E. 1998. Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Eng. 11:739-747. Shnel, J. 1996. Image library of biological macromolecules. Comput. Appl. Biosci. 12:227-229. Protein Databases on the Internet

Siddiqui, A.S. and Barton, G.J. 1995. Continuous and discontinuous domains: An algorithm for the automatic generation of reliable protein domain definitions. Protein Sci. 4:872-884.

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Sobolev, V., Sorokine, A., Prilusky, J., Abola, E.E., and Edelman, M. 1999. Automated analysis of interatomic contacts in proteins. Bioinformatics. 15:327-332 States, D.J., Harris, N.L., and Hunter, L. 1993. Computationally efficient representation of classes in protein sequence megaclassification. Proc. Intel. Syst. Mol. Biol. 1:387-394. Tatusov, R.L., Koonin, E.V., and Lipman, D.J. 1997. A genomic perspective on protein families. Science 278:631-637. University of Wisconsin. 1999. BioMagResBank. University of Wisconsin, Madison, Wis. Vriend, G. 1990. WHAT IF: A molecular modelling and drug design program. J. Mol. Graphics 8:52-56. Wallace, A.C., Laskowski, R.A., and Thornton, J.M. 1996. Derivation of 3D coordinate templates for searching structural databases: Application to Ser-His-Asp catalytic triads in the serine proteinases and lipases. Protein Sci. 5:1001-1013. Wu, C., Shivakumar, S., and Huang, H. 1999. Proclass protein family database. Nucl. Acids Res. 27:272-274.

INTERNET RESOURCES The Web addresses of the databases mentioned in this unit are listed in Table 2.6.1. Readers can find more protein databases and their related tools in the following Web pages, which collect a large number of useful links. http://compbio.ornl.gov/structure/resource/ Oak Ridge National Laboratory’s resources of protein modeling tools. http://www.public.iastate.edu/~pedro/research_tools.html Pedro’s biomolecular research tools. http://www.expasy.org/alinks.html Amos’ WWW links page.

Contributed by Dong Xu and Ying Xu Oak Ridge National Laboratory Oak Ridge, Tennessee

The authors thank Drs. Edward C. Uberbacher, Michael A. Unseren, Jay Snoddy, and Gwo-liang Chen for helpful discussions. This work is supported by the Office of Biological and Environmental Research, U.S. Department of Energy, under Contract DE-AC05-00OR22725, managed by UT-Battelle, LLC.

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Protein Tertiary Structure Prediction

UNIT 2.7

Predicting the three-dimensional structures of proteins from their amino acid sequences using computational methods is a very important and challenging problem in contemporary molecular biology. The three-dimensional structures of proteins not only define their biological functions, but also provide a key to engineering proteins and to designing drugs that target proteins related to disease. Protein tertiary structure prediction provides timely tools on account of rapid progress being made in genome sequencing, which is proceeding at a rate many times faster than experimental determination of protein structures. In conjunction with supporting experimental data, these tools can provide valuable insights into protein structure and function. In particular, predicted structures often give experimentalists some direction for further studies. Protein tertiary structure prediction is not only important, but also becomes more and more practical. Many nontrivial structure predictions (Nilges and Brünger, 1993; Hu et al., 1995; Madej et al., 1995) obtained prior to experimental structures turned out to be fairly accurate. Most notably, the success of protein structure prediction has been demonstrated in community-wide experiments on the Critical Assessment of Techniques for Protein Structure Prediction (CASP; see CASP, 1995, 1997, 1999). In each of the CASP experiments, crystallographers and NMR spectroscopists were solicited for the names of proteins whose structures were likely to be solved before a given deadline. The sequences of these target proteins were made available, and predictors submitted their models by the deadline. These blind predictions were then assessed through comparison with the experimental structures. It has been shown that many predictions in CASP have good qualities. There are three types of tertiary structure predictions: homology or comparative modeling, fold recognition, and ab initio structure prediction. Homology modeling constructs the coordinates of all the atoms in a query protein based on sequence alignment between the query protein and another protein of known three-dimensional structure. Fold recognition identifies a suitable fold for the query sequence from a structure library and provides an alignment between the query protein and the fold. It consists of two approaches, the sequence profile approach and the sequence-structure comparison (threading) approach. The ab initio structure prediction attempts to predict protein structure based directly on physicochemical principles. Homology modeling has been widely used. It can provide relatively reliable atomic coordinates with a low root mean square deviation (RMSD) between a model and a high-quality experimental structure. Fold recognition, which often generates useful backbone structures, also starts to gain ground. The ab initio prediction is still far from general application, although a few isolated successful predictions have been reported. The boundaries between the different types of predictions are becoming blurred as researchers start to combine them together. For example, one can use the alignment derived from fold recognition in homology modeling, or assemble partial structures predicted by threading in ab initio prediction. Both homology modeling and fold recognition rest on the foundation that the three-dimensional structures of proteins have been better conserved during evolution than their sequences. The relationship between a query protein and its template in a structure database can be classified at different hierarchical levels according to their structural and evolutionary relationship. A widely used classification consists of family, superfamily, and fold (Murzin et al., 1995). A query protein and its template are clustered into a family if they have clear evolutionary relationship with significant sequence identity between them (often 25% or higher). If two proteins have low sequence identity, but their structural Contributed by Dong Xu and Ying Xu Current Protocols in Protein Science (2000) 2.7.1-2.7.17 Copyright © 2000 by John Wiley & Sons, Inc.

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and functional features suggest that a common evolutionary origin is probable, then they are placed in the same superfamily. If two proteins have the same major secondary structures in the same arrangement and with the same topological connections, but they have no significant sequence similarity and may not have a common evolutionary origin, they are defined as having the same fold. It is estimated that the number of folds in all genomes ranges from 650 to 15,000, or less than one thousandth of the number of proteins (Wang, 1998). Different methods of structure predictions are better suited to proteins with different relationship to their templates. When a query protein and its template belong to the same family and they have high sequence identity to produce an unambiguous sequence alignment, homology modeling is the best choice and it typically produces a high-quality model including sidechain atoms. About 17% of identified genes fall into this category (Sanchez and Sali, 1998). The sequence profile approach is suitable for the following two cases: (1) a query protein and its template belong to the same family, but they do not have sufficient sequence identity to produce an unambiguous sequence alignment, or (2) they belong to the same superfamily, and the multiple-sequence alignment of the query protein has a strong pattern. For these two cases, which are also referred to as “remote homologs,” the sequence profile approach typically identifies the structure template with high confidence and good alignment. It extends the genome-wide coverage from 17% to ∼30% to 40% (Gerstein, 1998). The threading approach becomes the major tool for the following two cases: (1) a query protein and its template belong to the same superfamily, but the query protein has few proteins with significant sequence similarities in sequence databases, or the multiple-sequence alignment of the query protein has a weak pattern, or (2) they belong to the same fold, and threading gives a good confidence level for the prediction. This category currently accounts for an additional ∼10% to 20% of identified genes (Jones, 1999). If a query protein and its template belong to the same fold, but threading cannot recognize the template with a good confidence level, or the template does not exist in the structure database (i.e., the query protein is a new fold), it can only be predicted by ab initio methods. In total, structure predictions for about half of the genes do not have to use ab initio prediction and can provide useful information reliably. This number is growing as more and more structures are being solved. This unit provides an overview for homology modeling, fold recognition, and ab initio prediction. It introduces several major protein structure prediction tools. For each tool, it addresses (1) the method, (2) the technical protocol, (3) the expected result, and (4) the confidence level of the prediction. The Web addresses of the major structure prediction tools are listed in Table 2.7.1. The reader can find more detailed information about the tools in their Web pages or manuals. The examples used in this unit are all from the third CASP experiment (CASP-3) held in 1998. This blind test provides an up-to-date benchmark regarding the results that a user can expect from these prediction tools. Among the examples used, the models for t0053, t0067, t0068, and t0074 are from the authors’ own predictions submitted to CASP-3 (Xu et al., 1999). All the results are based on the programs and databases as of August, 1999. HOMOLOGY MODELING

Protein Tertiary Structure Prediction

A homology modeling method typically consists of three steps: (1) identification of protein templates with known three-dimensional structures and production of an alignment between the query sequence and its templates, (2) building a model for the query protein based on its alignment with the template structures, and (3) evaluation of the quality of the model.

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Table 2.7.1

Selected Protein Structure Prediction Tools

Program

Web site

Typea

Sequence alignment: ALIGN

http://www2.igh.cnrs.fr/bin/align-guess.cgi

Server

BLAST FASTA

http://www.ncbi.nlm.nih.gov/BLAST/ http://www.embl-heidelberg.de/cgi/fasta-wrapper-free

Server Server

KESTREL SSEARCH

http://www.cse.ucsc.edu/research/kestrel/ http://vega.igh.cnrs.fr/bin/ssearch-guess.cgi

Server Server

Homology modeling: COMPOSER

http://www-cryst.bioc.cam.ac.uk

SGI executable

CONGEN

http://www.tripos.com/software/composer.html http://www.congenomics.com/congen/congen toc.html

SGI module (GUI) Executables

CPHmodels DRAGON

http://www.cbs.dtu.dk/services/CPHmodels/ http://www.nimr.mrc.ac.uk/∼mathbio/a-aszodi/dragon.html

Server SGI executable

LOOK MODELLER

http://www.mag.com/products/look.html http://guitar.rockefeller.edu/modeller/

Module (GUI) Executables

SwissModel

http://www.msi.com/solutions/products/insight/modules/Modeler.html SGI module (GUI) Server http://www.expasy.ch/swissmod/SWISS-MODEL.html

WHAT IF

http://www.sander.embl-heidelberg.de/whatif/

Executables

http://www.ncbi.nlm.nih.gov/BLAST/ http://www.cse.ucsc.edu/research/compbio/sam.html

Server Server

Sequence profile methods: PSI-BLAST SAM-T99

Singleton-potential threading: 123D http://www-lmmb.ncifcrf.gov/∼nicka/123D.html

Server

SAS TOPITS

http://www.biochem.ucl.ac.uk/bsm/sas/ http://dodo.cpmc.columbia.edu/predictprotein/

Server Server

UCLA-DOE

http://www.doe-mbi.ucla.edu/people/frsvr/frsvr.html

Server

Pairwise-potential threading: NCBI Package PROFIT

http://www.ncbi.nlm.nih.gov/Structure/ http://lore.came.sbg.ac.at

Executables Executables

PROSPECT THREADER

http://compbio.ornl.gov/structure/prospect/ http://globin.bio.warwick.ac.uk/∼jones/threader.html

Executables Executables

ToPLign

http://cartan.gmd.de/ToPLign.html

Server

aExecutables without a specified machine type can be run on multiple platforms. Abbreviations: GUI, Graphic User Interface; SGI, Silicon Graphic,

Inc.

Template Search and Alignment Conventional homology modeling requires that the sequence similarity between a query sequence and its template sequence be significant, so that using a pairwise sequence-sequence alignment can identify the template and produce an unambiguous alignment. The most widely used sequence-sequence alignment tool is BLAST (UNIT 2.5), which is very fast. Using BLAST to search against the Protein Data Bank (PDB; Bernstein et al., 1977) is probably the best starting point for homology modeling. If the identified templates or alignments have any uncertainty, more sensitive but slower methods (e.g., FASTA; Pearson and Lipman, 1988) can be used. An even slower but much more sensitive method

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is the Smith-Waterman alignment (Smith and Waterman, 1981), which has been implemented in SSEARCH and KESTREL (Hughey, 1996). Templates and alignments using fold recognition methods, as described in the following two sections, can also be used for homology modeling. In fact, it has been shown in CASP-3 that profile-based alignment methods and threading methods can produce more accurate alignments than single-sequence alignment methods even when the query protein and its template have some sequence similarity (CASP, 1999). It is sometimes important to find as many templates as possible with a multiple-sequence alignment. Template search and alignment are essential for the correctness and the quality of a homology model. Homology modeling programs always generate a structure for any query sequence using the conformation of the template structures and the alignments between the query protein and its templates. If the templates or the alignments are incorrect, the output model will certainly be a result of “garbage in, garbage out.” Building Atomic Models Different homology modeling methods use different ways to construct a three-dimensional model from given templates and alignments. Some automated homology modeling servers, e.g., SwissModel (UNIT 2.8; Peitsch, 1996) and CPHmodels (Lund et al., 1997), provide interfaces for submitting a sequence and obtaining the model interactively or through e-mail. The use of SwissModel is described in detail in UNIT 2.8. These servers are fast and easy to use. The program WHAT IF (Vriend, 1990) provides the option to construct a crude model quickly or to build a structure using a better, but much slower, method (several hours for a large protein, e.g., >300 amino acids). Another program is COMPOSER (Srinivasan and Blundell, 1993), which has a free academic version and is also integrated into the commercial molecular modeling package SYBYL (Tripos, 1999). It has a specific tool for dealing with loop regions, which contain gaps in the alignment. COMPOSER under SYBYL also provides an interactive Graphic User Interface (GUI) for model building, which allows a user to edit at each step. The most widely used homology modeling program is MODELLER (Sali and Blundell, 1993), which has a free academic version as well as a commercial version integrated into the molecular modeling package INSIGHT (Molecular Simulations, 1998). The model structure starts with an extended strand, and then folds into a compact one by satisfying spatial restraints derived from the alignment between the query sequence and its templates of known structures. In particular, it tries to preserve main chain dihedral angles or hydrogen bonding features from the template structures. Meanwhile, MODELLER uses physical force fields to prevent interatomic clashes. In loop regions with gaps in the alignment, MODELLER uses statistical information derived from the alignment of many proteins of known three-dimensional structure. The final three-dimensional model is obtained by optimization through conjugate gradients and molecular dynamics with simulated annealing. MODELLER provides a wide range of options for structure construction and refinement.

Protein Tertiary Structure Prediction

t0074 (the second EH domain of eps15), which was predicted by the authors, can be used as a CASP-3 example to show some basic features of MODELLER. Constructing a model requires at least three input files: the three-dimensional coordinate file for the template, the alignment file between the query protein and the template, and a script file to run MODELLER. The template found for t0074 is the PDB entry 1ahr (calcium-binding protein), which has three-dimensional coordinates recorded in the file 1ahr.atm (the same file as 1ahr.pdb in the PDB database). The alignment between t0074 and 1ahr is saved in the file 1ahr.ali using the PIR database format as follows:

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>P1;t0074 sequence:t0074: 1 : : 72 : : casp-t0074: : PWAVKPE--DKAKYDAIFDSLSP-VNGFLSGDKVKPVLLNS--KLPVDILGRVWELSDIDHDGML DRDEFAVAMFLV* >P1;1ahr structureX:1ahr: 73 : : 148 : : template ARKMK-D--SEEEIREAFRVFDKDGNGFISAAELRHVMTNLGEKLTDEEVDEMIREADIDGDGQV NYEEFVTMMTSK*

where the first line of each entry specifies the protein code after the “>P1", the second line describes the type, the name, and the residue ranges, and the third line provides the sequence. The following is a sample script file, run.top, for building the model for the query protein t0074: INCLUDE

# Include predefined routines

SET TOPLIB = ’${LIB}/top_allh.lib’ # topology library SET PARLIB = ’${LIB}/par.lib’ # parameters library SET HYDROGEN_IO = ’ON’ SET HETATM_IO = ’ON’

# include hydrogen atoms # include HETATM entries in template

SET ALNFILE = ’1ahr.ali’ SET KNOWNS = ’1ahr’ SET SEQUENCE = ’t0074’

# alignment filename # template name # target sequence name

CALL ROUTINE = ’model’

# do homology modeling

The text following “#” is commentary that will be ignored when the script is run. To run the script, simply type mod run.top

It typically takes several minutes to an hour to run a MODELLER job, depending on the protein sizes and the machine used. After the job mod run.top is finished, a file (t0074.B99990001) containing the three-dimensional coordinates for the atomic model of t0074 is generated, as shown in Fig. 2.7.1A. MODELLER can easily incorporate more sophisticated modeling protocols. For example, several more templates can be used at the same time by changing the SET KNOWNS entry in run.top. For example, to add three templates in addition to “1ahr,” SET KNOWNS would be changed to: SET KNOWNS = ’1ahr’ ’4cln’ ’1cmg’ ’1cdla’

and the alignments of all four templates with the query sequence would be provided in 1ahr.ali. MODELLER can also use specified information (such as NMR data, secondary structure types, and disulfide bonds) as spatial restraints. The following shows an example for building a model with the constraint of disulfide bonds between certain residues: SUBROUTINE ROUTINE = ’special_patches’ # A disulfide between residues 5 and 60: PATCH RESIDUE_TYPE = ’DISU’, RESIDUE_IDS = ’5’ # A disulfide between residues 22 and 38: PATCH RESIDUE_TYPE = ’DISU’, RESIDUE_IDS = ’22’ RETURN END_SUBROUTINE

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A

B

Figure 2.7.1 A comparison between the predicted structure using MODELLER (thick lines) and the experimental structure (thin lines) for CASP-3 targets t0074 (A) and t0068 (B). This figure was made using visual molecular dynamics (VMD; Humphrey et al., 1996).

Model Assessment and Refinement The quality of a model depends primarily on the sequence identity between the query protein and the template. The higher the sequence identity, the more accurate a structure homology modeling can provide. As shown in Figure 2.7.1A, there are some conformational differences between the model and the experimental structure, mainly due to the structural differences between the template and the query protein. In this case, the sequence identity between the query protein and the template is only 15%. For another CASP-3 target, t0068, the sequence identity between the query protein and the template is 25%. As shown in Figure 2.7.1B, the model superimposes the experimental structure better than that of t0074. For high sequence identity, homology modeling often produces models with an all-atom RMSD of <2 Å between the model and the experimental structure (similar to the typical quality of an NMR structure). Another challenge in homology modeling is construction of regions with large alignment gaps. Although loops with short alignment gaps can often be modeled successfully, insertions of ∼8 residues or more in the query sequence usually cannot be modeled reliably.

Protein Tertiary Structure Prediction

It is important to assess models using other computational tools. When the alignment is correct, a homology model often has good stereochemistry and interresidue packing with a smaller backbone RMSD to the experimental structure than the template used. When the alignment has errors or long gaps, it is likely to generate errors, including (1) bad backbone conformations, e.g., artificial cis peptide bonds; (2) poor stereochemistry, e.g., unwanted D-amino residues; or (3) unfavorable interresidue packing. These errors can be detected using programs such as WHAT IF (Vriend, 1990) and PROCHECK (Laskowski et al., 1993). The overall quality of a model can be further assessed by PROVE (PROtein

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Volume Evaluator; Pontius et al., 1996), which checks the departures of the assessed structure from the standard atomic volumes in high-quality experimental structures. If errors are found, the alignment can be adjusted and the model can be rebuilt. Another method is to generate multiple models and find the best model with the least errors. It may be necessary to repeat the process of alignment, model construction, and assessment until a satisfactory model is obtained. SEQUENCE PROFILE METHODS Sequence profile methods detect remote homologs based on a profile in multiple-sequence alignments. Pairwise sequence alignments requires high sequence identity (typically 25% or more) for reliable results. A profile of close homologs can significantly increase the underlying signal while reducing noise, and hence a much lower sequence identity (as low as 15%) is needed for a high-quality alignment. As a huge number of protein sequences are being solved, sequence profile methods become more and more useful for finding a remote homolog with known structure to use as the template for a query protein. There are two successful tools that use sequence profile methods: PSI-BLAST (Altschul et al., 1997; UNIT 2.5) and SAM-T98 (for Sequence Alignment Modeling system; Karplus et al., 1998; Park et al., 1998). The two programs use different methods, as described below. PSI-BLAST is very fast, and the results of each iteration can be returned from the Web server in seconds. It also provides a statistical significance assessment value (EXPECT) for reporting the number of matches that would be expected by chance, given the profile used at the current iteration, according to a stochastic model. PSI-BLAST also allows users to select parameters and proteins for building sequence profiles interactively. On the other hand, SAM-T98 is slower but more sensitive. Compared to PSI-BLAST, SAM-T98 detects more remote homologs and generates fewer false positives at any level of true positives (Park et al., 1998). A statistical significance assessment like the PSIBLAST’s EXPECT value is not available in SAM-T98, but has been added in the newly released SAM-T99. Unlike PSI-BLAST, SAM-T98 and SAM-T99 as e-mail servers do not have the ability to interactively edit parameters and proteins for building sequence profiles during a search process. Users can search using both PSI-BLAST and SAM-T99 and compare the results when any uncertainty exists. PSI-BLAST The Position-Specific Iterated BLAST (PSI-BLAST) program (Altschul et al., 1997) searches the protein database using a position-specific score matrix derived from a multiple-sequence alignment on similar sequences found by BLAST. The search is carried out iteratively until a satisfactory match is found or the search is converged (typically a total of three to four iterations). At each iteration, the position-specific score matrix is updated using the new sequences in addition to the sequences found in previous iterations. One can use PSI-BLAST with a default setting for parameters. However, more remote homologs can be found by trying different settings. CASP-3 target t0081 (methylglyoxal synthase) can be used as an example. PSI-BLAST can be run via the Web server at http://www.ncbi.nlm.nih.gov/cgi-bin/BLAST/ nph_psi_blast. After submitting the sequence for a search using the default setting, the returned result is shown in Figure 2.7.2. If a user continues to use the default in the second iteration, the search will be converged without hitting any new matches with known structure. However, if a user selects the five additional entries whose EXPECT values are <0.01 and then carries out the search using the default setting in subsequent iterations, a

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Figure 2.7.2 The first iteration result of PSI-BLAST for the CASP-3 target t0081. The entries above the “Run PSI-Blast iteration 2" button will be used by the default (with the EXPECT value, listed at the end of each entry, <0.001). The entries below the button are optional to use in the search of the next iteration.

template with known structure will be found at the fourth iteration. The template is carbamoyl phosphate synthetase (chain B of 1jdb in the PDB), whose alignment to t0081 is shown as follows: Score = 74.7 bits (181), Expect = 3e-13 Identities = 23/147 (15%), Positives = 46/147 (30%), Gaps = 9/147 (6%) Query: 2

Sbjct: 990

ELTTRTLPARKHIALVAHDHCKQMLMSWVERHQPLLEQHVLYATGTTGNLISRATGMNVN + T+ L + K+ ++ + L + L AT T ++ A G+N LGSNSTMKKHGRALLSVREGDKERVVDLAAKL--LKQGFELDATHGTAIVLGEA-GINPR AMLSGPMGGDQQVGALISEGKIDVLIFFWDPLNAVPHDPDVKALLRLATVWNIPVATNVA + G + I G+ +I A+ D + + R A + + T + LVNKVHEGRP-HIQDRIKNGEYTYIINTTSGRRAIE---DSRVIRRSALQYKVHYDTTLN

Query: 122

148

Sbjct: 933 Query: 62

TADFIIQSPHFNDAVDILIPDYQRYLA + N + Q A Sbjct: 1046 GG--FATAMALNADATEKVISVQEMHA

Protein Tertiary Structure Prediction

61 989 121 1045

1070

It turned out that the PDB entry 1jdb is the correct template for t0081, and that the above alignment basically agrees with the structure-structure alignment. This example shows that the profile derived from the multiple-sequence alignment can significantly increase the sensitivity and reliability of a sequence search. However, a user should be careful when interpreting the meaning of the EXPECT value (3 × 10−13 in this case). Because EXPECT values are based on the profile used at the current iteration, the validity of using a low EXPECT value as evidence for relationship of a template to the original query protein is contingent upon previous iterations being accurate (Altschul and Koonin, 1998). The significance level for the match between a hit sequence and the query protein is limited to the maximum EXPECT value of all the sequences selected along the search

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path. Hence, changing the default setting to include more proteins to build the profile can be tricky. If improper entries are selected in the iterations, a conflated profile may be built, and some resulting matches could give wrong templates even when their EXPECT values are low. In the example of t0081, the manually added five entries have relatively small EXPECT values; more importantly, they are all synthetases, which are functionally related to the query protein (methylglyoxal synthase). Using these heuristics may help to avoid a conflated profile when adding more entries. Hidden Markov Models Hidden Markov models (HMMs) detect remote homologs with known structures using the following steps (Krogh et al., 1994): (1) a standard sequence-based search to find homologs (not necessarily with known structures) for a query sequence; (2) construction of an HMM based on the alignments between the query sequence and its homologs to describe the position-dependent amino acid (including deletion and insertion) probability distributions; and (3) a search from a library of templates (sequences of proteins in the PDB) to match the constructed HMM model. Similarly, a separate HMM can be constructed for each template, and the query sequence can be scored against each model. Among several computer packages based on HMMs, the most widely used package for finding a structure template of a query protein is SAM-T98/SAM-T99 (Karplus et al., 1998; Park et al., 1998), a particular application of the Sequence Alignment and Modeling system (SAM; Hughey and Krogh, 1996). The SAM-T99 server for identifying templates in the PDB is at http://www. cse.ucsc.edu/research/compbio/sam.html. To run a search, a user needs to enter the returning e-mail address and the query sequence. The result will automatically be sent back via e-mail. The following are the templates that SAM-T99 found for the CASP-3 target t0083 (cyanase): % Sequence ID 1lliA 1lliB 1lrp 1lmb4 1lmb3 1lmdC 1lmdD 1lmdB 1lmdA 1b0nA

Length 92 92 92 92 92 236 236 236 236 111

Simple −17.10 −17.10 −16.51 − 16.51 −16.51 −16.12 −16.12 −16.12 −16.12 −14.99

Reverse −10.38 −10.38 −9.95 −9.95 −9.95 −9.61 −9.61 −9.61 −9.61 −9.21

X count

where simple and reverse are two different scores. Reverse scores are more reliable than simple scores, and the templates are sorted by reverse scores. The first hit, 1lliA, is a good template for t0083, and the SAM-T99 alignment between t0083 and 1lliA is basically correct, as shown in the following: ; 1 user-seq-28992.seqs(1) ; 2 1lliA 1 MIQSQINRNIRLDLADAIL....LSKAKKDLSFAEIADGTGLAEAFVTAALLGQQALPADAARLV 2 STKKKPLTQEQLEDARRLKaiyeKKKNELGLSQESLADKLGMGQSGIGALFNGINALNAYNAALL 1 GAKLDLDEDSILLLQMIPLRGCIDDRIPTDPTMYRFYEMLQVYGTTLKALVHEKFGDGIISAINF 2 AKILKVSVEEFSPSIARE--------------IYEMYEAVS-----------------------1 KLDVKKVADPEGGERAVITLDGKYLPTKPF 2 -----------------------------Quantitation of Protein Interactions

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THREADING Protein threading (Bowie et al., 1991; Godzik et al., 1992; Jones et al., 1992; Sippl and Weitckus, 1992; Bryant and Altschul, 1995; Alexandrov et al., 1996; Fischer et al., 1996; Xu et al., 1998; Crawford, 1999) is the most promising and practical tool for fold recognition as demonstrated in the CASP tests. The basic idea of threading can be summarized as follows. Given a query protein sequence, s, of unknown structure, threading searches all structure templates, T, to find the best fit for s. A threading requires four components (Smith et al., 1997): (1) a library, T, of representative three-dimensional protein structures for use as templates; (2) an energy function to describe the fitness between s and t, where t is a single template in T; (3) a threading algorithm to search for the lowest energy among the possible alignments for a given s-t pair; and (4) a criterion to estimate the confidence level of the predicted structure. The threading approach can be further subdivided into two categories: (1) singleton-potential threading, which only considers the preference of amino acids in the query sequence at single sites of the templates, and (2) pairwise-potential threading that uses the preference on pairs (or even triplets) of amino acids in the query sequence within a contact distance when they are aligned to a given structure. Pairwise-potential threading can also include the singleton energy. Both methods allow gaps in alignments, but the gaps in pairwise-potential threading are often assumed to be in noncore regions (loops and possibly edges of α helices and β sheets) to reduce the search space. Such an assumption is generally true in the structure-structure alignment. In general, singletonpotential threading is faster, whereas pairwise-potential threading is more sensitive in detecting the correct templates. The threading approach generally has a better chance of selecting the right template among the top hits in the ranking than does the sequence profile approach. However, it is difficult to tell whether or not the template of the query protein is in the threading database (i.e., the top hits may be false positive matches). Some templates with similar folds to the query protein may be missed mainly due to the limitation of the energy functions used. A key difficulty for threading is that the structure profile and the residue pairs derived from the template may not describe the corresponding information in the query protein, due to the structure difference between the two proteins, even when they share a common fold. This is a more serious problem in the fold category than in the superfamily category. In CASP-3, almost every protein in the superfamily category was predicted correctly by at least one threading program. However, few proteins in the fold category were predicted correctly by any method. Singleton-Potential Threading Singleton-potential threading constructs a one-dimensional structure profile for each residue position in a template structure using local three-dimensional environmental information such as secondary structure type, degree of environmental polarity, and the fraction of the residue surface accessible to solvent. The energy function is based on the compatibility of the 20 common amino acids for each position in the one-dimensional structure profile. The compatibility is derived from the statistics of the whole template database. Optimal one-dimensional alignments between a query sequence and a template can be determined by dynamic programming. The final template is selected according to the optimal score or its statistical significance. Singleton-potential threading can incorporate secondary structure predictions and position-dependent profiles based on multiple-sequence alignments into the energy function. Protein Tertiary Structure Prediction

Several servers are available for singleton-potential threading, e.g., 123D (Alexandrov et al., 1996), TOPITS (Rost, 1995), SAS (Milburn et al., 1998), and the UCLA-DOE Structure Prediction Server (Fischer and Eisenberg, 1996). The following example uses

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the 123D server, which is located at http://www-lmmb.ncifcrf.gov/∼nicka/run123D.html, and which returns the results interactively from the Web page. An improved version of 123D can be found at http://cartan.gmd.de/ToPLign.html, which sends back results via e-mail. The following output shows the best templates selected by 123D for the CASP-3 target t0079 (the MarA protein): PDB structure raw score 1mseCx 105 a.a. 352 1sra_x 151 a.a. 274 1rcb_x 129 a.a. 58 1yrnBx 78 a.a. 48 1tam_x 120 a.a. 31 1mmoGx 162 a.a. 28 1octCx 131 a.a. 17 1bip_x 122 a.a. 13 ...

Z-score 2.16 1.51 −0.28 −0.37 −0.51 −0.53 −0.62 −0.66

a.a. aligned 99 116 109 73 106 105 100 101

% identities 18 10 17 24 13 24 19 14

Each entry of the output shows the template name, size, raw threading score, Z-score, number of aligned residues, and the sequence identity of the optimal alignment. The Z-score attempts to show the statistical significance of the alignment, but it may not work well in some cases. The optimal alignment between t0079 and 1mseC (c-Myb DNA-binding domain) returned by 123D is as follows: 1mseCx 352 bbb aaaaaaaaaa aaaaaa aaaaaaaaaa MTMSRRNTDAITIHSILDWIEDNLESPLSLEKVSERSGYSKWHLQRMFKKETGHSLGQYI * .. * ::. : . *.: : * .:*.: : *: *. .*. MLIKG------------PWTKEEDQRVIKLVQ---KYGPKRWSV--IAKHLKGR-IGKQC aaaaaaaaaaaaa aa aaaa aaaaaa aaaaa aaaaaaaaaaaa aaaaaaaaa RSRKMTEIAQKLK-----ESNEPILY-LAERYGFESQQTLTRTFKNYFDVPPHKYRMTNM *.* . :.* *... *:* . :*.* :. . :.: :.. * .... ..* RERWHNHLNPEVKKTSWTEEEDRIIYQAHKRLG-NRWAEIAKLLPGRTDNAIKNHWNSTM aaaaa aaaaa aaaaaaaaaaaaaa aaaaaaaa aaaaaaaa QGESRFLHPLNHYNS :.: . RRK-----------V

Among the five lines in the alignment, the second and fourth lines are the query protein sequence and the template sequence, respectively. The first and fifth lines are the predicted secondary structures and the secondary structures of the template, respectively, where a and b are α helix and β sheet, respectively. The third line is the match between the query sequence residues and the template residues, indicating where the two alignment residues are identical (*), similar (:), or related (.). Compared to the experimental structure of t0079, the template 1mseC found by 123D is a correct fold, although its alignment contains apparent errors. Both t0079 and 1mseC are DNA-binding proteins with helixturn-helix motifs. Even though the alignment is not perfect, the prediction may still provide useful information for the query protein. Pairwise-Potential Threading Pairwise-potential threading considers the propensity of two amino acids to be within a specified distance using a score function compiled from a database of structures, typically in addition to the singleton energy. In the recent CASP-3, it was clear that the top performers for fold recognition were among the groups using pairwise-potential threading methods. Several pairwise-potential threading programs are available, including the NCBI Threading Package (Bryant and Lawrence, 1993), PROFIT (Sippl and Weitckus,

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1992), PROSPECT (Xu et al., 1998), and THREADER (Jones et al., 1992). PROSPECT is used below to demonstrate how pairwise-potential threading works. PROSPECT (PROtein Structure Prediction and Evaluation Computer Toolkit) guarantees to find the globally optimal alignments for a given energy function with pairwise interactions. The system runs efficiently when using a distance cutoff. Two sets of threading templates are available: protein chains (defined by an FSSP nonredundant set; Holm and Sander, 1996) and compact domains (defined by the DALI nonredundant domain library; Holm and Sander, 1998). The system allows a user to input experimental knowledge and results from other computational tools into the threading process as restraints. The restraints include disulfide bonds, a match between a residue on the query sequence and a residue on a template, the secondary structure prediction by the neural net program PHD (an automatic e-mail server at http://dodo.cpmc.columbia.edu/predictprotein/; Rost and Sander, 1993), and the position-dependent profile based on the multiple-sequence alignment using SAM (Hughey and Krogh, 1996). PROSPECT also provides a confidence assessment using the Z-score and the compactness of the model. It has an interface to MODELLER for generating atomic structures. PROSPECT can be run in a very simple mode, but includes a wide range of options available to advanced users. The detailed manual of PROSPECT can be found at http://compbio.ornl.gov/structure/prospect/. The CASP-3 target t0057 (the CbiK protein) is used here as an example. To run the program in a default mode, a user only needs to prepare a file, t0053.seq, for the sequence of the protein, and to run the command: prospect t0053.seq

After the program is finished, another file, t0053.sort, will be generated for the ranking of the templates, while a third file, t0053.out, will record alignments and other detailed information. One can find the template with the optimal score (1ak1) and its alignment. To obtain the atomic structure for the query protein based on the threading result, one can simply run: prospect -m t0053.out 1ak1

Advanced options can be used to refine the prediction and yield a better result. For example, the authors refined the alignment between t0053 and the template 1ak1 using the information of an active site. Through a detailed analysis, it was found that 1ak1 has an active site at His-183 (Al-Karadaghi et al., 1997). Searching the BLOCK database (Heniko and Heniko, 1994), His-145 of t0053 was identified as the corresponding active site. PROSPECT was then run using the constraint that His-145 of t0053 is aligned with His-183 of 1ak1. This requires a configuration file, t0053.conf, to include: ANCHOR: anchor: 145:183 EDN

The output gives the detailed energies and assessment: Threading between t0053.seq and 1ak1 ... The raw score of the alignment: −2112.5 (−2113) Mutation = −494.0; Singleton = −872.0; Pairwise = −177.0; InternalPair = 9.5; GapPenalty = 331.0; SSFit = 0.0; AnchorMatch = −910.0 (1-anchor); PairConstrn = 0.0 (0-pair). Protein Tertiary Structure Prediction

Z-score = 11.07 Normalized radius of gyration = 1.83

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where different energy terms are recorded. For example, Pairwise is the pairwise energy between cores, InternalPair is the pairwise energy within cores, SSFit is the fitness energy between the PHD prediction and the secondary structures assigned from the PROSPECT alignment, AnchorMatch is the contribution of matching the predefined alignments between the anchor residues on the query sequence and the positions on the template, and PairConstrn is the contribution of matching the predefined pairs between the residues on the query sequence. The index of normalized radius of gyration gives an estimate for the compactness of the aligned portion in threading. If the value is >3.0, the aligned portion is insufficiently compact, indicating a bad match unless the template is a multidomain protein. The final alignment is as follows, where matches between the two aligned residues are shown to be identical (|), similar (:), or related (.): The full alignment with 264 residues in the target sequence MKKALLVVSFGTSYHDTCEKNIVACERDLAASCPDRD-L--FRA-F--TSGMI-IRKLRQ | |||:..||.|.: . | |: : | : . |. : : . -KMGLLVMAYGTPYKEEDIERYYTHIRR--GRKPEPEMLQDLKDRYEAIGGISPLAQITE RDGIDIDTPLQAL-QKLAAQGYQDV------AIQSLHII--NG-DEYEKIV--------.:: | : :. | . .. . .| | || QQAHNLEQHLNEIQDEITFKAYIGLKHIEPFIEDAVAEMHKDGITEAVSIVLAPHFSTFS --REVQLLRPLFTRL-TLGVPLLSSHNDYVQLMQALRQQMPSLR--------QTEKVVFM : :| | .. . | | : : . .. VQSYNKRAKEEAEKLGGLTITSVESWYDEPKFVTYWVDRVKETYASMPEDERENAMLIVS GH-------GASHHAFA-AYACLDHMMTAQRFP-ARVGAV------ES-Y-PEVDILIDS | . . || :. |:| | AHSLPEKIKEFGDPYPDQLHESAKLIAEGAGVSEYAVGWQSEGNTPDPWLGPDVQDLTRD L-RDEGVTGVHLMP-------L-MLVAGDHAINDMASDDGDSWKMRFNAAGIPATPWLSG | | :| | :| | :. | | |: | .. LFEQKGYQAFVYVPVGFVADHLEVLYDNDYECKVVTDDIGASYY----------RPEMPLGENPAIRAMFVAHLHQALNMAVEEAA | . . | -NAKPEFIDALATVVLKKLG------Identities in the aligned portion = 39/319 (12.2%) Residue range in the template: 5 : : 309 :

The refined alignment basically agrees with the structure-structure alignment, and it is significantly better than the original one without the constraint. The structure using the refined alignment is shown in Figure 2.7.3. The target t0053 and the template 1ak1 belong to the same superfamily. PROSPECT is also able to predict some proteins in the fold category. Figure 2.7.4 shows a prediction for the CASP-3 target t0067 using a template in a different superfamily but the same fold (chain A of 1tup). From both figures, one can get an idea about the quality of the models using threading. It should be noted that even when a perfect alignment is achieved, some conformational differences between the model and the experimental structure are expected on the backbones. The sidechain prediction based on threading alignment is unreliable. Although structure models derived from threading often have lower quality than those from homology modeling with significant sequence similarities, they are typically much better than the models generated from ab initio predictions and can often provide useful information for understanding protein function. Quantitation of Protein Interactions

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A

B

Figure 2.7.3 A comparison between predicted (A) and experimental (B) structures for the CASP-3 target t0053. Cylinders indicate α helices, strands indicate β sheets, dark lines indicate turns, and thin lines indicate loops. This figure was made using the INSIGHT II package (Molecular Simulations, 1998).

A

Protein Tertiary Structure Prediction

B

Figure 2.7.4 A comparison between predicted (A) and experimental (B) structures for the CASP-3 target t0067. Cylinders indicate α helices, strands indicate β sheets, dark lines indicate turns, and thin lines indicate loops. This figure was made using the INSIGHT II package (Molecular Simulations, 1998).

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AB INITIO PREDICTION An ab initio protein structure prediction derives a structure model through the optimization of an energy function that describes the physical properties or statistical preferences of amino acids. After decades of extensive effort, the ab initio tertiary structure prediction from sequence has proven to be extremely difficult. To run an ab initio prediction program requires long computing times, and the prediction results are generally unreliable. However, some recent developments using hierarchical approaches from local structures to the global structure seems to give ab initio predictions some new hope for generating low-resolution structures. When local structures are more or less defined, assembling them significantly reduces the computational search space compared to folding individual residues, and hence increases the chance of predicting a good structure. The optimization process is typically carried out using a genetic algorithm (Unger and Moult, 1992) or Monte Carlo simulations (Skolnick and Kolinski, 1991). Local structures can be built based on empirically derived data of preferred torsion angles in secondary structure elements, as done by the program LINUS (Local Independently Nucleated Units of Structure; Srinivasan and Rose, 1995). The “mini-threading” method (Simons et al., 1997) may be a more efficient way to build local structures. Mini-threading obtains the matches between short structure segments of template and the query sequence for building local structures. Some success of mini-threading has been demonstrated in CASP-3 (CASP, 1999). However, ab initio prediction programs are typically unavailable to the public. Therefore, no technical details about running these programs are described in this unit. COMMENTARY Although computation models often provide valuable information, they are nonetheless predictions no matter how good the methods are. A user should always keep in mind the general quality and the confidence level of the models derived by each method, as described above, when using them to draw any conclusion. It is usually rewarding to use different methods. The consensus and variations in different predictions may provide a clue about the reliability of a prediction. Predictions from other computational tools, e.g., secondary structure prediction, solvent accessibility prediction, and residue contact analysis through correlated mutation in a protein family, can also be used as input or to provide independent assessments in fold recognition and ab initio prediction. Whenever any experimental information for a structure is available, the predictor should incorporate the information in the tools or at least use the information to verify the output model. Last but not least, it is important to visually check the model in a molecular graphics program and manually adjust the model (e.g., change the alignment), if necessary. LITERATURE CITED Alexandrov, N.N., Nussinov, R., and Zimmer, R.M. 1996. Fast protein fold recognition via sequence to structure alignment and contact capacity potentials. In Biocomputing: Proceedings of the 1996 Pacific Symposium (L. Hunter and T. Klein, eds.) pp. 53-72. World Scientific Publishing, Singapore. Al-Karadaghi, S., Hansson, M., Nikonov, S., Jonsson, B., and Hederstedt, L. 1997. Crystal structure of ferrochelatase: The terminal enzyme in heme biosynthesis. Structure 5:1501-1510. Altschul, S.F. and Koonin, E.V. 1998. Iterated profile searches with PSI-BLAST—a tool for discovery in protein databases. Trends Biochem. Sci. 23:444-447. Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. 1997. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucl. Acids Res. 25:3389-3402. Bernstein, F.C., Koetzle, T.F., Williams, G.J.B., Meyer, E.F., Brice, M.D., Rodgers, J.R., Kennard, O., Shimanouchi, T., and Tasumi, M. 1977. The protein data bank: A computer based archival file for macromolecular structures. J. Mol. Biol. 112:535-542.

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Bowie, J.U., Luthy, R., and Eisenberg, D. 1991. A method to identify protein sequences that fold into a known three-dimensional structure. Science 253:164-170. Bryant, S.H. and Altschul, S.F. 1995. Statistics of sequence-structure threading. Curr. Opinion Struct. Biol. 5:236-244. Bryant, S.H. and Lawrence, C.E. 1993. An empirical energy function for threading protein sequence through the folding motif. Proteins Struct. Funct. Genet. 16:92-112. CASP (Critical Assessment of Techniques for Protein Structure Prediction). 1995. Protein structure prediction issue. Proteins Struct. Funct. Genet. 23:295-462. CASP. 1997. Protein structure prediction issue. Proteins Struct. Funct. Genet. 29:1-230. CASP. 1999. Protein structure prediction issue. Proteins Struct. Funct. Genet. 37:1-237. Crawford, O.H. 1999. A fast, stochastic algorithm for protein threading. Bioinformatics 15:66-71. Fischer, D. and Eisenberg, D. 1996. Fold recognition using sequence-derived predictions. Protein Sci. 5:947-955. Fischer, D., Rice, D., Bowie, J.U., and Eisenberg, D. 1996. Assigning amino acid sequences to 3-dimensional protein folds. FASEB J. 10:126-136. Gerstein, M. 1998. Patterns of protein-fold usage in eight microbial genomes: A comprehensive structural census. Proteins Struct. Funct. Genet. 33:518-534. Godzik, A., Skolnick, J., and Kolinski, A. 1992. A topology fingerprint approach to the inverse folding problem. J. Mol. Biol. 227:227-238. Heniko, S. and Heniko, J.G. 1994. Protein family classification based on searching a database of blocks. Genomics 19:97-107. Holm, L. and Sander, C. 1996. Mapping the protein universe. Science 273:595-602. Holm, L. and Sander, C. 1998. Dictionary of recurrent domains in protein structures. Proteins Struct. Funct. Genet. 33:88-96. Hu, X., Xu, D., Hamer, K., Schulten, K., Koepke, J., and Michel, H. 1995. Predicting the structure of the light-harvesting complex II of Rhodospirillum molischianum. Protein Sci. 4:1670-1682. Hughey, R. 1996. Parallel hardware for sequence comparison and alignment. CABIOS 12:473-479. Hughey, R. and Krogh, A. 1996. Hidden Markov models for sequence analysis: Extension and analysis of the basic method. CABIOS 12:95-107. Humphrey, W.F., Dalke, A., and Schulten, K. 1996. VMD—visual molecular dynamics. J. Mol. Graphics 14:33-38. Jones, D.T. 1999. GenTHREADER: An efficient and reliable protein fold recognition method for genomic sequences. J. Mol. Biol. 287:797-815. Jones, D.T., Taylor, W.R., and Thornton, J.M. 1992. A new approach to protein fold recognition. Nature 358:86-89. Karplus, K., Barrett, C., and Hughey, R. 1998. Hidden Markov models for detecting remote protein homologies. Bioinformatics 14:846-856. Krogh, A., Brown, M., Mian, I.S., Sjolander, K., and Haussler, D. 1994. Hidden Markov models in computational biology: Applications to protein modeling. J. Mol. Biol. 235:1501-1531. Laskowski, R.A., MacArthur, M.W., Moss, D.S., and Thornton, J.M. 1993. PROCHECK: A program to check the stereochemical quality of protein structures. J. Appl. Crystallogr. 26:283-291. Lund, O., Frimand, K., Gorodkin, J., Bohr, H., Bohr, J., Hansen, J., and Brunak, S. 1997. Protein distance constraints predicted by neural networks and probability density functions. Protein Eng. 10:1241-1248. Madej, T., Gibrat, J.F., and Bryant, S.H. 1995. Threading analysis suggests that the obese gene product may be a helical cytokine. FEBS Lett. 373:13-18. Milburn, D., Laskowski, R.A., and Thornton, J.M. 1998. Sequences annotated by structure: A tool to facilitate the use of structural information in sequence analysis. Protein Eng. 11:855-859. Molecular Simulations. 1998. Insight II (Release 98.0). Molecular Simultions, San Diego, Calif. Murzin, A.G., Brenner, S.E., Hubbard, T., and Chothia, C. 1995. SCOP: A structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol. 247:536-540. Nilges, M. and Brünger, A. 1993. Successful prediction of the coiled coil geometry of the gcn4 leucine zipper domain by simulated annealing: Comparison to the x-ray structure. Proteins Struct. Funct. Genet. 15:133-146. Protein Tertiary Structure Prediction

Park, J., Karplus, K., Barrett, C., Hughey, R., Haussler, D., Hubbard, T., and Chothia, C. 1998. Sequence comparisons using multiple sequences detect twice as many remote homologues as pairwise methods. J. Mol. Biol. 284:1201-1210.

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Pearson, W.R. and Lipman, D.J. 1988. Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. U.S.A. 85:2444-2448. Peitsch, M.C. 1996. ProMod and Swiss-Model: Internet-based tools for automated comparative protein modelling. Biochem. Soc. Trans. 24:274-279. Pontius, J., Richelle, J., and Wodak, S.J. 1996. Quality assessment of protein 3D structures using standard atomic volumes. J. Mol. Biol. 264:121-136. Rost, B. 1995. TOPITS: Threading one-dimensional predictions into three-dimensional structures. ISMB 3:314-321. Rost, B. and Sander, C. 1993. Prediction of protein secondary structure at better than 70% accuracy. J. Mol. Biol. 232:584-599. Sali, A. and Blundell, T.L. 1993. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234:779-815. Sanchez, R. and Sali, A. 1998. Large-scale protein structure modeling of the Saccharomyces cerevisiae genome. Proc. Natl. Acad. Sci. U.S.A. 95:13597-13602. Simons, K.T., Kooperberg, C., Huang, E., and Baker, D. 1997. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J. Mol. Biol. 268:209-225. Sippl, M.J. and Weitckus, S. 1992. Detection of native-like models for amino acid sequences of unknown three-dimensional structure in a database of known protein conformations. Proteins Struct. Funct. Genet. 13:258-271. Skolnick, J. and Kolinski, A. 1991. Dynamic monte carlo simulations of a new lattice model of globular protein folding, structure and dynamics. J. Mol. Biol. 221:499-531. Smith, T.F. and Waterman, M.S. 1981. Comparison of biosequences. Adv. Appl. Math. 2:482-489. Smith, T., Conte, L.L., Bienkowska, J., Gaitatzes, C., Rogers, R., and Lathrop, R. 1997. Current limitations to protein threading approaches. J. Comp. Biol. 4:217-225. Srinivasan, B.N. and Blundell, T.L. 1993. An evaluation of the performance of an automated procedure for comparative modelling of protein tertiary structure. Protein Eng. 6:501-512. Srinivasan, R. and Rose, G. 1995. LINUS—a hierarchic procedure to predict the fold of a protein. Proteins Struct. Funct. Genet. 22:81-99. Tripos. 1999. SYBYL 6.5.3. Tripos, Inc., St. Louis. Unger, R. and Moult, J. 1992. Potential of genetic algorithms in protein folding and protein engineering simulations. J. Mol. Biol. 5:637-645. Vriend, G. 1990. WHAT IF: A molecular modelling and drug design program. J. Mol. Graphics 8:52-56. Wang, Z.X. 1998. A re-estimation for the total numbers of protein folds and superfamilies. Protein Eng. 11:621-626. Xu, Y., Xu, D., and Uberbacher, E.C. 1998. An efficient computational method for globally optimal threading. J. Comp. Biol. 5:597-614. Xu, Y., Xu, D., Crawford, O.H., Einstein, J.R., Larimer, F., Uberbacher, E.C., Unseren, M.A., and Zhang, G. 1999. Protein threading by PROSPECT: A prediction experiment in CASP3. Protein Eng. 12:101-109.

Contributed by Dong Xu and Ying Xu Oak Ridge National Laboratory Oak Ridge, Tennessee

The authors thank Drs. Edward C. Uberbacher, J. Ralph Einstein, and Oakley H. Crawford for helpful discussions, and Drs. Stephen Altschul, Kevin Karplus, and Richard Hughey for their constructive comments on this manuscript. This research was sponsored by the Office of Biological and Environmental Research, U.S. Department of Energy, under contract no. DE-AC05-96OR22464 with Lockheed Martin Energy Research Corporation.

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Protein Tertiary Structure Modeling As an experimental science, biology increasingly involves large-scale automated approaches. This data production and gathering activity has profoundly changed the way in which data and information can be handled. The best known manifestation of this paradigm shift is genome sequencing activity, which yields ever increasing numbers of novel genes, for which no biological function is known. It is the proteins that are the “working” molecules responsible for most biological processes. Understanding their modes of action will allow us to unveil their biological role, and how their mutants cause diseases. In the past, proteins and their corresponding genes were generally discovered during functional experiments, which provided the biological context for their role, and which implicitly linked the genes to their function. The genome sequencing approach to biology has completely reversed this paradigm; one now has to discover the function of the large number of newly sequenced genes and their protein products. A widely used means to unravel the function of a protein is to create molecular mutants and observe their deviation from normal behavior. Experience has proven that the efficiency of this experimental approach is considerably enhanced by the insights the 3-D structure of the protein can provide. This can now be achieved by either experimental or, at least in part, by theoretical methods known as protein structure prediction. Experimental protein structure determination methods are often hampered by technical difficulties which, even if they can be overcome, are time and resource intensive. The number of known 3-D structures of proteins thus only represents a small fraction of the known protein sequences. Indeed, over 230,000 protein sequences are logged in the SwissProt/trEMBL database (Bairoch and Apweiler, 1999), while fewer than 10,000 protein sequences have a known 3-D structure and are stored in the Protein Data Bank (http://www.rcsb.org/pdb/). In this context it is not surprising that theoretical approaches have been explored, of which comparative protein modeling is by far the most reliable. Comparative protein modeling follows four steps: It starts with the quest for at least one protein of known structure that shares a significant degree of sequence identity with the se-

quence to be modeled, or target sequence (step 1). These modeling templates serve as the structural basis on which the target model will be built. Then, the target and template sequences are aligned (step 2). This sequence alignment is the key element of the modeling procedure, since it serves as the guide while building the structural “scaffold” of the model. Indeed, each position in the alignment defines the 3-D position of the target sequence’s amino acids. This structural scaffold is then used to build an atomic representation of all amino acids shared by the target and template sequence (step 3), but it is not sufficient to build the portions of the structure that differ from the modeling templates the same way. These deviant sequences are rebuilt using the basic rules that govern protein structures (step 4). This can now be done automatically using the SwissModel World Wide Web server (Guex et al., 1999). This unit will concentrate on delivering sufficient background and guidelines to build protein models.

GLOSSARY For the sake of clarity, a few terms used throughout the unit are defined below: comparative protein modeling often also called “homology modeling” or “knowledgebased modeling”; this is the process by which a model is built. core part of a protein structure that can be considered largely conserved within a protein family (Chothia and Lesk, 1986). ExPDB SwissModel template structure database; contains one entry for each individual protein chain of the PDB. First Approach simplest SwissModel request; initiated by submission of a sequence through the World Wide Web-interface. Alignment between target and template sequences is generated automatically. Optimise Mode more complex SwissModel request; initiated by submitting a project file generated with SPDBV. Allows for manual adjustment of the sequence alignment between target and template sequences. PDB Protein Data Bank; repository of experimentally solved protein structures. P(n) BLAST (Altschul et al., 1990) unlikelihood probabilities (see UNIT 2.5). protein chain a single polypeptide chain; PDB files can contain more than one protein chain.

Contributed by Nicolas Guex, Torsten Schwede, and Manuel C. Peitsch Current Protocols in Protein Science (2000) 2.8.1-2.8.17 Copyright © 2000 by John Wiley & Sons, Inc.

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reference template generally the template sharing the highest degree of sequence similarity with the target. rmsd root mean square deviation between a set of atoms (Å); represents the average displacement of atoms in 3D-space between two comparable structures. SPDBV Swiss-PdbViewer; an interactive software to display, analyze, and modify protein structures. target sequence protein sequence to be modeled. template structure(s) experimentally solved structure(s) used as template(s) for model construction (PDB or ExPDB file). template sequence(s) protein sequence(s) of the template(s) defined above.

ACCESSING SWISSMODEL PROGRAMS AND DOCUMENTATION SwissModel is an Internet-based service for automated comparative modeling of protein tertiary structures. SwissModel requests can be submitted through forms maintained on the ExPASy World Wide Web site, at http://www. expasy.ch/swissmod/SwissModel.html. It is advisable to install the Swiss-PdbViewer (SPDBV) software, which is available for PC, Macintosh, SGI, and i86LINUX platforms (Guex et al., 1999). This software can also b e d ownloaded from ExPASy at http://www.expasy.ch/spdbv/mainpage.htm. SPDBV is an interactive sequence-to-structure workbench, which acts as a client to SwissModel and allows the displaying, analyzing, and modifying of protein structures. By default, SwissModel will return SPDBV project files, which contain not only the coordinates of the model, but also all spatially superposed templates and their corresponding deduced structural alignment. SPDBV can then be used to adjust the alignment between target and template sequences, and to submit the corresponding modeling request.

ExPDB DATABASE

Protein Tertiary Structure Modeling

SwissModel uses ExPDB files as modeling templates. These are derived from the PDB database. Modeling procedures are frequently applied to a protein monomer, and many PDB files contain more than one protein chain. Hence, the ExPDB database has been created, in which each chain from a given PDB file has been split into a different entry. For example, the PDB entry 4MDH contains two protein chains (A and B). This generates the two cor-

responding ExPDB entries 4MDHA and 4MDHB. Some PDB files contain only one chain without a specific chain name (e.g., 1CRN), in this case, an “_” is added to the ExPDB name (1CRN_). SwissModel has been designed to build proteins, thus only amino acids (or modified amino acids) are kept in ExPDB files. This means that all N-terminal blocking groups (e.g., acetyl) and water molecules are removed. However, enzymatic cofactors, phosphorylated residues, and ions are kept, provided they are in close contact with the protein chain. Although they are not yet used during the modeling procedure per se, they are present in the SPDBV project files returned to the user. This allows for a quick inspection of the model in order to see whether these non-proteinaceous molecules could be conserved in the model.

FORMATTING A FIRST APPROACH MODELING REQUEST Mandatory Items As the modeling procedure can take more than 1 hr, depending on the size and complexity of the model to be built, all results are returned by e-mail. Hence one must provide a valid e-mail address, name, and a title for the request. The preferred format for entering the target sequence is the FASTA format (for a description, see UNIT 2.5). Alternatively, if the sequence is already in the SwissProt database, its accession code can be provided (e.g., P02761, used in the example below). Other formats, such as raw sequence, GCG, and SwissProt, are also accepted. Extra spaces and numbers are skipped. This is the minimal information to be submitted for model building. However, a number of options are available for submitting additional information.

Options The automatic template selection is done by comparing the target sequence with ExPDB template sequence. The BLASTP2 P(n) threshold value used to select the modeling templates can be altered. By default a value of 10−5 is used. A lower value (10−10) will select for templates sharing a higher degree of identity with the target sequence, while a higher value (10−4) will allow more remotely similar templates to be used. Up to five templates may be selected by the modeling procedure. However, it may occur that a non-optimal mix of templates is selected. This is the case when several

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distinct structures of the same protein are available in the template database (for example pro-enzyme/mature enzyme). It is then advisable to explicitly provide the name of the ExPDB template(s) to be used.

Finding the Best Templates As discussed above, SwissModel may not choose the optimal template(s), as there is no biological context taken into account by the template-selection procedure. To address this problem, it would be best to have some contextual information about the available templates, as this would allow the user to select the templates based on the experimental conditions under which their structures were elucidated. Indeed, template structures elucidated in the presence or absence of substrates, cofactors, and other variants of the biological unit are not identical. The ExPDB database can be searched with the target sequence for available templates through the World Wide Web server by clicking on the “Search Template” link at, http://www.expasy.ch/swissmod/SwissModel .html. All one needs to do is to provide the target sequence (in FASTA format) in the World Wide Web search form. The resulting report is sorted by BLASTP2 P(n) and contains the name of the proposed ExPDB templates, the method used to elucidate them (X-RAY, NMR), the resolution (for X-RAY), and a short description. A hyperlink to the corresponding PDB entry allows one to reach detailed information about the structure used to generate the ExPDB template (the REMARK fields in PDB files are usually quite instructive). Details of aligned regions between the sequence to be modeled and the template can also be reached through a hyperlink. Up to five templates can be selected for modeling. The first template listed is likely to be the most appropriate one. However, it may happen that several templates have the same P(n) value. Quite often, the server reports multiple entries for the same protein (e.g., multiple chains from a homo-oligomeric protein structure) and it is sufficient to select only one of these. When the same protein structure has been solved twice at different resolutions (e.g., 1CRN 1.5 Å and 1CNR 1.05 Å), only the structure with the best resolution should be selected (in this case 1CNR). Adding other templates with different sequences sometimes helps during side-chain modeling. However, there is no general rule, and it is often worth trying with and without additional templates.

SwissModel will automatically reject templates that cannot be used concurrently with the reference template (first one of the list). This will happen whenever a template cannot be automatically superposed onto the reference one. This limitation can be superseded using the “Optimise” mode.

Using Private Templates Whenever possible, use the ExPDB templates maintained on the server, as they have been processed for optimal use by SwissModel. However, in certain cases one might want to use templates that are not yet available in the ExPDB database. Both “First Approach” and “Optimise” modes allow the use of private templates. To ensure a high probability of success, those templates must comply with these rules: they must (1) be in PDB format, (2) contain only one protein chain with all backbone atoms (no gaps), and (3) be devoid of non-protein atoms (which are marked as “HETATM” in PDB files).

VIEWING SWISSMODEL RESULTS One can specify that the results be delivered in normal mode, in which case only the coordinates of the model, together with a detailed log of the procedure, are sent. The short mode will return just the coordinates of the model, without a log file. The default mode will return a detailed log o f th e m od elin g p ro ce du re an d a SwissPdbViewer project file, which will contain the final model and the template(s), superposed in 3D-space. This allows for the model to be analyzed and compared to its templates. If some regions of the model are dubious due to a bad initial sequence alignment, they can be modified and the altered alignment directly submitted to the SwissModel server as an Optimise mode request (see below). Optionally, a “WhatCheck” report (Hooft et al., 1996) of the model can be sent by the s e r v e r. T h e a n a l y s i s p e r f o r m ed b y WhatCheck includes atom nomenclature, bond-length deviation, side-chain planarity, packing, and many more. The report can be used to assess the accuracy of the model and detect regions that might need fine-tuning. Note that WhatCheck reports are text files that can be opened with SPDBV. Clicking on a report line will automatically center the view on the problem, which allows for quick inspection. Other features can be analyzed directly with SPDBV (for further details refer to the section on Model Analysis).

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By default, the models are sent as e-mail attachments. If your e-mail program does not support attachments, you can specify to receive the results in plain ASCII format. In this case, you will need to copy the content of the mail into a text file that will end with a file extension “.pdb” before dragging it to the SPDBV icon.

EXAMPLES OF SWISSMODEL REQUESTS In this section, various modeling examples are reviewed step by step. They are sorted by level of complexity. As new templates are added to the database, when new proteins are experimentally solved, the results will vary and running the same query again might not deliver exactly the same output.

Protein Tertiary Structure Modeling

First Approach A typical modeling request is shown in Figure 2.8.1. The sequence of rat fatty acid–binding protein (P02761) has been submitted through the “First Approach” mode. The server just acknowledges that the “request submission was successful,” and mentions that all further communications will come via e-mail. As soon as the server starts to build the model, a message containing a unique process identification is sent, as shown in Figure 2.8.2. Further e-mails containing log reports and model coordinates are received up to several hours later depending on the complexity of the model to build, the server load, or the Internet traffic. The report log is divided into two sections. The first section contains the results of the

Figure 2.8.1 Submitting a First Approach modeling request to the SwissModel server.

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>>>>>

Welcome to the SWISS-MODEL Protein Modelling Server

<<<<<

==================================================================== Dear JIM, Title of your Request: Process identification is:

MUP_RAT AAAa002Of

Swiss-Model ‘(SWISS-MODEL Version 3.5)’ started on 18-Aug-1999 The modelling procedure is now in progress, and its results should be sent to you shortly. ==================================================================== If results of this search are reported or published, please mention the publications listed below. (end of message truncated for brevity)

Figure 2.8.2 Message sent when the server starts to build the model. The unique process identification (AAAa002Of) is added in the subject of all subsequent e-mails. This is useful when several modeling requests are treated concurrently.

template identification performed with BLASTP2 (Fig. 2.8.3). The putative template candidates are shown sorted by the percent identity with the target sequence. Templates that share less than 25% identity with the target are automatically discarded, as their automatic alignment is usually not reliable enough to allow for blind model building (see section on Model Accuracy). The regions where the target and template sequences can be aligned are shown graphically. If the suitable templates are clustered in more than one region, the target sequence is split into two or more segments that are modeled separately and labeled Batch.1, Batch.2, and so on up to Batch.n. This occurs e.g., for membrane proteins that have an intra and extracellular domain elucidated by distinct experiments. The second part of the log file contains a summary of the modeling procedure (Fig. 2.8.4). Although some steps will vary from request to request, it typically contains a list of the templates that have been loaded, followed by the result of their 3D-superposition. The first template loaded is considered a reference template, which will guide the modeling procedure. Additional templates that cannot be superposed onto the reference template are discarded. In the case presented here the only template retained is 1MUP_. Recently two experimentally solved structures of rat lipid bind-

ing protein have been deposited in the PDB database (accession codes 2A2G and 2A2U). They may be used to assess the models built in this tutorial based on suboptimal templates. The target sequence is then aligned onto the selected template(s), and the overhangs are discarded (residues that cannot be modeled because they are before or after the first/last residues of the reference template). Once everything is in place, the model is built starting with the core, followed by a loop reconstruction (if the target contains insertions/deletions relative to the reference template). Finally a few energy minimization steps are performed using the GROMOS96 (van Gunsteren et al., 1996) force field as implemented in SPDBV. The final energy is reported at the end of the log file.

First Approach Using Specific Templates The same protein as in the example above has been submitted to SwissModel, but this time modeling templates have been explicitly specified (see Finding the Best Templates for conceptual background, and Optimise Mode for a concrete example). As 1MUP_ is more similar to the target protein than 1A3YB, it is given first to force SwissModel to use it as the reference (Fig. 2.8.5). This is likely to provide a better model than putting 1A3YB first. Specifying the templates will allow SwissModel to skip the template search, and to start

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directly with the pair-wise alignment step. Then the modeling procedure is performed and the reported results (Fig. 2.8.6) are quite similar to those reported in the previous section (Figs. 2.8.3 and 2.8.4). However, in this case the second template (1A3YB) can be superposed onto the reference template (1MUP_). This

adds a step to the procedure during which a structural alignment between the two templates is generated. In addition, information contained in both templates will be used and averaged to build the model. The reference template will be used alone in all regions where additional templates diverge from it too greatly. It may happen

Figure 2.8.3 First part of the modeling log file, containing a summary of the template identification process (see text).

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that the averaging procedure produces small distortions of the backbone (in our case at GLU 50) that will be repaired by the GROMOS96 force field used during the final modeling steps. The remaining steps are essentially the same as for the standard procedure. Depending on the number of templates used, the final energy may differ. In this case the final energy is −4154.777 KJ/mol compared to −6030.488 KJ/mol in the previous example. An all atom comparison between the two models yields 0.98 Å rmsd (for 1279 atoms), while the 157 Cα atoms deviate by 0.35 Å. In

both cases, WhatCheck judges the model satisfactory.

Optimise Mode The “Optimise” mode can be used either to optimize a model generated by the “First Approach” mode, or directly. In both cases this can only be done from SPDBV. In the “Optimise” mode the modeling procedure is uncoupled from the automated template selection, superposition, and target-sequence alignment. Those steps need to be performed manually within SPDBV, which provides a higher level of flexi-

ProModII trace log for Batch.1 ============================================================ ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII:

ProMod version 3.5b3 date Jul 19 1999 17:15 SPDBV version 3.5b3 Loop version 2.60 LoopDB version 2.60 Parameters version 3.5 Topologies version 3.5 Loading Template: 1MUP_.pdb 1MUP: some sidechain atoms are missing --> reconstructing them Loading Template: 1OBPB.pdb Loading Template: 1OBPA.pdb Loading Template: 1PBOA.pdb Loading Template: 1PBOB.pdb Loading Raw Sequence Iterative Template Fitting Protein does not fit well; Removing Layer 1OBPB (if you really want to include this template, use the optimise Iterative Template Fitting Protein does not fit well; Removing Layer 1OBPA (if you really want to include this template, use the optimise Iterative Template Fitting Protein does not fit well; Removing Layer 1PBOA (if you really want to include this template, use the optimise Iterative Template Fitting Protein does not fit well; Removing Layer 1PBOB (if you really want to include this template, use the optimise Aligning Raw Sequence Refining Raw Sequence Alignment N-terminal overhang trimmed for chain ‘ ’. Start at residue: 6 C-terminal overhang trimmed for chain ‘ ’. End at residue: 162 adding blocking groups Averaging Sidechains Adding Missing Sidechains Optimizing Sidechains Dumping Preliminary Model Adding Hydrogens Optimizing loops and OXT (nb = 1) Current Total Energy: -6030.488 KJ/mol Removing Hydrogens Fixing Atom Nomenclature Dumping Sequence Alignment Done.

mode) mode) mode) mode)

Figure 2.8.4 Second part of the modeling log file, containing a summary of action taken during the modeling procedure (see text).

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Figure 2.8.5 Submitting a First Approach modeling request to the SwissModel server with specific EXPDB templates.

Protein Tertiary Structure Modeling

bility, and a real-time visual feedback to help refine the guiding sequence alignment. Hence insertions/deletions can be finely adjusted, and many modeling requests that would otherwise fail in the First Approach mode can be built satisfactorily. The following example describes a typical workflow step by step: 1. Fetch the SwissProt entry Q21735 from the ExPASy site at http://www.expasy.ch/sprot/. 2. Ask for a FASTA-formatted sequence (Fig. 2.8.7). 3. Prepare a text file named Q21735.txt, which will be used later. 4. Paste the FASTA-formatted sequence into the “search template” form on the SwissModel World Wide Web site at http://www. expasy.ch/swissmod/SwissModel.html to find suitable templates. SwissModel returns a list of possible templates (Fig. 2.8.8) aligned onto the sequence to model (Fig. 2.8.9). 5. Download the ExPDB file 1OUNA, the template with the best BLAST score, and save on a local disk as 1OUNA.pdb. Note that other templates give similar scores, but they have been solved at a lower resolution.

The next part of the procedure only outlines the conceptual steps that need to be taken within SPDBV. It is not a full tutorial about the manipulation of SPDBV; this can be found at http://www.expasy.ch/spdbv/. The name of the menu from which a function can be called is shown in [brackets]. 6. Open the ExPDB file 1OUNA.pdb in SPDBV. 7. Use “Load raw sequence to model” [SwissModel] to open the target sequence file Q21735.txt. 8. Open the alignment window [Windows] and click on the name of the target sequence to select it. 9. Use the “Magic Fit” function [Tools]. The alignment should look like the one presented in the Figure 2.8.10. 10. Click “submit a modeling request” [SwissModel]. A SPDBV project file will be saved, containing the target protein, the template, and the alignment. A web-browser window will open with a SwissModel submission form. Note: if it is your first submission, you will be prompted to provide your e-mail address to SPDBV.

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AlignMaster output ============================================================ Length of target sequence: 181 residues Reading user-defined template list Extracting template sequences Running pair-wise alignments with target sequence Sequence identity of templates with target: 1MUP_.pdb: 63.7 % identity 1A3YB.pdb: 34 % identity Looking for template groups Global alignment overview: Target Seq: 1MUP_.pdb 1A3YB.pdb

|============================================================| | ---------------------------------------------------| -----------------------------------------------

AlignMaster found 1 regions to model separately: 1: Using template(s) 1A3YB.pdb 1MUP_.pdb Creating Batch files for ProMod (if any): Batch.1: residues 15 - 181 of submitted sequence. Exiting AlignMaster ProModII trace log for Batch.1 ============================================================ ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII:

ProMod version 3.5b3 date Jul 19 1999 17:15 SPDBV version 3.5b3 Loop version 2.60 LoopDB version 2.60 Parameters version 3.5 Topologies version 3.5 Loading Template: 1MUP_.pdb : some sidechain atoms are missing --> reconstructing them Loading Template: 1A3YB.pdb : some sidechain atoms are missing --> reconstructing them Loading Raw Sequence Iterative Template Fitting Generating Structural Alignment Aligning Raw Sequence Refining Raw Sequence Alignment N-terminal overhang trimmed for chain ‘ ’. Start at residue: 6 C-terminal overhang trimmed for chain ‘ ’. End at residue: 162 adding blocking groups Weighting Backbones Averaging Sidechains Adding Missing Sidechains Small Ligation (C-N < 3.0A) ignored; GROMOS will repair it at residue GLU 50 Optimizing Sidechains Dumping Preliminary Model Adding Hydrogens Optimizing loops and OXT (nb = 1) Current Total Energy: -4154.777 KJ/mol Removing Hydrogens Fixing Atom Nomenclature Dumping Sequence Alignment Done.

Figure 2.8.6 Log file for the optimise mode when modeling templates are specified.

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>Q21735 MSFNPDYESVAKAFIQHYYSKFDVGDGMSRAQGLSDLYDPENSYMTFEGQ QAKGRDGILQKFTTLGFTKIQRAITVIDSQPLYDGSIQVMVLGQLKTDED PINPFSQVFILRPNNQGSYFIGNEIFRLDLHNN Figure 2.8.7 Amino acid sequence of the SwissProt entry Q21735 in FASTA format.

Figure 2.8.8 Results of a template search for the SwissProt entry Q21735.

Protein Tertiary Structure Modeling

11. Select the saved project file by clicking on the [Browse] button in the Web form. Make sure that your name and e-mail address are correct. Optionally you may ask for a WhatCheck report. 12. Submit the request. The results will come by e-mail. The modeling log file (Fig. 2.8.11) will contain the same kind of information as presented earlier (Fig. 2.8.6), with an additional section for each loop that had to be reconstructed (insertions/deletions). In order to build a loop, SwissModel defines two anchor residues—one at each end—that will not move during the procedure. It then attempts to generate a set of acceptable solutions for each

insertion/deletion. If the initially defined anchor residues are not suitable (as no acceptable solution is found), then different anchor residues are selected until at least one acceptable loop is found. For each loop the best solution is reported in the log file, which shows the residues used as anchor points, the number of close contacts (clashes), an energy evaluation with the GROMOS96 force field (as implemented in SPDBV), and a mean-force potential evaluation. This part of the modeling procedure is under constant development, and the reported summary may differ slightly in the future. In general the final force-field energy (FF) should be as low as possible (negative values), while as few clashes as possible should be

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Figure 2.8.9 Alignment between Q21735 and the best template identified.

reported. Furthermore a solution should be found for each insertion/deletion without the need for more than one or two anchor residue reselections. In this example, no loop was found with anchor residues GLY25 and ARG 30, so more flexibility was given to this loop by using ALA31 as a new anchor point. Rebuilding of the next loop (anchor residues ALA31 and LEU34) also failed and needed a second pass, using residues ARG30 and LEU34 as anchor points. In summary, the complete region from GLY25 to LEU34 has been rebuilt. This is most likely an indication that the alignment was not optimal. In order to correct this,

select the residue ASP25 of 1OUNA in the alignment window and move it to the right with the arrow key, until the alignment looks like the one presented in Figure 2.8.12. Then select ASP35 of 1OUNA and move it to the left by one position. Finally select ASP110 and ALA111 and shift them by one position to the right. The new alignment can now be resubmitted to the SwissModel server. The results of this new attempt are shown in Figure 2.8.13. This time, the loop rebuilding procedure seems to find better solutions, and the final global energy is significantly lower than with the default alignment. Figure 2.8.14 shows a comparison of the loop region between and Asn123 derived

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by the First Approach mode (A and B) and the Optimise Mode (C and D). The molecular interactions of the loop based on the automatic alignment (A) are less favorable compared to those found after manually optimizing the placement of insertions in the sequence alignment (C). A ribbon representation of the same loops (B and D) illustrates that the manual adjustment of the insertion into the turn between the two strands reduces the distortions in the rebuilt loop.

It should however be noted that loops are always the least reliable parts of the model. The automated modeling procedure generally returns a reasonable starting point, which can be optimised in only a few steps. The other loops rebuilt in this example are worth considering in more detail, and alternative alignments should be explored. The main reason for SwissModel failures is the misplacement of insertions and deletions in the sequence alignment. Although

A

B Q21735 1OUNA

1 2

MSFNPDYESV AKAFIQHYYS KFDVGDGMSR AQGLSDLYDP ENSYMTFEGQ GDKPIWEQI GSSFIQHYYQ LFDND----R TQ-LGAIYI- DASCLTWEGQ . .* * . . .****** ** * .* *. .* . * .* ***

Q21735 1OUNA

51 45

QAKGRDGILQ KFTTLGFTKI QRAITVIDSQ PLYDGSIQVM VLGQLKTDED QFQGKAAIVE KLSSLPFQKI QHSITAQDHQ PTPDSCIISM VVGQLKADED * *. .*.. *...* * ** *..** * * * *. * * *.****.***

Q21735 1OUNA

101 95

PINPFSQVFI LRPNNQGSYF IGNEIFRLDL HNN PIMGFHQMFL LKNINDA-WV CTNDMFRLAL HNF ** * *.*. *. *.. *..*** * **

Figure 2.8.10 (A) Automatic alignment between Q21735 and its modeling template (1OUNA) as shown within SPDBV. (B) Complete alignment, shown for clarity.

(......) ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: (......) ProModII:

Building CSP loop with anchor residues Number of Ligations found: 19 all loops are bad; continuing CSP with Building CSP loop with anchor residues Number of Ligations found: 244 ACCEPTING loop 113: clash= 1 FF= Building CSP loop with anchor residues Number of Ligations found: 1 all loops are bad; continuing CSP with Building CSP loop with anchor residues Number of Ligations found: 36 ACCEPTING loop 15: clash= 0 FF= Building CSP loop with anchor residues Number of Ligations found: 4 ACCEPTING loop 2: clash= 1 FF= Building CSP loop with anchor residues Number of Ligations found: 5 ACCEPTING loop 4: clash= 0 FF= Current Total Energy:

GLY 25 and ARG 30 larger segment GLY 25 and ALA 31 176.0 PP=-23.72 ALA 31 and LEU 34 larger segment ARG 30 and LEU 34 -185.0 PP=-23.27 ASP 39 and ASN 42 208.2 PP=-23.67 GLY 117 and PHE 120 8.1 PP=-23.38

-5003.404 KJ/mol

Figure 2.8.11 Log file for the optimise mode with default alignment.

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many anchor points for loop building will be tested, it can happen that no acceptable solution can be found. In this case, it is most likely that the guiding alignment is so flawed that model building is completely unrealistic. The log file will have a long succession of consecutive attempts (Fig. 2.8.15) followed by the general error message “Aborting: too many unfruitful attempts to find a compatible spare part”.

Optimise Mode for Modeling a Dimer The functional biological unit of the above example is in fact a homodimer. As a mono-

mer was successfully built, it is worth trying to model the homodimer. As ExPDB files contain only one chain, the templates to build multichain models must be either constructed or obtained elsewhere. The PQS Protein Quaternary Structure Server which is at the EBI (http://pqs.ebi.ac.uk/) may give useful information about the oligomeric state of a protein. Indeed, in this example the original PDB entry (1OUN) contains a properly defined biological unit. It could be used directly in the “optimise mode” as the file contains only amino acids. However, this is not the case for

A

B Q21735 1OUNA

2 2

SFNPDYE SVAKAFIQHY YSKFDVGDGM SRAQGLSDLY DPENSYMTFE GDKPIWE QIGSSFIQHY YQLFDN---- DRTQ-LGAIY ID-ASCLTWE . .* * .. .***** * ** *.* *. .* * .* *

Q21735 1OUNA

49 43

GQQAKGRDGI LQKFTTLGFT KIQRAITVID SQPLYDGSIQ VMVLGQLKTD GQQFQGKAAI VEKLSSLPFQ KIQHSITAQD HQPTPDSCII SMVVGQLKAD *** *. .* ..*...* * ***..** * ** *. * **.****.*

Q21735 1OUNA

99 93

EDPINPFSQV FILRPNNQGS YFIGNEIFRL DLHNN -EDPIMGFHQM FLLKNIN-DA WVCTNDMFRL ALHNF **** * *. *.*. * . *..*** ***

Figure 2.8.12 (A) Manually refined alignment between Q21735 and its modeling template (1OUNA) as shown within SPDBV. (B) Complete alignment, shown for clarity.

(...) ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: ProModII: (...) ProModII:

Building CSP loop with anchor residues Building CSP loop with anchor residues Number of Ligations found: 250 ACCEPTING loop 9: clash= 0 FF= Building CSP loop with anchor residues Number of Ligations found: 1 ACCEPTING loop 0: clash= 0 FF= Building CSP loop with anchor residues Building CSP loop with anchor residues Number of Ligations found: 42 ACCEPTING loop 16: clash= 0 FF= Building CSP loop with anchor residues Building CSP loop with anchor residues Number of Ligations found: 21 ACCEPTING loop 19: clash= 0 FF= Current Total Energy:

GLY 25 and ARG 30 VAL 24 and ARG 30 121.8 PP=-23.78 GLN 32 and SER 35 -98.2 PP=-23.40 PRO 40 and SER 43 ASP 39 and SER 43 -53.6 PP=-22.37 ASN 115 and SER 118 ASN 114 and SER 118 -414.2 PP=-22.62

-5753.147 KJ/mol

Figure 2.8.13 Partial Log file for the optimise mode with manually optimised alignment.

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all PDB files, and as the template file has to follow the rules set forth under “Using private templates” it might be necessary to prepare a compatible file using the steps described below: 1. Open the ExPDB file of the first protein chain (1OUNA) in SPDBV.

2. Select all HETATM [Select; Group Kind]. 3. Remove selected residues [Build]. 4. Open the ExPDB file of the second protein chain (1OUNB). 5. Select all residues in both layers [Select]. Holding down the shift key before and while

Figure 2.8.14 Loop from Ile110 to Ans133 rebuilt with the default alignment. (A) Backbone representation where Cα are emphasized with bigger atoms. Hydrogen bonds are drawn as dotted lines. (B) Ribbon representation of the same loop. (C and D) Same region rebuilt after manual optimisation of the alignment. The H-bonding pattern is more regular, which makes a longer β-sheet.

ProModII: Finding Spare-Part ProModII: Finding Spare-Part ProModII: all loops are bad; (...) ProModII: Finding Spare-Part ProModII: all loops are bad; ProModII: Finding Spare-Part ProModII: Finding Spare-Part ProModII: Finding Spare-Part ProModII: all loops are bad; ProModII: *** Aborting: too compatible spare part

Protein Tertiary Structure Modeling

loop with anchor residues THR 83 and THR 94 loop with anchor residues THR 83 and GLN 95 continuing scan with larger segment loop with anchor residues CYS 73 and continuing scan with larger segment loop with anchor residues CYS 73 and loop with anchor residues CYS 73 and loop with anchor residues CYS 73 and continuing scan with larger segment many unfruitful attempts to find a

GLY 99 PRO 100 ARG 101 SER 102

Figure 2.8.15 Typical example of a Log file for a model that failed because a long loop had to be rebuilt (between THR83 and THR94). As no suitable solution was found after several attempts to gain more flexibility using ever increasing number of residues, the modeling procedure stopped. In most cases, the only way of addressing this problem is to move the gap location in SPDBV, and resubmit an “optimise mode” modeling request.

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>Q21735 MSFNPDYESVAKAFIQHYYSKFDVGDGMSRAQGLSDLYDPENSYMTFEGQ QAKGRDGILQKFTTLGFTKIQRAITVIDSQPLYDGSIQVMVLGQLKTDED PINPFSQVFILRPNNQGSYFIGNEIFRLDLHNN; MSFNPDYESVAKAFIQHYYSKFDVGDGMSRAQGLSDLYDPENSYMTFEGQ QAKGRDGILQKFTTLGFTKIQRAITVIDSQPLYDGSIQVMVLGQLKTDED PINPFSQVFILRPNNQGSYFIGNEIFRLDLHNN

Figure 2.8.16 Twice the amino acid sequence of the SwissProt entry Q21735 separated by a semicolon. This allows for dimer modeling.

selecting the menu item will apply the command on all layers. 6. Create a merged layer from selection [Edit]. This will open a new file containing the biological unit. 7. Close the two original layers (1ONUA and 1ONUB). 8. Save the new modeling template [File]. The rest of the modeling procedure is essentially the same as before: 9. Edit the target sequence in a text editor so that it contains twice the same chain, separated by a semicolon (Fig. 2.8.16). 10. Use “Load raw sequence to model” [SwissModel] to open the edited target sequence file. A different chain name is automatically attributed to each chain. 11. Open the alignment window [Windows] and click on the name of the target sequence to select it. 12. Use the “Magic Fit” function [Tools]. Each target chain is aligned onto its corresponding template chain. 13. Alter the alignment for each monomer, so that gaps are placed at the same position as in Figure 2.8.12. 14. Submit the request following the steps defined previously (see Optimise Mode, steps 10 to 12,).

COMMENTARY Sequence Alignment The most frequent reason for unsuccessful modeling attempts by SwissModel is an incorrect sequence alignment. This can be either generated automatically or provided by the user. Although the automatic mode contains a procedure that will try to optimise the alignment, it is still error prone. As the degree of sequence identity between the target sequence and the reference template decreases, the risk of obtaining no model or worse a severely flawed model increases. For those cases, it is

worth casting a critical eye on the alignment with SPDBV. It might be useful to submit several alternate alignments, and then use the WhatCheck and SPDBV analysis tools to select the best model. As a rule, gaps should be placed at the tip of secondary structure elements, to allow maximum flexibility during loop rebuilding. Also, for soluble proteins, it is worth checking that the polarity of the alignment is correct (i.e., all charged side chains are pointing into the solvent while hydrophobic residues should be mainly pointing into the core of the protein).

Model Accuracy As outlined above, the quality of a model greatly depends on the quality of the alignment used by SwissModel. However, this is not the only reason for structural errors in protein models. For instance, large conformational changes cannot be predicted as the model backbone is constructed by extrapolation of the template backbone (Guex and Peitsch, 1997). In order to assess the value and accuracy of modeling procedures, protein models have been compared with their experimentally elucidated counterpart(s). One calls these structures “experimental control structures.” An evaluation of the performance of SwissModel can be obtained from the World Wide Web page in section “Reliability of models” in the form of a table showing the relationship between sequence identity and backbone rmsd. It is important to realize that the accuracy of a protein model is essentially limited by the structural deviation of the best template relative to the experimental control structure. As a consequence, the Cα atoms of protein models which share 35% to 50% sequence identity with their templates, will generally deviate by 1.0 to 1.5 Å from their experimental counter parts, as do similarly related experimental structures (Chothia and Lesk, 1986). Structural differences between predicted and experimental structures have two sources: (1) the errors in-

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herent to the modeling procedures and (2) the variations caused by the molecular environment and data collection method incorporated into experimentally elucidated structures which will be used as modeling templates. Indeed, crystallographic structures of identical proteins can vary not only because of experimental errors and differences in data collection conditions and refinement (illustrated in Tilton et al., 1992), but also because of different crystal-lattice contacts and the presence or absence of ligands. One of the most interesting examples in which several structures of the same protein, determined by different methods, were compared involves interleukin-4 (IL-4; Harrison et al., 1995). This cytokine consists of a 130-residue 4-helix bundle, and its structure was elucidated by x-ray crystallography as well as by NMR. The backbones of three IL-4 crystal structures (PDB entries 1RCB, 2INT, and 1HIK) show an rmsd of 0.4 to 0.9 Å, while those of three IL-4 NMR forms (PDB entries 1ITM, 1CYL, and 2CYK) give an rmsd of 1.2 to 2.8 Å. These values illustrate the structural differences due to experimental procedures and the molecular environment at the time of data collection. Therefore, “a protein model derived by comparative methods cannot be more accurate than the difference between the NMR and crystallographic structure of the same protein” (Harrison et al., 1995). The accuracy of the model determines the extent to which it can be used. “Low-resolution” models, those derived from templates sharing less than 70% residue identity with the target, are helpful to rationalize site-directed mutagenesis experiments aimed at the identification of residues essential for a given molecular recognition and binding process. On the other hand, models based on more closely related templates may be useful during a compound optimisation process. For example, models of closely related species variants of an enzyme, may allow the optimisation of drug specificity.

structures which were used to build a specific part of the model. This “Model B-factor” is included in the PDB file returned to the user and occupies the B-FACTOR field. For visual inspection, open the project file in SPDBV and color the model by B-factor. Parts that appear in red have been reconstructed from scratch (such as insertions and deletions) and are likely to be where most of the problems reside. The color gradient ranges from green, when only one template was used, to dark blue when five templates were used for a specific region. Third, inspect the Ramachandran plot. In addition to the backbone analysis provided by the WhatCheck report, an interactive graphical representation can be provided by SPDBV. As few residues as possible should lay outside the allowed regions (blue), and most residues should be in the optimal regions (yellow). It should be noted that glycine residues (depicted as squares) are allowed to reside out of the allowed regions as they do not have a side chain. Furthermore, within SPDBV, the protein can be colored by “protein problems,” showing prolines with bad backbone conformation (red), and residues laying out of allowed Ramachandranplot regions (yellow). A cluster of such residues is a good indication that there is something wrong in the area. Buried residues with unsatisfied H-bonding capabilities are displayed in orange. Finally, compute the force-field energy of the protein and color the structure accordingly. Residues with a high energy clearly bear some structural problems. These can be of two types: first stereochemical distortions, meaning that they have out-of-average bond length, bond angles, and torsion angles. Second, proximity problems, residues can be too close to other parts of the structure, meaning that they will bear high nonbonded energies. A residue with a high overall energy usually indicates that a wrong side chain rotamer (combination of torsion angles) was selected during the modeling procedure. This can usually be fixed by selecting another rotamer with the mutate tool of SPDBV.

Model Analysis

Protein Tertiary Structure Modeling

SwissModel returns the model with its template(s) and, when selected, a WhatCheck report. The model quality can be assessed at different levels. First, assess the quality of the sequence alignment. The rules described in the “Sequence Alignment” section should be satisfied. Second, SwissModel computes a confidence factor for each atom in the model structures. This factor gives an indication of the number of

Very Large Scale Protein Modeling There is no doubt that the scrutiny of multiple sequence alignments is more informative than the analysis of an isolated sequence. This allows the identification of conserved and variable residues, and improves the rationalization of site-directed mutagenesis experiments. Likewise, comparative structure analysis involving several members of a protein family, as opposed to single-structure inspection, is ex-

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pected to be invaluable for the understanding of their functional differences. Thus it is increasingly valuable to have models for as many members of a protein family as possible. Following 3DCrunch (Guex et al., 1999), the very large scale comparative protein modeling (Peitsch, 1997) experiment run in 1998 (in collaboration with SGI), a database of protein models has been generated (now containing over 80,000 model structures) that can be accessed at http://www.expasy.ch/ swissmod/ SM_3DCrunch.html. Another database containing models of Saccharomyces cerevisiae, Mycoplasma genitalium, and Methanococcus jannaschii proteins can be reached at http://pipe.rockefeller.edu/modbase/ (Sánchez and Sali, 1998). These databases will evolve over the next year to incorporate new models generated with better algorithms, and will allow cross-searching in order to provide a one-stop shop.

LITERATURE CITED Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J. 1990. Basic local alignment search tool. J. Mol. Biol. 215:403-410. Bairoch, A. and Apweiler, R. 1999. The SwissProt protein sequence data bank and its supplement TrEMBL in 1999. Nucl. Acids Res. 27:49-54. Chothia, C. and Lesk, A.M. 1986. The relation between the divergence of sequence and structure in proteins. EMBO J. 5:823-826 Guex, N. and Peitsch, M.C. 1997. SwissModel and SwissPdbViewer: An environment for comparative protein modeling. Electrophoresis 18:27142723.

Guex, N., Diemand, A., and Peitsch, M.C. 1999. Protein modeling for all. Trends Biochem. Sci. 24:364-367. Harrison, R.W., Chatterjee, D., and Weber, I.T. 1995. Analysis of six protein structures predicted by comparative modeling techniques. Proteins Struct. Func. Genet. 23:463-471. Hooft, R.W.W., Vriend, G., Sander, C., and Abola, E.E. 1996. Errors in protein structures. Nature 381:272-272. Peitsch, M.C. 1997. Large scale protein modeling and model repository. In Proceedings of the Fifth International Conference on Intelligent Systems for Molecular Biology, Vol. 5 (T. Gaasterland, P. Karp, K. Karplus, C. Ouzonis, C. Sander, and A. Valencia, eds.) pp. 234-236. AAAI Press, Menlo Park, Calif. Sánchez, R. and Sali, A. 1998. Large-scale protein structure modeling of the Saccharomyces cerevisiae genome. Proc. Natl. Acad. Sci. U.S.A. 95:13597-13602. Tilton, R.F., Dewan, J.C., and Petsko, G.A. 1992. Effects of temperature on protein structure and dynamics: X-ray crystallographic studies of the protein ribonuclease-A at nine different temperatures from 98 to 320 K. Biochemistry 31:2469-2481. van Gunsteren, W.F., Billeter, S.R., Eising, A.A., Hünenberger, P.H., Krüger, P., Mark, A.E., Scott, W.R.P., and Tinoni, I.G. 1996. In Biomolecular Simulation: The GROMOS96 Manual and User Guide. Vdf Hochschulverlag ETH, Zurich (http://igc.ethz.ch/gromos/).

Contributed by Nicolas Guex, Torsten Schwede, and Manuel C. Peitsch Glaxo Wellcome Experimental Research Geneva, Switzerland

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Comparative Protein Structure Prediction

UNIT 2.9

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. The number of protein sequences that can be modeled, as well as the accuracy of the predictions, is increasing steadily because of growth in the number of known protein structures and improvements in the modeling software. It is currently possible to model, with useful accuracy, significant parts of approximately one-half of all known protein sequences (Pieper et al., 2002). Despite progress in ab initio protein structure prediction (Baker, 2000; Bonneau and Baker, 2001), comparative modeling remains the only method that can reliably predict the 3-D structure of a protein with an accuracy comparable to a low-resolution experimentally determined structure (Marti-Renom et al., 2000). Even models with errors may be useful, because some aspects of function can be predicted from only coarse structural features of a model (Marti-Renom et al., 2000; Baker and Sali, 2001). There are several computer programs and Web servers that automate the comparative modeling process. The first Web server for automated comparative modeling was the Swiss-Model server (http://www.expasy.ch/swissmod/), followed by CPHModels (http://www.cbs.dtu.dk/services/CPHmodels/), SDSC1 (http://cl.sdsc.edu/hm.html), FAMS (http://physchem.pharm.kitasato-u.ac.jp/FAMS/fams.html), MODWEB (http://guitar.rockefeller.edu/modweb/), and ESyPred3D (http://www.fundp.ac.be/urbm/ bioinfo/esypred/). Several of these servers are being evaluated by EVA-CM (http:// cubic.bioc.columbia.edu/eva), a Web server for assessing protein structure prediction methods in an automated, continuous and large-scale fashion (Eyrich et al., 2001; Marti-Renom et al., 2002). While the Web servers are convenient and useful, the best results in the difficult or unusual modeling cases, such as problematic alignments, modeling of loops, existence of multiple conformational states, and modeling of ligand binding, are still obtained by nonautomated, expert use of the various modeling tools. A number of resources useful in comparative modeling are listed in Table 2.9.1 (an online version of this table is available at http://guitar.rockefeller.edu/bioinformatics_resources.shtml). Next, we describe generic considerations in all four steps of comparative modeling (see Steps in Comparative Modeling, below, and Fig. 2.9.1), typical modeling errors (see Errors in Comparative Models, below, and Fig. 2.9.2), and applications of comparative protein structure models (see Applications of Comparative Modeling, below, and Fig. 2.9.3). Finally, we illustrate these considerations in practice by discussing in detail one application of our program MODELLER (Sali and Blundell, 1993; Sali and Overington, 1994; Fiser et al., 2000).

Computational Analysis Contributed by Marc A. Marti-Renom, Bozidar Yerkovich, and Andrej Sali Current Protocols in Protein Science (2002) 2.9.1-2.9.22 Copyright © 2002 by John Wiley & Sons, Inc.

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Table 2.9.1

Programs and Web Servers Useful in Comparative Modeling

Name

Typea WWW addressb

Databases CATH GenBank GeneCensus MODBASE PDB PRESAGE SCOP TrEMBL

S S S S S S S S

http://www.biochem.ucl.ac.uk/bsm/cath/ http://www.ncbi.nlm.nih.gov/Genbank/ http://bioinfo.mbb.yale.edu/genome/ http://guitar.rockefeller.edu/modbase/ http://www.rcsb.org/pdb/ http://presage.berkeley.edu http://scop.mrc-lmb.cam.ac.uk/scop/ http://srs.ebi.ac.uk

Orengo et al. (2002) Blundell et al. (1987) Gerstein and Levitt (1997) Pieper et al. (2002) Westbrook et al. (2002) Brenner et al. (1999) Lo Conte et al. (2002) Bairoch and Apweiler (2000)

Template search 123D BLAST DALI FastA MATCHMAKER PHD, TOPITS PROFIT THREADER FRSVR

S S S S P S P P S

http://123d.ncifcrf.gov/123D+.html http://www.ncbi.nlm.nih.gov/BLAST/ http://www2.ebi.ac.uk/dali/ http://www.ebi.ac.uk/fasta33/ http://bioinformatics.burnham-inst.org http://cubic.bioc.columbia.edu/predictprotein/ http://www.came.sbg.ac.at http://insulin.brunel.ac.uk/~jones/threader.html http://fold.doe-mbi.ucla.edu

Alexandrov et al. (1996) Altschul et al. (1990) Holm and Sander (1999) Pearson (1990) Godzik et al. (1992) Rost (1995) Flockner et al. (1995) Jones et al. (1992) Fischer and Eisenberg (1996)

http://searchlauncher.bcm.tmc.edu http://www.ncbi.nlm.nih.gov/gorf/bl2.html http://blocks.fhcrc.org/blocks/blockmkr/make_blocks.html

Smith et al. (1996) Altschul et al. (1997) Henikoff et al. (1995)

http://www2.ebi.ac.uk/clustalw/ http://www2.ebi.ac.uk/fasta3/ http://pbil.ibcp.fr

Thompson et al. (1994) Pearson (1990) Corpet (1988)

http://www.tripos.com/software/composer.html http://www.congenomics.com/congen/congen.html http://www.molsoft.com http://www.accelrys.com http://guitar.rockefeller.edu/modeller/modeller.html http://www.accelrys.com http://www.tripos.com http://www.fccc.edu/research/labs/dunbrack/scwrl/ http://www.expasy.org/swissmod/SWISS-MODEL.html http://www.cmbi.kun.nl/whatif/

Sutcliffe et al. (1987) Bruccoleri and Karplus (1990) —c —d Sali and Blundell (1993)e —d —f Bower et al. (1997) Peitsch and Jongeneel (1993) Vriend (1990)

http://www.fundp.ac.be/sciences/biologie/bms/CGI/anolea.html http://urchin.bmrb.wisc.edu/~jurgen/aqua/ http://biotech.embl-heidelberg.de:8400 http://www.doe-mbi.ucla.edu/Services/ERRAT/ http://www.biochem.ucl.ac.uk/~roman/procheck/procheck.html http://www.came.sbg.ac.at http://www.ucmb.ulb.ac.be/UCMB/PROVE http://www.ysbl.york.ac.uk/~oldfield/squid/ http://www.doe-mbi.ucla.edu/Services/Verify_3D/ http://www.sander.embl-heidelberg.de/whatcheck/

Melo and Feytmans (1998) Laskowski et al. (1996) Laskowski et al. (1998) Colovos and Yeates (1993) Laskowski et al. (1998) Sippl (1993) Pontius et al. (1996) Oldfield (1992) Luthy et al. (1992) Hooft et al. (1996a)

Sequence alignment BCM SERVER S BLAST2 S BLOCK S MAKER CLUSTAL S FASTA3 S MULTALIN S Modeling COMPOSER P CONGEN P ICM P InsightII P MODELLER P QUANTA P SYBYL P SCWRL P SWISS-MOD S WHAT IF P Model evaluation ANOLEA S AQUA P S BIOTECHg ERRAT S PROCHECK P ProsaII P PROVE S SQUID P VERIFY3D S WHATCHECK P

Reference

continued

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Table 2.9.1

Name

Programs and Web Servers Useful in Comparative Modeling, continued

Typea WWW addressb

Methods evaluation CASP S CAFASP S EVA S LiveBench S

http://predictioncenter.llnl.gov http://cafasp.bioinfo.pl http://cubic.bioc.columbia.edu/eva/ http://bioinfo.pl/LiveBench/

Reference Moult et al. (2001) Fischer et al. (2001) Eyrich et al. (2001) Bujnicki et al. (2001)

aS, server; P, program. bSome of the sites are mirrored on additional computers. cMolSoft Inc., San Diego, Calif. dAccelrys Inc., San Diego, Calif. eSee Internet Resources for additional information. fTripos Inc., St Louis, Mo. gThe BIOTECH server uses PROCHECK and WHATCHECK for structure evaluation.

STEPS IN COMPARATIVE MODELING Fold Assignment and Template Selection The starting point in comparative modeling is to identify all protein structures related to the target sequence, and then select those structures that will be used as templates. This step is facilitated by numerous protein sequence and structure databases and by the use of database-scanning software available on the Web (Altschul et al., 1994; Barton, 1998; Holm and Sander, 1996; also see Table 2.9.1). Templates can be found using the target sequence as a query for searching structure databases such as the Protein Data Bank (Westbrook et al., 2002), SCOP (Lo Conte et al., 2002), DALI (Holm and Sander, 1999), and CATH (Orengo et al., 2002). The probability of finding a related protein of known structure for a sequence picked randomly from a genome ranges from 20% to 70% (Fischer and Eisenberg, 1997; Huynen et al., 1998; Jones, 1999; Rychlewski et al., 1998; Sanchez and Sali, 1998; Pieper et al., 2002). There are three main classes of protein comparison methods that are useful in fold identification. The first class includes the methods that compare the target sequence with each of the database sequences independently, using pairwise sequence-sequence comparison (Apostolico and Giancarlo, 1998). The performance of these methods in searching for related protein sequences and structures has been evaluated exhaustively. Frequently used programs in this class include FASTA (Pearson and Lipman, 1988; Pearson, 1995) and BLAST (UNIT 2.5; Altschul et al., 1990). The second set of methods relies on multiple sequence comparisons to improve the sensitivity of the search (Altschul et al., 1997; Henikoff and Henikoff, 1994; Gribskov, 1994; Krogh et al., 1994; Rychlewski et al., 1998). A widely used program in this class is PSI-BLAST (Altschul et al., 1997), which iteratively expands the set of homologs of the target sequence. For a given sequence, an initial set of homologs from a sequence database is collected, a weighted multiple alignment is made from the query sequence and its homologs, a position-specific scoring matrix is constructed from the alignment, and the matrix is used to search the database for additional homologs. These steps are repeated until no additional homologs are found. In comparison to BLAST, PSI-BLAST finds homologs of known structure for approximately twice as many sequences (Park et al., 1998; Sternberg et al., 1999).

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start target sequence

template structure

identify related structures

select templates

align target sequence with template structures

alignment target

KLTDSQNFDEYMKALGVGFATRQVGNVTKPTVIISQEGGKVV...

template

KLVSSENFDDYMKEVGVGFATRKVAGMAKPNMIISVNGDLVT...

build a model for the target using information from template structures target model

no

model OK?

yes

end

Pseudo energy

evaluate the model

Residue index

Figure 2.9.1 Steps in comparative protein structure modeling.

Comparative Protein Structure Prediction

The third class of methods are the so-called threading or 3-D template matching methods (Bowie et al., 1991; Jones et al., 1992; Godzik et al., 1992; reviewed in Jones, 1997; Smith et al., 1997; Torda, 1997; Levitt, 1997; David et al., 2000). These methods rely on pairwise comparison of a protein sequence and a protein of known structure. Whether or not a given target sequence adopts any one of the many known 3-D folds is predicted by an optimization of the alignment with respect to a structure-dependent scoring function, independently for each sequence-structure pair—i.e., the target sequence is threaded through a library of 3-D folds. These methods are especially useful when there are no

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sequences clearly related to the modeling target, and thus the search cannot benefit from the increased sensitivity of the sequence profile methods. A useful fold-assignment approach is to accept an uncertain assignment provided by any of the methods, build a full-atom comparative model of the target sequence based on this match, and make the final decision about whether or not the match is real by evaluating the resulting comparative model (Sanchez and Sali, 1997; Guenther et al., 1997; Miwa et al., 1999). Once a list of all related protein structures has been obtained, it is necessary to select those templates that are appropriate for the given modeling problem. Usually, a higher overall sequence similarity between the target and the template sequence yields a better model. In any case, several other factors should be taken into account when selecting the templates:

A

B

D

C

E

Figure 2.9.2 Typical errors in comparative modeling. (A). Errors in side-chain packing. The Trp 109 residue in the crystal structure of mouse cellular retinoic acid binding protein I (thin line) is compared with its model (thick line), and with the template mouse adipocyte lipid-binding protein (broken line). (B) Distortions and shifts in correctly aligned regions. A region in the crystal structure of mouse cellular retinoic acid binding protein I is compared with its model and with the template fatty acid binding protein using the same representation as in panel A. (C) Errors in regions without a template. The Cα trace of the 112–117 loop is shown for the X-ray structure of human eosinophil neurotoxin (thin line), its model (thick line), and the template ribonuclease A structure (residues 111–117; broken line). (D) Errors due to misalignments. The N-terminal region in the crystal structure of human eosinophil neurotoxin (thin line) is compared with its model (thick line). The corresponding region of the alignment with the template ribonuclease A is shown. The black lines show correct equivalences, that is, residues whose Cα atoms are within 5 Å of each other in the optimal least-squares superposition of the two X-ray structures. The “a” characters in the bottom line indicate helical residues. (E) Errors due to an incorrect template. The X-ray structure of α-trichosanthin (thin line) is compared with its model (thick line) which was calculated using indole-3-glycerophosphate synthase as the template.

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syy
 

[[sty[Å [[syyw

}

STUVWXYZ[\]T]^WTX\ _`\a]YXS_ V`SXZYXYZ[]YV[X_bcdeXYZ ^XZ]YVS Vd\fXYZ[dg[_]\cd_d^`\U^`Sh bc`VX\TXdY[dg[bcdT`XY[b]cTY`cS

uy

eXcTU]^[S\c``YXYZ[]YV Vd\fXYZ[dg[S_]^^[^XZ]YVS

qy

ndV`^[]\\Uc]\W

w[`~U`Y\`[XV`YTXTW

[[stu[Å [[vuw

V`gXYXYZ[]YTXidVW `bXTdb`S

[qtu[Å [xyw

{

_d^`\U^]c[c`b^]\`_`YT[XY jkc]W[\cWST]^^dZc]baW V`SXZYXYZ[\aX_`c]Sh[ST]i^` \cWST]^^Xl]i^`[e]cX]YTS SUbbdcTXYZ[SXT`kVXc`\T`V _UT]Z`Y`SXS c`gXYXYZ[mno[STcU\TUc`S

XYSXZYXgX\]YT[S`~U`Y\`[SX_X^]cXTW

gXTTXYZ[XYTd[^dpkc`Sd^UTXdY `^`\TcdY[V`YSXTW

Comparative Protein Structure Prediction

zty[Å xy[]]

|

gXYVXYZ[gUY\TXdY]^[SXT`S[iW qkr[_dTXg[S`]c\aXYZ STcU\TUc`[gcd_[Sb]cS` `jb`cX_`YT]^[c`STc]XYTS ]YYdT]TXYZ[gUY\TXdY[iW gd^V[]SSXZY_`YT `ST]i^XSaXYZ[`ed^UTXdY]cW c`^]TXdYSaXbS

Figure 2.9.3 Accuracy and application of protein structure models. The vertical axis indicates the different ranges of applicability of comparative protein structure modeling, the corresponding accuracy of protein structure models, and their sample applications. (A) The docosahexaenoic fatty acid ligand (darker strand) was docked into a high-accuracy comparative model of brain lipid-binding protein (right), modeled based on its 62% sequence identity to the crystallographic structure of adipocyte lipid-binding protein (PDB code, 1adl). A number of fatty acids were ranked for their affinity to brain lipid-binding protein consistently with site-directed mutagenesis and affinity chromatography experiments (Xu et al., 1996), even though the ligand-specificity profile of this protein is different from that of the template structure. Typical overall accuracy of a comparative model in this range of sequence similarity is indicated by a comparison of a model for adipocyte fatty acid binding protein with its actual structure (left). (B) A putative proteoglycan binding patch was identified on a medium-accuracy comparative model of mouse mast cell protease 7 (right), modeled based on its 39% sequence identity to the crystallographic structure of bovine pancreatic trypsin (PDB code, 2ptn) that does not bind proteoglycans. The prediction was confirmed by site-directed mutagenesis and heparin-affinity chromatography experiments (Matsumoto et al., 1995). Typical accuracy of a comparative model in this range of sequence similarity is indicated by a comparison of a trypsin model with the actual structure. (C) A molecular model of the whole yeast ribosome (right) was calculated by fitting atomic rRNA and protein models into the electron density of the 80S ribosomal particle, obtained by electron microscopy at 15 Å resolution (Beckmann et al., 2001). Most of the models for 40 out of the 75 ribosomal proteins were based on approximately 30% sequence identity to their template structures. Typical accuracy of a comparative model in this range of sequence similarity is indicated by a comparison of a model for a domain in L2 protein from B. Stearothermophilus with the actual structure (PDB code, 1rl2).

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1. The family of proteins, which includes the target and the templates, can frequently be organized in subfamilies. The construction of a multiple alignment and a phylogenetic tree (Felsenstein, 1985) can help in selecting the template from the subfamily that is closest to the target sequence. 2. The template environment should be compared to the required environment for the model. The term “environment” is used in a broad sense and includes all factors that determine protein structure, except its sequence (e.g., solvent, pH, ligands, and quaternary interactions). 3. The quality of the experimental template structure is another important factor in template selection. The resolution and the R-factor of a crystallographic structure and the number of restraints per residue for an NMR structure are indicative of its accuracy. The priority of the criteria for template selection depends on the purpose of the comparative model. For instance, if a protein-ligand model is to be constructed, the choice of the template that contains a similar ligand is probably more important than the resolution of the template. On the other hand, if the model is to be used to analyze the geometry of the active site of an enzyme, it is preferable to use a high-resolution template. It is not necessary to select only one template. In fact, the use of several templates approximately equidistant from the target sequence generally increases the model accuracy (Srinivasan and Blundell, 1993; Sanchez and Sali, 1997). Target-Template Alignment Most fold-assignment methods produce an alignment between the target sequence and template structures. However, this is often not the optimal target-template alignment for comparative modeling. Searching methods are usually tuned for detection of remote relationships, not for optimal alignments. Therefore, once templates have been selected, a specialized method should be used to align the target sequence with the template structures (Taylor, 1996; Holm and Sander, 1996; Briffeuil et al., 1998; Baxevanis, 1998; Smith, 1999). For closely related protein sequences with identity higher than 40%, the alignment is almost always correct. Regions of low local sequence similarity become common when the overall sequence identity is below 40% (Saqi et al., 1998). The alignment becomes difficult in the “twilight zone” of less than 30% sequence identity (Rost, 1999). As the sequence similarity decreases, alignments contain an increasingly large number of gaps and alignment errors, regardless of whether they are prepared automatically or manually. For example, only 20% of the residues are likely to be correctly aligned when two proteins share 30% sequence identity (Johnson and Overington, 1993). Maximal effort to obtain the most accurate alignment possible is needed, because no current comparative modeling method can recover from an incorrect alignment. There are a great variety of protein sequence alignment methods, many of which are based on dynamic programming techniques (Needleman and Wunsch, 1970; Smith and Waterman, 1981). A frequently used program for multiple sequence alignment is CLUSTAL (Thompson et al., 1994; Higgins et al., 1996), which is also available as a Web server (Table 2.9.1). In the more difficult alignment cases, it is frequently beneficial to rely on multiple structure and sequence information (Barton and Sternberg, 1987; Taylor et al., 1994). First, the alignment of the potential templates is prepared by superposing their structures. Next, the sequences that are clearly related to the templates and are easily aligned with them are added to the alignment. The same is done for the target sequence. Finally, the two profiles are aligned with each other, taking structural information into account as much as possible (Koretke et al., 1998; Thompson et al., 1994; Yang and Honig, 2000; Al Lazikani et al., 2001a).

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Model Building Once an initial target-template alignment has been built, a variety of methods can be used to construct a 3-D model for the target protein. The original and still widely used method is modeling by rigid-body assembly (Browne et al., 1969; Greer, 1990; Blundell et al., 1987). Another family of methods, based on modeling by segment matching, relies on the approximate positions of conserved atoms in the templates (Jones and Thirup, 1986; Unger et al., 1989; Claessens et al., 1989; Levitt, 1992). The third group of methods, modeling by satisfaction of spatial restraints, uses either distance geometry or optimization techniques to satisfy spatial restraints obtained from the alignment (Havel and Snow, 1991; Srinivasan et al., 1993; Sali and Blundell, 1993; Brocklehurst and Perham, 1993; Aszodi and Taylor, 1996; Kolinski et al., 2001). Accuracies of the various model-building methods are relatively similar when used optimally (Marti-Renom et al., 2002). Other factors, such as template selection and alignment accuracy, usually have a larger impact on the model accuracy, especially for models based on less than 40% sequence identity to the templates. There are many reviews of comparative model building methods (Blundell et al., 1987; Sanchez and Sali, 1997; Sali, 1995; Johnson et al., 1994; Bajorath et al., 1993; Marti-Renom et al., 2000; Al Lazikani et al., 2001b). A number of programs and Web servers for comparative modeling are listed in Table 2.9.1. Model Evaluation The quality of the predicted model determines the information that can be extracted from it. Thus, estimating the accuracy of 3-D protein models is essential for interpreting them. The model can be evaluated as a whole, as well as in the individual regions. There are many model-evaluation programs and servers (Laskowski et al., 1998; Wilson et al., 1993; also see Table 2.9.1). The first step in model evaluation is to determine if the model has the correct fold (Sanchez and Sali, 1998). A model will have the correct fold if the correct template is picked and if that template is aligned at least approximately correctly with the target sequence. The confidence in the fold of a model is generally increased by a high sequence similarity with the closest template, a significant energy-based Z-score (Sippl, 1993; Sanchez and Sali, 1998), or conservation of the key functional or structural residues in the target sequence. Once the fold of a model is accepted, a more detailed evaluation of the overall model accuracy can be obtained based on the similarity between the target and template sequences (Sanchez and Sali, 1998). Sequence identity above 30% is a relatively good predictor of the expected accuracy. The reasons are the well-known relationship between structural and sequence similarities of two proteins (Chothia and Lesk, 1986), the “geometrical” nature of modeling that forces the model to be as close to the template as possible (Sali and Blundell, 1993), and the inability of any current modeling procedure to recover from an incorrect alignment (Sanchez and Sali, 1997). The dispersion of the model-target structural overlap increases with the decrease in sequence identity. If the target-template sequence identity falls below 30%, the sequence identity becomes unreliable as a measure of accuracy of a single model. Models that deviate significantly from the average accuracy are frequent. It is in such cases that model-evaluation methods are particularly useful.

Comparative Protein Structure Prediction

In addition to the target-template sequence similarity, the environment can strongly influence the accuracy of a model. For instance, some calcium-binding proteins undergo large conformational changes when bound to calcium. If a calcium-free template is used to model the calcium-bound state of the target, it is likely that the model will be incorrect, irrespective of the target-template similarity or accuracy of the template structure (Pawlowski et al., 1996). This qualification also applies to the experimental determination

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of protein structure; a structure must be determined in the functionally meaningful environment. A basic requirement for a model is to have good stereochemistry. Some useful programs for evaluating stereochemistry (see Table 2.9.1 for URLs) are PROCHECK (Laskowski et al., 1998), PROCHECK-NMR (Laskowski et al., 1996), AQUA (Laskowski et al., 1996), SQUID (Oldfield, 1992), and WHATCHECK (Hooft et al., 1996b). The features of a model that these programs check include bond lengths, bond angles, peptide bond and side-chain ring planarities, chirality, main-chain and side-chain torsion angles, and clashes between nonbonded pairs of atoms. There are also methods for testing 3-D models that implicitly take into account many spatial features compiled from high-resolution protein structures. These methods are based on 3-D profiles and statistical potentials of mean force (Sippl, 1990; Luthy et al., 1992). Programs implementing this approach (see Table 2.9.1 for URLs) include VERIFY3D (Luthy et al., 1992), PROSAII (Sippl, 1993), HARMONY (Topham et al., 1994), and ANOLEA (Melo and Feytmans, 1998). The programs evaluate the environment of each residue in a model with respect to the expected environment as found in the high-resolution X-ray structures. There is a concern about the theoretical validity of the energy profiles for detecting regional errors in models (Fiser et al., 2000). It is likely that the contributions of the individual residues to the overall free energy of folding vary widely, even when normalized by the number of atoms or interactions made. If this expectation is correct, the correlation between the model errors and energy peaks is greatly weakened, resulting in the loss of predictive power of the energy profile. Despite these concerns, error profiles have been useful in some applications (Miwa et al., 1999). ERRORS IN COMPARATIVE MODELS As the similarity between the target and the templates decreases, the errors in the model increase. Errors in comparative models can be divided into five categories (Sanchez and Sali, 1997; also see Fig. 2.9.2): 1. Errors in side-chain packing (Fig. 2.9.2A). As the sequences diverge, the packing of side chains in the protein core changes. Sometimes even the conformation of identical side chains is not conserved, a pitfall for many comparative modeling methods. Side-chain errors are critical if they occur in regions that are involved in protein function, such as active sites and ligand-binding sites. 2. Distortions and shifts in correctly aligned regions (Fig. 2.9.2B). As a consequence of sequence divergence, the main-chain conformation changes, even if the overall fold remains the same. Therefore, it is possible that in some correctly aligned segments of a model the template is locally different (<3 Å) from the target, resulting in errors in that region. The structural differences are sometimes not due to differences in sequence, but are a consequence of artifacts in structure determination or structure determination in different environments (e.g., packing of subunits in a crystal). The simultaneous use of several templates can minimize this kind of an error (Srinivasan and Blundell, 1993; Sanchez and Sali, 1997). 3. Errors in regions without a template (Fig. 2.9.2C). Segments of the target sequence that have no equivalent region in the template structure (i.e., insertions or loops) are the most difficult regions to model. If the insertion is relatively short, less than 9 residues long, some methods can correctly predict the conformation of the backbone (van Vlijmen and Karplus, 1997; Fiser et al., 2000). Conditions for successful prediction are the correct alignment and an accurately modeled environment surrounding the insertion.

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4. Errors due to misalignments (Fig. 2.9.2D). The largest source of errors in comparative modeling are misalignments, especially when the target-template sequence identity decreases below 30%. However, alignment errors can be minimized in two ways. First, it is usually possible to use a large number of sequences to construct a multiple alignment, even if most of these sequences do not have known structures. Multiple alignments are generally more reliable than pairwise alignments (Barton and Sternberg, 1987; Taylor et al., 1994). The second way of improving the alignment is to iteratively modify those regions in the alignment that correspond to predicted errors in the model (Sanchez and Sali, 1997). 5. Incorrect templates (Fig. 2.9.2E). This is a potential problem when distantly related proteins are used as templates (i.e., <25% sequence identity). Distinguishing between a model based on an incorrect template and a model based on an incorrect alignment with a correct template is difficult. In both cases, the evaluation methods will predict an unreliable model. The conservation of the key functional or structural residues in the target sequence increases the confidence in a given fold assignment. An informative way to test protein structure modeling methods is provided by EVA-CM (Eyrich et al., 2001) and LiveBench (Bujnicki et al., 2001) See Table 2.9.1 for the URLs of the EVA and LiveBench servers. APPLICATIONS OF COMPARATIVE MODELING Comparative modeling is an increasingly efficient way to obtain useful information about the proteins of interest. For example, comparative models can be helpful in designing mutants to test hypotheses about a protein’s function (Boissel et al., 1993; Wu et al., 1999), identifying active and binding sites (Sheng et al., 1996), identifying, designing, and improving ligands for a given binding site (Ring et al., 1993), modeling substrate specificity (Xu et al., 1996), predicting antigenic epitopes (Sali et al., 1993), simulating protein-protein docking (Vakser, 1997), inferring function from a calculated electrostatic potential around the protein (Matsumoto et al., 1995), facilitating molecular replacement in X-ray structure determination (Howell et al., 1992), refining models based on NMR constraints (Modi et al., 1996), testing and improving a sequence-structure alignment (Wolf et al., 1998), confirming a remote structural relationship (Guenther et al., 1997; Miwa et al., 1999), and rationalizing known experimental observations. For exhaustive reviews of comparative modeling applications see Johnson et al. (1994), Fiser and Sali (2002), and Baker and Sali (2001). Fortunately, a 3-D model does not have to be absolutely perfect to be helpful in biology, as demonstrated by the applications listed above. However, the type of a question that can be addressed with a particular model does depend on its accuracy (Figure 2.9.3). Three levels of model accuracy and some of the corresponding applications are as follows. 1. At the low end of the spectrum, there are models that are based on less than 30% sequence identity and that sometimes have less than 50% of their atoms within 3.5 Å of their correct positions. Such models still have the correct fold, which is frequently sufficient to predict its approximate biochemical function. More specifically, only nine out of 80 fold families known in 1994 contained proteins (domains) that were not in the same functional class, although 32% of all protein structures belonged to one of the nine superfolds (Orengo et al., 1994). Models in this low range of accuracy, combined with model evaluation, can be used for confirming or rejecting a match between remotely related proteins (Sanchez and Sali, 1997, 1998). Comparative Protein Structure Prediction

2. In the middle of the accuracy spectrum are the models based on ~30% to 50% sequence identity, corresponding to 85% of the residues modeled within 3.5 Å of their correct positions. Fortunately, the active and binding sites are frequently more

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conserved than the rest of the fold, and are thus modeled more accurately (Sanchez and Sali, 1998). In general, medium-resolution models frequently allow refinement of the functional prediction based on sequence alone, because ligand binding is most directly determined by the structure of the binding site rather than by its sequence. It is frequently possible to correctly predict important features of the target protein that do not occur in the template structure. For example, the location of a binding site can be predicted from clusters of charged residues (Matsumoto et al., 1995), and the size of a ligand may be predicted from the volume of the binding site cleft (Xu et al., 1996). Medium-resolution models can also be used to construct site-directed mutants with altered or destroyed binding capacity, which in turn could test hypotheses about the sequence-structure-function relationships. Other problems that can be addressed with medium-resolution comparative models include designing proteins that have compact structures without long tails, loops, and exposed hydrophobic residues, for better crystallization, or with added disulfide bonds for extra stability. 3. The high end of the accuracy spectrum corresponds to models based on more than 50% sequence identity. The average accuracy of these models approaches that of low-resolution X-ray structures (3 Å resolution) or medium-resolution NMR structures (10 distance restraints per residue; Sanchez and Sali, 1997). The alignments on which these models are based generally contain almost no errors. In addition to the already listed applications, high-quality models can be used for docking of small ligands (Ring et al., 1993) or whole proteins onto a given protein (Totrov and Abagyan, 1994; Vakser, 1997). EXAMPLE OF COMPARATIVE MODELING: MODELING LACTATE DEHYDROGENASE FROM TRICHOMONAS VAGINALIS BASED ON A SINGLE TEMPLATE This section contains an example of a typical comparative modeling application. It demonstrates each of the five steps of comparative modeling, using the program MODELLER6. All files described in this section, including the MODELLER program, are available at http://guitar.rockefeller.edu/modeller/tutorials.shtml (also see Internet Resources). A novel gene for lactate dehydrogenase was identified from the genomic sequence of Trichomonas vaginalis (TvLDH). The corresponding protein had a higher similarity to the malate dehydrogenase of the same species (TvMDH) than to any other LDH. The authors hypothesized that TvLDH arose from TvMDH by convergent evolution relatively recently (Wu et al., 1999). Comparative models were constructed for TvLDH and TvMDH to study the sequences in the structural context and to suggest site-directed mutagenesis experiments for elucidating specificity changes in this apparent case of convergent evolution of enzymatic specificity. The native and mutated enzymes were expressed and their activities were compared (Wu et al., 1999). Searching for Structures Related to TvLDH It is necessary to put the target TvLDH sequence into the PIR format readable by MODELLER (see Internet Resources for MODELLER Web site; file TvLDH.ali). The first line of the file (see Fig. 2.9.4) contains the sequence code, in the format >P1;code. The second line, with ten fields separated by colons, generally contains information about the structure file, if applicable. Only two of these fields are used for sequences: sequence (indicating that the file contains a sequence without known structure) and TvLDH (the model file name). The rest of the file contains the sequence of TvLDH, with a “*” marking its end. A search for potentially related sequences of known structure can be performed by the SEQUENCE_SEARCH command of MODELLER. The following

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>P1;TvLDH sequence:TvLDH:::::::0.00: 0.00 MSEAAHVLITGAAGQIGYILSHWIASGELYG-DRQVYLHLLDIPPAMNRLTALTMELEDCAFPHLAGFVATTDPK AAFKDIDCAFLVASMPLKPGQVRADLISSNSVIFKNTGEYLSKWAKPSVKVLVIGNPDNTNCEIAMLHAKNLKPE NFSSLSMLDQNRAYYEVASKLGVDVKDVHDIIVWGNHGESMVADLTQATFTKEGKTQKVVDVLDHDYVFDTFFKK IGHRAWDILEHRGFTSAASPTKAAIQHMKAWLFGTAPGEVLSMGIPVPEGNPYGIKPGVVFSFPCNVDKEGKIHV VEGFKVNDWLREKLDFTEKDLFHEKEIALNHLAQGG*

Figure 2.9.4 File TvLDH.ali. Sequence file in the PIR MODELLER format.

SET SEARCH_RANDOMIZATIONS = 100 SEQUENCE_SEARCH FILE = 'TvLDH.ali', ALIGN_CODES = 'TvLDH', DATA_FILE = ON

Figure 2.9.5 File seqsearch.top. The TOP script file for template research using MODELLER.

script uses the query sequence TvLDH assigned to the variable ALIGN_CODES from the file TvLDH.ali assigned to the variable FILE (file seqsearch.top; Fig. 2.9.5). The SEQUENCE_SEARCH command has many options (see Internet Resources for MODELLER Web site), but in this example only SEARCH_RANDOMIZATIONS and DATA_FILE are set to non-default values. SEARCH_RANDOMIZATIONS specifies the number of times the query sequence is randomized during the calculation of the significance score for each sequence-sequence comparison. The higher the number of randomizations, the more accurate the significance score. DATA_FILE = ON triggers creation of an additional summary output file (seqsearch.dat). Selecting a Template The output of the seqsearch.top script is written to the seqsearch.log file. MODELLER always produces a log file. Errors and warnings in log files can be found by searching for the E> and W> strings, respectively. At the end of the log file, MODELLER lists the hits, sorted by alignment significance. Because the log file is sometimes very long, a separate data file (seqsearch.dat) is created that contains the summary of the search (Fig. 2.9.6). The example in Figure 2.9.6 shows only the top 10 hits from such a file.

Comparative Protein Structure Prediction

The most important columns in the SEQUENCE_SEARCH output are the CODE_2, %ID, and SIGNI columns. The CODE_2 column reports the code of the PDB sequence that was compared with the target sequence. The PDB code in each line is the representative of a group of PDB sequences that share 40% or more sequence identity to each other and have less than 30 residues or 30% sequence length difference. All the members of the group can be found in the MODELLER CHAINS_3.0_40_XN.grp file. The LEN1 and LEN2 are lengths of the protein sequences in the CODE_1 and CODE_2 columns, respectively. NID represents the number of aligned residues. The %ID1 and %ID2 columns report the percentage sequence identities between TvLDH and a PDB sequence, normalized by their lengths, respectively. In general, a %ID value above ∼25% indicates a potential template unless the alignment is short (i.e., less than 100 residues). A better measure of the significance of the alignment is given by the SIGNI column (see Internet Resources for MODELLER Web site). A value above 6.0 is generally significant irrespective of the sequence identity and length. In this example, one protein family represented by 1bdmA shows significant similarity with the target sequence, at more than 40%

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# CODE_1

CODE_2

1 2 3 4 5 6 7 8 9 10

1bdmA 1lldA 1ceqA 2hlpA 1ldnA 1hyhA 2cmd 1db3A 91dtA 1cdb

TvLDH TvLDH TvLDH TvLDH TvLDH TvLDH TvLDH TvLDH TvLDH TvLDH

LEN1 LEN2 NID

335 335 335 335 335 335 335 335 335 335

318 153 313 103 304 95 303 86 316 91 297 88 312 108 335 91 331 95 105 69

% IDI

45.7 30.7 28.4 25.7 27.2 26.3 32.2 27.2 28.4 29.6

%ID2

48.1 32.9 31.3 28.4 28.8 29.6 34.6 27.2 28.7 65.7

SCORE

SIGNI

212557. 183190. 179636. 177791. 180669. 175969. 182079. 181928. 181720. 80141.

28.9 10.1 9.2 8.9 7.4 6.9 6.6 4.9 4.7 3.8

Figure 2.9.6 Top 10 hits from file seqsearch.dat. The summary output file for the SEQUENCE_SEARCH command in MODELLER.

sequence identity. While some other hits are also significant, the differences between 1bdmA and other top scoring hits are so pronounced that we use only the first hit as the template. As expected, 1bdmA is a malate dehydrogenase (from a thermophilic bacterium). Other structures closely related to 1bdmA (and thus not scanned against by SEQUENCE_SEARCH) can be extracted from the CHAINS_3.0_40_XN.grp file: 1b8vA, 1bmdA, 1b8uA, 1b8pA, 1bdmA, 1bdmB, 4mdhA, 5mdhA, 7mdhA, 7mdhB, and 7mdhC. All these proteins are malate dehydrogenases. During the project, all of them, as well as other malate and lactate dehydrogenase structures, were compared and considered as templates (there were 19 structures in total). However, for the sake of illustration, we will investigate only four of the proteins that are sequentially most similar to the target: 1bmdA, 4mdhA, 5mdhA, and 7mdhA. The script in Figure 2.9.7 performs all pairwise comparisons among the selected proteins (file compare.top). The READ_ALIGNMENT command shown in Figure 2.9.7 reads the protein sequences and information about their PDB files. MALIGN calculates their multiple sequence alignment, used as the starting point for the multiple structure alignment. The MALIGN3D command performs an iterative least-squares superposition of the four 3-D structures. The COMPARE command compares the structures according to the alignment constructed by MALIGN3D. It does not make an alignment, but it calculates the RMS and DRMS deviations between atomic positions and distances, differences between the main-chain and side-chain dihedral angles, percentage sequence identities, and several other measures. Finally, the ID_TABLE command writes a file with pairwise sequence distances that can be used directly as the input to the DENDROGRAM command—or the clustering programs in the PHYLIP package (Felsenstein, 1985). DENDROGRAM calculates a clustering tree from the input matrix of pairwise distances, which helps visualizing

READ_ALIGNMENT FILE = ’$(LIB)/CHAINS_all.seq’,; ALIGN_CODES = ’1bmdA’ ’4mdhA’ ’5mdhA’ ’7aidhA’ MALIGN MALIGN3D COMPARE ID_TABLE DENDROGRAM

Figure 2.9.7 TOP script file compare.top.

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>> Least-squares superposition (FIT)

:

T

Atom types for superposition/RMS (FIT_ATOMS) : CA Atom type for position average/variability ( DISTANCE_ATOMS[1]): CA Position comparison (FIT_ATOMS): Cutoff for RMS calculation:

3.5000

Upper = RMS, Lower = numb equiv positions 1bmdA 0.000 310 308 320

1bmdA 4mdhA 5mdhA 7mdhA

4mdhA 1.038 0.000 329 306

5mdhA 0.979 0.504 0.000 307

7mdhA 0.992 1.210 1.173 0.000

>> Sequence comparison: Diag=numb res, Upper=numb equiv res, Lower = % seq ID 1bmdA 4mdhA 5mdhA 7mdhA

1bmdA 327 51 51 48

4mdhA 168 333 98 41

5mdhA 168 328 333 41

7mdhA 158 137 138 351 1bmdA @1.9

49.0000

4mdhA @2.5

2.0000

5mdhA @2.4

55.5000

7mdhA @2.4

+ + + + + + + + + + 57.6400 48.0100 38.3800 28.7500 19.1200 52.8250 43.1950 33.5650 23.9350 14.3050 4.6750

+ + + 9.4900 -0.1400

Figure 2.9.8 Excerpts from the log file compare.log.

differences among the template candidates. Excerpts from the log file are shown in Figure 2.9.8 (file compare.log). The comparison in Figure 2.9.8 shows that 5mdhA and 4mdhA are almost identical, both sequentially and structurally. They were solved at similar resolutions, 2.4 and 2.5 Å, respectively. However, 4mdhA has a better crystallographic R-factor (16.7 versus 20%), eliminating 5mdhA. Inspection of the PDB file for 7mdhA reveals that its crystallographic refinement was based on 1bmdA. In addition, 7mdhA was refined at a lower resolution than 1bmdA (2.4 versus 1.9), eliminating 7mdhA. These observations leave only 1bmdA and 4mdhA as potential templates. Finally, 4mdhA is selected because of the higher overall sequence similarity to the target sequence. Aligning TvLDF with the Template

Comparative Protein Structure Prediction

A good way of aligning the sequence of TvLDH with the structure of 4mdhA is the ALIGN2D command in MODELLER. Although ALIGN2D is based on a dynamic programming algorithm (Needleman and Wunsch, 1970), it is different from standard sequence-sequence alignment methods because it takes into account structural information from the template when constructing an alignment. This task is achieved through a variable gap penalty function that tends to place gaps in solvent-exposed and curved

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READ_MODEL FILE = '4mdh.pdb' SEQUENCE_TO_ALI ALIGN_CODES = '4mdhA', ATOM_FILES = '4mdhA' READ_ALIGNMENT FILE = 'TvLDH.ali', ALIGN_CODES = ALIGN_CODES 'TvLDH', ADD_SEQUENCE = ON ALIGN2D WRITE_ALIGNMENT FILE='TvLDH-4mdhA.ali', ALIGNMENT_FORMAT = 'PIR' WRITE_ALIGNMENT FILE='TvLDH-4mdhA.pap', ALIGNMENT_FORMAT = 'PAP'

Figure 2.9.9 The align2d.top file that contains the MODELLER script executing the ALIGN2D command.

_aln.pos 10 20 30 40 50 60 GSEPIRVLVTGAAGQIAYSLLYSIGNGSVFGKDQPIILVLLDITPMMGVLDGVLMELQDCALPLLKDV 4mdhA MSEAAHVLITGAAGQIGYI LSHWIASGELYG-DRQVYLHLLDIPPA MNRLTALTMELEDCA FPHLAGF TvLDH ** ******* * * * * * * * **** * * * *** *** * * _consrvd **

90 _aln.p 70 80 100 110 120 130 IATDKEEIAFKDLDVAILVGSMPRRDGMERKDLLKANVKIFKCQGAALDKYAKKSVKVIVVGNPANTN 4mdhA VATTDPKAAFKDIDCAFLVASMPLKPGQVRADLISSNSVIFKNTGEYLSKWAKPSVKVLVIGNPDNTN TvLDH **** * * ** *** * * ** * *** * * * ** **** * *** *** _consrvd ** _aln.pos 140 150 170 180 190 200 160 CLTASKSAPSIPKENFSCLTRLDHNRAKAQIALKLGVTSDDVKNVIIWGNHSSTQYPDVNHAKVKLQA 4mdhA CEIAMLHAKNLKPENFSSLSMLDQNRAYYEVASKLGVDVKDVHDIIVWGNHGESMVADLTQATFTKEG TvLDH * **** * ** *** * **** ** * **** * * _consrvd * *

Figure 2.9.10 The alignment file between sequences TvLDH and 4mdhA in the PAP MODELLER format.

regions, outside secondary structure segments, and between two Cα positions that are close in space. As a result, the alignment errors are reduced by approximately one third relative to those that occur with standard sequence alignment techniques. This improvement becomes more important as the similarity between the sequences decreases and the number of gaps increases. In the current example, the template-target similarity is so high that almost any alignment method with reasonable parameters will result in the same alignment. The MODELLER script shown in Figure 2.9.9 aligns the TvLDH sequence in file TvLDH.seq with the 4mdhA structure in the PDB file 4mdh.pdb (file align2d.top). In the first line of the script shown in Figure 2.9.9, MODELLER reads the 4mdhA structure file. The SEQUENCE_TO_ALI command transfers the sequence to the alignment array and assigns it the name 4mdhA (ALIGN_CODES). The third line reads the TvLDH sequence from file TvLDH.ali, assigns it the name TvLDH (ALIGN_CODES), and adds it to the alignment array (ADD_SEQUENCE = ON). The fourth line executes the ALIGN2D command to perform the alignment. Finally, the alignment is written out in two formats, PIR (TvLDH-4mdhA.ali) and PAP (TvLDH-4mdhA.pap). The PIR format is used by MODELLER in the subsequent model-building stage. The PAP alignment format is easier to inspect visually. Due to the high target-template similarity, there are only a few gaps in the alignment. In the PAP format, all identical positions are marked with a “*” (file TvLDH-4mdhA.pap; Fig. 2.9.10).

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INCLUDE SET ALNFILE = ’TvLDH-4mdhA.ali’ SET KNOWNS = ’4mdhA’ SET SEQUENCE = ’TvLDH’ SET STARTING_MODEL = 1 SET ENDING_MODEL = 5 CALL, ROUTINE = ’model’

Figure 2.9.11 TOP script file model-single top.

Model Building Once a target-template alignment is constructed, MODELLER calculates a 3-D model of the target completely automatically. The script in Figure 2.9.11 will generate five models of TvLDH based on the 4mdhA template structure and the alignment in file TvLDH4mdh.ali (file model-single.top). The first line (see Fig. 2.9.11) includes MODELLER variable and routine definitions. The following five lines set parameter values for the “model” routine. ALNFILE names the file that contains the target-template alignment in the PIR format. KNOWNS defines the known template structure(s) in ALNFILE (TvLDH-4mdh.ali). SEQUENCE defines the name of the target sequence in ALNFILE. STARTING_MODEL and ENDING_MODEL define the number of models that are calculated (their indices will run from 1 to 5). The last line in the file calls the “model” routine that actually calculates the models. The most important output files are model-single.log, which reports warnings, errors, and other useful information including the input restraints used for modeling that remain violated in the final model, and TvLDH.B99990001, TvLDH.B9990002, etc., which contain the model coordinates in the PDB format. The models can be viewed by any program that reads the PDB format, such as ModView (http://guitar.rockefeller.edu/modview/; Ilyin et al., 2002). Evaluating a Model If several models are calculated for the same target, the “best” model can be selected by picking the model with the lowest value of the MODELLER objective function, which is reported in the second line of the model PDB file. The value of the objective function in MODELLER is not an absolute measure, in the sense that it can only be used to rank models calculated from the same alignment. Once a final model is selected, there are many ways to assess it. In this example, PROSAII (Sippl, 1993) is used to evaluate the model fold and PROCHECK (Laskowski et al., 1998) is used to check the model’s stereochemistry. Before any external evaluation of the model, one should check the log file from the modeling run for runtime errors (model-single.log) and restraint violations; see the MODELLER manual for details (at the MODELLER Web site listed in Internet Resources for MODELLER Web site). Both PROSAII and PROCHECK confirm that a reasonable model was obtained, with a Z-score comparable to that of the template (-10.53 and -12.69 for the model and the template, respectively). Comparative Protein Structure Prediction

More detailed examples of MODELLER applications can be found in Fiser and Sali (2002).

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CONCLUSION Over the past few years, there has been a gradual increase in both the accuracy of comparative models and the fraction of protein sequences that can be modeled with useful accuracy (Marti-Renom et al., 2000; Baker and Sali, 2001; Pieper et al., 2002). The magnitude of errors in fold assignment, alignment, and the modeling of side-chains, loops, distortions, and rigid-body shifts has decreased measurably. These improvements are a consequence both of better techniques and a larger number of known protein sequences and structures. Nevertheless, all the errors remain significant and demand future methodological improvements. In addition, there is a great need for more accurate detection of errors in a given protein structure model. Error detection is useful both for refinement and interpretation of the models. It is now possible to predict by comparative modeling significant segments of approximately one half of all known protein sequences (Pieper et al., 2002). One half of these models are in the least accurate class, based on less than 30% sequence identity to known protein structures. The remaining 35% and 15% of the models are in the medium- (<50% sequence identity) and high-accuracy classes (>50% identity), respectively. The fraction of protein sequences that can be modeled by comparative modeling is currently increasing by approximately 4% per year (Sanchez et al., 2000). It has been estimated that globular protein domains cluster in only a few thousand fold families, approximately 900 of which have already been structurally defined (Holm and Sander, 1996; Lo Conte et al., 2000). Assuming the current growth rate in the number of known protein structures, the structure of at least one member of most of the globular folds will be determined in less than 10 years (Holm and Sander, 1996). However, there are some classes of proteins, including membrane proteins, that will not be amenable to modeling without improvements in structure determination and modeling techniques. To maximize the number of proteins that can be modeled reliably, a concerted effort towards structure determination of new folds by X-ray crystallography and nuclear magnetic resonance spectroscopy is in order, as envisioned by structural genomics (Terwilliger et al., 1998; Sali, 1998; Montelione and Anderson, 1999; Zarembinski et al., 1998; Burley et al., 1999; Vitkup et al., 2001; Sanchez et al., 2000). It has been estimated that 90% of all globular and membrane proteins can be organized into approximately 16,000 families containing protein domains with more than 30% sequence identity to each other (Vitkup et al., 2001). 3000 of these families are already structurally defined; the others present suitable targets for structural genomics. The full potential of the genome sequencing projects will only be realized once all protein functions are assigned and understood. This aim will be facilitated by integrating genomic sequence information with databases arising from functional and structural genomics. Comparative modeling will play an important bridging role in these efforts. ACKNOWLEDGEMENTS We are grateful to all members of our research group, especially to Dr. András Fiser, for many discussions about comparative structure modeling. Marc A. Marti-Renom was a Burroughs Wellcome Fund Postdoctoral Fellow and is currently a Rockefeller University Presidential Postdoctoral Fellow. Andrej Sali is an Irma T. Hirschl Trust Career Scientist. Support by The Merck Genome Research Institute, Mathers Foundation, and NIH (GM 54762) is also acknowledged. This unit is based on Marti-Renom et al. (2000) and Fiser and Sali (2002).

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lattice modeling for protein structure prediction and refinement. Proteins 44:133-149. Koretke, K.K., Luthey-Schulten, Z., and Wolynes, P.G. 1998. Self-consistently optimized energy functions for protein structure prediction by molecular dynamics. Proc. Natl. Acad. Sci. U.S.A. 95:2932-2937. Krogh, A., Brown, M., Mian, I.S., Sjolander, K., and Haussler, D. 1994. Hidden Markov models in computational biology: Applications to protein modeling. J. Mol. Biol. 235:1501-1531. Laskowski, R.A., Rullmannn, J.A., MacArthur, M.W., Kaptein, R., and Thornton, J.M. 1996. AQUA and PROCHECK-NMR: Programs for checking the quality of protein structures solved by NMR. J. Biomol. NMR. 8:477-486.

Montelione, G.T., Anderson S. 1999. Structural genomics: Keystone for a Human Proteome Project. Nat. Struct. Biol. 6:11-12. Moult, J., Fidelis, K., Zemla, A., and Hubbard, T. 2001. Critical assessment of methods of protein structure prediction (CASP): Round IV. Proteins 45 Suppl 5:2-7. Needleman, S.B. and Wunsch, C.D. 1970. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48:443-453.

Laskowski, R.A., MacArthur, M.W., and Thornton, J.M. 1998. Validation of protein models derived from experiment. Curr. Opin. Struct. Biol. 8:631639.

Oldfield, T.J. 1992. SQUID: A program for the analysis and display of data from crystallography and molecular dynamics. J. Mol. Graph. 10:247-252.

Levitt, M. 1992. Accurate modeling of protein conformation by automatic segment matching. J. Mol. Biol. 226:507-533.

Orengo, C.A., Jones, D.T., and Thornton, J.M. 1994. Protein superfamilies and domain superfolds. Nature 372:631-634.

Levitt, M. 1997. Competitive assessment of protein fold recognition and alignment accuracy. Proteins Suppl 1:92-104.

Orengo, C.A., Bray, J.E., Buchan, D.W., Harrison, A., Lee, D., Pearl, F.M., Sillitoe, I., Todd, A.E., and Thornton, J.M. 2002. The CATH protein family database: A resource for structural and functional annotation of genomes. Proteomics 2:11-21.

Lo Conte, L., Ailey, B., Hubbard, T.J., Brenner, S.E., Murzin, A.G., and Chothia C. 2000. SCOP: A structural classification of proteins database. Nucleic Acids. Res 28:257-259. Lo Conte, L., Brenner, S.E., Hubbard, T.J., Chothia, C., and Murzin, A.G. 2002. SCOP database in 2002: Refinements accommodate structural genomics. Nucleic Acids Res. 30:264-267. Luthy, R., Bowie, J.U., and Eisenberg, D. 1992. Assessment of protein models with three-dimensional profiles. Nature 356:83-85. Marti-Renom, M.A., Stuart, A., Fiser, A., Sanchez, R., Melo, F., and Sali, A. 2000. Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 29:291-325. Marti-Renom, M.A., Madhusudhan, M.S., Fiser, A., Rost, B., and Sali, A. 2002. Reliability of assessment of protein structure prediction methods. Structure 10:435-440. Matsumoto, R., Sali, A., Ghildyal, N., Karplus, M., and Stevens, R.L. 1995. Packaging of proteases and proteoglycans in the granules of mast cells and other hematopoietic cells: A cluster of histidines on mouse mast cell protease 7 regulates its binding to heparin serglycin proteoglycans. J. Biol. Chem. 270:19524-19531. Melo, F. and Feytmans, E. 1998. Assessing protein structures with a non-local atomic interaction energy. J. Mol. Biol. 277:1141-1152.

Comparative Protein Structure Prediction

Modi, S., Paine, M.J., Sutcliffe, M.J., Lian, L.Y., Primrose, W.U., Wolf, C.R., and Roberts, G.C. 1996. A model for human cytochrome P450 2D6 based on homology modeling and NMR studies of substrate binding. Biochemistry 35:4540-4550.

Miwa, J.M., Ibanez-Tallon, I., Crabtree, G.W., Sanchez, R., Sali, A., Role, L.W., and Heintz, N. 1999. lynx1, an endogenous toxin-like modulator of nicotinic acetylcholine receptors in the mammalian CNS. Neuron 23:105-114.

Park, J., Karplus, K., Barrett, C., Hughey, R., Haussler, D., Hubbard, T., and Chothia, C. 1998. Sequence comparisons using multiple sequences detect three times as many remote homologues as pairwise methods. J. Mol. Biol. 284:12011210. Pawlowski, K., Bierzynski, A., and Godzik, A. 1996. Structural diversity in a family of homologous proteins. J. Mol. Biol. 258:349-366. Pearson, W.R. 1990. Rapid and sensitive sequence comparison with FASTP and FASTA. Methods Enzymol. 183:63-98. Pearson, W.R. 1995. Comparison of methods for searching protein sequence databases. Protein Sci. 4:1145-1160. Pearson, W.R. and Lipman D.J. 1988. Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. U.S.A. 85:2444-2448. Peitsch, M.C. and Jongeneel, C.V. 1993. A 3-D model for the CD40 ligand predicts that it is a compact trimer similar to the tumor necrosis factors. Int. Immunol. 5:233-238. Pieper, U., Eswar, N., Ilyin, V.A., Stuart, A., and Sali, A. 2002. ModBase, a database of annotated comparative protein structure models. Nucleic Acids Res. 30:255-259. Pontius, J., Richelle, J., and Wodak, S.J. 1996. Deviations from standard atomic volumes as a quality measure for protein crystal structures. J. Mol. Biol. 264:121-136. Ring, C.S., Sun, E., McKerrow, J.H., Lee, G.K., Rosenthal, P.J., Kuntz, I.D., and Cohen, F.E. 1993. Structure-based inhibitor design by using

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protein models for the development of antiparasitic agents. Proc. Natl. Acad. Sci. U.S.A. 90:3583-3587.

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Smith, T.F. 1999. The art of matchmaking: Sequence alignment methods and their structural implications. Structure Fold Des. 7:R7-R12.

Rost, B. 1999. Twilight zone of protein sequence alignments. Protein Eng. 12:85-94.

Smith, T.F. and Waterman, M.S. 1981. Overlapping genes and information theory. J. Theor. Biol. 91:379-380.

Rychlewski, L., Zhang, B., and Godzik, A. 1998. Fold and function predictions for Mycoplasma genitalium proteins. Fold. Des. 3:229-238. Sali, A. 1995. Comparative protein modeling by satisfaction of spatial restraints. Mol. Med. Today 1:270-277. Sali, A. 1998. 100,000 protein structures for the biologist. Nat. Struct. Biol. 5:1029-1032. Sali, A. and Blundell T.L. 1993. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234:779-815. Sali, A. and Overington, J.P. 1994. Derivation of rules for comparative protein modeling from a database of protein structure alignments. Protein Sci. 3:1582-1596. Sali, A., Matsumoto, R., McNeil, H.P., Karplus, M., and Stevens, R.L. 1993. Three-dimensional models of four mouse mast cell chymases: Identification of proteoglycan binding regions and protease-specific antigenic epitopes. J. Biol. Chem. 268:9023-9034. Sanchez, R. and Sali, A. 1997. Advances in comparative protein-structure modelling. Curr. Opin. Struct. Biol. 7:206-214. Sanchez, R. and Sali A. 1998. Large-scale protein structure modeling of the Saccharomyces cerevisiae genome. Proc. Natl. Acad. Sci. U.S.A. 95:13597-13602. Sanchez, R., Pieper, U., Melo, F., Eswar, N., MartiRenom, M.A., Madhusudhan, M.S., Mirkovic, N., and Sali, A. 2000. Protein structure modeling for structural genomics. Nat. Struct. Biol. 7:986990. Saqi, M.A., Russell, R.B., and Sternberg, M.J. 1998. Misleading local sequence alignments: Implications for comparative protein modelling. Protein Eng. 11:627-630. Sheng, Y., Sali, A., Herzog, H., Lahnstein, J., and Krilis, S.A. 1996. Site-directed mutagenesis of recombinant human beta 2-glycoprotein I identifies a cluster of lysine residues that are critical for phospholipid binding and anti-cardiolipin antibody activity. J. Immunol. 157:3744-3751. Sippl, M.J. 1990. Calculation of conformational ensembles from potentials of mean force: An approach to the knowledge-based prediction of local structures in globular proteins. J. Mol. Biol. 213:859-883. Sippl, M.J. 1993. Recognition of errors in three-dimensional structures of proteins. Proteins 17:355-362. Smith, R.F., Wiese, B.A., Wojzynski, M.K., Davison, D.B., and Worley, K.C. 1996. BCM Search Launcher: An integrated interface to molecular

Smith, T.F., Lo, Conte, L., Bienkowska, J., Gaitatzes, C., Rogers, R.G., Jr., and Lathrop, R. 1997. Current limitations to protein threading approaches. J. Comput. Biol. 4:217-225. Srinivasan, N. and Blundell, T.L. 1993. An evaluation of the performance of an automated procedure for comparative modelling of protein tertiary structure. Protein Eng. 6:501-512. Srinivasan, S., March, C.J., and Sudarsanam, S. 1993. An automated method for modeling proteins on known templates using distance geometry. Protein Sci. 2:277-289. Sternberg, M.J., Bates, P.A., Kelley, L.A., and MacCallum, R.M. 1999. Progress in protein structure prediction: Assessment of CASP3. Curr. Opin. Struct. Biol. 9:368-373. Sutcliffe, M.J., Haneef, I., Carney, D., and Blundell, T.L. 1987. Knowledge based modelling of homologous proteins, Part I: Three-dimensional frameworks derived from the simultaneous superposition of multiple structures. Protein Eng. 1:377-384. Taylor, W.R. 1996. Multiple protein sequence alignment: Algorithms and gap insertion. Methods Enzymol. 266:343-367. Taylor, W.R., Flores, T.P., and Orengo, C.A. 1994. Multiple protein structure alignment. Protein Sci. 3:1858-1870. Terwilliger, T.C., Waldo, G., Peat, T.S., Newman, J.M., Chu, K., and Berendzen, J. 1998. Class-directed structure determination: Foundation for a protein structure initiative. Protein Sci. 7:1851-1856. Thompson, J.D., Higgins, D.G., and Gibson, T.J. 1994. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22:4673-4680. Topham, C.M., Srinivasan, N., Thorpe, C.J., Overington, J.P., and Kalsheker, N.A. 1994. Comparative modelling of major house dust mite allergen Der p I: Structure validation using an extended environmental amino acid propensity table. Protein Eng. 7:869-894. Torda, A.E. 1997. Perspectives in protein-fold recognition. Curr. Opin. Struct. Biol. 7:200-205. Totrov, M. and Abagyan, R. 1994. Detailed ab initio prediction of lysozyme-antibody complex with 1.6 A accuracy. Nat. Struct. Biol. 1:259-263. Unger, R., Harel, D., Wherland, S., and Sussman, J.L. 1989. A 3D building blocks approach to analyzing and predicting structure of proteins. Proteins 5:355-373.

Computational Analysis

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Vakser, I.A. 1997. Evaluation of GRAMM lowresolution docking methodology on the hemagglutinin-antibody complex. Proteins Supp l 1:226-230. van Vlijmen, H.W. and Karplus, M. 1997. PDBbased protein loop prediction: Parameters for selection and methods for optimization. J. Mol. Biol. 267:975-1001. Vitkup, D., Melamud, E., Moult, J., and Sander, C. 2001. Completeness in structural genomics. Nat. Struct. Biol. 8:559-566. Vriend, G. 1990. WHAT IF: A molecular modeling and drug design program. J. Mol. Graph. 8:56. Westbrook, J., Feng, Z., Jain, S., Bhat, T.N., Thanki, N., Ravichandran, V., Gilliland, G.L., Bluhm, W., Weissig, H., Greer, D.S., Bourne, P.E., and Berman, H.M. 2002. The protein data bank: Unifying the archive. Nucleic Acids Res. 30:245-248.

Xu, L.Z., Sanchez, R., Sali, A., and Heintz, N. 1996. Ligand specificity of brain lipid-binding protein. J. Biol. Chem. 271:24711-24719. Yang, A.S. and Honig, B. 2000. An integrated approach to the analysis and modeling of protein sequences and structures. III. A comparative study of sequence conservation in protein structural families using multiple structural alignments. J. Mol. Biol. 301:691-711. Zarembinski, T.I., Hung, L.W., Mueller-Dieckmann, H.J., Kim, K.K., Yokota, H., Kim, R., and Kim, S.H. 1998. Structure-based assignment of the biochemical function of a hypothetical protein: A test case of structural genomics. Proc. Natl. Acad. Sci. U.S.A. 95:15189-15193.

INTERNET RESOURCES http://guitar.rockefeller.edu/modeller/

Wilson, C., Gregoret, L.M., and Agard, D.A. 1993. Modeling side-chain conformation for homologous proteins using an energy-based rotamer search. J. Mol. Biol. 229:996-1006.

MODELLER, A Protein Structure Modeling Program, Release 6v0. Sali, A., Fiser, A., Sanchez, R., Marti-Renom, M.A., Jerkovic, B., Badretdinov, A., Melo, F., Overington, J., and Feyfant, E. 2001.

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Table 2.9.1 lists a large number of additional Web sites useful in comparative modeling.

Wu, G., Fiser, A., ter Kuile, B., Sali, A., and Muller, M. 1999. Convergent evolution of Trichomonas vaginalis lactate dehydrogenase from malate dehydrogenase. Proc. Natl. Acad. Sci. U.S.A. 96:6285-6290.

Contributed by Marc A. Marti-Renom, Bozidar Yerkovich, and Andrej Sali The Rockefeller University New York, New York

Comparative Protein Structure Prediction

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Protein Charge Determination

UNIT 2.10

To many researchers the problem of protein charge determination has been solved by the creation of extensive protein data banks and the availability of computer programs for the calculation of net charge from sequence data and a set of pK values for proton dissociation from ionizable groups (Jakubke and Jeschkeit, 1977; Skoog and Wichman, 1986). However, the inaccuracy of that approach was recognized long before the advent of protein data banks and computer programs to facilitate its adoption. Although improvements to these algorithms can be implemented, there is no alternative to experimental measurement for reliable estimation of protein valence. Of the various methods available (Winzor, 2004), capillary zone electrophoresis affords the simplest and most economical approach. This unit also provides a brief discussion of the major pitfalls inherent in the calculation of net charge from protein sequence data.

CALCULATION OF PROTEIN CHARGE FROM SEQUENCE DATA The popularity of this method of charge estimation resides in its availability as an accessible option within the computer software of any protein data bank. Consequently, the reliability of the calculated valence depends upon the adequacy of the quantitative treatment incorporated into the program, which is generally not questioned by its users. On the grounds that the fractional dissociation (αi ) of any ionizable group i is related to pH by the Henderson−Hasselbalch equation:

Equation 2.10.1

αi may be calculated from pKi , the negative logarithm of the effective dissociation constant. In the usual charge determination procedure (Jakubke and Jeschkeit, 1977; Skoog and Wichman, 1986), the pKi for each type of ionizable group is assigned a magnitude. Knowledge of the amino acid
sequence (or composition) of a protein is then used to calculate the net charge, zP , as (zP )i , where the protein charge arising from ni ionizable groups of type i, (zP )i , is given by:

Equation 2.10.2

where the ionizable group, i, is anionic (zi is negative) OR

Equation 2.10.3

where the ionizable group, i, is cationic (zi is positive). However, pKi cannot be assigned a fixed magnitude because of a dependence upon the overall charge of the protein; it is more difficult to dissociate a proton from a negatively charged molecule than from one with a net positive charge. This variation of pKi can be taken into account (Compton and O’Grady, 1991) by means of the theoretical expression for proton dissociation that has been in existence for over 80 years (Linderstrøm-Lang, Computational Analysis Contributed by Donald J. Winzor Current Protocols in Protein Science (2005) 2.10.1-2.10.8 C 2005 by John Wiley & Sons, Inc. Copyright


2.10.1 Supplement 41

1924; Linderstrøm-Lang and Nielsen, 1959). Specifically, pKi in Equation 2.10.1 needs to be replaced by the relationship

Equation 2.10.4

where the factor 0.868wzP takes into account the work required to remove the dissociating proton from the charged protein molecule. (pKint )i refers to the intrinsic dissociation constant for ionizable groups i in a molecule with net zero charge. Because of the need to specify the correct magnitude of zP for the calculation of pKi , and hence αi , Equations 2.10.2 to 2.10.4 must be used iteratively to calculate protein valence (Compton and O’Grady, 1991; Winzor, 2003). A problem with this approach is the need to assign a magnitude to the electrostatic interaction factor, w, in Equation 2.10.4. Although Linderstrøm-Lang (1924) provided a theoretical expression for evaluating w from the protein radius and the ionic strength of the medium, the value so obtained invariably exceeds the experimental estimate of its magnitude (Tanford et al., 1955a,b). In addition to errors in zP stemming from that source, there are those that arise from assuming that all charged groups exhibit normal ionization behavior—an assumption that is demonstrably invalid for the tyrosine residues of ribonuclease (Tanford et al., 1955a) and the carboxyl groups of bovine serum albumin (Tanford et al., 1955b). Finally, the neglected consequences of the binding of ions other than protons and hydroxyls preclude any meaningful attempt to calculate protein valence. For example, the calculated isoelectric point (pH associated with net zero charge) of 8.2 for aldolase clearly does not describe the experimental situation (Velick, 1949) in phosphate and acetate buffers with a range of ionic strengths (Fig. 2.10.1)—allowance for the binding of chloride ions also needs to be taken into account. Unfortunately, quantification of chloride binding by proteins (Carr, 1953, 1956; Scatchard et al., 1957) is restricted to measurements at very low ionic strength because of sensitivity limitations in the electrochemical estimation of chloride concentration. Protein valence, therefore, needs to be obtained by experimental measurement under the conditions of interest.

Protein Charge Determination

Figure 2.10.1 Effect of ionic strength (I ) on the isoelectric point of aldolase in acetate and phosphate buffers. The data, taken from Velick (1949), were acquired by interpolating electrophoretic mobility measurements obtained by the moving boundary method.

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ELECTROPHORETIC DETERMINATION OF NET PROTEIN CHARGE The study of proteins by electrophoresis became popular after refinement of the moving boundary technique by Tiselius (1937). Moving boundary electrophoresis afforded an unequivocal means of characterizing proteins on the basis of their migration rate (dx/dt) under the influence of an electrical field (potential gradient), E. The electrophoretic mobility, uP , defined as

Equation 2.10.5

is related to the charge/size ratio of a protein molecule. Most mobility data have been obtained by moving boundary electrophoresis (Tiselius, 1937), but the demise of that technique now necessitates the use of methods such as gel electrophoresis (Chapter 10; Rodbard and Chrambach, 1971) and capillary zone electrophoresis (UNIT 10.13; K´alm´an et al., 1995). These methods have proven more popular than their predecessor because of greater economy in terms of protein requirements. In an electrochromatographic procedure, such as gel electrophoresis, the experimentally determined mobility from a single run necessarily reflects the combined consequences of chromatographic (molecular sieving) and electrophoretic migration. For monophasic buffer systems, this electrochromatographic mobility (uP  ) can be obtained via Equation 2.10.5 from the distance migrated by the protein zone during electrophoresis for time t in response to an applied electric field E based on the applied voltage and the length of the gel tube. Because the extent of protein retardation by molecular sieving effects decreases with increasing gel porosity (decreasing gel concentration), it is advantageous to obtain the linear relationship between the logarithm of uP  and the gel concentration (Ferguson, 1964; Hedrich and Smith, 1968) to eliminate the chromatographic contribution to uP  and hence determine the electrophoretic mobility uP (Rodbard and Chrambach, 1971; Butterman et al., 1988). The need for multiple electrophoresis runs in two series of gels with different porosities (Butterman et al., 1988) is obviously a disadvantage of the electrophoretic mobility determination method. On the other hand, a single experiment suffices for the measurement of mobility by capillary electrophoresis. Because of the experimental design of the apparatus, migration is described by a retention time, tP , the time taken by the protein zone to move a fixed distance (x) from the point of sample application to the detector. For an electric field strength, E, taken as the applied voltage divided by the total length of the capillary across which the voltage is applied, the retention time is related to electrophoretic mobility by the expression:

Equation 2.10.6

Allowance is made for the contribution of electroosmotic flow to protein migration by the inclusion of a retardation time (tM ) for an uncharged marker (mesityl oxide). The reliability of mobility measurement by capillary zone electrophoresis is obviously dependent upon the relative magnitudes of the (x/tP ) and (x/tM ) terms in Equation 2.10.6. In some instances, tP and tM may differ by as little as 2% (Gao et al., 1994), whereupon the magnitude of uP obtained from Equation 2.10.6 is subject to considerable uncertainty. Fortunately, the accuracy of mobility measurement can be improved by coating the inner walls of the capillary tube with a non-crosslinked polyacrylamide layer to decrease the extent of electroosmosis (Hjert´en, 1985; K´alm´an et al., 1995).

Computational Analysis

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Figure 2.10.2 Time course of absorbance (200 nm) at the detection point, located at distance x (20 cm) from the point of sample application, in a zonal capillary electrophoresis experiment on β-lactoglobulin in 0.05 M phosphate buffer, pH 6.7. tP , the time required for the protein zone to migrate the 20 cm under the influence of an electric field strength (E) of 260 V/cm, is the protein characteristic required for determination of electrophoretic mobility (uP ) from Equation 2.10.6. The electrophoretic time-course has been inferred from Figure 3b in Gao et al. (1997).

Capillary zone electrophoresis is currently of limited utility for protein charge estimation because buffers are usually selected on the basis of resolving power rather than physiological considerations. However, the feasibility of conducting capillary zone electrophoresis in buffer with much higher ionic strength is illustrated in Figure 2.10.2, which presents an electropherogram (Gao et al., 1997) for β-lactoglobulin in 0.05 M phosphate buffer, pH 6.7 (I ≈ 0.1 M). Clearly, tP can still be defined sufficiently well for accurate determination of uP via Equation 2.10.6.

RELATIONSHIP BETWEEN MOBILITY AND PROTEIN CHARGE The interpretation of electrophoretic mobility in terms of protein charge has traditionally been considered equivocal because of reliance of the theory upon a microscopic model of electric migration that is known to be imperfect in some respects (Overbeek and Lijklema, 1959). However, the major cause for concern about the validity of protein charge determination from mobility data has been the discrepancies between such estimates and those deduced from pH-titration curves (Longsworth, 1941; Barnett and Bull, 1960). It is now evident that the discrepancy reflected error in the pH titration values rather than in those deduced from electrophoretic mobility (Winzor, 2004). For a uniformly charged spherical molecule, the dependence of electrophoretic mobility upon protein valence is given by the relationship (Abramson et al., 1942):

Equation 2.10.7

Protein Charge Determination

where e denotes electronic charge (1.602 × 10−19 C), κ is the inverse screening length (Debye and H¨uckel, 1923), and f(κRP ) the Henry function (Henry, 1931). The inclusion of Rb , the radius of a buffer ion, makes allowance for the fact that (RP + Rb ) is the distance of closest approach by buffer ion to the center of the protein. η is the dynamic viscosity of the buffer medium (0.00089 N·sec/m2 for water at 25◦ C). Allowance for protein asymmetry entails the incorporation of an additional term ψ (0 < ψ < 1) into the

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Figure 2.10.3 pH dependence of the net charge for ovalbumin (I = 0.1 M) deduced from published electrophoretic mobility measurements (Longsworth, 1941; Creeth and Winzor, 1962). Open symbols denote values calculated by the application of Equation 2.10.7, which assumes spherical geometry for the protein, whereas closed symbols are values of zP based on a modified version of Equation 2.10.7 that takes into account the prolate-ellipsoidal shape of ovalbumin.

numerator of Equation 2.10.7—a modification of the procedure that has been described in detail elsewhere (Winzor, 2004; Winzor et al., 2004). The starting point for the application of Equation √ 2.10.7 is the calculation of the inverse screening length (m−1 ) as κ = 3.27 × 109 I , where I is the molar ionic strength (Abramson et al., 1942). Also required is the assignment of magnitudes to the radius of a buffer ion Rb (0.25 nm) and that of the protein RP , considered to be the Stokes radius. The Henry function may be calculated with sufficient accuracy by the approximate relationship

Equation 2.10.8

with a = 2.8 for κRP < 10, and 2.5 for κRP > 10 (Rodbard and Chrambach, 1971). In the absence of any extensive sets of mobility data obtained by capillary zone electrophoresis, the use of electrophoretic mobility results for the determination of protein valence is illustrated in Figure 2.10.3, which presents the pH dependence of ovalbumin valence that is deduced from electrophoretic mobilities (Longsworth, 1941; Creeth and Winzor, 1962) obtained by the moving boundary technique. Open symbols summarize the interpretation of those data for ovalbumin (I = 0.1 M, 0◦ C) in terms of Equation 2.10.7 with a protein radius (RP ) of 2.9 nm (Creeth and Winzor, 1962; Jeffrey et al., 1977) and a value of 0.00179 N·sec/m2 for the buffer viscosity (η) at 0◦ C (Abramson et al., 1942). However, these estimates of zP refer to a spherical model of ovalbumin, which is more appropriately represented as a prolate ellipsoid with an axial ratio of 1.95 (Harding et al., 1999). Valence estimates for this model (closed symbols, Fig. 2.10.4), which are 32% higher, provide a better indication of the pH dependence of the net charge on ovalbumin (Winzor, 2004). Computational Analysis

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Figure 2.10.4 Estimation of protein valence by capillary zone electrophoresis and the “charge ladder” approach. Published electrophoretic mobilities for Staphylococcal nuclease and mutant variants (Table 4 of Kalm ´ an ´ et al., 1995) are plotted in accordance with Equation 2.10.10, the quantitative basis for valence estimation by the “charge ladder” method. The designated value of zP for each set of conditions is the reciprocal of the slope of the best-fit description of the data in such terms.

VALENCE MEASUREMENT BY THE “CHARGE LADDER” APPROACH A capillary zone electrophoresis procedure has been developed for the determination of protein charge on the basis of mobility differences stemming from alteration of the protein charge either by mutation (K´alm´an et al., 1995) or by chemical modification (Gao et al., 1994). Provided that such modification has no effect on the size and shape of the protein molecule, it follows from Equation 2.10.7 that

Equation 2.10.9

where the P and D subscripts refer to the native protein and its modified derivative, respectively. It has been proposed (K´alm´an et al., 1995) that (zP − zD ) can be deduced from the nature of the mutation in that replacement of an aspartate residue by alanine should change the net charge by +1, whereas substitution of arginine for aspartate should lead to an increase of +2 in net charge. Equation 2.10.9 is therefore rearranged to the form:

Equation 2.10.10 Protein Charge Determination

The valence of the native protein is therefore deduced as the reciprocal of the slope of the dependence of (uP − uD )/uP upon (zP − zD ). From the illustration of this procedure in Figure 2.10.4 for Staphylococcal nuclease (K´alm´an et al., 1995) at pH 6.8 and pH 8.9,

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it is evident that uncertainty surrounds the estimate of zP . Of particular concern is the variation in mobility between mutants bearing the same charge—an observation that invalidates the assumed identity of size and shape for the native protein and all modified forms thereof (Winzor et al., 2004). The lack of any requirement for information about the size and shape of the protein is certainly an attractive feature of this “charge ladder” method for estimating protein valence by capillary zone electrophoresis (Gao et al., 1994; K´alm´an et al., 1995). Unfortunately, valence values determined by this procedure may be prone to considerable error because of failure of the experimental system to comply rigorously with the requirement that chemical modification or mutation has no effect whatsoever on the overall threedimensional characteristics of the protein. A preferred approach would therefore be the interpretation of uP for the native protein in terms of the classical electrophoretic theory described above (Winzor et al., 2004).

CONCLUDING REMARKS This treatment of protein charge determination covers an aspect of protein chemistry with which few biochemists are familiar. Indeed, it refutes the method of charge determination with which protein chemists are most familiar—the calculation of net charge from protein sequence data. Because zP depends not only upon pH but also on the buffer/electrolyte composition of the environment, there is no realistic alternative to the experimental measurement of protein valence. Knowledge of the Stokes radius for a relatively symmetrical (spherical) protein allows the calculation of net charge (via Equation 2.10.7) from the electrophoretic mobility, which is readily measured by capillary zone electrophoresis. In that regard, an improved estimate of zP can be obtained from the same electrophoretic mobility measurements in instances where a prolate ellipsoid provides a more reasonable representation of the three-dimensional protein structure: the only asymmetric model for which the electrophoretic mobility is currently amenable to quantitative interpretation in terms of net protein charge.

Literature Cited Abramson, H.A., Moyer, L.S., and Gorin, M.H. 1942. Electrophoresis of Proteins. Reinhold, New York. Barnett, L.B. and Bull, H.B. 1960. Electrophoresis of ribonuclease and of β-lactoglobulin: Isoelectric points of proteins. Arch. Biochem. Biophys. 89:162-172. Butterman, M., Tietz, D., Orb´an, L., and Chrambach, A. 1988. Ferguson plots based on absolute mobilities in polyacrylamide gel electrophoresis: Dependence of linearity of polymerization conditions and application to the determination of free mobility. Electrophoresis 9:293-298. Carr, C.W. 1953. Studies on the binding of small ions in protein solutions with the use of membrane electrodes. III. The binding of chloride in solutions of various proteins. Arch. Biochem. Biophys. 46:417-423. Carr, C.W. 1956. Studies on the binding of small ions in protein solutions with the use of membrane electrodes. VI. The binding of sodium and potassium ions in solutions of various proteins. Arch. Biochem. Biophys. 62:476-484. Compton, B.J. and O’Grady, E.A. 1991. Role of charge suppression and ionic strength in free zone electrophoresis of proteins. Anal. Chem. 63:2597-2602.

Creeth, J.M. and Winzor, D.J. 1962. Physicochemical studies on ovalbumin 4. Characterization of an iodine-modified derivative by electrophoresis and sedimentation. Biochem. J. 83:566574. Debye, P. and H¨uckel, E. 1923. Theory of electrolytes. I. Lowering of freezing point and related phenomena. Physik. Z. 24:185-206. Ferguson, K.A. 1964. Starch gel electrophoresis: Application to the classification of pituitary proteins and polypeptides. Metabolism 13:9851002. Gao, J., Gomez, F.A., H¨arter, R., and Whitesides, G.M. 1994. Determination of the effective charge of a protein in solution by capillary zone electrophoresis. Proc. Natl. Acad. Sci. U.S.A. 91:12027-12030. Gao, J.Y., Dubin, P.L., and Muhoberac, B.M. 1997. Measurement of the binding of proteins to polyelectrolytes by frontal analysis continuous capillary electrophoresis. Anal. Chem. 69:29452951. Harding, S.E., Horton, J.C., Jones, S., Thornton, J.M., and Winzor, D.J. 1999. COVOL: An interactive program for evaluating second virial coefficients from the triaxial shape or dimensions of rigid macromolecules. Biophys. J. 76:24322438.

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Hedrich, J.L. and Smith, A.J. 1968. Size and charge isomer separation and estimation of molecular weights by disk electrophoresis. Arch. Biochem. Biophys. 126:155-164.

Scatchard, G., Coleman, J.S., and Shan, A.L. 1957. Physical chemistry of protein solutions. VII. The binding of some small ions to bovine serum albumin. J. Am. Chem. Soc. 79:12-20.

Henry, D.C. 1931. The cataphoresis of suspended particles. I. The equation of cataphoresis. Proc. R. Soc. London A. 133:106-129.

Skoog, B. and Wichman, A. 1986. Calculation of the isoelectric points of polypeptides from the amino acid composition. Trends Anal. Chem. 5:82-83.

Hjert´en, S. 1985. High-performance electrophoresis: Elimination of electroendosmosis and solute sorption. J. Chromatogr. 347:191-198. Jakubke, H. and Jeschkeit, H. 1977. Amino Acids, Peptides and Proteins. John Wiley & Sons, New York. Jeffrey, P.D., Nichol, L.W., Turner, D.R., and Winzor, D.J. 1977. The combination of molecular covolume and frictional coefficient to determine the shape and axial ratio of a rigid macromolecule. J. Phys. Chem. 81:566-574. K´alm´an, F., Ma, S., Fox, R.O., and Horv´ath, C. 1995. Capillary electrophoresis of S. nuclease mutants. J. Chromatogr. A 705:135-154. Linderstrøm-Lang, K. 1924. The ionization of proteins. Compt. Rend. Trav. Lab. Carlsberg 15(7):1-29. Linderstrøm-Lang, K. and Nielsen, S.O. 1959. Acid−base equilibria in proteins. In Electrophoresis: Theory, Methods and Applications (M. Bier, ed.) pp. 35-89. Academic Press, New York. Longsworth, L.G. 1941. The influence of pH on the mobility and diffusion of ovalbumin. Ann. N.Y. Acad. Sci. 41:267-285. Overbeek, J.T.G. and Lijklema, J. 1959. Electric potentials in colloidal systems. In Electrophoresis: Theory, Methods and Applications (M. Bier, ed.) pp. 1-33. Academic Press, New York. Rodbard, D. and Chrambach, A. 1971. Estimation of the molecular radius, free mobility and valence using polyacrylamide gel electrophoresis. Anal. Biochem. 40:95-134.

Tanford, C., Hauenstein, J.D., and Rands, D.G. 1955a. Phenolic hydroxyl ionization in proteins. II. Ribonuclease. J. Am. Chem. Soc. 77:64096413. Tanford, C., Swanson, S.A., and Shore, J.S. 1955b. Hydrogen ion equilibria of bovine serum albumin. J. Am. Chem. Soc. 77:6414-6428. Tiselius, A. 1937. A new apparatus for electrophoretic analysis of colloidal mixtures. Trans. Faraday Soc. 33:524-531. Velick, S.F. 1949. The interaction of enzymes with small ions. I. An electrophoretic and equilibrium analysis of aldolase in phosphate and acetate buffers. J. Phys. Colloid Chem. 53:135-149. Winzor, D.J. 2003. Classical approach to interpretation of the charge-dependence of peptide mobilities obtained by capillary zone electrophoresis. J. Chromatogr. A1015:199-204. Winzor, D.J. 2004. Determination of the net charge (valence) of a protein: A fundamental but elusive parameter. Anal. Biochem. 325:1-20. Winzor, D.J., Jones, S., and Harding, S.E. 2004. Determination of protein charge by capillary electrophoresis. Anal. Biochem. 333:225-229.

Contributed by Donald J. Winzor University of Queensland Brisbane, Queensland

Protein Charge Determination

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Basic Protein Sequence Analysis

UNIT 2.11

Protein sequence analysis can be performed from many perspectives and with a vast array of bioinformatics methods. This unit will focus on those analyses that can be performed using only a computer (desktop workstation or laptop), Internet access, and a laboratory notebook to record results. All the resources needed are accessible via publicly available Web servers and databases, and require little or no computational expertise. These tools enable biologists to predict the structure of a protein, the presence of functional motifs or domains, cellular localization (e.g., membrane-bound, secreted, chloroplast, or nuclear), and post-translational modifications. Integration of all these data enables the biologist to make a more informed prediction of the likely molecular functions of proteins of interest. A plethora of tools are available for these tasks, and in choosing preferred methods for each of the separate protocols, the authors have been guided primarily by two criteria: user-friendliness and utility. Some tools are extremely powerful, but inappropriate for the computational novice. Other tools may be easy to use, but relatively ineffectual. We have kept working biologists in mind, and asked ourselves which tools our biologist colleagues would find easiest to use and understand. With these criteria in mind, we selected one or two protocols for each task. Identifying structural domains using different types of servers are presented (Basic Protocol 1 and Alternate Protocols 1 and 2), as are procedures for predicting transmembrane regions and subcellular localization (Basic Protocol 2 and Alternate Protocol 3) and key functional residues and motifs (Basic Protocol 3). Each procedure also includes a brief discussion of some of the Web servers and databases that we feel deserve attention. Support Protocols for each method present a strategy for the integration of the results from the different procedures (Support Protocols 1 to 7).

IDENTIFYING STRUCTURAL AND FUNCTIONAL DOMAINS USING INTEGRATED META-SERVERS

BASIC PROTOCOL 1

Below is a protocol for the use of integrated meta-servers. Meta-servers include prediction results from several independent prediction tools to produce a consensus prediction. This helps a biologist avoid false positive predictions. Our primary protocol for this task involves the use of the SMART (Simple Modular Architecture Research Tool) server provided by the European Molecular Biology Laboratory (EMBL) at Heidelberg (Schultz et al., 1998; Letunic et al., 2004). This server integrates structure and function prediction tools with pre-calculated analyses (including domain structure prediction, intron/exon boundary detection and ortholog identification) for proteins in the Swiss-Prot and TrEMBL databases (Boeckmann et al., 2003) and all proteomes analyzed by the Ensembl group (Hubbard et al., 2002). The extensive results and appealing interface make a biologist’s work much easier, and this is our reason for recommending the use of this server.

Computational Analysis Contributed by Nandini Krishnamurthy and Kimmen V. Sj¨olander Current Protocols in Protein Science (2005) 2.11.1-2.11.24 C 2005 by John Wiley & Sons, Inc. Copyright


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Figure 2.11.1 FASTA format of AVR2 HUMAN protein sequence. The Swiss-Prot accession number is P27037 and the Swiss-Prot ID is AVR2 HUMAN.

Materials Input data The sequence to be analyzed should be in FASTA format, or may alternatively be submitted as raw sequence (the single-letter representation of the protein sequence without the definition line). An example FASTA format is shown in Figure 2.11.1. In cases where the sequence of interest is in the Swiss-Prot or TrEMBL databases, the accession number is normally sufficient to retrieve precalculated results. Otherwise, the protein sequence is required as described in the protocol below. 1. Point the browser at the SMART Web site (http://smart.embl-heidelberg.de/). On the SMART main page that appears, click either SMART MODE: NORMAL or SMART MODE: GENOMIC. In NORMAL SMART, the database contains SwissProt, SP-TrEMBL and stable Ensembl proteomes. In GENOMIC SMART, only the proteomes of completely sequenced genomes are used. We recommend the use of NORMAL SMART. In the page that now appears, paste the sequence of interest in FASTA format in the box provided. If the sequence is from Swiss-Prot, TREMBL, or the EMBL database, one could alternatively submit the Accession number or Entry name in the box marked Sequence ID or ACC (Fig. 2.11.2). 2. Select additional analyses. At this point, one can either click the Sequence SMART button or select additional analyses. In this example (shown in Fig. 2.11.3), we have selected all available analyses. a. Outlier homologs. This option will search sequence databases derived from proteins of solved structures and return the ID and E-value (see Table 2.11.1 for definition) of the top hit and the database searched. The search is done on PDB (Protein Data Bank, a database of solved structures) and position-specific iterated BLAST (PSI-BLAST) profiles derived from SCOP (Structural Classification Of Proteins; Table 2.11.1), a system of classifying proteins based on structural and functional similarity (Murzin et al., 1995).

b. PFAM domains. This option searches for the presence of PFAM domains (Bateman et al., 2002) in the query sequence. Since SMART (as of November, 2004) has 667 hidden Markov models (HMMs), and PFAM (August, 2004) has 7503 HMMs, searching PFAM HMMs increases the likelihood of a match.

c. Signal peptides. This gives information on whether the query sequence contains a signal peptide, using the SigCleave program. Basic Protein Sequence Analysis

d. Internal repeats. This option shows any repeated motifs found in the query.

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Figure 2.11.2 SMART sequence submission form. (A) Submission of sequence in FASTA format. (B) Submission using the Swiss-Prot accession number in the box marked with arrow.

Figure 2.11.3

Sequence submission options in SMART.

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Table 2.11.1 Definitions of Acronyms and Abbreviations Used in this Unit, continued

Acronym/ abbreviation

Full name

Description

3D-PSSM

3-D Position Specific Scoring Matrix

The 3D-PSSM is a protein fold recognition server that uses 1-D and 3-D sequence profiles coupled with secondary structure and solvation potential information

BLAST

Basic Local Alignment Search Tool

BLAST is a sequence comparison algorithm optimized for speed, used to search sequence databases for optimal local alignments to a query. Variants of BLAST enable searches of protein databases using nucleotide queries and vice versa.

CDART

Conserved Domain Architecture Retrieval Tool

For a given protein query sequence, CDART displays the predicted functional/structural domains that make up the protein and lists proteins with similar domain architectures

EMBL

European Molecular Biology Laboratory

EMBL is an international research organization with its main laboratory in Heidelberg, Germany, and four outstations in Hinxton, U.K. (the European Bioinformatics Institute, EBI), Grenoble, France, Hamburg, Germany, and Monterotondo, Italy.

E-value

Expectation value

For a given score S and a database size, the E-value is the number of hits with scores equivalent to or better than S that are expected to occur in a database of that size by chance. The lower the E value, the more significant the score.

HMM

Hidden Markov Model

HMMs for proteins are statistical models (akin to profiles and PSSMs) that represent the amino acid preferences and insertion/deletion likelihoods at each position. They are used to detect remote homologs and to generate alignments of new sequences to a family.

MSA

Multiple Sequence Alignment

A multiple sequence alignment refers to an alignment of more than two sequences; pairwise alignment refers to an alignment of two sequences. Gap characters (typically, dots or dashes) are inserted between amino acids so that all sequences have the same length, and can be arranged in a matrix to display the amino acids conserved across the family. MSAs are used as input to construction of PSSMs and HMMs, to estimate phylogenetic trees, and for numerous other tasks.

NCBI

National Center for Biotechnology Information

The NCBI is the main resource for computational biologists in the U.S., and includes a number of important tools (e.g., BLAST) and databases. Databases include the CDD (Conserved Domain Database), GenBank (a comprehensive sequence database), PubMed (for retrieving papers in biomedical literature), OMIM (Online Mendelian Inheritance in Man), and a host of other important resources. continued

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Table 2.11.1 Definitions of Acronyms and Abbreviations Used in this Unit, continued

Acronym/ abbreviation

Full name

Description

PFAM

Protein Families

PFAM is a large collection of multiple sequence alignments and hidden Markov models representing many protein domains and families. It has a web interface for identification of domains in a given protein, and includes hyperlinks to relevant literature and functional/structural information for many domains.

PSI-BLAST

Position-Specific Iterated BLAST

An iterative search using the BLAST algorithm and profile construction, enabling identification of remote homologs

PSIPRED

PSIPRED is a simple and reliable secondary-structure prediction method, incorporating two feed-forward neural networks, which perform an analysis on output obtained from PSI-BLAST to predict whether a position is likely to be found in an α helix, β strand, or coil.

PSSM

Position-specific scoring matrix

A PSSM is a statistical representation of the amino acids in a multiple sequence alignment, and is used to provide a log-odds score for each amino acid at each position in the alignment. PSSMs, HMMs, and profiles are related.

RasMol

Raster Display of Molecules.

Molecular visualization freeware to visualize 3-D structure of proteins

RPSBLAST

Reverse PSI-BLAST

RPS-BLAST (Reverse PSI-BLAST) searches a query sequence against a database of profiles (i.e., the reverse of PSI-BLAST that searches a profile against a database of sequences).

SAWTED

Structure Assignment With Text Description

SAWTED compares annotations of the query and closely related sequences with annotations associated with database hits having weak scores. This is included to improve the ability to differentiate between true homologs and spurious matches (i.e., SAWTED enables sequences with weak scores but similar annotations to be given more credence in homology search).

SCOP

Structure Classification of Proteins

The SCOP database classifies protein structural domains into a hierarchy (class, fold, superfamily and family), based on functional and structural data. Proteins in the same SCOP superfamily are expected to be related by divergent evolution from a common ancestral protein and to share a similar (albeit potentially high-level) function. Proteins in different SCOP folds are assumed not to be homologous.

SMART

Simple Modular Architecture Research Tool

The SMART Web server allows rapid identification of the domain architecture of a given protein, and includes precomputed results for many sequences

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Figure 2.11.4 SMART results for AVR2 HUMAN. Integrated results from several servers show an N-terminal signal peptide, two PFAM domains, a transmembrane domain, and a low-complexity region. This black and white facsimile of the figure is intended only as a placeholder; for full-color version of figure go to http://www.interscience.wiley.com/c p/colorfigures.htm.

3. Select the Sequence SMART button. This will retrieve precalculated results (when available) or search the sequence against the library of HMMs available. 4. Examine predicted domains and motifs. The features represented in the example (Fig. 2.11.4) are: a. Signal peptide: the small red box at the N-terminus. b. Two PFAM domains: Activin receptor and Pkinase. c. Low-complexity region: pink box at C-terminus. d. Transmembrane domain: Blue vertical box. 5. Place the mouse cursor over predicted domains and unidentified regions to see their start and end amino acid positions. 6. Click on the domains to get a detailed description of the domain. 7. Scroll down to see the E-value of the predicted matches. 8. Analyze results (see Support Protocol 1). SUPPORT PROTOCOL 1

GUIDELINES FOR UNDERSTANDING RESULTS OF ANALYSES FROM INTEGRATED META-SERVERS The domains in SMART (see Basic Protocol 1) are represented by seed alignments (alignments used to build the HMMs). Homologs are gathered by different iterative methods and included in a multiple sequence alignment (MSA) after estimating the statistical significance of sequence similarities and examining experimental biological information, if available. To reduce redundancy in a family of sequences representing a domain, a phylogenetic tree is constructed using the MSA of all the sequences in the family, and, from every branch that is less than a distance of 0.2 on the tree, only a single sequence is chosen. This corresponds to about 80% sequence identity. These sequences are then used in the seed alignment from which the profiles and HMMs representing a domain are constructed (Schulz et al., 1998).

Basic Protein Sequence Analysis

Interpreting the Significance of a Match to a Profile Most similarity search methods employ E-values (Expect values) to report the statistical significance of a match. How an E-value is calculated varies with each method. Generally, the E-value represents the number of matches expected to be found merely by chance. For instance if an E-value is 10, then 10 matches are expected to be found by chance alone. In general, an E-value <0.001 is a good indicator of the credibility of predicted homology, and the likelihood of a spurious result increases as the E-value approaches (or exceeds) 1. A significant E-value implies similarity in structure; the functional specificity may have changed. To confirm functional similarity, it is critical to check for agreement

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at key functional residues (particularly active site residues in the case of an enzyme), and for a high percent identity (as a conservative rule of thumb, we recommend requiring a minimum of 40% identity). If the profile or HMM represents a structural domain, the query should align over a significant fraction of the profile/HMM. As a basic rule of thumb, we recommend requiring a minimum of 75% coverage. Note that the SMART output does not provide this information (i.e., percent identity, coverage of the profile and agreement with key positions); these criteria will need to be confirmed separately.

Factors Affecting E-Value E-values are affected by the length of the profile or HMM, with longer profile/HMMs producing more significant E-values for related sequences and shorter profile/HMMs producing weaker E-values. Weak E-values are particularly common when the profile or HMM is based on very short motifs (e.g., the PFAM Leucine Rich Repeat HMMs, having between 20 and 25 residues). Because a near-exact match to a short motif can occur by chance alone, E-values will be correspondingly high (i.e., approaching or exceeding 1) even for closely related sequences. For this reason, many HMM and profile libraries employ empirically determined score cutoffs instead of E-values (see, e.g., the PFAM gathering threshold); these tend to be more relaxed for shorter profile/HMMs than for longer. Another factor that affects E-values is the size of the database searched. The probability of finding a match by chance increases with the size of the database searched. The upshot of this is that a match between a given query and a hit will have different E-values depending on the database searched. For example, if the E-value of a match between query X and hit Y is 0.01 on searching the National Center for Biotechnology Information (NCBI) nonredundant database (2,182,528 sequences as of November, 2004), then it will be smaller (e.g., 0.001), and hence more significant, on searching Swiss-Prot (159,078 sequences as of November, 2004) using the same search method. The database size becomes an issue especially when E-values are not extremely significant.

IDENTIFYING STRUCTURAL AND FUNCTIONAL DOMAINS USING THE NCBI CD-SEARCH

ALTERNATE PROTOCOL 1

The Conserved Domain Database (CDD; Marchler-Bauer et al., 2003) at NCBI is a database of Position Specific Scoring Matrices (PSSMs) generated from alignments imported from PFAM and SMART and automated alignments from COGs (Cluster of Orthologous Groups; Tatusov, 2001) CD-Search is a tool that uses RPS-BLAST (Reverse PSI-BLAST) to search a sequence against a database of profiles, namely the CDD (Marchler-Bauer et al., 2004). This is the reverse of PSI-BLAST, which searches a profile against a database of sequences.

Materials See Basic Protocol 1. 1. Point the browser at the CDD (http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml). 2. Input a sequence in FASTA format (or, alternatively, the NCBI accession or GenBank accession number) in the box provided (Fig. 2.11.5). 3. Set the Search Database from the pull-down menu of that name. The default option is CDD, but users can also search SMART, PFAM, and COGs. 4. Set other parameters using the “click here for advanced options” link, if desired. 5. Click Submit.

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Figure 2.11.5

CD-Search sequence submission form.

Figure 2.11.6 Results from CD-Search. (A) The output gives a graphic display and E-values of hits. (B) Pairwise alignment of the query with the top hit. This black and white facsimile of the figure is intended only as a placeholder; for full-color version of figure go to http://www.interscience.wiley.com/c p/colorfigures.htm. Basic Protein Sequence Analysis

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6. The CDD results page returns a graphic display of domains in the query, the Evalues for each hit, and the alignments (Fig. 2.11.6). Analyze results (see Support Protocol 2).

GUIDELINES FOR UNDERSTANDING RESULTS OF ANALYSES FROM THE NCBI CD-SEARCH

SUPPORT PROTOCOL 2

See Alternate Protocol 1 for steps in carrying out this search. 1. The first few lines give details about the version of RPS-BLAST used, the definition line of the query sequence, and the database searched. Because the CDD has regular updates, we recommend noting these data for record-keeping purposes. 2. In the graphic display, the query sequence is represented as a black bar with a ruler above indicating its length. Low-complexity regions are marked in cyan. 3. The domain matches are shown below in different colors depending on the E-value of the hits. The best hits are marked in red, the second best in blue and so on. The CDD contains multiple PSSMs for the same structural domains; these may have differences in length and coverage of the query, and have correspondingly different E-values. Hits to conserved domains that are related by the Conserved Domain Architecture Retrieval Tool (CDART; Geer et al., 2002; also see Table 2.11.1) resource are indicated by the same color. In the example in Figure 2.11.6, the hits to different PSSMs representing kinase domains are all colored red. 4. A jagged edge seen in the hit display indicates incomplete coverage of the domain (a default minimum of 80% coverage is used by the CD search). For example the SPS1 domain in Figure 2.11.6 contains a jagged right edge, indicating that the alignment to the query does not contain this region of the domain. 5. Below the graphical display is a list of hits along with their E-values. A pink dot before the hit indicates the presence of a structure for the predicted hit. Clicking on the dot will display the structure. Prior installation of Cn3D software is required (http://www.ncbi.nlm.nih.gov/Structure/CN3D/cn3d.shtml). 6. Scrolling down the page displays the pairwise alignment between the query and a representative sequence for the PSSM. Identical residues between the query and the hit sequence are colored red, similar residues are colored blue, and masked-out regions are in italics.

Viewing a multiple sequence alignment (MSA) of the query to a conserved domain 7. Clicking on the graphical display of a domain brings up a multiple sequence alignment of the query to selected PSSM training sequences (Fig. 2.11.7). By default, sequences with high similarity to the query protein are shown. This page gives information about the source of the domain alignment, PubMed references, description, and other relevant data. The default MSA viewing option is Hypertext; our guidelines for interpreting results assume this option is used. 8. Interpreting the MSA: aligned positions are in uppercase, unaligned positions are in lowercase, and gaps are indicated by dashes (-). Residues labeled “unaligned” are inserts between positions in the predicted consensus structure. Profiles and hidden Markov models (HMMs) are constructed to represent the conserved structural elements of a protein family or domain. Amino acids corresponding to additional structure (e.g., extended loop regions) will be displayed in lowercase. These lowercase letters are indicative of insertions relative to the consensus structure. Missing parts of the consensus structure are indicated with dash characters (--).

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Figure 2.11.7 Multiple sequence alignment of AVR2 HUMAN kinase domain with a conserved kinase domain hit. The columns marked with # have been identified as critical for kinase function.

9. For domains with a solved structure, one can view the structure using the View 3D Structure option. The coloring scheme of the alignment can be changed to indicate different levels of conservation. For instance using the option Identity from the “color bits” pull-down menu shows identical residues in the alignment in red, aligned residues in blue, and unaligned residues and columns containing many gaps in gray. 10. Features defining a family are highlighted with # above the corresponding columns in the MSA. The evidence for the feature is presented at the bottom of the MSA. This is useful to predict if the query sequence also contains residues important for the family. In our example, clicking on the S-TKc shows a catalytic loop as feature 1 and the 3-D structure as evidence.

Basic Protein Sequence Analysis

Interpreting the significance of results 11. If E-values are weak, it may be helpful to examine the PSSM hits to see if all refer to the same structural or functional class, or if competing hypotheses for a region are presented by the CDD analysis. Checking for significant percent identity and coverage (as described in the SMART discussion earlier in this unit) is important to avoid spurious and misleading predictions.

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12. The Advanced Search mode allows users to change the maximum allowed E-value cutoff. This is generally done if no matches are possible with more restrictive cutoffs. If a permissive threshold (e.g., E-value 1) is used, false positive matches should be expected. 13. Though the domains represented in CD are similar to those in PFAM and SMART, the search and scoring mechanisms are very different. While PFAM uses profile HMM scoring, CD-Search employs RPS-BLAST. Hence, the E-values that are reported from PFAM and CD-Search for the same PFAM domain may not be identical. To interpret the significance of E-values and the effect of profile/HMM lengths on E-values, see the guidelines described in Support Protocol 1.

PREDICTING STRUCTURAL DOMAINS AND SECONDARY STRUCTURE USING 3D-PSSM

ALTERNATE PROTOCOL 2

3D-PSSM is a Web server designed explicitly and exclusively for protein structure prediction, which incorporates a variety of advanced remote homolog detection and threading methods (Kelley et al., 2000). We present 3D-PSSM as a supportive procedure in cases where other servers are unable to provide clues to molecular function or structure for regions of the protein. In these cases, the advanced methods incorporated in the 3DPSSM workflow can often give insights not available via other servers for identifying homologous structures. In contrast to PFAM, the NCBI CDD, and many other servers that model both structural domains and domains having no known structure, any match to a 3D-PSSM domain implies a solved structure for that region. One of the great benefits of 3D-PSSM is the automatic construction of a homology model for any regions in the query matching one or more of the 3D-PSSM models. The 3D-PSSM server is continually updated. As of February, 2005, 3D-PSSM contains a library of 9864 PSSMs representing structural domains.

Materials See Basic Protocol 1. 1. Point the browser at http://www.sbg.bio.ic.ac.uk/servers/3dpssm/. 2. Click on Recognise a Fold (Fig. 2.11.8A) for the sequence submission form. 3. Enter a valid e-mail address. 4. Paste the query sequence in FASTA format. The query sequence must be <800 residues. The maximum number of residues allowed per submission by 3D-PSSM is restricted to 800. If the protein of interest has >800 residues, we suggest that it be submitted in two parts with overlap. For example, for a protein of 1000 residues, submit region 1 to 600 first, and 400 to 1000 next. 5. Click Submit. Results of the run will be returned by e-mail. The e-mail also contains a hyperlink to the Web version of the results. Results are stored only for 5 days in the server (we recommend downloading results from the Web site). 6. Click on the hyperlink in the e-mail to open a Web page with the results (Fig. 2.11.8B). Analyze results (see Support Protocol 3).

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Figure 2.11.8 page.

SUPPORT PROTOCOL 3

Basic Protein Sequence Analysis

Submission form and results page from 3D-PSSM server. (A) The submission form; (B) the results

GUIDELINES FOR UNDERSTANDING RESULTS OF ANALYSES FROM THE 3D-PSSM SERVER We recommend 3D-PSSM (see Alternate Protocol 2) as an alternative for domain identification for those cases where regions of a protein have no significant matches to domains in the CDD or SMART databases. In these cases, the advanced methods used by 3DPSSM may be able to identify a weak but potentially significant match to a protein of solved structure and shed light on the functional role of the protein. Caution must be employed when examining the 3D-PSSM results where the E-value is only marginally significant (e.g., >0.001), as the potential for a false positive match increases as the E-value approaches 1.

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Figure 2.11.9 Sequence alignments produced by 3D-PSSM (A) Alignment of query to its homologs. (B) Pairwise alignment and comparison of secondary structure and solvent accessibility predictions for the query with that of the hit (structural domain). This black and white facsimile of the figure is intended only as a placeholder; for full-color version of figure go to http://www.interscience.wiley.com/c p/colorfigures.htm.

The 3D-PSSM Web server produces a great deal of interesting and informative results, some of which are beyond the scope of this unit. In this protocol, we highlight a selection of the most interesting results. We encourage users to explore further. 1. The first page of the results is separated into two frames. The upper frame contains a summary of results. The lower frame shows the secondary structure of the query sequence predicted by PSIPRED (Jones, 1999; also see Table 2.11.1). We focus here on results in the upper panel. 2. Click on View Multiple Sequence Alignment to examine the alignment of the query and predicted sequence homologs (Fig. 2.11.9A). The 3D-PSSM Web server uses this alignment to construct a PSSM and score proteins of solved structure, as part of a bidirectional search for structural homologies. Each sequence in the alignment has a hyperlink to the corresponding GenBank file at NCBI. The scores and E-value of each hit, and percent ID to query are also available.

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3. Return to the original results page and scroll down to view matches to structural domains. The top 20 structural hits are shown in a tabular format. The table contains the following information. a. Alignment. A link to the sequence alignment of the query to the hit (structural domain) and comparison of the secondary structure and solvent accessibility of residues between query and hit (Fig. 2.11.9B).

b. Fold Library. The ID of the structural hit (d[pdbcode][chain][region]), along with the pairwise ID between query and hit, are shown. Clicking on the ID brings up a page of the fold library containing information about the hit structural domain, including an MSA of the hit with its sequence and structural homologs.

c. Models. A link to the predicted 3-D model of the query protein based on the alignment to the hit which can be viewed by the protein visualization software RasMol (see Table 2.11.1) or Chime.

d. E-value. The E-value of the hit is shown, colored according to level of confidence, red indicating very high confidence.

e. Other information. The other information in the table includes SAWTED (Table 2.11.1) E-value for text matching between query and definitions of hits, SCOP classification of hit, and other data.

If the E-value to a structural hit is >.001, the following analyses are recommended to assess the credibility of the predicted homology 4. Check the sequence alignment. The query and structure should align with few insertions or gap characters, and have significant pairwise identity. As a rule of thumb, a pairwise identity of 25% or more over 80 amino acids can be used to infer homology (assuming very few gaps). As the percent identity drops below this level, the potential for a spurious match by chance alone increases. In these cases, it is critical to confirm that the alignment shows agreement at known critical residues. 5. Compare the agreement between predicted and known secondary structure. Compare the predicted secondary structure of the query with that of hit. The secondary structures are indicated as follows: H for Helix; E for Strand; C for Coil. Agreement between helical and sheet structure is generally more important than in the more variable coil regions. 6. Compare the agreement in the conserved core structure. The hydrophobic core region of the structural domain hit is marked in the MSA page as Core (Fig. 2.11.9B), numerically labeled to indicate how buried the residues are (9 for very buried and making many contacts and 0 for exposed and making few contacts) and color coded to indicate important core residues (red for important core residues). Significant gaps or inserts in the conserved core elements of the structure should be considered evidence against the predicted homology. An important issue to keep in mind is that 3D-PSSM may only identify one structural domain in a multidomain protein. To enable identification of domains in the remaining region(s) of the query, separate submissions of unknown regions are necessary. Basic Protein Sequence Analysis

There are a few other Web servers and databases enabling integrated searches, as shown in Table 2.11.2.

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Table 2.11.2 Web Servers Providing Domain Identification Tools

Web server

Description

URL

SMART

Structural, functional, and localization prediction

http://smart.embl-heidelberg.de/

PFAM

Includes both structural and functional domains

http://www.sanger.ac.uk/Software/Pfam/ http://pfam.wustl.edu

SUPERFAMILY

Based on the Astral PDB90 database of structural domains

http://supfam.mrc-lmb.cam.ac.uk/ SUPERFAMILY/hmm.html

SAM-T02

The UCSC structure prediction HMM library

http://www.cse.ucsc.edu/research/compbio/ HMM-apps/T02-query.html

TigrFAMs

From TIGR, a Web server of HMMs for structure (and function) prediction

http://tigrblast.tigr.org/web-hmm/

PhyloFacts

Includes subfamily HMMs for structural domains.

http://phylogenomics.berkeley.edu/phylofacts/

NCBI Conserved Domain Database

Includes protein domains with solved structures

http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi

3D-PSSM

Includes a secondary structure prediction to obtain predictions of 3-D fold

http://www.sbg.bio.ic.ac.uk/∼3dpssm/

PREDICTING HELICAL TRANSMEMBRANE REGIONS AND SUBCELLULAR LOCALIZATION

BASIC PROTOCOL 2

The molecular function of a protein is context-dependent; the subcellular localization of a protein plays a large role in determining its partners and pathway interactions. The cellular sorting machinery makes use of sequence signals targeting a protein for secretion, targeting it to different organelles, or targeting it for insertion into the membrane. Bioinformatics methods take advantage of these motifs and patterns as well as other sequence attributes to predict where a protein is localized. These methods are reviewed in this protocol; see UNITS 2.2 & 2.3 for discussion of earlier methods for this type of analysis. Our primary recommended resource for this protocol makes use of a variety of software tools developed by the Center for Biological Sequence Analysis at the Technical University of Denmark (http://www.cbs.dtu.dk/services/). This resource enables the detection of helical transmembrane domains, signal peptides, and other organellar targeting signals. Due to the biological importance of transmembrane proteins, we discuss the detection of these domains first.

Prediction of helical transmembrane regions in protein of interest Transmembrane predictions involve not only the location of probable membrane helices in a protein, but also the full topology of the protein with respect to the membrane (i.e., which regions of the protein span the membrane, which regions are cytoplasmic, and which are extracellular). Among the several Web servers available for transmembrane prediction, we describe the use of the TMHMM server. The TMHMM server uses sophisticated hidden Markov model methodologies to predict transmembrane regions in proteins, incorporating information on hydrophobicity, helix length, charge bias, and topological constraints into its model (Krogh et al., 2001). It has a user-friendly Web interface, produces quick results, and allows batch-mode submissions of up to 4000 sequences. Computational Analysis

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Materials Input data Protein sequence file, in either raw or FASTA format (Fig. 2.11.1). Alternatively, a file containing multiple input sequences in FASTA format can be used. 1. Point the browser at the TMHMM server (http://www.cbs.dtu.dk/services/TMHMM/). 2. Paste in the sequence(s), or upload a file directly from the local computer (recommended when one wants to analyze large numbers of sequences). To upload a file, use the Browse button below the text “Submission of a local file in FASTA format.” Each file can contain at most 4000 sequences. 3. Set display options. If submitting a single sequence, we recommend setting the display as “Extensive, with graphics.” This will provide results in the most informationrich format (see below). The recommended option for multiple sequence search is “One line per protein.” This returns the results in a tab-delimited format that can be saved as a text file to be opened in a program like Excel. 4. Click Submit. After a short time the results will be displayed. 5. Only 10 job (including batch mode) submissions per day are allowed per user. 6. Analyze results (see Support Protocol 4). SUPPORT PROTOCOL 4

GUIDELINES FOR UNDERSTANDING RESULTS OF PREDICTIONS OF HELICAL TRANSMEMBRANE REGIONS AND SUBCELLULAR LOCALIZATION For steps in predicting helical transmembrane regions and subcellular localization, see Basic Protocol 2. 1. Depending on the selection during submission, the results could be either in “long” or “short” format. The long format is shown in Figure 2.11.10. 2. View summary of results containing the following information: a. Length. Length of query protein.

b. Number of predicted TMHs. Number of predicted TM helices in the query.

c. Exp number of AAs in TMHs. Number of amino acids predicted to be in TM helix. If this number is greater than 18, then it is predicted to be a TM.

d. Exp number, first 60 AAs. Number of amino acids expected to be in TM helix in the first 60 residues. A warning POSSIBLE N-term signal sequence is generated if this number exceeds 10.

e. Total prob of N-in. This represents the probability of N-terminal region being on the cytoplasmic side.

Basic Protein Sequence Analysis

3. View plot. The plot summarizes the various signals used by the TM server. The x axis corresponds to the amino acid positions while the y axis corresponds to the likelihood of an individual position being predicted to be in a TM. Note that the y axis continues beyond a value of 1; the region above 1 is used to display the topology. Since the

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Figure 2.11.10 TMHMM ouput for AVR2 HUMAN. Shown are results in the “extensive with graphics” format. A summary of results is followed by a graphic display. The x axis on the graph represents amino acid positions in the query sequence and the y axis represents the probability of a residue to be in a TM. The peaks indicate positions with higher probability of being a TM domain. The predictions of TM and topology are indicated above a y axis value of 1. The region predicted to be inside (cytoplasmic) is represented with a blue line (marked as INSIDE in this figure) and the region on the outside (extracellular) is represented in pink (labeled OUTSIDE in this figure). This black and white facsimile of the figure is intended only as a placeholder; for full-color version of figure go to http://www.interscience.wiley.com/c p/colorfigures.htm.

TMHMM server integrates a variety of signals from the protein, occasionally the predicted number of helices is lower than the number of peaks in the plot. 4. View raw data. Below the plot is a hyperlink to the raw data for the plot. 5. Cautions a. Accuracy of topology predictions. The accuracy of topology prediction (i.e., which parts of the proteins are extracellular and which are cytoplasmic) is known to be one of the most challenging aspects of transmembrane predictions. Even when the location of transmembrane domains is predicted correctly, the topology can be reversed.

b. Potential for false positives and false negatives. All TM prediction servers are known to have problems with false positive and false negative predictions. False positives arise often for hydrophobic stretches and signal peptides. False negatives can arise for any number of reasons, and are particularly common in noncanonical TM stretches containing some polar or charged residues.

c. Incorporating prediction of globular structure. It can be helpful to integrate domain detection, particularly of globular domains, when interpreting TM predictions. If a predicted TM occurs in a region that has a significant match to a protein of solved structure, we recommend examining what is known about that structure. A very limited number of solved structures include

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transmembrane domains. The vast majority of solved structures represent globular (soluble) domains that do not include transmembrane domains. If a predicted TM domain is contained in a region with a significant match to a globular protein, we would discount the likelihood of the TM domain. ALTERNATE PROTOCOL 3

PREDICTING THE SUBCELLULAR LOCALIZATION OF A PROTEIN USING TargetP Cells have evolved a complex machinery for enabling proteins to be targeted to specific sub-cellular locations. Targeted proteins have specific signals recognized by the machinery. For secreted proteins and those targeted to the organelles such as mitochondria or chloroplast, the targeting signals are amino-terminal peptides recognized by the translocation machinery (Schatz and Dobberstein, 1996). The targeting signals for different locations have specific features. For instance, the aminoterminal signal peptide (∼20 amino acids) that targets proteins for secretion starts with a positively charged segment followed by a central hydrophobic region and a polar segment containing the cleavage site recognized by the signal peptidase. Different approaches have been developed to predict protein localization (Emanuelsson and von Heijne, 2001). Most methods specialize in predicting one specific location (e.g., SignalP for predicting signal peptides). In this protocol we review TargetP, an integrated approach that distinguishes proteins targeted to different locations in the cell such as mitochondria, chloroplast, and the secretory pathway.

Materials See Basic Protocol 2. 1. Point the browser at the TargetP server (http://www.cbs.dtu.dk/services/TargetP/). 2. Paste in the sequence(s), or upload a file directly from the local computer. 3. Identify the origin of the protein sequence (i.e., plant or animal). If it is of plant origin, TargetP will search for chloroplast localization signals. 4. Select the option to perform a cleavage site prediction. 5. Use default settings for specificity cutoffs. Click the “Submit sequence/file” button. 6. Analyze results (see Support Protocol 5). SUPPORT PROTOCOL 5

GUIDELINES FOR UNDERSTANDING RESULTS PREDICTING THE SUBCELLULAR LOCALIZATION OF A PROTEIN USING TargetP Results (see Alternate Protocol 3) include scores for different predicted cellular locations (Fig. 2.11.11).

SP : Signal peptide mTP : Mitochondrial target peptide cTP : Chloroplast (if plant sequence is used) Other: For other locations Along with the scores, a “reliability class” (RC) is also calculated. This gives information on the difference between the highest score and the second best.

Basic Protein Sequence Analysis

RC RC RC RC RC

1: 2: 3: 4: 5:

diff > 0.800 0.800 > diff > 0.600 0.600 > diff > 0.400 0.400 > diff > 0.200 0.200 > diff

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Figure 2.11.11

TargetP prediction of the subcellular localization of AVR2 HUMAN.

Table 2.11.3 Cellular Localization Prediction

Web server

Description

URL

TMHMM

The TMHMM transmembrane helix prediction server

http://www.cbs.dtu.dk/services/TMHMM/

DAS

The DAS transmembrane helix prediction server

http://www.sbc.su.se/∼miklos/DAS/

SignalP

The SignalP signal peptide prediction server

http://www.cbs.dtu.dk/services/SignalP/

ChloroP

The ChloroP chloroplast targeting prediction server

http://www.cbs.dtu.dk/services/ChloroP/

PredictNLS

Nuclear localization signal prediction

http://cubic.bioc.columbia.edu/predictNLS/

In interpreting TargetP results, it is important to consider both the predicted location as well as the RC score. An RC of 1 or 2 is a good indicator of reliability. In our example, the score for SP is 0.853 and the RC value is 1, both supporting the prediction of presence of a signal peptide in the protein (Fig. 2.11.11). TargetP is known to have greater specificity in prediction compared to other methods in this class, but this comes at a cost of sensitivity. A null prediction does not imply that the protein is cytoplasmic. By contrast, cleavage-site prediction is less precise. Finally it is critical to indicate whether the input sequence is of plant or animal origin so that the appropriate localization tests can be performed; it is otherwise difficult to distinguish between chloroplast and mitochondrial signals. Other resources for prediction of transmembrane helices or subcellular localization can be found in Table 2.11.3.

PREDICTING KEY FUNCTIONAL RESIDUES AND MOTIFS USING THE PROSITE WEB SERVER

BASIC PROTOCOL 3

Not all residues in a protein are created equal. Residues in active sites, e.g., the catalytic triad of serine proteases, are critical for enzymatic activity. Other positions may be involved in substrate recognition and binding. Still other positions may be conserved for structural reasons. Over the years, biologists have identified a large number of key residues and conserved motifs that are useful in defining and recognizing protein families. Our primary recommended resource for this protocol is PROSITE. The PROSITE Web server enables a biologist to determine whether any experimentally characterized motifs are present in a sequence of interest (Sigrist et al., 2002; Hulo et al., 2004). This resource enables the detection of different protein motifs, post-translational modification sites, and domains. Since domain detection is provided through other servers (presented earlier in this unit), this protocol focuses on the use of the PROSITE Web server for detecting the presence of conserved motifs and functional residues.

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Materials Input data Sequence in raw or FASTA format (Fig. 2.11.1) 1. Point the browser to the PROSITE Web site (http://us.expasy.org/prosite/). 2. Input the sequence in FASTA format in the sequence box provided. 3. Choose the option to exclude patterns with a high probability of occurrence. This will decrease the likelihood of spurious predictions. 4. Click the Quick Scan button to search for conserved motifs in the PROSITE database. 5. Analyze results (see Support Protocol 6).

SUPPORT PROTOCOL 6

GUIDELINES FOR UNDERSTANDING RESULTS OF SEARCHES DONE USING THE PROSITE WEB SERVER The PROSITE Web server (see Basic Protocol 3) includes both pattern and profile search. Profiles are usually constructed to represent domains and protein families, while patterns are confined to the represention of short conserved motifs and critical residues. When a query protein is submitted, both patterns and profiles are searched and the results are returned as “hits by profile” (Fig. 2.11.12A) and “hits by patterns” (Fig. 2.11.12B). For each detected feature, the position and the sequence motif are provided along with a link to detailed information about each feature. Placing the mouse cursor over predicted domains in the graphical display highlights the corresponding region in the sequence. Pattern detection has certain advantages relative to profile search. First, pattern detection is extremely fast. Second, and potentially of greater interest to biologists, the results are easily interpreted; the motifs are typically quite short, and critical residues defining the motif can be examined easily. There are two main disadvantages to pattern detection, relative to profile search. First, a strict agreement with a pattern is required, resulting in lower sensitivity than the more permissive profile searches, which allow substitutions. Second, the results from pattern searches are not given scores; the pattern is either present or it is not. This makes is difficult to evaluate the significance of the match. In addition, some patterns have a high associated false-positive rate (this is more common in very short or highly variable patterns). For example, the post-translational modification site for N-glycosylation is Asn-Xaa-Ser/Thr (where Xaa represents any amino acid). However, the presence of this motif may not be sufficient to ensure glycosylation, as the process is also dependent on the structure of the protein (Sigrist et al., 2002). The PROSITE Web server contains information to help reduce the likelihood of errors. First, each pattern includes information regarding the number of hits obtained while scanning the Swiss-Prot database with that pattern, the number of false positives and false negatives, and results from pattern search using database randomization. These data should be consulted to obtain an understanding of the likely significance of a match to this pattern. In addition, the PROSITE profile searches can provide additional support for a functional classification based on motif and pattern detection. For instance, the presence of a serine/threonine protein kinase signature pattern along with detection of kinase domain for AVR2 HUMAN strengthens the significance of the prediction.

Basic Protein Sequence Analysis

An example of a six-amino-acid PROSITE pattern is shown below. G-{EDRKHPFYW}-x(2)-[STAGCN]-{P}[G is the N-myristoylation site]

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Figure 2.11.12 Prediction of patterns by PROSITE for AVR2 HUMAN. (A) results of profile search; (B) results of pattern search.

Residues in straight brackets ([]) designate residues that are favorable at a position. Residues in curly brackets ({}) designate those amino acids that are not allowed at a position (i.e., in this example, the amino acids EDRKHPFYW are not allowed at position 2). The pattern x(2) indicates any two amino acids. Other resources for similar tasks can be found in Table 2.11.4. Computational Analysis

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Table 2.11.4 Predicting Key Functional Residues and Motifs

Web server

Description

URL

Evolutionary Trace University of Cambridge implementation of Evolutionary Trace algorithm

http://www-cryst.bioc.cam.ac.uk/∼jiye/ evoltrace/evoltrace.html

MEME

The MEME motif discovery software

http://meme.sdsc.edu/meme/Website/intro.html

PROSITE

Detects previously characterized motifs characterizing protein families, as well as post-translational modifications

http://www.expasy.ch/prosite

SUPPORT PROTOCOL 7

HOMOLOG IDENTIFICATION As a support protocol, we encourage biologists to gather homologs for their sequence of interest and to construct and analyze a multiple sequence alignment of these homologs. The BLAST family of methods (Altschul et al., 1990) is by far the most popular means for identifying homologs in database search; we refer readers to UNIT 2.5 in this manual or the BLAST tutorial at the NCBI Website (http://www.ncbi.nlm.nih.gov/ Education/BLASTinfo/tut1.html). Homolog identification is useful for a variety of reasons. First, homologs can give clues to the overall molecular function of a protein. When inferring molecular function by homology, it is critical to confirm that the query and hit have the same overall fold (i.e., are globally alignable), that they are orthologous, and that the database annotation of the hit is based on experiment and not homology (reviewed in Eisen, 1998, and in Sj¨olander, 2004). Gathering homologs is also the first step in construction of a multiple sequence alignment and the (somewhat esoteric) art of alignment gazing. Alignment gazing can help a biologist discriminate between true homologs and spurious inclusions, and assists in the identification of key functional residues. Sequences inserting large numbers of amino acids, that do not align over long regions, or that do not agree at conserved positions (especially longer conserved motifs) should be removed from the alignment. The final set of high-confidence homologs can be used to supplement the structure-prediction efforts (i.e., by parallel analyses using all the servers and approaches described in this unit). In addition, the multiple sequence alignment can be used as input into motif-discovery systems such as MEME. This is particularly helpful when there is limited (or no) success in analysis of the query sequence. Consensus approaches such as these have been shown to dramatically improve prediction accuracy.

COMMENTARY Background Information

Basic Protein Sequence Analysis

Each of the methods presented in this unit has the potential to produce errors of different types. When examining results, it is important to keep in mind the types of errors that are possible with each method, and to attempt to separate those predictions that are highly credible from those that are potentially misleading. Homology-based function prediction is particularly prone to systematic error; domain shuffling, gene duplication, and speciation produce families of proteins sharing some regions of similarity but with different functions and

overall folds. Because of these issues, even a significant E-value between a query and a database hit is not sufficient for inference of molecular function. To avoid these errors, phylogenomic inference of a protein’s molecular function in the context of its related family members is critical. A detailed description of this approach is beyond the scope of this unit; overviews of the issues and methodologies for phylogenomic analysis are presented in Eisen (1998) and Sj¨olander (2004). For these reasons, this unit focuses less on obtaining a prediction of precise molecular

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function and more on a structural annotation of a protein. The protocols presented here have been selected to enable a biologist to label regions in the protein as functional or structural domains, to identify key residues and motifs, and to predict a protein’s cellular localization. Most homolog-detection methods use either profile- or HMM-based strategies. Both profiles and HMMs are generalizations of multiple sequence alignment of homologs. They differ in how they estimate gap parameters; profiles have a fixed cost for insertions and deletions, while HMMs learn them from the training sequences used in the MSA, thus varying the cost for different positions. Both methods are dependent on precision of homolog gathering, alignment accuracy, and estimation of amino acid substitutions. Domain-based approaches to protein annotation have a critical limitation. The profiles (and HMMs and PSSMs) used to detect the presence of these domains are typically optimized for remote homolog detection. This makes them very effective at providing clues to a protein’s fold or function that might otherwise not be achievable. However, because some protein folds are able to provide a multiplicity of distinct functions, a significant score to a profile for a domain may mean only that the query has a similar fold, but may not actually imply a common molecular function. We strongly advise checking for agreement at key functional residues (where this information is available). The use of consensus approaches to predicting a protein’s structure can help avoid errors, provided that the individual methods are orthogonal. For instance, since both SMART and the CDD include PFAM profile HMMs in their structure-prediction process, a consensus structure prediction between the NCBI CDD or SMART and PFAM is much less informative than a consensus of the NCBI CDD and the 3D-PSSM structure prediction Web server (which uses an entirely different approach). Methods for predicting helical transmembrane domains often rely on weak nonspecific signals such as hydrophobicity, and hence have a fairly high frequency of false-positive as well as false-negative predictions (Chen et al., 2002). To boost accuracy in this task, we strongly encourage biologists to examine alternative hypotheses. Does the predicted transmembrane domain occur in a region predicted (with a significant E-value) to be a globular structure? Is it found at the amino-terminus (where it is more likely to be a signal peptide)? In either case, the probability of the

region being a transmembrane domain should be discounted. In summary, our recommendation for enhancing prediction accuracy involves integrating results and information from a variety of different resources, and biological data should be incorporated wherever possible.

Critical Parameters and Troubleshooting On occasion, a Web server will report an error for certain input sequences. In some cases, hidden characters may make identification of the problem difficult. If this happens, try truncating the definition line to the name of the protein, inserting a carriage return after the definition line, and then resubmitting the sequence.

Literature Cited Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J. 1990. Basic local alignment search tool. J. Mol. Biol. 215:403-410. Bateman, A., Birney, E., Cerruti, L., Durbin, R., Etwiller, L., Eddy, S.R., Griffiths-Jones, S., Howe, K.L., Marshall, M., and Sonnhammer, E.L. 2002. The Pfam protein families database. Nucleic Acids Res. 30:276-280. Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.C., Estreicher, A., Gasteiger, E., Martin, M.J., Michoud, K., O’Donovan, C., Phan, I., Pilbout, S., and Schneider, M. 2003. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31:365-370. Chen, C.P., Kernytsky, A., and Rost, B. 2002. Transmembrane helix predictions revisited. Protein Sci. 11:2774-2791. Eisen, J.A. 1998. Phylogenomics: Improving functional predictions for uncharacterized genes by evolutionary analysis. Genome Res. 8:163-167. Emanuelsson, O. and von Heijne, G. 2001. Prediction of organellar targeting signals. Biochim. Biophys. Acta 1541:114-119. Geer, L.Y., Domrachev, M., Lipman, D.J., and Bryant, S.H. 2002. CDART: Protein homology by domain architecture. Genome Res. 12:16191623. Hubbard, T., Barker, D., Birney, E., Cameron, G., Chen, Y., Clark, L., Cox, T., Cuff, J., Curwen, V., Down, T., Durbin, R., Eyras, E., Gilbert, J., Hammond, M., Huminiecki, L., Kasprzyk, A., Lehvaslaiho, H., Lijnzaad, P., Melsopp, C., Mongin, E., Pettett, R., Pocock, M., Potter, S., Rust, A., Schmidt, E., Searle, S., Slater, G., Smith, J., Spooner, W., Stabenau, A., Stalker, J., Stupka, E., Ureta-Vidal, A., Vastrik, I., and Clamp, M. 2002. The Ensembl genome database project. Nucleic Acids Res. 30:38-41. Hulo, N., Sigrist, C.J., Le Saux, V., LangendijkGenevaux, P.S., Bordoli, L., Gattiker, A., De Castro, E., Bucher, P., and Bairoch, A. 2004.

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Recent improvements to the PROSITE database. Nucleic Acids Res. 32:D134-D137. Jones, D.T. 1999. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292:195-202. Kelley, L.A., MacCallum, R.M., and Sternberg, M.J. 2000. Enhanced genome annotation using structural profiles in the program 3D-PSSM. J. Mol. Biol. 299:499-520. Krogh, A., Larsson, B., von Heijne, G., and Sonnhammer, E.L. 2001. Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes. J. Mol. Biol. 305:567-580. Letunic, I., Copley, R.R., Schmidt, S., Ciccarelli, F.D., Doerks, T., Schultz, J., Ponting, C.P., and Bork, P. 2004. SMART 4.0: Towards genomic data integration. Nucleic Acids Res. 32:D142D144. Marchler-Bauer, A., Anderson, J.B., DeWeeseScott, C., Fedorova, N.D., Geer, L.Y., He, S., Hurwitz, D.I., Jackson, J.D., Jacobs, A.R., Lanczycki, C.J., Liebert, C.A., Liu, C., Madej, T., Marchler, G.H., Mazumder, R., Nikolskaya, A.N., Panchenko, A.R., Rao, B.S., Shoemaker, B.A., Simonyan, V., Song, J.S., Thiessen, P.A., Vasudevan, S., Wang, Y., Yamashita, R.A., Yin, J.J., and Bryant, S.H. 2003. CDD: A curated Entrez database of conserved domain alignments. Nucleic Acids Res. 31:383-387. Marchler-Bauer, A. and Bryant, S.H. 2004. CDSearch: Protein domain annotations on the fly. Nucleic Acids Res. 32:W327-W331.

Murzin, A.G., Brenner, S.E., Hubbard, T., and Chothia, C. 1995. SCOP: A structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol. 247:536540. Schatz, G. and Dobberstein, B. 1996. Common principles of protein translocation across membranes. Science 271:1519-1526. Schultz, J., Milpetz, F., Bork, P., and Ponting, C.P. 1998. SMART, a simple modular architecture research tool: Identification of signaling domains. Proc. Natl. Acad. Sci. U.S.A. 95:5857-5864. Sigrist, C.J., Cerutti, L., Hulo, N., Gattiker, A., Falquet, L., Pagni, M., Bairoch, A., and Bucher, P. 2002. PROSITE: A documented database using patterns and profiles as motif descriptors. Brief. Bioinform. 3:265-274. Sj¨olander, K. 2004. Phylogenomic inference of protein molecular function: Advances and challenges. Bioinformatics 20:170-179. Tatusov, R.L., Natale, D.A., Garkavtsev, I.V., Tatusova, T.A., Shankavaram, U.T., Rao, B.S., Kiryutin, B., Galperin, M.Y., Fedorova, N.D., and Koonin, E.V. 2001. The COG database: New developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Res. 29:22-28.

Contributed by Nandini Krishnamurthy and Kimmen V. Sj¨olander University of California Berkeley, California

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Minimotif Miner: A Computational Tool to Investigate Protein Function, Disease, and Genetic Diversity

UNIT 2.12

Investigating protein function is a very time-consuming, expensive process. Thus, computational tools that aid in prediction of protein function are valuable resources. The Minimotif Miner (MnM) Web site (http://mnm.engr.uconn.edu) has collected information on a large number of short functional motifs, from other databases and primary literature, into a single database. Short motifs are <15 amino acids long, distinguishing them from longer protein domains. The MnM Web tool searches protein queries for the presence of these motifs. Scoring by several approaches allows users to rank and select motifs. By cross-mapping motifs with single nucleotide polymorphisms or disease-associated mutations, users can identify motifs that are altered and generate new hypotheses for mechanisms of genetic diversity and disease. Importantly, MnM is a tool for identifying short functional motifs, not for identifying protein domains, for which numerous resources already exist that are discussed in other units of this chapter (e.g., UNITS 2.3, 2.7, & 2.11). This unit employs Grb2 as an example to describe how MnM can be used to investigate protein function (see Mapping and Ranking Motifs with Minimotif Miner, below). Grb2 is a adaptor protein comprising SH2 and SH3 domains, which links growth-factor receptors to the MAP kinase signal transduction pathways, among other functions (Davis, 1995; Wang and Moran, 1996; Yoon et al., 1997). Analysis of Grb2 with MnM predicts a number of previously known motifs and several motifs that have yet to be studied. Since the definitions of short motifs, by their nature, have lower complexity and higher redundancy than larger protein domains, false-positive predictions are a limitation. Therefore, this unit provides a description of how scores based on increased frequency of occurrence, prediction of motifs on the protein surface, and evolutionary conservation can be used to rank motifs, thereby reducing false-positive predictions. If the protein studied has a known three-dimensional structure, then short motifs predicted by MnM or other short-motif-prediction programs can be mapped onto the surface of the protein (see Mapping Motifs onto Protein Surfaces). A protocol for using PyMOL (a structural display program) to map motifs onto protein surfaces is provided. This approach is much more accurate for identifying motifs on the protein’s surface than the surface prediction score, another approach that MnM uses for ranking motifs (see Interpretation and Selecting Motifs, below). This analysis can also provide insight into the spatial relationship among motifs. The results of MnM can be cross-mapped with known mutations or polymorphisms (see Using Minimotif Miner to Investigate Disease and Genetic Diversity, below). This allows researchers to determine whether mutations identified in patients with a disease perturb an existing motif or introduce a new motif into the protein of interest. This effectively allows investigators to generate new hypotheses for disease pathology. Similarly, if polymorphisms in the coding region of a protein alter the presence or absence of a motif in a protein, this analysis can provide new hypotheses of how such genetic diversity encodes different phenotypes.

Computational Analysis Contributed by Martin R. Schiller Current Protocols in Protein Science (2007) 2.12.1-2.12.14 C 2007 by John Wiley & Sons, Inc. Copyright


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MAPPING AND RANKING MOTIFS WITH MINIMOTIF MINER Input page. The MnM input window accepts protein sequence queries as RefSeq accession numbers or as protein sequences (Fig. 2.12.1; Balla et al., 2006). RefSeq accession numbers for proteins can be readily retrieved using a hyperlink on the input page to the HomoloGene Web site, which can be searched with keywords (Wheeler et al., 2000). Using Grb2 as an example, this keyword retrieves HomoloGene Cluster 1576. This cluster contains RefSeq accession numbers for human Grb2 (NP 002077) and its orthologs from several other species (Pruitt and Maglott, 2001). If using a RefSeq number, the species of the protein sequence should be selected from the pull-down menu on the input page. This is important only for scoring and ranking motifs on the results page. Another pull-down menu allows the user to restrict motifs to the subcellular compartment where they are found. The user then submits the query. Results page. The Results page contains a Protein Sequence window, Motif Table, and Protein Details Table. The protein sequence is initially displayed with all identified motifs highlighted magenta. When human Grb2 is analyzed, 40 different motifs are highlighted. Pop-up balloons for each motif can be visualized by holding the mouse cursor over the motif. The second window on the Results page is a Motif Table that contains information about the motifs highlighted in the sequence window. This table shows the identity of the motif that was found in the protein sequence, the expected and observed number of each motif in the proteome selected from the input page, a ratio of these numbers (Enrichment factor), a short annotation of the motif function, the positions of the first amino acid of the motif in the Grb2 sequence, and a hyperlinked reference to at least one paper that published the consensus motif definition. Selection of a radio button and the View Selected Motif button will highlight only the single selected motif in the Protein Sequence window. The Results page also contains a Protein Details Table that contains database hyperlinks related to the query (Fig. 2.12.2). The content of this table is only displayed if a RefSeq accession number is entered on the input page.

Minimotif Miner

Figure 2.12.1 MnM Input page, which is used to enter queries. Simplified instructions for entering a query are numbered. Queries can be either a RefSeq accession number (2a) or a protein sequence (2b).

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Figure 2.12.2 Protein Details Table. The accession number for Grb2 was entered on the Input page (see Fig. 2.12.1) and the table shown here was returned, containing relevant information about the query. Database cross-referencing is output only with entry of a RefSeq accession number.

Figure 2.12.3 Protein Sequence window of the MnM results page. Buttons under the window allow mapping of domains (cyan-, green- and yellow-highlighted text) and polymorphisms (capitalized, with blue highlighting). These functions only work with entry of a RefSeq accession number. For the color version of this figure go to http://www.currentprotocols.com.

The Results page has several interactive buttons to aid the user in investigating the identified short motifs. A RefSeq accession number must be used on the input page for these buttons to become active. The View SNPs button will display any known single nucleotide polymorphisms (SNPs) from the dbSNP database with a blue highlight (Fig. 2.12.3; Wheeler et al., 2000). Holding the cursor over the highlighted region reveals a pop-up balloon with more information about the SNP, including its ID number. In the case of human Grb2, SNP-ID # 11552057 is retrieved, which is an allelic variation producing either a Phe or Ile at amino acid 19. The View Domains button will display any protein domains defined by the NCBI Conserved Domain Database (CDD) annotated in the RefSeq database (Marchler-Bauer et al., 2003). These are highlighted yellow, green, or cyan. Again, holding the cursor over the highlighted domain reveals a pop-up balloon

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Table 2.12.1 Scores for ranking short motifs in Grb2a

Motif

Annotation

FS

ECS

SPS

YXXP

SH2 ligand(PLCγ)

2.8

−1.55

0

YXXM

SH2 ligand(PI3K)

8.6

0.56

0

PXXP

SH3 ligand

1.1

−11.1

1

RXX[S,T]V

CamKII substrate

9.8

0.57

1

RXSXSP XP

14-3-3 ligand

172

0.53

1

LXXRR

Erk docking site

13.5

0.56

1

[W,F]W

EH binding motif

7.6

0.56

0

a Abbreviations: FS, frequency score; ECS, evolutionary conservation score; SPS, surface prediction score.

Superscript “P” indicates a phosphorylated residue.

with information about the domain. In the case of Grb2, one SH2 and two SH3 domains are revealed. If there are no SNPs or domains present in the query, then the text in these buttons becomes gray. A View Homologous Proteins button will analyze all orthologs in the same HomoloGene cluster as the query. This analysis adds new information to the Motif Table and sorts the motifs according to degree of evolutionary conservation. The second column of the table now lists the percentage of species that contain the motif and the accession numbers and Taxon IDs of orthologs in the HomoloGene cluster (Wheeler et al., 2000). Taxon IDs are numerical identifiers of species in the Taxonomy database, and these definitions can be retrieved from the MnM help page or from the Taxonomy database (Wheeler et al., 2000). In addition to the frequencies of each motif in the proteome, the expected and observed occurrences and ratio of these numbers for each motif in the query are shown. This is discussed further in the Interpretation and Selecting Motifs section, below. Analysis of Grb2 reveals four Grb2 orthologs from mouse, rat, mosquito, and fly. The 14-3-3 binding motif at the top of the Motif Table is conserved at the same position in Grb2 from all four species, and thus has a conservation of 100%, which is shown in Table 2.12.1.

INTERPRETATION AND SELECTING MOTIFS Three scores are determined for ranking motifs. Above the Motif Table there are buttons for Frequency Score (FS), Surface Prediction Score (SPS), and Evolutionary Conservation Score (ECS). These scores should be used in combination with the known biology of the protein to select motifs for further study.

Frequency Score (FS) FS represents the frequency of occurrence of the motif in the query when compared to all protein sequences in the entire proteome; higher scores are better. An FS >5 is globally more likely to be experimentally confirmed; however motifs with scores <5 are also commonly confirmed and should not be ruled out based on FS alone. Analysis of >2300 experimentally identified motifs that are annotated in the Swiss-Prot database shows that FS is globally larger for motifs that have been experimentally confirmed than for predicted motifs. An example of a prediction with a high FS came from the analysis of EFF-1, a protein that is necessary for fusion of body-wall muscle cells in C. elegans. MnM analysis of EFF-1 revealed a 14-3-3 motif with a high FS score that was subsequently confirmed by biochemical and genetic analyses (Balla et al., 2006). FS has the advantage that it favors motifs rarely found in other proteins, and also motifs Minimotif Miner

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that have multiple copies in a single protein. In many cases, short motifs are found in multiple copies, as exemplified by proline-rich regions with multiple motifs that bind SH3 domains. FS is limited by compartment specificity of motifs, protein surface accessibility of motifs, and random occurrence of motifs, and favors motifs with high complexity and low degeneracy. Therefore, other means of ranking motifs should be used in conjunction with the frequency score. Analysis of human Grb2 yields 40 motifs with FS values ranging from 0.4 to 172. The FS for several motifs in Grb2 are shown in Table 2.12.1. The 14-3-3 motif, which has a score of 172, would be considered more likely to be of biological relevance than the SH3 ligand motif, which has a score of only 1.1 A score of 1.0 would indicate a motif observed at its expected statistical frequency.

Surface Prediction Score (SPS) Given their functions, short motifs should have at least some, if not all, residues on the protein surface. Thus, minimotifs with buried residues are less likely to be functional. The SPS provides a measure of how likely a motif is to be on the protein surface. The scores range between 0 and 1.0, with 1.0 indicating a higher likelihood of the motif being on the surface. Surface prediction was confirmed with two datasets containing nearly 1000 protein structures, and produced an accuracy of 65% to 75% (Balla et al., 2006). For analysis of human Grb2, SPS ranges from 0 to 1.0. A subset of SPS for the 40 identified motifs in Grb2 is shown in Table 2.12.1. It would be expected that the Erk docking site, with a SPS of 1, would be more likely to be on the surface and less likely to be false-positive than the predicted PI3K SH2 ligand, which has an SPS of 0 and is predicted to be buried. Since the three-dimensional structure of Grb2 is known, the motifs shown in Table 2.12.1 were mapped onto backbone and surface plots of the Grb2 crystal structure using MolMol (Fig. 2.12.4; Maignan et al., 1995; Koradi et al., 1996). The two Ser residues in a 14-3-3 binding motif (blue) are surface-exposed, and thus are accessible for Grb2 phosphorylation and binding to 14-3-3. This motif had one of the highest SPS scores. Similarly, residues in the Erk docking site (orange) are exposed on the surface of the Grb2 SH2 domain and had the highest SPS score. The SPS score has limitations. Some motifs may require a specific structure beyond being on the surface, and just because a motif is on the surface does not necessarily mean it is functional. Furthermore, this surface prediction algorithm yields ∼25% to 35% false-positive predictions. Therefore, it is highly recommended that, for proteins with known structures in the PDB, the motifs be mapped onto the surface of the protein. A simplified description on using the PyMOL program to map motifs on protein surfaces is described under Mapping Motifs onto Protein Surfaces, below (also see Delano, 2004). Minimally, SPS should be used in conjunction with other scoring methods.

Evolutionary Conservation Score (ECS) As a quantitative alternative to the View Homologous Proteins function, ECS serves as a quantitative measure of evolutionary conservation of short motifs. The ECS score is generated from a series of BLAST alignments (Balla et al., 2006). This algorithm generates positive or slightly negative scores where a motif is conserved, and strongly penalizes, with relatively large negative scores, if a motif is missing in homologs. The algorithm also takes into account the overall conservation of homologs. ECS, like the FS, has been validated. Examples of ECS scoring for Grb2 are shown in Table 2.12.1. All motifs in Table 2.12.1 have slightly positive or negative ECS values, reflecting conservation of the motif in most

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Figure 2.12.4 Location of conserved minimotifs on the three-dimensional structure of human Grb2. Minimotifs shown in Table 2.12.1 were mapped onto the crystal structure of human Grb2 (1GRI) using MolMol (Koradi et al., 1996). Grb2 contains two SH3 domains (yellow and cyan) and one SH2 domain (light pink). Backbone/stick diagrams (top) and surface plots (bottom) are rotated 180o about the z axis (right). Domains are labeled in the bottom right structure representation. Motifs mapped onto the Grb2 structure are colored: SH2 ligand (PI3K) (red), 14-3-3 ligand (blue), CamKII phosphorylation site (magenta), SH3 ligand (brown), Erk docking site (orange), EF hand (green), and SH2 binding motif(PLCγ) (yellow). For the color version of this figure go to http://www.currentprotocols.com.

of the Grb2 orthologs. An alignment of several Grb2 orthologs is shown in Figure 2.12.5. The PXXP motif has an ECS of −10, reflecting its poor conservation in only 2 of the 6 orthologs. Given the low FS and ECS values for the PXXP motif, it is less likely to be biologically relevant in Grb2. Since evolutionary conservation is considered an important indicator of functional regions in proteins, ECS or View Homologous Proteins can be used to select motifs for further study. However, it must be kept in mind that motifs may be utilized differently among divergent species or that species can have slightly different motif definitions. For example KDEL> is an endoplasmic reticulum retention signal in mammals, while HDEL> is used in yeast (Pelham, 1991; Townsley et al., 1994). In these motif definitions “>” indicates the motif must be on the C-terminus of the protein. The user should be aware that ECS scores are computationally intensive and only currently available through e-mail. In addition to using scoring to select motifs for further study, it is also important to incorporate information about the known biology of the protein. Some of the highscoring motifs in Grb2 participate in functions already known to be associated with the MAP kinase signaling pathway and are consistent with the known biological function of Grb2. For example, the Erk docking site and 14-3-3 binding sites have relatively high FS, ECS, and SPS scores. Erk is one of the kinases in the MAP kinase cascade, and 14-3-3 is already known to bind to Raf, another kinase in the MAP kinase pathway (Davis, 1995; Thorson et al., 1998). It is expected that motifs with functions highly related to the known biology of the protein query are less likely to be false-positive predictions. Minimotif Miner

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Figure 2.12.5 Alignment of Grb2 protein sequences. Alignment of human, mouse, rat, worm, fly, and mosquito Grb2 sequences generated with a multiple sequence alignment tool and Boxshade (http://www.ch.embnet.org/software/BOX doc.html ). SH2 domains are colored green, SH3 domains are colored cyan, and novel minimotifs identified by SMS analysis are colored red. For the color version of this figure go to http://www.currentprotocols.com.

MAPPING MOTIFS ONTO PROTEIN SURFACES If the protein studied has a known three-dimensional structure, then short motifs predicted by MnM or other short-motif prediction programs can be mapped onto the surface of the protein. The SPS score alone has an accuracy of only 65% to 75% for predicting the surface expression of a particular motif. Therefore, if the Protein Data Bank (PDB) contains a structure for the protein being studied, mapping the motifs onto the surface of the protein is a superior approach. Furthermore, by mapping motifs onto the protein surface, additional motif information can be gained to help select other motifs for further consideration. In cases where the structure is known, the spatial relationships of motifs

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can be determined. The structure of a particular motif can be compared to other structures in which the motif ligand is bound to its protein partner in order to further reduce falsepositive predictions. Another consideration pertains to multidomain proteins such as Src, where a motif may be buried between domains and may become accessible only upon other protein-protein interactions (Lerner and Smithgall, 2002; Cowan-Jacob et al., 2005). An example of motif mapping onto the surface of Grb2 is shown in Fig. 2.12.4. Below, a simplified protocol is described for using PyMOL protein structure visualization software to map short motifs onto the surface of proteins with known structure (Delano, 2004). PyMOL is shareware software and instructions for downloading and installing PyMOL are available from the PyMOL Web site (http://pymol.sourceforge.net/). Once the program is installed, follow the instructions below, as done for the Grb2 PDB file 1GRI.pdb similar to Figure 2.12.4 (Maignan et al., 1995). These are simplified instructions; a comprehensive manual is available from the PyMOL Web site (Delano, 2004). MolMol or other structure-plotting programs can also be used to generate surface plots and map motifs (Koradi et al., 1996). 1. Download and save the relevant .pdb file from the Protein Data Bank (1GRI.pdb for Grb2). 2. Download and install PyMOL from http://pymol.sourceforge.net/. 3. Open the PyMOL program. There is a Command Window and Viewer Window.

4. In the Command Window, locate the menu bar and select Open from the File menu, then select the downloaded .pdb file. 5. In the Command Window, choose Display from the menu bar and select Sequence to display the protein sequence in the Viewer Window. 6. In the upper right corner of the Viewer Window, click the “H” button, then select “everything” from the menu that appears. This will hide the molecule.

7. In the upper right corner of the Viewer Window, click the “S” button, then select “ribbon” from the menu that appears. This will show a ribbon plot of the protein molecule.

8. In the Viewer Window, select motif residues in the sequence with the mouse. A new selection (sele) will appear on the right side of window.

9. In the upper right corner of the Viewer Window, to the right of the PDB name, click the “C” button, then choose the color for the backbone. 10. In the upper right corner of the Viewer Window, to the right of “sele,” click the “A” button, choose “rename selection,” type the new motif name, and click Enter, to name the motif. 11. In the upper right corner of the Viewer Window, to the right of the motif name, click the “S” Button, then select “sticks” from the menu that appears to show the motif residues. 12. In the upper right corner of the Viewer Window, to the right of the motif name, click the “C” button, then choose the color for the motif residues. Minimotif Miner

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13. In the upper right corner of the Viewer Window, to the right of the PDB name, click the “S” button, then select “surface” to show the surface. 14. Repeat steps 8 to 12 to color other motifs. 15. Use the mouse to rotate the molecule (by dragging) or zoom the molecule (via the pop-up menu) to view motif(s) on the protein surface. 16. In the upper right corner of the Viewer Window, to the right of the PDB name, click the “H” button, then select “surface” to hide the surface and determine if residues are buried.

USING MINIMOTIF MINER TO INVESTIGATE DISEASE AND GENETIC DIVERSITY If a user is interested in quantitative trait loci, disease-associated mutations, or SNPs that contribute to plant, animal, and human diversity, MnM can be used to investigate the mechanisms by which these substitutions induce a change in phenotype. MnM is useful not only for analyzing missense and nonsense mutations or substitutions, but also for analyzing mutations that affect splicing and use of alternative start/stop codons. Mutations and allelic variations can be identified from numerous resources. Although beyond the scope of this unit, some examples of these databases are provided. The Human Genome Mutation Database (HGMD; http://www.hgmd.cf.ac.uk/ac/index.php) contains over 30,000 mutations that are associated with a variety of diseases (Cooper et al., 1998). The molecular genetics section of the Online Mendelian Inheritance in Man (OMIM; (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM) database entries often has a summary of papers that describe allelic variation and diseaseassociated mutations. There are also a fairly large number of smaller databases focused on a specific diseases; these databases contain published and unpublished mutations (Claustres et al., 2002). MnM will map single nucleotide polymorphisms from dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/), which contains millions of missense SNPs from several species. There are several additional sources of SNPs. Quantitative trait loci (QTL) are often mapped with SNPs and numerous smaller projects provide a source of additional SNPs (Teufel et al., 2006). A list of many SNP databases can be found at http://hgvbase.cgb.ki.se/databases.htm. Once the protein and mutations/polymorphisms of interest are identified, the amino acid changes resulting from these mutations should be mapped onto the protein sequence. This can be compared to a motif analysis of the protein query by MnM. That comparison will allow the investigator to identify motifs eliminated by mutations in the protein query. To determine whether any motifs are introduced by a mutation, the sequence containing the mutations must be used on the input page. An example analysis of motifs eliminated by mutations in presenilin 1 is shown in Figure 2.12.6. Certain mutations in the presenilin 1 gene are associated with Alzheimer’s disease (Cruts et al., 1996). An alignment of the presenilin 1 proteins from several species is shown in Figure 2.12.6. Mutations found in patients with Alzheimer’s disease are highlighted red. A small subset of the motifs that were identified by analysis with MnM are indicated. Figure 2.12.6 shows that some mutations lie within motifs. For example, mutations have been identified in patients for all three consensus positions of a class II SH3 binding motif. Thus, one arrives at the novel hypothesis that binding of an SH3 domain-containing protein to presenilin 1 is important for its role in disease. Similar analysis can be done for almost all genes implicated to play a role in development or progression of disease. Computational Analysis

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Figure 2.12.6

Legend at right.

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DISCUSSION Predicting protein domains using consensus motifs or positional scoring matrices has proven useful for extrapolating protein function (Letunic et al., 2002; Bateman et al., 2004). This is primarily because there is a very low false-positive domain prediction and a minimal number of false-negative results. Shorter motifs that confer binding, trafficking, or post-translational modification activities to proteins can be equally as important as a functional domain. Because short motifs, by their nature, have low complexity, many false-positive results are expected when trying to correlate motif prediction with function. This is the likely reason why short-motif prediction has not yet developed into as useful an approach as domain prediction. MnM can be used to investigate new functions in proteins as well as functional consequences of mutations or genetic variability. Short motif prediction has several advantages. Because motif prediction is computationally driven, it is fast. For analysis of a typical protein, 50 to 100 motifs are predicted within seconds. Another advantage is that the hypotheses generated with MnM are readily testable. By simply introducing a point mutation that eliminates the short motif and testing for the known function of the motif, it is fairly straightforward to design and perform experiments that test MnM predictions. While there are several Web tools for predicting short motifs, there are two main advantages in using MnM over other motif-searching programs. (1) MnM provides a near-comprehensive view of motifs that are present in other smaller databases. Since most researchers are not aware of several hundred short functional motifs, this Web resource allows investigators to identify new potential functions in proteins. (2) MnM also provides several means for ranking motifs using independent pieces of information. This is important considering that the false-positive rate for short motif prediction is relatively high when compared to protein domain prediction. The most critical consideration in prediction of short motifs is the false-positive rate. There are several other tools that can be used to study motifs. These should be used in conjunction with MnM when ever possible. MnM is the most comprehensive short motif database with >300 nonredundant motifs. Scansite (http://scansite.mit.edu/ motifscan seq.phtml) predicts motifs using position-specific scoring matrices that were built from phage display and random peptide library data (Obenauer et al., 2003). Scansite, while less comprehensive than MnM with 63 motifs, has an advantage over MnM in that experimental data are used to generate the position-specific scoring matrices used for motif prediction. The Eukaryotic Linear Motif (ELM; http://elm.eu.org/) server has ∼80 motifs and several functions for examining motifs that MnM does not have (Puntervoll et al., 2003). “ELM instance” indicates experimentally validated motifs. This server links to pattern-hit initiated BLAST (phi-BLAST; (http://www.ncbi.nlm.nih.gov/blast/), which can be used to search for other proteins that contain the exact sequence of the experimentally identified instance of the motif. The ELM globular domain filter allows users Figure 2.12.6 (at left) Short motifs in presenilin 1 eliminated by mutations in patients with Alzheimer’s disease. Presenilin 1 sequences from human (NP 000012), cow (NP 777146), dog (AAR97724), mouse (NP 032969), rat, (NP 062036), frog (AAH64246), and zebrafish (AAH54639) were aligned and colored using multiple sequence alignment (Smith et al., 1996) and Boxshade (http://www.ch.embnet.org/software/BOX doc.html). Amino acids highlighted black are highly conserved; those highlighted gray are conservative substitutions; those highlighted white are not conserved; and those highlighted red are mutated in human patients with Alzheimer’s disease (http://www.alzforum.org). Amino acids with text colored green are minimotifs that overlap with disease-associated mutations, and those with cyan text are of interest to presenilin function. Motifs are annotated in colored text. For the color version of this figure go to http://www. currentprotocols.com.

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to search only interdomain regions that are thought to be more likely to contain functional motifs (Puntervoll et al., 2003). A few motifs are known to have species-specific consensus definitions, and the ELM server allows species-specific searches. The BLAST Web site has a link with preset options that allows users to search for short, nearly exact matches. In practice, this is useful for motifs of >4 to 5 residues in length (Altschul et al., 1997). The myHITS Web site (http://myhits.isb-sib.ch) is more specialized for this purpose and can be used to search several databases for user-entered motifs that contain degenerate motif definitions using Prosite syntax (Pagni et al., 2001; Falquet et al., 2002). Some motifs such as ubiquitination, O-glycosylation, sulfation, and GPI-anchor attachment are not readily reduced to consensus sequence definitions. Thus, individual servers focused on these motifs can be used to investigate several motifs. A number of these motif predictors are collected at the ExPASy Proteomics Server (Schneider et al., 2004; http://www.expasy.org/). There are also several Web sites that focus on a particular type of motif such as myristoylation, phosphorylation, and proteolysis (Schneider et al., 2004). These sites will generally be superior to MnM, Scansite, and ELM in their ability to predict these specific motifs. The intention of MnM is to inform users of potential functions rather than to flawlessly predict them. Thus, in selecting a motif for experimental analysis, it is recommended that the evolutionary conservation, location of protein surface, increased frequency of occurrence, location in interdomain regions, predictions by other motif programs, and known biology of the protein query all be used to select motifs for study. MnM and other short-motif analysis programs are for prediction, and should be used to generate experimental leads and new hypotheses. All short-motif programs, including MnM, yield a significant number of false-positive predictions, which is the major limitation in motif predictions. MnM, ELM, and Scansite each attempt to limit falsepositive predictions in different ways; thus, using each program will provide additional information in selecting the best motifs for experimental pursuit. It must also be taken into consideration that, for the majority of motifs, every possible amino acid substitution at each position has not been examined; thus, most definitions could possibly be more specific. Furthermore, a number of motifs have been examined using random peptide libraries, or by phage display. While this does assess most, if not all, sequence permutations of the motif, the peptide is out of context of the native protein and may not adopt a structural orientation that is necessary for binding. Thus, motif definitions determined by this approach may also yield false-positive results. Despite these limitations, short-motif analysis will produce many new leads for investigating function. For example, analyzing human dynamin, p53, paxillin, prion, and vAbl with MnM produces 277 motif predictions, of which 18 ± 4% have been previously experimentally confirmed.

ACKNOWLEDGEMENTS This research was funded in part by NIH grant MH65567 to Martin Schiller, grants CCR-9912395 and ITR-0326155 to Sanguthevar Rajasekaran, and ES09847 to Michael Gryk.

LITERATURE CITED Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. 1997. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25:3389-3402. Minimotif Miner

Balla, S., Thapar, V., Luong, T., Faghri, T., Huang, C.H., Rajasekaran, S., del Campo, J.J., Shin,

J.H., Mohler, W.A., Maciejewski, M.W., Gryk, M., Piccirillo, B., Schiller, S.R., and Schiller, M.R. 2006. Minimotif Miner, a tool for investigating protein function. Nat. Methods 3:175177. Bateman, A., Coin, L., Durbin, R., Finn, R.D., Hollich, V., Griffiths-Jones, S., Khanna, A., Marshall, M., Moxon, S., Sonnhammer, E.L.,

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Studholme, D.J., Yeats, C., and Eddy, S.R. 2004. The Pfam protein families database. Nucleic Acids Res. 32:D138-D141. Claustres, M., Horaitis, O., Vanevski, M., and Cotton, R.G.H. 2002. Time for a unified system of mutation description and reporting: A review of locus-specific mutation Databases. Genome Res. 12:680-688. Cooper, D.N., Ball, E.V., and Krawczak, M. 1998. The human gene mutation database. Nucleic Acids Res. 26:285-287. Cowan-Jacob, S.W., Fendrich, G., Manley, P.W., Jahnke, W., Fabbro, D., Liebetanz, J., and Meyer, T. 2005. The crystal structure of a c-Src complex in an active conformation suggests possible steps in c-Src activation. Structure 13:861871. Cruts, M., Hendricks, L., and Broeckhoven, C. 1996. The Presenilin genes: A new gene family involved in Alzheimer’s disease pathology. Hum. Mol. Genet. 5:1449-1455. Davis, R.J. 1995. Transcriptional regulation by MAP kinases. Mol. Reprod. Dev. 42:459467. Delano, W.L. 2004. Use of PYMOL as a communications tool for molecular science. Abstr. Pap. Am. Chem. Soc. 228:U313-U314. Falquet, L., Pagni, M., Bucher, P., Hulo, N., Sigrist, C.J., Hofmann, K., and Bairoch, A. 2002. The PROSITE database: Its status in 2002. Nucleic Acids Res. 30:235-238. Koradi, R., Billeter, M., and Wuthrich, K. 1996. MOLMOL: A program for display and analysis of macromolecular structures. J. Mol. Graph. 14:29-32; 51-55. Lerner, E.C. and Smithgall, T.E. 2002. SH3dependent stimulation of Src-family kinase autophosphorylation without tail release from the SH2 domain in vivo. Nat. Struct. Biol. 9:365369.

cell signaling interactions using short sequence motifs. Nucleic Acids Res. 31:3635-3641. Pagni, M., Iseli, C., Junier, T., Falquet, L., Jongeneel, V., and Bucher, P. 2001. trEST, trGEN and Hits: Access to databases of predicted protein sequences. Nucleic Acids Res. 29:148-151. Pelham, H.R.B. 1991. Recycling of proteins betweeen the Endoplasmic Reticulum and the Golgi Complex. Curr. Opin. Cell Biol. 3:585591. Pruitt, K.D. and Maglott, D.R. 2001. RefSeq and LocusLink: NCBI gene-centered resources. Nucleic Acids Res. 29:137-140. Puntervoll, P., Linding, R., Gemund, C., ChabanisDavidson, S., Mattingsdal, M., Cameron, S., Martin, D.M., Ausiello, G., Brannetti, B., Costantini, A., Ferre, F., Maselli, V., Via, A., Cesareni, G., Diella, F., Superti-Furga, G., Wyrwicz, L., Ramu, C., McGuigan, C., Gudavalli, R., Letunic, I., Bork, P., Rychlewski, L., Kuster, B., Helmer-Citterich, M., Hunter, W.N., Aasland, R., and Gibson, T.J. 2003. ELM server: A new resource for investigating short functional sites in modular eukaryotic proteins. Nucleic Acids Res. 31:3625-3630. Schneider, M., Tognolli, M., and Bairoch, A. 2004. The Swiss-Prot protein knowledgebase and ExPASy: Providing the plant community with high quality proteomic data and tools. Plant Physiol. Biochem. 42:1013-1021. Smith, R.F., Wiese, B.A., Wojzynski, M.K., Davison, D.B., and Worley, K.C. 1996. BCM Search Launcher: An integrated interface to molecular biology data base search and analysis services available on the World Wide Web. Genome Res. 6:454-462. Teufel, A., Krupp, M., Weinmann, A., and Galle, P.R. 2006. Current bioinformatics tools in genomic biomedical research. Int. J. Mol. Med. 17:967-973.

Letunic, I., Goodstadt, L., Dickens, N.J., Doerks, T., Schultz, J., Mott, R., Ciccarelli, F., Copley, R.R., Ponting, C.P., and Bork, P. 2002. Recent improvements to the SMART domain-based sequence annotation resource. Nucleic Acids Res. 30:242-244.

Thorson, J.A., Yu, L.W.K., Hsu, A.L., Shih, N.Y., Graves, P.R., Tanner, J.W., Allen, P.M., PiwnicaWorms, H., and Shaw, A.S. 1998. 14-3-3 proteins are required for maintenance of Raf-1 phosphorylation and kinase activity. Mol. Cell. Biol. 18:5229-5238.

Maignan, S., Guilloteau, J.P., Fromage, N., Arnoux, B., Becquart, J., and Ducruix, A. 1995. Crystal structure of the mammalian Grb2 adaptor. Science 268:291-293.

Townsley, F.M., Frigerio, G., and Pelham, H.R.B. 1994. Retrieval of Hdel proteins is Required for growth of yeast cells. J. Cell Biol. 127:21-28.

Marchler-Bauer, A., Anderson, J.B., DeWeeseScott, C., Fedorova, N.D., Geer, L.Y., He, S., Hurwitz, D.I., Jackson, J.D., Jacobs, A.R., Lanczycki, C.J., Liebert, C.A., Liu, C., Madej, T., Marchler, G.H., Mazumder, R., Nikolskaya, A.N., Panchenko, A.R., Rao, B.S., Shoemaker, B.A., Simonyan, V., Song, J.S., Thiessen, P.A., Vasudevan, S., Wang, Y., Yamashita, R.A., Yin, J.J., and Bryant, S.H. 2003. CDD: A curated Entrez database of conserved domain alignments. Nucleic Acids Res. 31:383-387.

Wheeler, D.L., Chappey, C., Lash, A.E., Leipe, D.D., Madden, T.L., Schuler, G.D., Tatusova, T.A., and Rapp, B.A. 2000. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 28:10-14.

Obenauer, J.C., Cantley, L.C., and Yaffe, M.B. 2003. Scansite 2.0: Proteome-wide prediction of

Wang, Z. and Moran, M.F. 1996. Requirement for the adapter protein Grb2 in EGF receptor endocytosis. Science 272:19355-1939.

Yoon, S.Y., Koh, W.S., Lee, M.K., Park, Y.M., and Han, M.Y. 1997. Dynamin II associates with Grb2 SH3 domain in Ras transformed NIH3T3 cells. Biochem. Biophys. Res. Comm. 234:539543.

Computational Analysis

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KEY REFERENCES Balla et al., 2006. See above. Describes the MnM database, algorithms used in scoring, and methods used to validation the scoring approaches.

INTERNET RESOURCES

http://elm.eu.org/ ELM: Online Web site for identifying short functional motifs. http://myhits.isb-sib.ch/cgi-bin/pattern search myHITS (pattern search): Searches several sequence databases for motifs.

http://mnm.engr.uconn.edu Minimotif Miner (MnM):,Online Web site for identifying short functional motifs.

http://www.ncbi.nlm.nih.gov/BLAST/ BLAST: Searches proteins for short, nearly exact matches.

http://pymol.sourceforge.net/ PyMOL: Shareware software for viewing motifs in protein structures.

http://scansite.mit.edu/motifscan seq.phtml SCANSITE: Online Web site for identifying short functional motifs.

http://hugin.ethz.ch/wuthrich/software/molmol/ index.html MolMol: Downloadable software for viewing protein structures.

http://ca.expasy.org/tools/ ExPASy: Contains a collection of tools for prediction of specific motifs.

http://www.rcsb.org/pdb/ Protein Data Bank (PDB): Repository of macromolecular structures. http://www.ncbi.nlm.nih.gov/omim/ McKusick, V.A. 2000. Online Mendelian Inheritance in Man (OMIM). McKusick-Nathans Institute for Genetic Medicine, Johns Hopkins University, (Baltimore, Md. and National Center for Biotechnology Information, National Library of Medicine, Bethesda, Md.

http://www.hgmd.cf.ac.uk/ac/index.php HGMD: The Human Genome Mutations Database, which contains a large collection of mutations known to be associated with human disease.

Contributed by Martin R. Schiller University of Connecticut Health Center Farmington, Connecticut

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CHAPTER 3 Detection and Assay Methods INTRODUCTION

T

he success or failure of protein-centered projects can frequently be traced to the quality of the analytical procedures used to characterize the sample at different stages. Qualitative and quantitative analysis can aid in definition of the sample for the purpose of designing separations. A knowledge of the properties of the desired protein (e.g., whether it has a high aromatic amino acid content) can suggest methods of analysis that will help locate the desired protein in a complex mixture. Establishing the properties of an isolated protein creates benchmarks that future researchers can use to evaluate their protocols and final product. Accurate quantitation of the amount of protein at the beginning, middle, and end of a series of steps is the only valid way to evaluate the overall yield of a procedure. The observation of significant loss of protein following a particular purification procedure, without a substantial increase in the purity of the desired protein, would indicate that the procedure should be omitted or revised.

Several spectroscopic procedures for characterizing protein samples are described in UNIT 3.1. Measuring the absorbance of the aromatic amino acids in a protein at different wavelengths yields a very useful measure of protein concentration. This is nondestructive and requires very little sample or time. A more qualitative, but much more sensitive, evaluation is provided by fluorescence spectroscopy. Another approach is amino acid analysis—qualitative analysis, to determine purity, and quantitative analysis, to provide concentrations—both of which are presented in UNIT 3.2. Procedures are also given for calculating amino acid ratios from primary analytical data. Significant advances that have improved the precision and sensitivity of amino acid analysis have reinvigorated this method, which had for some years been neglected. A common method for enhancing the sensitivity of protein detection is to radiolabel the proteins, as detailed in UNIT 3.3. This approach may also be employed to help quantitate the binding of peptide to other molecules or for radioimmunoassays. Several procedures for iodination (introduction of 125I or 131I) are presented; the choice between them will depend on the sample type, the content of residues sensitive to oxidation, and the amino acids that are critical to function. Procedures for handling radioactive samples, for separating labeled from unlabeled peptides, and for iodination to equimolar or higher levels (using a mixture of radioactive and nonradioactive iodine isotopes) are also provided. When Tyr or His residues are absent, or cannot be iodinated because they are critical for function, alternative methods involving the incorporation of 14C or 3H may be employed instead. The methods described include labeling by acetylation at amino groups on lysine or at the amino terminus using the Bolton-Hunter reagent or acetic anhydride, by reductive alkylation, and by labeling at Lys residues with iodoacetic acid or iodoacetamide. Two synthetic methods, reduction of a peptide containing iodo-Tyr residues and incorporation of labeled amino acids, are also presented. Measurement of the total protein content of a sample is discussed in UNIT 3.4. Three methods based on reduction of Cu2+ to Cu+ followed by chelation of the cuprous ion by various reagents (biuret, Hartree-Lowry, and bicinchinonic acid) are presented. In addition, a quantitative measure of total protein based on protein hydrolysis and the ninhydrin Contributed by Ben M. Dunn Current Protocols in Protein Science (2001) 3.0.1-3.0.2 Copyright © 2001 by John Wiley & Sons, Inc.

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reaction is described. The use of UV absorption, Coomassie dye binding, and weight analysis to determine protein content complete the presentation. Important considerations for performing these assays, such as dealing with interfering substances, are detailed throughout the unit. Some aspects involved in the design of enzyme assays are discussed in UNIT 3.5. Parameters that affect both enzyme and substrate are evaluated and suggestions to optimize the quantitation of enzymatic activity are enumerated. The proper concentration range for conducting initial velocity measurements is presented, and different methods of data presentation and analysis are discussed. Tagging a protein with a group that permits facile detection is an excellent way, for example, to study the movement of a protein through internalization or compartmentalization. A variety of reagents are available for attaching biotin to functional groups or the surface of a protein (UNIT 3.6). Detection and localization can be achieved by the strong and specific binding of avidin or streptavidin. These reactions can also be used to label proteins at cell surfaces. Another highly sensitive approach to the detection of proteins is the incorporation of radiolabeled amino acids into proteins. Integration of a particular radiolabeled amino acid (i.e., [35S]D-methionine or [3H]leucine) can be achieved using the normal cellular machinery (UNIT 3.7). Both pulse-labeling methods that permit study of cellular trafficking and long-term labeling to increase the detection of relatively rare proteins are described. The occurrence of selenium in proteins in the form of selenocysteine is now recognized as essential for several enzymatic functions. Therefore, methods for analysis of selenocysteine in proteins have become more important (UNIT 3.8). A future update will consider selenomethionine incorporation and analysis, as it can be helpful in the determination of three-dimensional structure by X-ray diffraction methods. The availability of genomic sequences for a growing number of organisms has magnified the need for techniques to analyze the protein content of biological fluids, cell and tissue extracts, and other samples. The use of small surfaces with defined properties to capture proteins from complex mixtures with subsequent identification by mass spectrometry is the topic of UNIT 3.9. Analytical assay methods to quantitate total protein content by biological activity will be presented in future updates to this chapter. Ben M. Dunn

Introduction

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Spectrophotometric Determination of Protein Concentration

UNIT 3.1

This unit describes methods for measuring the concentration of a protein in solution using absorbance spectroscopy. The absorbance, A, is a linear function of the molar concentration, c, according to the Beer-Lambert Law: A=ε×l ×c Equation 3.1.1

where ε is the molar absorption coefficient (M−1cm−1) and l is the cell pathlength (cm). It is important to have an accurate value of ε to determine c from A. Basic Protocol 1 shows how to calculate ε for a protein, making use of the average ε values for the three chromophores in proteins (the side chains of tryptophan, tyrosine, and cystine), as determined from an analysis of a large set of proteins with known ε values. Basic Protocol 2 describes how ε can be determined for a folded protein by measuring the absorbance of two solutions of the protein with identical concentrations—one containing just buffer and one containing buffer plus 6 M guanidine⋅HCl. Basic Protocol 3 shows how to measure the concentration of a protein using the Beer-Lambert law after ε has been either calculated or measured. Basic Protocol 4 describes a more sensitive method that is especially useful for proteins that contain few tryptophan or tyrosine residues. Basic Protocol 5 provides an easy way to estimate the total protein concentration in crude protein extracts during the early steps of a purification procedure. CALCULATION OF THE MOLAR ABSORPTION COEFFICIENT (ε) OF A PROTEIN

BASIC PROTOCOL 1

The molar absorption coefficient of a protein at 280 nm, ε280 (in M–1 cm–1 ), is calculated using the following equation (Pace et al., 1995; Pace and Schmid, 1997): ε 280 = (5500 × nTrp ) + (1490 × nTyr ) + (125 × nS-S ) Equation 3.1.2

where the numbers are the molar absorbances for tryptophan (Trp), tyrosine (Tyr), and cystine (i.e., the disulfide bond, S-S), from the first column in Table 3.1.1, and nTrp = number of Trp residues, nTyr = number of Tyr residues, and nS-S = number of disulfide bonds in the protein. This method is simple, fast, and reliable. The amino acid composition of the protein and the number of disulfide bonds are the only information needed. 1. Count the number of Trp residues (nTrp), Tyr residues (nTyr), and disulfide bonds in the protein sequence. 2. Use Equation 3.1.2 and the numbers from step 1 to calculate ε280.

Detection and Assay Methods Contributed by Gerald R. Grimsley and C. Nick Pace Current Protocols in Protein Science (2003) 3.1.1-3.1.9 Copyright © 2003 by John Wiley & Sons, Inc.

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Table 3.1.1

Values of ε for Trp, Tyr, and Disulfide Bonds Used to Determine ε of a Proteina

Average values of ε280 in folded proteinsb (M– 1cm– 1) Trp Tyr S-S

5500 1490 125

Values of ε for model compounds in 6.0 M guanidine⋅HCl at specified wavelengthsc (M– 1cm– 1) 276 nm 5400 1450 145

278 nm 5600 1400 127

279 nm 5660 1345 120

280 nm 5690 1280 120

282 nm 5600 1200 100

aReproduced from Pace and Schmid (1997) with permission from IRL press. bFrom Pace et al. (1995). cData are for N-acetyl-L-tryptophanamide, Gly-L-Tyr-Gly, and for S-S (disulfide bond) in 6.0 M guanidine⋅HCl, 0.02 M phosphate buffer, pH 6.5. Values from Gill and von Hippel (1989); original data from Edelhoch (1967).

BASIC PROTOCOL 2

DETERMINATION OF THE MOLAR ABSORPTION COEFFICIENT (ε) FOR A FOLDED PROTEIN In this protocol, the value of ε for the unfolded protein in 6.0 M guanidine⋅HCl (εU) is first calculated from the number of Trp, Tyr, and disulfide bonds with Equation 3.1.2, but using the ε values for those residues determined in 6 M guanidine⋅HCl, which are given in Table 3.1.1. These ε values are based on experimental studies of appropriate model compounds. This procedure is generally called the Edelhoch method (Edelhoch, 1967). Edelhoch showed that ε values for model compounds determined in 6.0 M guanidine⋅HCl could accurately reproduce the absorbance spectrum of an unfolded protein in 6.0 M guanidine⋅HCl. Values of ε for the reference compounds are given in Table 3.1.1 at five wavelengths between 276 and 282 nm. The εU for the unfolded protein is calculated at the wavelength of the absorbance maximum of the native protein. The ε value for the folded protein (εF) is determined by preparing solutions of folded and unfolded protein with identical concentrations, then measuring their absorbances, AF and AU. Since the concentrations of folded and unfolded protein are equal, εF and εU are related by: AF ε = F AU εU Equation 3.1.3

and εF can be calculated using: A  εF = εU ×  F   AU  Equation 3.1.4

The steps for determining the molar absorption coefficient of the folded protein are given below. Spectrophotometric Determination of Protein Concentration

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Materials 8 M nitric acid Ethanol 20 mM potassium phosphate buffer, pH 6.5 (APPENDIX 2E) Protein stock solution: ∼1 mg protein in 0.25 ml of 20 mM potassium phosphate buffer, pH 6.5 (see APPENDIX 2E for buffer) 6.6 M guanidine⋅HCl (APPENDIX 3A) in 40 mM potassium phosphate buffer, pH 6.5 (APPENDIX 2E); also see Critical Parameters Double-beam absorbance spectrophotometer Two quartz cuvettes, 10 mm long × 4 mm wide Prepare spectrophotometer and cuvettes 1. Turn on the spectrophotometer and UV and visible lamps. Allow a 30-min warm up period. Thermostatting is not required when the ambient temperature is between 20° and 30°C.

2. Clean cuvettes thoroughly by soaking in an 8 M nitric acid bath and then washing with distilled water. Rinse with ethanol and air dry. Obtain spectrum of native protein 3. Add 910 µl of 20 mM potassium phosphate buffer, pH 6.5 to both the sample and reference cuvettes and scan the absorbance between 350 and 250 nm. This baseline should be flat or deviate by no more than 0.005 AU from the instrument’s baseline with the cells removed.

4. Record the baseline. In addition, individually measure the absorbance values at 276, 278, 279, 280, and 282 nm, as the protein absorbance maximum will typically be in this range. See Critical Parameters for discussion of the linear range of the spectrophotometer.

5. Add 90 µl of the protein stock solution to the sample cuvette and 90 µl of 20 mM potassium phosphate buffer, pH 6.5 to the reference cuvette. Mix the solutions well in the cells. Scan and record the absorbance values at 276, 278, 279, 280, and 282 nm as in steps 3 and 4 above. Repeat the measurement to check reproducibility. 6. Subtract the baseline and the individual absorbance values of the buffer at 276, 278, 279, 280, and 282 nm. Obtain spectrum of unfolded protein 7. Obtain a spectrum of protein as in steps 1 to 6 above, but use 910 µl of 6.6 M guanidine⋅HCl in 40 mM potassium phosphate buffer, pH 6.5 in place of 20 mM potassium phosphate buffer, pH 6.5. 8. Calculate the value of εU for the unfolded protein between 276 nm and 282 nm as described in Basic Protocol 1, using the values of ε for the residues in 6.0 M guanidine⋅HCl (Table 3.1.1).

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Calculate molar absorption coefficient for the folded protein 9. Correct for light scattering, if necessary (see Critical Parameters and Troubleshooting). An absorbance greater than zero at wavelengths above 320 nm generally results from light scattering.

10. Determine the ratios AF/AU at 276 to 282 nm and use Equation 3.1.4 to calculate εF in this wavelength range. BASIC PROTOCOL 3

DETERMINATION OF PROTEIN CONCENTRATION BY ABSORBANCE SPECTROSCOPY USING THE MOLAR ABSORPTION COEFFICIENT This protocol describes how to measure the concentration of a pure protein in solution using the molar absorption coefficient determined in Basic Protocol 1 or 2. Materials Solution of pure protein in either distilled water or buffer Spectrophotometer with UV and visible lamps Quartz cuvette 1. Turn on the spectrophotometer and UV and visible lamps. Allow a 30-min warm up period. 2. Zero the spectrophotometer with a solvent blank (distilled water or buffer) at the wavelength where ε was determined and also at 330 nm. The reading at 330 nm serves to detect light scattering (see Critical Parameters and Troubleshooting).

3. Remove the cuvette, discard the solvent, and thoroughly dry the cuvette. Add the protein solution and replace the cuvette in the spectrophotometer. 4. Record the absorbance of the sample at the appropriate wavelengths. 5. If necessary, correct for light scattering (see Critical Parameters and Troubleshooting). 6. Calculate the protein concentration (c) in mg/ml: c = ( A280 × mol. wt.) /(ε280 × l ) Equation 3.1.5

The path length, l, is usually 1 cm.

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DETERMINATION OF PROTEIN CONCENTRATION BY ABSORPTION SPECTROSCOPY AT 205 nm

BASIC PROTOCOL 4

If the protein of interest contains few or no Trp or Tyr residues, the concentration may be determined by measuring the absorbance at 205 nm (A205). This is generally called the Scopes method (Scopes, 1974). Most of the absorbance at 205 nm is due to peptide bonds, but Scopes described a method for estimating ε at 205 nm (ε205) for proteins that also contain some Trp or Tyr residues. The Scopes method is the method of choice for proteins that do not contain Trp or Tyr residues. It works best for dilute protein solutions (1 to 100 µg/ml) that are in nonabsorbing buffers or solvents. Materials Protein sample Brij 35 solution: 0.01% (v/v) Brij 35 (Sigma) in an aqueous solution appropriate for dissolving the sample protein Spectrophotometer with UV and visible lamps Quartz cuvette 1. Dissolve protein in Brij 35 solution. 2. Turn on the spectrophotometer and UV lamp. Allow a 30-min warm up period. Set the wavelength to 205 nm. 3. Zero the spectrophotometer with the Brij 35 solution without protein. 4. Measure the A205 of the sample protein. 5a. If ε205 is known for the protein, use Equation 3.1.5 to calculate the concentration, substituting A205 for A280 and ε205 for ε280. 5b. If ε205 is not known, estimate the concentration of the sample protein (c) in mg/ml as follows:

c (mg/ml) = A205 /(31 × l )

Equation 3.1.6

SPECTROPHOTOMETRIC DETERMINATION OF TOTAL PROTEIN CONCENTRATION IN CRUDE PROTEIN EXTRACTS

BASIC PROTOCOL 5

A reasonable estimate of the protein concentration in an extract can be obtained by assuming that the absorbance at 280 nm of a 1% solution of the protein (A2801%) equals 13. This value is the average of the measured (A2801%) values for 116 proteins of various molecular weights and amino acid compositions (Pace et al., 1995). This protocol is most useful for monitoring protein concentration in the early stages of a purification procedure. Materials Sample of protein extract Spectrophotometer with UV and visible lamps Quartz cuvette Detection and Assay Methods

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1. Turn on the spectrophotometer and UV and visible lamps. Allow a 30-min warm up period. 2. Zero the spectrophotometer at 280 nm with a solvent blank (distilled water or buffer). 3. Remove the cuvette, discard the solvent, and thoroughly dry the cuvette. Add the protein extract and replace the cuvette in the spectrophotometer. 4. Record the absorbance of the sample at 280 nm. See Critical Parameters for discussion of the linear range of the spectrophotometer.

5. Estimate the protein concentration by assuming that a 1 mg/ml solution of protein will have an absorbance of 1.3. COMMENTARY Background Information

Spectrophotometric Determination of Protein Concentration

There are many methods available for determining protein concentration. Most of these methods rely either on colorimetric assays (e.g., UNIT 3.4) or the use of UV absorbance spectroscopy (Stoscheck, 1990). The choice of method depends upon a variety of factors, including the amount of protein present, the specificity of the method, the presence of interfering substances, and the amino acid composition of the protein. The Lowry method (UNIT 3.4) is a colorimetric method that has been widely used by protein chemists for the past 50 years (Lowry et al., 1951). The fact that Lowry’s publication of the methods is the most cited paper in the history of biochemistry attests to its popularity. In the mid-1970s, the Bradford method (Bradford, 1976; UNIT 3.4), based on binding of a blue dye to the protein, became popular and eventually supplanted the Lowry method. However, colorimetric assays are generally only useful for monitoring protein purification and should not be used when an accurate protein concentration is required. For the accurate determination of protein concentration, UV absorbance spectroscopy is the preferred method. UV absorbance spectroscopy is one of the oldest methods for determining protein concentration (Warburg and Christian, 1942) and has numerous advantages. The protein solution is used directly, without the addition of reagents, and is not modified or inactivated by the process. No incubation period is required, so measurements can be made rapidly and with high reproducibility. Finally, the protein concentration can be determined accurately from absorbance measurements using the Beer-Lambert Law (Equation 3.1.1). An accurate value of ε is essential when using Equation 3.1.1. Determination of ε requires an accurate measurement of A and c. The

measurement of A is straightforward (see Schmid, 1997 for an excellent discussion of how absorbance spectroscopy can be used to study proteins), but the measurement of c is not. The four techniques most often used to measure c are amino acid analysis (UNIT 3.2), Kjelkahl nitrogen determination, the dry weight method, and the Edelhoch method. The Edelhoch method is the simplest and most reliable experimental method to determine c, and consequently ε (Pace et al., 1995). Basic Protocol 1 describes the simplest method of estimating ε. Nevertheless, it is reliable, and in a test with 80 proteins it gave an average deviation from measured values of <4% (Pace et al., 1995). Basic Protocol 2 requires absorbance measurements on two solutions, but it is still simple and straightforward and leads to an experimental determination of ε. This method should be used when an accurate ε value is crucial. Basic Protocol 4 should be used for the rare proteins that contain neither Trp nor Tyr residues.

Critical Parameters and Troubleshooting Chromophores Basic Protocol 1 cannot be used if the protein of interest contains a tightly bound ligand or prosthetic group that absorbs between 250 and 300 nm. By comparing the protein spectrum with the absorbance spectra of Trp and Tyr (Schmid, 1997), one can often detect nonprotein contributions. For example, bound nucleotides lead to a decrease in the ratio A280/A260. If the disulfide bond content is unknown, assume for cytosolic proteins that the number of disulfide bonds (nS-S) is zero and assume for secretory proteins that nS-S = nCys/2 (Pace and Schmid, 1997). Since the molar absorption co-

3.1.6 Supplement 33

Current Protocols in Protein Science

Table 3.1.2

Absorbances of Various Salt and Buffer Components in the Far-UV Rangea

Compoundb

NaClO4 NaF, KF Boric acid NaCl Na2HPO4 NaH2PO4 Sodium acetate Glycine Diethylamine NaOH, pH 12 Boric acid/NaOH, pH 9.1 Tricine, pH 8.5 Tris, pH 8.0 HEPES, pH 7.5 PIPES, pH 7.0 MOPS, pH 7.0 MES, pH 6.0 Cacodylate, pH 6.0

No absorbance Absorbance of a 0.01 M solution in a 0.1-cm cell above 170 nm 170 nm 180 nm 205 nm 210 nm 195 nm 220 nm 220 nm 240 nm 230 nm 200 nm

210 nm 0 0 0 0 0 0 0.03 0.03 0.4 >0.5 0

200 nm 0 0 0 0.02 0.05 0 0.17 0.1 >0.5 >2 0

190 nm 0 0 0 >0.5 0.3 0.01 >0.5 >0.5 >0.5 >2 0.09

180 nm 0 0 0 0.5 >0.5 0.15 >0.5 >0.5 >0.5 >2 0.3

230 nm 220 nm 230 nm 230 nm 230 nm 230 nm 210 nm

0.22 0.02 0.37 0.20 0.10 0.07 0.01

0.44 0.13 0.5 0.49 0.34 0.29 0.20

>0.5 0.24 >0.5 0.29 0.28 0.29 0.22

>0.5 >0.5 >0.5 >0.5 >0.5 >0.5 >0.5

aTable from Schmid (1997) reproduced with permission from IRL press. bBuffers titrated with 1 M NaOH or 0.5 M H SO to the indicated pH values. 2 4

efficient for the disulfide bond is small, errors in the number of disulfide bonds have only a small effect on the calculated absorption coefficient. Basic Protocol 1 works best for globular proteins that contain at least one Trp residue (Pace et al., 1995). Guanidine⋅HCl When performing Basic Protocol 2, use ultrapure guanidine⋅HCl. Since guanidine⋅HCl is quite hygroscopic, it is difficult to prepare stock solutions by weight. Consequently, the molarity of guanidine⋅HCl stock solutions is generally based on refractive index measurements (see Pace and Scholtz, 1997, on how to prepare guanidine⋅HCl stock solutions). For the purposes of this protocol, the exact molarity of the guanidine⋅HCl stock solution is not critical, only that it be near 6 M. This can be approximated by adding 1 g of guanidine⋅HCl per ml of distilled water. A 40 mM buffer is used with the guanidine⋅HCl because in a 6.6 M solution, guanidine⋅HCl occupies about half of the volume of the solution, so the final buffer concentration is actually ∼20 mM. The buffer concentration here is not crucial as long as the correct

pH is maintained. Be sure to filter all solvents to remove dust particles (a 0.45-µm syringe filter works best). Careful pipetting and mixing of all solutions is essential for the reproducibility of this procedure. Light scattering When measuring the absorbance in Basic Protocols 2 and 3, a correction must be made if there is significant light scattering. The absorbance of the protein should be zero above 320 nm. A simple method to correct for light scattering is to multiply the absorbance at 330 nm by 1.929 to get the light scattering contribution at 280 nm or by 1.986 to get the light scattering contribution at 278 nm, then subtract the resulting values from AF and AU as measured at these wavelengths (Pace et al., 1995). This assumes that the light scattering contribution varies as the inverse fourth power of the wavelength and is discussed in detail by Leach and Scheraga (1960). Spectrometer linear range For the most accurate results, the maximum absorbance near 280 should be between 0.2 and

Detection and Assay Methods

3.1.7 Current Protocols in Protein Science

Supplement 33

Table 3.1.3

Concentration Limits of Interfering Reagents for A205 and A280 Protein Assaysa

Reagentsb

A205

A280

Ammonium sulfate Brij 35 DTT EDTA Glycerol KCl 2-ME NaCl NaOH Phosphate buffer SDS Sucrose Tris buffer Triton X-100 TCA Urea

9% (w/v) 1% (v/v) 0.1 mM 0.2 mM 5% (v/v) 50 mM <10 mM 0.6 M 25 mM 50 mM 0.10% (w/v) 0.5 M 40 mM <0.01% (v/v) <1% (w/v) <0.1 M

>50% (w/v) 1% (v/v) 3 mM 30 mM 40% (v/v) 100 mM 10 mM >1 M >1 M 1M 0.10% (w/v) 2M 0.5 M 0.02% (v/v) 10% (w/v) >1 M

aValues from Stoscheck (1990). bAbbreviations: DTT, dithiothreitol; EDTA, ethylenediaminetetraacetic acid; 2-ME.

1.0. When the absorbance is outside this range, make a new protein solution so that A280 is within this range. Finally, be sure that the total volume in the cell is sufficient that the light beam strikes the solution rather than the meniscus. The height of the light beam can be examined visually in the visible range (e.g., near 500 nm) by inserting a strip of white paper into the cell. Buffer effects The Brij 35 solution in Basic Protocol 4 is added to prevent protein adsorption onto vessel surfaces (Stoscheck, 1990), a significant problem when working with very dilute solutions. In addition, at 205 nm, a number of common solvents and buffers may contribute substantially to the total absorbance. Table 3.1.2 provides helpful information for selecting buffers when working in the far-UV region. Table 3.1.3 lists acceptable concentrations of potentially interfering reagents for use at 205 or 280 nm.

Spectrophotometric Determination of Protein Concentration

Nucleic acid interference Basic Protocol 5 is most useful for estimating protein concentration where there is little nucleic acid present. Nucleic acids contribute substantially to A280. To obtain a better estimate of protein concentration in the presence of nucleic acid, measure the absorbance at 260 nm

and 280 nm and calculate the protein concentration, c (in mg/ml), as follows (Stoscheck, 1990):

c = (1.55 × A280 ) − (0.76 × A260 ) Equation 3.1.7

Anticipated Results Accurate protein concentrations can be obtained by measuring the absorbance near 280 nm and then using the Beer-Lambert Law. The accuracy is limited mainly by the accuracy of ε. Equation 3.1.2 can be used to estimate ε quite accurately, but the best approach is to measure rather than predict ε.

Time Considerations All of the protocols described in this unit can be performed in <1 hr for a single sample.

Literature Cited Bradford, M.M. 1976. A rapid and sensitive method for quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72:248-254. Edelhoch, H. 1967. Spectroscopic determination of tryptophan and tyrosine in proteins. Biochemistry 6:1948-1954.

3.1.8 Supplement 33

Current Protocols in Protein Science

Gill, S.C. and von Hippel, P.H. 1989. Calculation of protein extinction coefficients from amino acid sequence data. Anal. Biochem. 182:319-326.

Scopes, R.K. 1974. Measurement of protein by spectrophotometry at 205 nm. Anal. Biochem. 59:277-282.

Leach, S.J. and Scheraga, H.A. 1960. Effect of light scattering on ultraviolet difference spectra. J. Am. Chem. Soc. 82:4790-4792.

Schmid, F.X. 1997. Optical spectroscopy to characterize protein conformation and conformational changes. In Protein Structure: A Practical Approach. (T.E. Creighton, ed.) pp. 261-297. IRL Press, Oxford, U.K.

Lowry, O.H., Rosebrough, J.J., Farr, A.L., and Randall, R.J. 1951. Protein measurement with the Folin phenol reagent. J. Biol. Chem. 193:265275.

Stoscheck, C.M. 1990. Quantitation of protein. Methods Enzymol. 182:50-68.

Pace, C.N. and Schmid, F.X. 1997. How to determine the molar absorbance coefficient of a protein. In Protein Structure: A Practical Approach. (T.E. Creighton, ed.) pp. 253-259. IRL Press, Oxford, U.K.

Warburg, O. and Christian, W. 1942. Isolierung und Kristallisation des Garungsferments Enolase. Biochem. Z. 310:384-421.

Pace, C.N. and Scholtz, J.M. 1997. Measuring the conformational stability of a protein. In Protein Structure: A Practical Approach (T.E. Creighton, ed.) pp. 299-321. IRL Press, Oxford, U.K.

Contributed by Gerald R. Grimsley and C. Nick Pace The Texas A&M University System Health Science Center College Station, Texas

Pace, C.N., Vajdos, F., Fee, L., Grimsley, G., and Gray, T. 1995. How to measure and predict the molar absorption coefficient of a protein. Prot. Sci. 4:2411-2423.

Detection and Assay Methods

3.1.9 Current Protocols in Protein Science

Supplement 33

Quantitative Amino Acid Analysis The use of quantitative amino acid analysis in the characterization of protein samples is often overlooked in many laboratories. This was typically due to the difficulties involved in obtaining reliable amino acid composition data, as well as to the advent of colorimetric dye-binding assays as alternative methods to determine protein concentration. However, many proteins do not bind dyes in the same way as the proteins normally used as standards, making quantitation by those techniques difficult. Fortunately, at many institutions, the creation of protein chemistry core laboratories devoted mainly to protein sequence analysis and peptide synthesis has provided new instrumentation that yields the necessary precision. This unit describes the information that can be derived from quantitative amino acid analysis, presents some details on sample preparation, and gives examples of the calculations that are needed.

INFORMATION OBTAINED FROM AMINO ACID ANALYSIS The benefits of amino acid composition data are two-fold. First, identifying the nature of the amino acids present in a sample can provide an important qualitative measure of the purity of the preparation. Amino acid analysis does not depend on the shape, charge, or function of a protein because the hydrolysis completely destroys structure and reduces the protein to its constituent amino acids. Although large proteins typically contain a mixture of all the commonly occurring amino acids, certain residues are relatively rare—Trp, Met, His, Cys, and occasionally Arg are present at lower than average ratios compared to Gly, Ala, Ser, and Leu. For a pure protein, the ratios of the amino acids should approach the theoretical levels to within ∼10%, with a few caveats. For a sample from a column fraction, for example, the presence of a higher than expected ratio of a certain amino acid is a very good indication that the sample is not pure and that further fractionation is indicated. In the same way, a lower than expected ratio could also indicate impurity— for example, the presence of a second protein that has a lower ratio of some significant amino acid present in higher amounts in the protein of interest. Because the expression of a protein in bacteria or other hosts utilizes a gene of known sequence, the expected amino acid composition

Contributed by Ben Dunn Current Protocols in Protein Science (1995) 3.2.1-3.2.3 Copyright © 2000 by John Wiley & Sons, Inc.

is also known (see Chapters 5 and 6 for expression and purification of recombinant proteins). Second, averaged composition yields an excellent measure of the amount of protein in a sample. On the ion-exchange-based Beckman 6300 amino acid analyzer running with ninhydrin detection chemistry, the minimum amount of the standard (a mixture of amino acids used to derive integration parameters as well as elution times) that is required is typically 1 nmol of each amino acid. Amounts as low as 250 pmol (0.25 nmol) of an amino acid can be measured with confidence from protein hydrolyzates. For a protein of 50 kDa mass, 1 nmol is 50 µg, an amount that can be obtained readily by purification of the protein from a high-expression system followed by purification by standard methods. For isolation of a trace protein from a natural source, this required amount may be too large to be feasible. However, a protein of 50 kDa mass contains ∼400 amino acids, or roughly 20 copies of each amino acid on average. Thus, the actual amount of protein required for detection and semiquantitative analysis could be as low as 0.05 nmol, or 2.5 µg. At that level, rare amino acids cannot be accurately quantitated, but the more common ones can be measured easily. Obviously, the more material available, the more accurate the analysis. Users should check with their local facility for specific advice on the amount of sample that can be processed by existing technologies. The advent of advanced instrumentation, such as the Applied Biosystems 420 amino acid analyzer and the Waters PicoTag system, has provided another increment in sensitivity. These instruments utilize precolumn derivatization followed by separation based on reversed-phase high-performance liquid chromatography (HPLC). With such analyzers, 100 pmol of each amino acid, or 5 pmol of the 50-kDa protein described above, can be quantitated accurately. Another important consideration in the design of a purification scheme that can be accomplished through the use of amino acid quantitation is the compilation of the mass balance—the relationship between how much protein is applied to the chromatography column and how much is recovered in a particular step. Amino acid analysis of a measured aliquot of the sample taken before application (typically ≤1%) along with aliquots of column frac-

UNIT 3.2

Detection and Assay Methods

3.2.1 CPPS

tions or pools, can be used to calculate the amounts of protein loaded and recovered. These calculations can provide essential information to assess the success of a particular purification procedure.

SAMPLE PREPARATION The success or failure of quantitative amino acid analysis is strongly dependent upon the quality of the sample, as well as upon the exact composition of the sample matrix (i.e., the aggregate of all components including salts, detergent, and dirt). The following section gives a few key recommendations for successful sample preparation.

Sample Concentration An ideal sample for amino acid analysis would contain 1 to 10 nmol protein in a volume of 10 to 100 µl water. A sample that has been dialyzed against a low concentration of buffer (if needed to maintain solubility) or pure water is optimal, because the presence of salt, detergent, high concentrations of buffers, or other agents may interfere with either hydrolysis or subsequent analysis. A sample that has been lyophilized to dryness is also usually acceptable, with some important considerations. First, lyophilization should be done in such a way as to concentrate the lyophilized residue at the bottom of a conical tube. Drying a sample in a round-bottom flask may leave the protein sample spread over a large surface area. Such samples are extremely difficult to handle and will not give satisfactory results. Second, lyophilization of a buffered solution will concentrate the salts as well as the protein. Therefore, it is recommended that the total ionic strength of the solution be <50 µM before lyophilization.

Estimate of Total Protein An estimate of the total amount of protein present is very helpful in planning an accurate analysis. This can be based on a colorimetric dye–binding assay, an A280 measurement, or an activity assay in which the results are compared to a standard value. All information about the sample should be communicated to the individual charged with conducting the analysis to ensure the maximum return on the sample. Many facilities provide a form with a series of questions to be answered concerning the sample. Quantitative Amino Acid Analysis

Samples from PVDF Blots With the advent of more sensitive analyzers

based on phenylthiohydantoin (PTH) chemistry, it is now possible to obtain compositional data on samples separated by electrophoresis and blotted onto membranes (UNIT 10.7) such as are used for protein sequence analysis. This is especially helpful in cases where a protein sample fails to yield any sequence during gas-phase Edman degradation. Acid hydrolysis and analysis of sample blot pieces recovered from the sequencer sample compartment yield a rough estimate of the amount of protein present. If the amount is larger than that normally required for sequence analysis (10 pmol), it may then be concluded that the protein sample was N-terminally blocked.

CALCULATION OF THE AVERAGE COMPOSITION Table 3.2.1 presents the results of analysis of a 24-kDa recombinant protein. The initial sample volume was 1.0 ml and was expected to contain ∼4 mg total protein. A 30-µl aliquot was taken for analysis and was estimated to contain 120 µg, or ∼5 nmol. The aliquot was transferred to a glass tube for hydrolysis and dried by application of a vacuum at room temperature. One milliliter of 6 M HCl was added, the tube was cooled in a dry ice bath, then a vacuum was applied and the tube sealed by heating in a gas flame. Hydrolysis was conducted for 24 hr at 124°C, followed by removal of the HCl under vacuum. The sample was taken up in 1.0 ml application buffer, 50 nmol norleucine was added as an internal standard, and a 50-µl aliquot was analyzed by ion-exchange chromatography on a Beckman 6300 system with ninhydrin detection. 1. The total number of nanomoles of amino acids detected is first calculated by summing the second column of Table 3.2.1, yielding 1446 nmol. 2. The total nanomoles is then divided by the total number of expected amino acids (based on protein sequence; in this case, 212) to obtain the average number of nanomoles per residue; 1446 nanomoles/212 residues = 6.82 nmol per residue. If the total number of amino acids is not known, it may be estimated by dividing the mol. wt. of the protein by 110. 3. The number of nanomoles of each amino acid measured is divided by the average number of nanomoles per residue (step 2) to give the ratio1 observed for that amino acid. For Asp, 155 ÷ 6.82 = 22.7. The resulting ratios are given in the fourth column and may be compared to the ratio expected listed in the third column. For most

3.2.2 Current Protocols in Protein Science

Table 3.2.1 Data Used to Calculate Amino Acid Composition for a 24-kDa Protein

Amino acid Asp Thr Ser Glu Pro Gly Ala Val Met Ile Leu Tyr Phe His Lys Arg

Nanomoles measured 155 78 63 153 52 157 90 159 24 88 138 43 56 16 121 53

Ratio expecteda

Ratio1 observed

Ratio2 observed

22.7 11.4 9.2 22.4 7.6 23.0 13.1 23.3 3.5 12.9 20.2 6.3 8.2 2.3 17.7 7.8

21.8 10.9 8.9 21.5 7.3 22.1 12.6 22.3 3.4 12.3 19.4 6.1 7.9 2.3 17.0 7.4

23 13 13 21 7 22 12 23 5 13 19 6 8 3 17 7

aRatio expected based on amino acid sequence of protein.

of the amino acids, the ratio1 observed agrees with the ratio expected. The value of average nanomoles per residue calculated in step 2 is based on the analysis of a 30-µl sample; the total sample of 1 ml contains ∼5.46 mg of protein (average moles/residue × grams/mole protein × 1000/30). This analysis utilized only 50 µl of the 1.0 ml final solution, so a sample size smaller than 3% of the original sample could have been used. Using an excess, however, provides the flexibility to repeat the determination if the first analysis gives peaks that are either too large or too small for accurate integration. The ratio1 observed for Ser, Thr, His, and Met are in this sample are ∼30%, 13%, 24%, and 30% below the expected values, respectively. These results are typical for these four amino acids, but for different reasons. The Ser and Thr results are low because these amino acids undergo partial degradation during acid hydrolysis. Ser and Thr losses of 10% to 40% are normal. The lower-than-expected values for His and Met are an anomaly that is most likely caused by the relatively low copy number of those two amino acids in the protein. Due to these known problems, most investigators choose to delete the values for sensitive residues (Ser, Thr) as well as the values for those amino acids appearing in low amounts (in this case, His, and Met) and calculate the average

number of nanomoles per residue independently of those amino acids. This gives the corrected ratios shown in the last column of Table 3.2.1 (ratio2 observed). In this example, the recalculated values are changed by as much as 0.9 due to the higher average number of nanomoles per residue (7.11). The deviation of ratio1 observed from the expected ratio ranges from 1% to 32% for the various amino acids, with an average error of 8.8%. When the high variation for Ser (32%), Met (32%), and His (24%) are removed from consideration, the average deviation of ratio2 observed from expected ratio drops to 4.1%. By standard criteria, this sample would be judged to be pure; of course, this result needs to be combined with electrophoretic separation (Chapter 10), chromatographic purification (Chapter 8), and perhaps mass spectroscopic analyses before a final assessment of the purity of the sample can be made.

Key Reference Ozols, J. 1990. Amino acid analysis. Methods Enzymol. 182:587-601. Excellent discussion of sample preparation issues and hydrolysis methods.

Contributed by Ben Dunn University of Florida Gainesville, Florida

Detection and Assay Methods

3.2.3 Current Protocols in Protein Science

Supplement 4

In Vitro Radiolabeling of Peptides and Proteins

UNIT 3.3

Radiolabeling of peptides or proteins is often performed to enhance the sensitivity of detection, to quantitate the binding of peptides to other molecules, or for radioimmunoassays. The choice of radioisotope depends in part on the amino acid residue(s) to be modified (Table 3.3.1). The most common and straightforward labeling strategy is radioiodination with 125I because of its relative ease of use, the high resulting specific radioactivities (curies per millimole), and the convenience of detecting γ emissions. Radioiodination may also be performed with 131I, which has a shorter half-life (8 days versus 60 days for 125I), giving higher specific radioactivities but limiting the useful lifetimes of the labeled products; in addition, 131I has higher-energy γ emissions, necessitating more shielding to protect workers. Iodination at tyrosine or histidine residues using Iodo-Beads (Basic Protocol 1) is the most convenient method, but chloramine T or Iodogen (Alternate Protocol 1) may give higher recoveries when working with small quantities of peptide, and lactoperoxidase (Alternate Protocol 2) results in less oxidative damage and lower specific radioactivities, and is commonly used when labeling whole cells. Modifications for achieving equimolar or higher (stoichiometric) rather than trace iodination are presented in Support Protocol 1, and the optional separation of unlabeled peptide from iodinated products by HPLC is described in Support Protocol 2. For peptides that do not contain tyrosine or histidine, or in which labeling of these amino acid residues interferes with biological activity, 125I or 131 I may be added onto primary amino groups (lysine residues and the peptide N-terminus) using Bolton-Hunter reagent (Basic Protocol 2). As an alternative to iodination, peptides or proteins can be labeled with 14C or 3H by acetylation of primary amino groups using anhydride (Basic Protocol 3), by reductive alkylation of primary amino groups using aldehyde and borohydride (Alternate Protocol 3), or by alkylation of cysteine sulfhydryl groups (Alternate Protocol 4). Figure 3.3.1 summarizes these radiolabeling strategies as well as the basic iodination reactions. Two methods are available to label a peptide without altering its structure: 3H labeling by catalytic reduction of (nonradioactive) 127I-labeled peptide (Basic Protocol 4) and de novo peptide synthesis using a radiolabeled form of any amino acid (Basic Protocol 5). However, these methods tend to be more expensive and laborious. Finally, selective Table 3.3.1

Peptide Labeling Strategies

Amino acid residue

Label

Method

Reference

Tyrosine Histidine, tyrosine Tyrosine Lysine, N-terminus Lysine, N-terminus

125I, 131I

Chloramine T, Iodo-Beads Chloramine T, Iodo-Beads Lactoperoxidase Bolton-Hunter Anhydride

Lysine, N-terminus Cysteine Tyrosine

14C, 3H

Any residue Carbohydrate groups

14C, 3H

Greenwood et al. (1963) Wolff and Covelli (1969) Marchalonis (1969) Bolton and Hunter (1973) Fraenkel-Conrat and Colloms (1967) Tack and Wilder (1981) Chersi et al. (1988) Amersham or Du Pont NEN literature Stewart and Young (1984) Gahmberg and Andersson (1977)

125I, 131I 125I, 131I 125I, 131I 14C, 3H

14C, 3H 3H

3H

Aldehyde/borohydride Iodoacetic acid Reduction of 127I Peptide synthesis Periodate/borohydride

Contributed by Ton N.M. Schumacher and Theodore J. Tsomides Current Protocols in Protein Science (1995) 3.3.1-3.3.19 Copyright © 2000 by John Wiley & Sons, Inc.

Detection and Assay Methods

3.3.1 CPPS

I

OH

A

I

OH

OH I

+NH 3

COO –

+NH 3

COO –

+NH 3

COO –

I

B N +NH 3

C

NH

N COO –

I

+NH 3

+

HO

COO–

N COO –

I

COO –

I HO

O N I

NH

+NH 3

O

I

+NH 3

+NH 3

NH

O I

O O

NH

I HO

COO –

NH I

O O

D

+NH 3

+ +NH 3

E

CH3 NH O

O O COO –

CH3 O CH3

COO –

CH3 NH CH3

+NH 3

+NH 3

+

COO –

CH2=O

NH

CH2=N

+

NaCNBH3

COO –

CH2=N

COO –

CH3–NH CH3

CH3 N

CH2=O

(CH3)2N

F SH +NH 3

NaCNBH3

+

I–CH2 –COO –

COO – (or I–CH2 –CONH2)

COO –

S-CH2 –COO – (or S–CH2 –CONH2) +NH 3

COO –

Figure 3.3.1 Common peptide radiolabeling reactions. (A) Iodination on tyrosine. (B) Iodination on histidine. (C) Bolton-Hunter iodination on lysine and N-terminus. (D) Acetylation on lysine and N-terminus. (E) Reductive alkylation on lysine and N-terminus. (F) Alkylation on cysteine.

In Vitro Radiolabeling of Peptides and Proteins

3.3.2 Current Protocols in Protein Science

labeling at the N-terminus of a peptide can be performed during peptide synthesis (Alternate Protocol 5). All peptide-labeling procedures described in this unit are also applicable to larger proteins; however, with larger proteins there is increased potential for idiosyncratic results because of tertiary structure and for undesired side reactions because of sensitive amino acid residues (e.g., tryptophan, methionine, and cysteine in the case of oxidative iodinations). To some extent these problems may be minimized through the use of milder labeling procedures (e.g., lactoperoxidase-catalyzed iodination). NOTE: Various methods for separating radiolabeling agents (e.g., free iodide) from labeled peptide products are described in Basic Protocol 1 and in UNITS 8.2 & 8.3, and any of these methods may be used in conjunction with any of the radiolabeling protocols in this unit. The choice of separation method should depend primarily on the properties of the peptide or protein (see Critical Parameters, discussion of iodination work-up). CAUTION: When working with 125I, 131I, 14C, 3H, or other radiochemicals, always observe appropriate safety precautions, including the use of gloves (consider use of a glove box with a charcoal filter, particularly for >5 mCi iodide), shielding, dosimetry monitoring, and proper disposal of radioactive waste. Consult a radiation safety specialist to ensure that all procedures are safe and in accordance with Nuclear Regulatory Commission guidelines. Oxidation converts iodide to a volatile and hazardous form; peptide-bound iodine is not volatile, but emits γ radiation and remains a potential hazard. Be sure to perform all steps that may give rise to volatile iodine in an externally vented hood. See APPENDIX 2B for additional guidelines. IODINATION AT TYROSINE OR HISTIDINE RESIDUES USING IODO-BEADS

BASIC PROTOCOL 1

Iodo-Beads are nonporous polystyrene beads with an immobilized oxidizing agent (N-chlorobenzenesulfonamide) capable of converting Na125I (or Na131I) to its reactive form (Markwell, 1982). Iodination occurs by electrophilic addition to tyrosine at the two positions ortho to the hydroxyl group, and on histidine at two imidazole carbon positions (Means and Feeney, 1971; Fig. 3.3.1). After reaction of a peptide with oxidized iodide, the mixture is transferred to a solid-phase extraction device (e.g., a Sep-Pak cartridge) to remove free iodide from the labeled peptide. Materials Iodo-Beads (Pierce) 0.1 M sodium phosphate, pH 6.0 or pH 8.5 Carrier-free sodium iodide-125 (Na125I; Amersham, Du Pont NEN, or ICN Biomedicals) or sodium iodide-131 (Na131I) 0.1 M sodium hydroxide Peptide, ideally dissolved in ≤0.4 ml of water or 0.1 M sodium phosphate Methanol HPLC solvent A: 0.1% (v/v) trifluoroacetic acid (TFA) in water HPLC solvent B: 0.1% (v/v) TFA in acetonitrile Filter paper 100-µl syringe with beveled tip, or hot pipettor (i.e., dedicated to radioactive use) Luer-tipped syringes (1-, 5-, and 20-ml) and one 23-G needle Solid-phase extraction device (e.g., Sep-Pak or Sep-Pak Plus C18 cartridge, Waters) Polypropylene or other plastic tubes (12 × 75 mm) Detection and Assay Methods

3.3.3 Current Protocols in Protein Science

1. Wash Iodo-Beads with 1 ml of 0.1 M sodium phosphate, pH 6.0 (for labeling primarily on tyrosine) or pH 8.5 (for labeling on histidine and tyrosine). Dry on filter paper and add to 0.5 ml of same buffer in capped microcentrifuge tube or small reaction vial. For trace labeling use two Iodo-Beads; for stoichiometric labeling more may be needed (see Support Protocol 1). Iodo-Beads are compatible with most salts (including azide), chaotropic agents, and detergents, but not with reducing agents or organic solvents that dissolve polystyrene such as dimethyl sulfoxide (DMSO) and dimethylformamide (DMF). Iodo-Beads must be stored in the presence of desiccant, and have a manufacturer-suggested shelf life of 1 year.

2. Add Na125I (or Na131I) to Iodo-Beads in the reaction tube using a 100-µl syringe with beveled tip or a hot pipettor. Cap tube and wait 5 min at room temperature, agitating the mixture occasionally. Rinse syringe with 0.1 M sodium hydroxide (125I is less volatile at high pH) and then with water before storing. The amount of radioactive iodide depends largely on the desired specific radioactivity, subject to safety and cost limitations; 1 to 5 mCi is typical. Use a fresh batch to minimize loss of radioisotope as volatile molecular iodine. Na125I is supplied in various concentrations of NaOH; the more dilute NaOH solutions are preferable since they are less likely to alter the pH of the reaction.

3. Add peptide to the reaction. Cap tube and wait 30 min, agitating occasionally. The amount of peptide is inversely related to the final specific radioactivity, unless iodinated products are separated from unlabeled peptide (see Support Protocol 2); 0.05 to 2.0 mg is typical. Reaction time can be 45 min or longer if desired, or just 5 to 10 min if oxidationsensitive residues in the peptide (tryptophan, methionine, cysteine) are of concern, and should be optimized empirically if needed.

4. During the iodination reaction (or before if more convenient), prepare a Sep-Pak C18 cartridge by attaching a syringe and passing 5 ml methanol through the cartridge, followed by 5 ml water and 10 to 20 ml HPLC solvent A. Maintain a flow rate of ∼2 ml/min, do not reverse the orientation of the cartridge when changing solvents, and avoid introducing air bubbles or allowing the cartridge to dry out. Although this Sep-Pak method offers the most efficient separation of unbound radioactive iodide from the peptide, anion-exchange (UNIT 8.2) or gel-filtration (UNIT 8.3) chromatography may be used as alternatives, especially for larger peptides at risk of denaturation under the conditions used in steps 4 to 8 (see Alternate Protocol 1, steps 1 and 4). Dialysis is another alternative for large peptides, but takes much longer.

5. Remove the mixture from the reaction tube using a 1-ml syringe fitted with a 23-G needle. Carefully remove the needle and slowly load mixture onto the preconditioned Sep-Pak cartridge, collecting the eluate into a radioactive liquid waste container. 6. Wash Sep-Pak cartridge with 20 ml HPLC solvent A, collecting the eluate as radioactive waste. Neutralize waste with 5 ml of 0.1 M sodium hydroxide. Unbound radioactive iodide will pass through the Sep-Pak cartridge under these conditions, while peptide will remain in the cartridge.

7. Elute two fractions from the Sep-Pak cartridge into 12 × 75–mm plastic tubes, the first using 4 ml of a 1:1 mixture of HPLC solvent A and HPLC solvent B, and the second using 4 ml HPLC solvent B. In Vitro Radiolabeling of Peptides and Proteins

Usually the first tube contains most of the labeled peptide, but a two-step elution improves overall recovery. Because unbound iodide is now removed, the tubes may be transferred out of the glove box or other iodination facility, as long as appropriate shielding is maintained.

3.3.4 Current Protocols in Protein Science

8. Dry the tubes in a Speedvac evaporator. Redissolve in water or desired buffer and pool the two fractions. 9. To determine the specific radioactivity of the labeled peptide, estimate peptide recovery or analyze an aliquot of the sample for peptide concentration (e.g., by a colorimetric assay) and simultaneously determine cpm (in a γ-radiation counter, possibly after serial dilutions in the presence of a carrier protein to reach a level of radioactivity within the linear counting range of the γ counter). Specific radioactivity (in cpm/ìg) = measured radioactivity (in cpm/ìl) ÷ peptide concentration (in ìg/ìl). To convert to millicuries, use 1 mCi = 2.22 × 109 dpm and cpm = dpm × counting efficiency of γ counter (often 50% to 75%, depending on the instrument). Alternatively, if iodinated products are separated from unlabeled peptide by HPLC and the molar ratio of incorporated iodide to peptide is known (or estimated), the specific radioactivity of the peptide may be calculated from that of carrier-free 125I (or 131I; see Support Protocol 2).

IODINATION AT TYROSINE OR HISTIDINE RESIDUES USING CHLORAMINE T OR IODOGEN

ALTERNATE PROTOCOL 1

Instead of using Iodo-Beads, Na125I can be oxidized to its reactive form using soluble chloramine T (N-chloro-4-methylbenzenesulfonamide sodium salt; Greenwood et al., 1963), the solid-phase reagent Iodogen (1,3,4,5-tetrachloro-3α,6α-diphenylglycoluril; Fraker and Speck, 1978), or the enzyme lactoperoxidase (see Alternate Protocol 2). Although slightly less convenient than Iodo-Beads, these methods may give higher recoveries when working with small quantities of peptide (<0.05 mg), which can adsorb to Iodo-Beads. The reagents are inexpensive and stable, and with Iodogen or lactoperoxidase, expose the peptide to relatively mild reaction conditions. Additional Materials (also see Basic Protocol 1) Anion-exchange resin, e.g., Dowex 1-X8, chloride form, 100 to 200 mesh (Bio-Rad) BSA solution: 1.0 mg/ml BSA in PBS, with 0.02% sodium azide (NaN3) 1.0 mg/ml chloramine T in 0.1 M sodium phosphate, prepared immediately before use Iodogen (Pierce), dichloromethane, and glass tubes or vials (optional alternate to chloramine T) Saturated solution of tyrosine in water (∼0.4 mg/ml at 25°C) 2.5 mg/ml sodium metabisulfite (Na2S2O5) in 0.1 M sodium phosphate, prepared immediately before use (optional alternate to tyrosine solution) Pasteur pipet or 1-ml syringe with glass wool plug inserted at bottom Gel-filtration column, e.g., Sephadex G-10 or G-25 (Pharmacia Biotech), or Excellulose Desalting Column (Pierce; optional alternate to anion-exchange column) 1. Suspend Dowex resin in water and pour into plugged Pasteur pipet. Wash with ≥20 ml BSA solution. Alternatively, equilibrate a gel-filtration column with BSA solution. Avoid introducing air bubbles into the column and do not allow column to dry out. Because short peptides (<20 residues) are difficult to separate completely from unbound radioactive iodide by gel-filtration columns, an anion-exchange column may be preferred. Highly negatively charged (e.g., phosphorylated) peptides and very short peptides (<10 residues) may be retained on an anion-exchange column and should therefore be desalted by solid-phase extraction (see Basic Protocol 1). The desalting method should be established in advance.

Detection and Assay Methods

3.3.5 Current Protocols in Protein Science

Supplement 4

2a. Chloramine T procedure: Place peptide in a small tube and buffer with 0.1 M sodium phosphate, pH 6.0 or 8.5 (see Basic Protocol 1, step 1); bring the total volume to 100 to 300 µl. Add Na125I (or Na131I), followed by 10 µl chloramine T (freshly prepared), and mix. Wait 1 min for reaction to occur. 2b. Iodogen procedure:Add sodium iodide to the peptide (as described in step 2a) in a glass tube previously coated with the solid-phase reagent Iodogen by dissolving the solid in dichloromethane and blowing off the solvent under a stream of dry nitrogen. Wait 10 min at room temperature. Iodogen-coated tubes may be stored for many months at room temperature in a desiccator, protected from light. Because the oxiding agent is water insoluble, peptide damage is kept to a minimum.

3. Add 50 µl saturated tyrosine solution to quench the reaction. Alternatively, the reaction may be terminated by adding 10 ìl sodium metabisulfite, although this reducing agent is potentially more damaging to peptides.

4. Load mixture onto a Dowex or gel-filtration column and collect 0.5-ml fractions in 12 × 75–mm plastic tubes, using BSA solution for elution. Monitor fractions with a hand-held γ counter, or count 1-µl aliquots of each fraction in a γ counter to locate the peptide-containing fractions. Radiolabeled peptide should pass through the column; unreacted iodide is retained longer and need not be eluted. Dispose of the entire column as radioactive sharps waste. ALTERNATE PROTOCOL 2

IODINATION AT TYROSINE OR HISTIDINE RESIDUES USING LACTOPEROXIDASE Although the degree of oxidative damage to peptides undergoing iodination is difficult to predict, the lactoperoxidase method results in less oxidative damage than chemical oxidative procedures (Marchalonis, 1969). Iodination is confined to tyrosine. Specific radioactivities are likely to be lower by this method. Because lactoperoxidase (mass 77,500 Da) will be iodinated during the reaction, it should be used only if the labeled peptide or protein of interest can be separated from labeled lactoperoxidase by gel filtration (UNIT 8.3), ion-exchange chromatography (UNIT 8.2), or some other means. Alternatively, it may be possible to use lactoperoxidase coupled to beads, or biotinylated lactoperoxidase and avidin-coated beads to remove the labeled enzyme (Sigma). Because the enzyme does not gain access to the cytoplasm, lactoperoxidase-catalyzed iodination is useful for selectively radiolabeling membrane proteins on whole cells, provided iodide concentrations are kept low and the proportion of dead cells is very small (Marchalonis et al., 1971). Additional Materials (also see Basic Protocol 1) 0.2 mg/ml lactoperoxidase in PBS 0.01% hydrogen peroxide (H2O2) in PBS (diluted from 30% stock immediately before use) 2 U/ml glucose oxidase in PBS and 0.1 M D-glucose in PBS (optional alternate to H2O2) 1. Place peptide in a tube with PBS, ideally at ≥1.0 mg/ml in a small volume. 2. Add 10 µl of 0.2 mg/ml lactoperoxidase.

In Vitro Radiolabeling of Peptides and Proteins

Azide should not be present in the reaction as it inhibits lactoperoxidase.

3.3.6 Supplement 4

Current Protocols in Protein Science

3. Add Na125I (or Na131I), cap tube, and mix. 4. Add 10 µl of H2O2. Wait 10 min at room temperature. The H2O2 concentration is critical and may need fine-tuning for best results. Alternatively, use a coupled glucose/glucose oxidase system to generate H2O2 in situ, i.e., treat with 10 ìl of glucose oxidase and 10 ìl of glucose for 10 min at 4°C (Hubbard and Cohn, 1975).

5. Terminate the reaction and remove unbound radioactive iodide (see Alternate Protocol 1, steps 1, 3, and 4). EQUIMOLAR IODINATION TO GIVE HIGH YIELDS OF PRODUCT Iodination of microgram to milligram amounts of a peptide using low millicurie amounts of carrier-free Na125I (or Na131I), such as those suggested in Basic Protocol 1 and Alternate Protocols 1 and 2, results in trace labeling (iodination of only a small fraction of the peptide molecules). For example, in a reaction between 1 mCi of 125I (∼600 pmol) and 500 µg of a peptide of mass 1000 Da (500 nmol) or a protein of mass 100,000 Da (5 nmol, or ∼200 nmol of tyrosine residues on average), there is at most only enough 125I to label well under 1% of the tyrosines. For proteins, trace labeling is often advantageous as it minimizes the chance that the protein’s activity will be lost (Eisen and Keston, 1949; Hunter and Greenwood, 1962); moreover, a sizable fraction of protein molecules will have one iodine atom on at least one of its tyrosines. However, for short peptides over 99% of the molecules remain unmodified, greatly diluting the specific radioactivities that are possible and complicating the interpretation of experiments in which labeled and unlabeled peptides differ significantly in their activities (see Support Protocol 2).

SUPPORT PROTOCOL 1

To label a higher proportion of a peptide, nonradioactive Na127I (“carrier”) can be added to 125I, giving a higher yield of iodinated product and resulting in a mixture of chemically equivalent 125I- and 127I-labeled peptides. Perform all steps for iodination as described using Iodo-Beads (see Basic Protocol 1) or chloramine T or Iodogen (see Alternate Protocol 1), with two modifications. First, add 0.1 M Na127I in water to the Na125I prior to using Na125I in the indicated step. The amount of 127I should be in moderate molar excess over the peptide (2- to 5-fold) to achieve high yields of iodinated products (70% to 95%). Further increasing the amount of 127I unnecessarily diminishes the products’ specific radioactivities: e.g., at a 4-fold molar excess of 127I, only up to 25% of the 125I added can be incorporated, at a 10-fold molar excess only 10% can be incorporated, etc. Second, a sufficient number of Iodo-Beads (or other oxidizing reagent) must be used to oxidize the total iodide (essentially 127I). Use the manufacturer-determined oxidizing capacity of 0.55 ± 0.05 µmol per Iodo-Bead. Note that Iodo-Beads rapidly develop a yellow-brown color when stoichiometric amounts of iodide are added; failure to observe this color indicates deterioration of the beads. For maximum specific radioactivities, label a small amount of peptide (e.g., 30 µg) with up to an equimolar amount of 125I (e.g., 40 mCi) and separate the unlabeled peptide from iodinated products by HPLC (see Support Protocol 2). It is also possible to iodinate with 127I only, in which case the products will be nonradioactive but chemically equivalent to the corresponding radiolabeled products. This approach is useful for practicing the iodination procedure and for assessing the effects of iodination on a peptide’s activity (Tsomides and Eisen, 1993), as well as for preparing an 127I-labeled peptide to be reductively tritiated (see Basic Protocol 4).

Detection and Assay Methods

3.3.7 Current Protocols in Protein Science

Supplement 3

SUPPORT PROTOCOL 2

SEPARATION OF UNLABELED PEPTIDE FROM IODINATED PRODUCTS BY HPLC Whether trace iodinated with 125I only or stoichiometrically iodinated with a mixture of 125 I and 127I, some peptide will remain unlabeled, and it may be desirable to separate this unlabeled component from the iodinated products. To obtain chemically homogeneous radiolabeled peptides, iodinated products can be resolved from each other and from unlabeled peptide by reversed-phase HPLC. (This optional procedure is distinct from the obligatory separation of peptide from unbound radioactive iodide by solid-phase extraction, anion-exchange chromatography, gel filtration, exhaustive dialysis, or other means as detailed in steps 4 to 8 of Basic Protocol 1.) Because iodine is a bulky, hydrophobic atom relative to hydrogen, the retention times of iodinated peptides will be greater than those of their unlabeled counterparts on reversed-phase HPLC columns (e.g., C18 for short peptides, C4 for longer or more hydrophobic peptides). In most iodinations, more than one product will be generated, even if the peptide has only a single tyrosine (see Fig. 3.3.1). Because the pKa of monoiodotyrosine is lower than that of tyrosine (Edelhoch, 1962), electrophilic addition of the second iodine atom may be accelerated relative to the first; however, this reaction will not go to completion even when iodide is present in molar excess over the peptide. When more than two or three iodinatable residues (tyrosine and histidine) are present (as in a protein), the labeled products may be too complex to allow for individual resolution by HPLC, but unlabeled peptide can still be removed from the radiolabeled mixture. Even peptides as long as 50 to 100 residues can be separated from their iodinated derivatives by reversed-phase HPLC. For peptides with a single iodinatable residue, the two products are usually easily separated by HPLC and can readily be identified by their specific radioactivities, which are in the ratio 1:2 for monoiodinated and diiodinated peptide, respectively. Figure 3.3.2 shows the result of stoichiometrically labeling 2.0 mg of an eight-residue peptide with 20

Figure 3.3.2 Stoichiometric iodination of the eight-residue peptide LSPFPYDL and HPLC separation of the unlabeled starting material (peak 1) from monoiodinated (peak 2) and diiodinated (peak 3) products. Two milligrams of peptide were iodinated with 20 mCi Na125I and 5 µmol Na127I at pH 7.0 using six Iodo-Beads, injected on a Vydac C4 semipreparative column, and eluted with an acetonitrile gradient of 1%/min at a flow rate of 3 ml/min. The specific radioactivities of the three peaks were <105 cpm/µg, 2.1 × 107 cpm/µg, and 4.6 × 107 cpm/µg, respectively.

cpm

1

2

3

A280

In Vitro Radiolabeling of Peptides and Proteins

30

Time (min)

60

3.3.8 Supplement 3

Current Protocols in Protein Science

mCi Na125I mixed with 5 µmol Na127I and separating the unreacted, monoiodinated, and diiodinated products by HPLC. If Edman degradation is available, labeled products may be characterized directly by detecting the monoiodinated and diiodinated derivatives of tyrosine and/or histidine (Tsomides and Eisen, 1993). Note that peptides containing tryptophan, methionine, or cysteine residues may undergo oxidation during iodination. The resulting oxidized products (both radiolabeled and unlabeled) may appear as separate peaks on HPLC, complicating the identification of products. When carrying out stoichiometric iodination with a mixture of 125I and 127I, specific radioactivities of the product mixture or of individual HPLC-purified products can be measured as described for the Iodo-Beads procedure (see Basic Protocol 1, step 9). However, when trace labeling with 125I only and removing unlabeled peptide by HPLC, specific radioactivities may simply be calculated as the specific radioactivity of carrierfree 125I (∼2000 Ci/mmol) multiplied by the molar ratio of 125I to peptide (e.g., 1 for a monoiodinated derivative, 2 for a diiodinated derivative, etc.). When a mixture of 125 I-labeled products is separated from unlabeled peptide by HPLC but not resolved into its individual components, the same calculation may be applied by taking the average molar ratio of 125I to peptide to be the number of iodinatable residues. For example, the specific radioactivity of a peptide of mass 1000 Da with two tyrosine residues that is labeled with 125I and separated from unlabeled peptide by HPLC is ∼5 × 109 cpm/µg, assuming an average of two 125I per labeled peptide. NOTE: Although HPLC fractions can be analyzed for radioactivity by transferring them to a γ counter, it is preferable to use an on-line radioisotope detector connected in series with an ultraviolet absorbance detector (see Fig. 3.3.2). IODINATION AT LYSINE RESIDUES OR N-TERMINUS USING BOLTON-HUNTER REAGENT

BASIC PROTOCOL 2

Many peptides do not contain tyrosine or histidine. Other peptides’ function may be altered if crucial tyrosine or histidine residues are iodinated or if damage to oxidativesensitive residues occurs during iodination. An alternate strategy is to label primary amino groups on lysine residues and/or at the N-terminus of the peptide using Bolton-Hunter reagent 3-(4-hydroxyphenyl)propionic acid N-hydroxysuccinimide ester (Bolton and Hunter, 1973). This reagent introduces a side chain similar to that of tyrosine (see Fig. 3.3.1). Of course, this method carries the potential drawback of altering peptide function if lysine residues (or the N-terminus) are critical for activity; unlike the methods above, peptide charge will be decreased by one unit for each site modified (at pH values below the amino group’s pKa value). Because lysine’s ε-amino group and the N-terminal α-amino group have different pKa values (∼10 vs. 8 to 9), it is possible, in principle, to achieve selective labeling by adjusting the pH (lower pH should favor the N-terminus). However, this is difficult in practice, and a mixture of products is to be expected from lysine-containing peptides unless the N-terminal residue is proline (secondary amine) or a protecting group is present on either lysine or the N-terminus. 125

I-labeled Bolton-Hunter reagent is commercially available in monoiodinated (∼2000 Ci/mmol) or diiodinated (∼4000 Ci/mmol) form. A cost-saving modification is to prepare 125 I-labeled Bolton-Hunter reagent from the less expensive unlabeled reagent by iodination using Iodo-Beads (see Basic Protocol 1) or chloramine T (see Alternate Protocol 1) followed by extraction of the product into anhydrous benzene. (In this case it is crucial to work quickly to minimize hydrolysis, and specific radioactivities will be lower unless unreacted Bolton-Hunter reagent is removed from the labeled product.) Alternatively, the

Detection and Assay Methods

3.3.9 Current Protocols in Protein Science

peptide can first be modified with unlabeled Bolton-Hunter reagent and then radioiodinated using any of the methods previously described. Because 125I-labeled Bolton-Hunter reagent suffers some degradation by competing hydrolysis during its reaction with peptide, this alternate approach gives a higher incorporation of radioactivity; however, it also results in the radiolabeling of any tyrosine or histidine residues present in the peptide, as well as exposure of the peptide to oxidative conditions. Materials 0.1 M sodium borate, pH 8.5 0.1% (w/v) gelatin in 0.1 M sodium borate, pH 8.5 125 I-labeled Bolton-Hunter reagent (Amersham, Du Pont NEN, or ICN Biomedicals); or substitute unlabeled Bolton-Hunter reagent and iodinate later Peptide, ideally ≥1 mg/ml in ≤100 µl of 0.1 M sodium borate, pH 8.5 1 M glycine in 0.1 M sodium borate, pH 8.5 Gel-filtration column, e.g., Sephadex G-10 Dry nitrogen tank and outlet tubing fitted with needle Polypropylene or other plastic tubes (12 × 75 mm) 1. Equilibrate a gel filtration column with 0.1% gelatin/0.1 M sodium borate, pH 8.5, to prevent loss of radiolabeled peptide by nonspecific adsorption. BSA is not recommended as a carrier protein in place of gelatin because albumin binds to Bolton-Hunter reagent. If gelatin is undesired in the final preparation (e.g., iodination is to be performed later), condition the column and then reequilibrate in buffer of choice. Alternatively, use a Sep-Pak cartridge instead of gel filtration to separate by-products and salts from the labeled peptide (see Basic Protocol 1, steps 4 to 8).

2. Immediately prior to use, evaporate 125I-labeled Bolton-Hunter reagent to dryness by inserting a needle through the rubber septum and admitting a gentle stream of dry nitrogen, using the manufacturer-supplied charcoal trap as a vent to contain any released volatile radioiodine. Do not splash solution onto upper parts of the vial. If using unlabeled Bolton-Hunter reagent, prepare a stock solution in anhydrous organic solvent and dilute into borate buffer at the time of reaction to achieve a 3- to 5-fold molar excess over peptide. Unlabeled Bolton-Hunter reagent is also available as a water-soluble (sulfo) derivative (at higher cost), obviating the need for organic solvent and offering an advantage for labeling cell surface proteins because the sulfo derivative is membrane impermeable.

3. Chill peptide in borate buffer to 0°C on ice. Add peptide to vial containing dried 125 I-labeled Bolton-Hunter reagent (or freshly dissolved unlabeled Bolton-Hunter reagent) and wait 15 to 30 min at 0°C, agitating the reaction occasionally. Incorporation is greatest when using high peptide concentrations. Phosphate or bicarbonate buffers, pH 8.5, may be used instead of borate, but avoid amine-containing buffers (ammonium, glycine, Tris) as well as azides and thiols. Being an N-hydroxysuccinimide ester, Bolton-Hunter reagent is sensitive to hydrolysis, with a half-life of a few hours at pH 7.0 and only ∼10 min at pH 8.5; however, its reaction with amino groups is greatly accelerated at the higher pH since more amine is in the deprotonated (active) form.

4. Add 100 µl of 1 M glycine in 0.1 M sodium borate, pH 8.5, to quench the reaction and wait 5 min, or use a corresponding amount of Tris or any other amine for quenching. In Vitro Radiolabeling of Peptides and Proteins

Longer incubations at 0°C (>4 hr) may improve yields and obviate the need for quenching by consuming all of the reagent.

3.3.10 Current Protocols in Protein Science

5. Load mixture onto gel filtration column and collect 0.5-ml fractions in 12 × 75–mm plastic tubes, using borate (or other) buffer for elution. Monitor fractions with a hand-held γ counter, or count aliquots of each fraction in a γ counter to locate the peptide-containing fractions. 6. Resolve products by HPLC if desired and determine specific radioactivities (see Support Protocol 2). 14

C OR 3H LABELING AT LYSINE RESIDUES OR N-TERMINUS BY ACETYLATION USING ANHYDRIDE

BASIC PROTOCOL 3

There are two main advantages of labeling with 14C or 3H instead of 125I. First, for peptides that are unacceptably altered as a result of iodination, different sites may be targeted for modification (i.e., lysine or cysteine), or the peptide may even be labeled without changing its structure by replacing carbon or hydrogen atoms with the corresponding radioisotope (see Basic Protocol 4 and Basic Protocol 5). Second, the half-lives of 14C and 3H are much longer than that of 125I (5760 and 12.26 years, respectively, vs. 60 days), so labeling need not be done often. However, the specific radioactivities of peptides labeled with 14C or 3H are generally correspondingly lower, and the labeling techniques are not as widely applied as radioiodination. Also, counting β emissions involves the use of a liquid scintillation cocktail and is therefore slightly more cumbersome than counting γ emissions. Acetylation with [14C]- or [3H]anhydride is a simple reaction that will modify amino groups (lysine and/or the N-terminus of a peptide; Fig. 3.3.1; Fraenkel-Conrat and Colloms, 1967). The most common side reaction is O-acetylation of tyrosine, which is reversible under mild alkali conditions (Means and Feeney, 1971). CAUTION: All procedures should be carried out in an externally vented hood. Materials Saturated sodium acetate solution Peptide, ideally ≥1 mg/ml in ≤100 µl H2O or sodium acetate [14C]- or [3H]acetic anhydride (Amersham, Du Pont NEN, ICN Biomedicals, or Sigma) Acetic anhydride, unlabeled (optional) 1. Add 1 vol of saturated sodium acetate solution to the peptide (unless already in this buffer) and cool to 0°C on ice. 2. Add [14C]- or [3H]acetic anhydride, with or without 2 molar equivalents of additional unlabeled acetic anhydride, in four equal installments at 10- to 15-min intervals. Because the anhydride is rapidly hydrolyzed, addition over several installments is the preferred method. The use of excess unlabeled anhydride increases the extent of reaction but dilutes the specific radioactivity of the products (analogous to stoichiometric vs. trace iodination; see Support Protocol 1).

3. Remove by-products by gel filtration (UNIT 8.3), dialysis (APPENDIX 3B), or solid-phase extraction (see Basic Protocol 1, steps 4 to 8), and count fractions in an appropriate liquid scintillation cocktail. Resolution of individual products may be possible by HPLC (see Support Protocol 2).

Detection and Assay Methods

3.3.11 Current Protocols in Protein Science

Supplement 2

ALTERNATE PROTOCOL 3

14

C OR 3H LABELING AT LYSINE RESIDUES OR N-TERMINUS BY REDUCTIVE ALKYLATION

An alternative two-step procedure used to label peptides is treatment with an aldehyde followed by reduction of the resulting Schiff base with sodium cyanoborohydride (Fig. 3.3.1; Tack and Wilder, 1981; Jentoft and Dearborn, 1983). Most aldehydes give the monosubstituted alkylamine product, but formaldehyde goes on to give the dimethyl product. 14C can be incorporated via the aldehyde, or 3H can be added via labeled sodium cyanoborohydride. The latter gives higher specific radioactivities, reportedly ≥50 Ci/mmol. One advantage of this approach over acetylation is that the net charge on amino groups is not substantially altered (there is only a small change in pKa value). CAUTION: All procedures should be carried out in an externally vented hood. Materials 0.2 M sodium borate, pH 9.0 Peptide, ideally ≥1 mg/ml in 0.1 to 1.0 ml of 0.2 M sodium borate, pH 9.0 [3H]sodium borohydride (Amersham, Du Pont NEN, or ICN Biomedicals) or [3H]sodium cyanoborohydride (Amersham) 0.01 M NaOH 37% (12.4 M) formaldehyde stock [14C]formaldehyde and unlabeled sodium cyanoborohydride (optional alternative for labeling with 14C instead of 3H) 1. Add an equal volume of 0.2 M sodium borate, pH 9.0, to the peptide (unless already in this buffer) and cool to 0°C on ice. 2. Dilute formaldehyde stock (12.4 M) to 0.1 to 0.2 M and add to peptide on ice to give a 2- to 6-fold molar excess over amino groups (e.g., 10 to 20 µl). More concentrated formaldehyde in this step may lead to peptide cross-linking. For 14C labeling, use [14C]formaldehyde in this step.

3. Immediately dissolve [3H]sodium borohydride (or [3H]sodium cyanoborohydride) in 0.01 M NaOH to a final concentration of 1.0 Ci/ml and add to peptide on ice for 10 min. Use a molar ratio of sodium borohydride to formaldehyde of between 0.25 and 0.40 (e.g., 30 to 50 µl). Sodium cyanoborohydride is a superior reagent for this purpose because it reduces the Schiff base formed initially between the peptide and formaldehyde without reducing disulfides or aldehydes. [3H]sodium borohydride or [3H]sodium cyanoborohydride should be stored frozen in small aliquots rather than frozen and thawed repeatedly. For 14C labeling, use unlabeled sodium cyanoborohydride in this step.

4. Remove by-products by gel filtration (UNIT 8.3), dialysis (APPENDIX 3B), or solid-phase extraction (see Basic Protocol 1, steps 4 to 8), and count fractions in an appropriate liquid scintillation cocktail. Resolution of individual products may be possible by HPLC (see Support Protocol 2).

In Vitro Radiolabeling of Peptides and Proteins

3.3.12 Supplement 2

Current Protocols in Protein Science

14

C OR 3H LABELING AT CYSTEINE RESIDUES USING IODOACETIC ACID OR IODOACETAMIDE

ALTERNATE PROTOCOL 4

To label cysteine residues, a peptide is reduced and allowed to react with either iodoacetic acid (to give the carboxymethyl derivative) or iodoacetamide (to give the carboxamidomethyl derivative, which is uncharged; see Fig. 3.3.1). For large peptides containing disulfide bonds, this procedure is often used to denature the peptide irreversibly. Carboxymethylcysteine content is readily determined by amino acid analysis following acid hydrolysis. [14C]- or [3H]iodoacetic acid and [14C]iodoacetamide are commercially available for radiolabeling. Possible side reactions are alkylation of histidine, methionine, or lysine, each of which can usually be controlled by pH and other reaction conditions (Crestfield et al., 1963; Means and Feeney, 1971). CAUTION: All procedures should be carried out in an externally vented hood. Materials 0.5 M Tris⋅Cl/6 M guanidine⋅HCl/0.002 M disodium EDTA, pH 8.6 Peptide, ideally ≥1 mg/ml in 0.1 to 1.0 ml 0.5 M Tris⋅Cl/6 M guanidine⋅HCl/0.002 M disodium EDTA, pH 8.6 Dithiothreitol 1.0 M NaOH [14C]- or [3H]iodoacetic acid or [14C]iodoacetamide (Amersham, Du Pont NEN, or ICN Biomedicals) Iodoacetic acid or iodoacetamide, unlabeled (optional) 1. Peptides containing disulfide bonds or significant tertiary structure require reduction and/or denaturation prior to alkylation of cysteine residues. For this, prepare a solution of peptide in 0.5 M Tris⋅Cl/6 M guanidine⋅HCl/0.002 M disodium EDTA (pH 8.6), incubate for 1 hr at 37°C, add dithiothreitol (50-fold molar excess over disulfide), and incubate overnight at 37°C. 2. Cool to room temperature and add desired amount of radiolabeled iodoacetic acid (or iodoacetamide) dissolved in 1⁄10 vol of 1.0 M NaOH for 30 min in the dark. Optionally, include additional unlabeled iodoacetic acid (or iodoacetamide) at a 2-fold molar excess over the dithiothreitol added to increase the extent of the reaction (analogous to stoichiometric vs. trace iodination; see Support Protocol 1).

3. Remove by-products by gel filtration (UNIT 8.3), dialysis (APPENDIX 3B), or solid-phase extraction (see Basic Protocol 1, steps 4 to 8), and count fractions in an appropriate liquid scintillation cocktail. Resolution of individual products may be possible by HPLC (see Support Protocol 2). 3

H LABELING AT TYROSINE RESIDUES BY REDUCTION OF 127I-LABELED PEPTIDE

BASIC PROTOCOL 4

It is possible to label one or more tyrosine residues of a peptide stoichiometrically with H to a relatively high specific radioactivity (e.g., 50 Ci/mmol). Since 3H substitutes for hydrogen, the peptide’s structure remains unaltered. However, this approach is more laborious and expensive than the previous methods, requiring the preparation of a 127 I-labeled peptide that is then catalytically reduced in the presence of tritium gas, leading to replacement of each 127I (or other halogen atom) with 3H. 3

The 127I-labeled peptide precursor can be produced in two ways: by stoichiometric iodination with 127I (see Basic Protocol 1, Support Protocol 1, and Support Protocol 2) or by incorporating a suitably protected 127I-labeled tyrosine derivative (either monoiodi-

Detection and Assay Methods

3.3.13 Current Protocols in Protein Science

nated or diiodinated) during de novo peptide synthesis (e.g., N-Boc-O-3-bromobenzyl3,5-diiodo-L-tyrosine, available from Peninsula). The 127I-labeled peptide is then submitted to a specialized facility for reductive tritiation (Amersham and Du Pont NEN offer this service). BASIC PROTOCOL 5

INTRODUCTION OF LABELED AMINO ACID RESIDUES DURING PEPTIDE SYNTHESIS The most specific and least disruptive labeling procedure is incorporation of a suitably protected radioactive amino acid into any desired position of a peptide during its solid-phase synthesis. However, because this requires an entire synthesis rather than use of just an aliquot of peptide, and because the protected derivatives of radioactive amino acids are not generally available and therefore must be prepared, it is not commonly done. Furthermore, introduction of a radioactive amino acid into a peptide synthesizer will be possible only if the operator of the instrument is prepared to work with radioactive samples. However, amino acids containing 14C, 3H, or 35S (for methionine) are available, procedures for attaching various protecting groups to these amino acids are quite well established, and the result may be an abundant supply of a radiolabeled peptide unperturbed in structure and not obtainable by any other means. Specific radioactivities are typically two to three orders of magnitude lower than for radioiodinated peptides. The simplest approach is to select a radioactive amino acid that requires protection during solid-phase peptide synthesis only on its α-amino group and not on its side chain (e.g., 14 C- or 3H-labeled alanine, isoleucine, leucine, valine, or phenylalanine, all available from Amersham, Du Pont NEN, ICN Biomedicals, or Sigma). If classical t-butyloxycarbonyl (t-BOC) chemistry is to be used during peptide synthesis, a variety of procedures are available for attaching t-BOC to α-amino groups (Stewart and Young, 1984); one such procedure is outlined below for leucine. Materials L-Leucine, unlabeled 14 3 L-[ C]- or L-[ H]leucine, 1 to 5 mCi Triethylamine 2-(t-butoxycarbonyloxyimino)-2-phenylacetonitrile (BOC-ON, Aldrich) Dioxane Diethyl ether, anhydrous 1.0 M HCl, 4°C Separatory funnel 1. Add 131 mg L-leucine (1 mmol) and the desired number of millicuries L-[14C]- or 3 L-[ H]leucine (supplied in water) to a 25-ml round-bottom flask. Other amino acids or their side chain protected derivatives can be substituted for leucine. Unlabeled amino acid is used because the amount of 14C- or 3H-labeled material is very small in molar terms (e.g., 2 mCi [3H]leucine is <1 ìmol); safety and cost considerations generally preclude the use of stoichiometric amounts of radiolabeled amino acid.

2. Add 210 µl triethylamine (1.5 mmol). 3. Add 271 mg BOC-ON (1.1 mmol).

In Vitro Radiolabeling of Peptides and Proteins

4. Add a mixture of 600 µl dioxane and enough water to bring the total volume of water to 600 µl; e.g., if the radiolabeled amino acid is supplied in 400 µl water, add 600 µl dioxane and 200 µl water.

3.3.14 Current Protocols in Protein Science

5. Stir with a magnetic stir bar 3 hr at room temperature. The yellow, heterogeneous reaction will become homogeneous. 6. Add 2 ml water and 2 ml diethyl ether and transfer the reaction mixture to a separatory funnel. The aqueous (bottom) layer contains the triethylammonium salt of the t-BOC-amino acid product; the ether (upper) layer contains an oxime by-product from the reaction (bright yellow).

7. Drain the aqueous layer and wash it three times with an equal volume of diethyl ether in the separatory funnel to remove unwanted oxime. 8. Add a small volume of 4°C 1.0 M HCl to bring the aqueous layer to pH 2 and precipitate the t-BOC-amino acid product. 9. Add an equal volume of diethyl ether to the aqueous layer in the separatory funnel, shake, and drain off the aqueous layer. Save the ether layer and repeat ether extraction of the aqueous layer two more times. Pool the combined ether layers into an Erlenmeyer flask. The t-BOC-amino acid product now partitions into the ether layer after being converted by HCl from a salt to the free acid. Some t-BOC-amino acids (serine, threonine) remain soluble in water and are best extracted if the aqueous layer is first saturated with NaCl before adding ether.

10. Allow the product to crystallize for several hours on ice or overnight at 4°C. First blowing off some ether with a gentle stream of dry nitrogen gas may be helpful. Wash the white, crystalline product carefully with cold diethyl ether. The t-BOC-amino acid should be checked for nonreactivity of its amino group by a ninhydrin (Sarin et al., 1981) or other analytical test prior to using it in peptide synthesis.

SELECTIVE LABELING ON N-TERMINUS DURING PEPTIDE SYNTHESIS

ALTERNATE PROTOCOL 5

The N-terminus of a peptide that contains one or more lysine amino groups can be selectively labeled after the peptide has been synthesized by solid-phase methods but before amino acid side chains (including that of lysine) have been deprotected. For example, N-terminal acetylation of a newly synthesized protected peptide can be used to label the peptide with 14C or 3H without affecting lysine residues that might be important for activity (see Basic Protocol 3). Alternatively, addition of a Bolton-Hunter group allows subsequent iodination at this position (see Basic Protocol 2); tyrosine, if present, will also be iodinated unless radiolabeled Bolton-Hunter reagent is used. Various other reagents can be used to modify a peptide at its N-terminus. For example, a dinitrophenyl (DNP) group can be attached specifically to the N-terminus of a protected resin-bound peptide using dinitrofluorobenzene (100 mol equivalents), N,N-diisopropylethylamine (20 mol equivalents), and dichloromethane as the solvent for the coupling reaction (2 hr at room temperature with shaking in the dark; T. Tsomides, unpub. observ.). Specific details of the protocols to be used for N-terminal labeling reactions depend on the particular chemistries involved in the peptide’s synthesis and cleavage. In particular, any label attached selectively to the N-terminus must be stable during the peptide side chain deprotection step (e.g., hydrogen fluoride or trifluoroacetic acid). In the example above, DNP is stable during deprotection with trifluoromethane sulfonic acid. Detection and Assay Methods

3.3.15 Current Protocols in Protein Science

Supplement 4

COMMENTARY Background Information

In Vitro Radiolabeling of Peptides and Proteins

Detection of minute quantities of a peptide or protein is often made possible by radiolabeling procedures. The most common labeling strategy, radioiodination with 125I, is relatively straightforward to perform and low in cost. Thus, a peptide with two tyrosine residues can be radiolabeled with 125I to a specific radioactivity of 5 × 109 cpm/µg (see Support Protocol 2), allowing its detection at subfemtomole levels, or several orders of magnitude lower than absorbance- or fluorescence-based detection methods (UNIT 3.1). The main drawback of radioiodination (other than the need to work safely with a potentially hazardous radioisotope) is that some peptides may be unreactive or may be damaged by the introduction of iodine atoms (each as large as a phenyl ring) or by the oxidative conditions typically employed during the labeling reaction. These problems may be more acute for a large protein with native tertiary structure and more oxidation-sensitive residues (tryptophan, methionine, and cysteine) than for a short peptide; nevertheless, even small peptides can be significantly altered in their biological activities by iodination. In most cases these problems can be obviated by modifying the radiolabeling strategy (see Table 3.3.1). Standard radioiodination methods rely on the oxidation of 125I (in the form of Na125I) to its reactive state (which can be considered “I+”) for electrophilic addition to the phenolic and imidazole side chains of the amino acids tyrosine and histidine, respectively (see Fig. 3.3.1). At a pH between 6 and 7, tyrosine labeling predominates, whereas at a higher pH there is also labeling on histidine (Wolff and Covelli, 1969; Tsomides and Eisen, 1993). Different methods of oxidizing the 125I include IodoBeads (a convenient immobilized form of chloramine T; Basic Protocol 1), soluble chloramine T, and Iodogen (a water-insoluble reagent plated onto tubes prior to use; Alternate Protocol 1). A gentler oxidative procedure uses the enzyme lactoperoxidase along with hydrogen peroxide (Alternate Protocol 2). All four methods can be expected to give reasonable specific radioactivities (lowest for lactoperoxidase), and their relative merits have been assessed for some peptides (Thean, 1990; Rizzino and Kazakoff, 1991). For peptides that lack tyrosine and histidine or that are unacceptably altered by standard

iodination procedures, an alternate strategy is to label free amino groups (lysine side chains and the peptide N-terminus, unless it is proline or blocked) using an iodinated acylating agent (e.g., Bolton-Hunter reagent; see Basic Protocol 2 and Fig. 3.3.1; Bolton and Hunter, 1973). This mild coupling reaction avoids direct contact of the peptide or protein with harsh oxidizing conditions, but the resulting labeled product is likely to have a lower specific radioactivity (because Bolton-Hunter reagent suffers competing hydrolysis during the reaction); a second drawback is the relatively high cost of this reagent. A similar though less commonly tried modification using an iodinated amidinating reagent (Wood et al., 1975) has the potential advantage that the positive charge on lysine (pKa ∼10) is retained in the final labeled product (an amidine with pKa 11.5 to 12.5). Besides labeling with 125I, it is straightforward to radiolabel peptides or proteins with 14C or 3H using various methods (see Table 3.3.1). This may be desirable if all iodination options fail, or if it is critical to obtain a radioactive product devoid of structural alterations. Usually the products of 14C or 3H labeling will have much lower specific radioactivities than the products of 125I labeling, reflecting the different disintegration rates for these radioisotopes (∼35,000 and 75 times lower for 14C and 3H, respectively, than for 125I). Also, counting β emissions requires liquid scintillants and is therefore less convenient than counting γ emissions, which requires no additives to the sample. However, the longer half-lives of 14C and 3H (5760 and 12.26 years, respectively) render the resulting products useful for much longer than 125I-labeled products (t1⁄2 ∼60 days). A selection of protocols for labeling a peptide with 14C or 3H is given in this unit: acetylation on amino groups with acetic anhydride (see Basic Protocol 3), reductive alkylation on amino groups with formaldehyde and sodium cyanoborohydride (see Alternate Protocol 3), and alkylation on cysteine with iodoacetic acid or iodoacetamide (see Alternate Protocol 4). Each of these procedures results in modification of the peptide structure, albeit on different residues than does standard radioiodination (Fig. 3.3.1). To label a peptide without altering its structure, two alternatives are available: 3H labeling by catalytic reduction of (nonradioactive) 127I-labeled tyrosine (see Basic Protocol 4), and de novo peptide synthesis using a radiolabeled (and suitably protected) amino acid

3.3.16 Supplement 4

Current Protocols in Protein Science

derivative (see Basic Protocol 5; Stewart and Young, 1984). These methods are much more laborious and costly, but may be useful when modification of a peptide’s structure is unacceptable.

Critical Parameters and Troubleshooting Amount of radioisotope The amount of radioisotope to be used is often a compromise among the desire for high specific radioactivities (curies per millimole or cpm per microgram), a practical limit to the amount of radioactivity that can be afforded and safely handled, and (particularly for a large protein) any risk of altering tertiary structure by excessive substitution or radiation damage. Commonly, 1 to 5 mCi of 125I (or 131I) is used; however, it is possible to scale up the 125I and increase specific radioactivities proportionately, because iodide is almost always limiting relative to iodinatable sites (e.g., 2 mCi 125I is only ∼1.2 nmol). Conversely, lowering the amount of peptide or protein will also improve specific radioactivities, but lowering the amount of substrate too far will cause unacceptable losses of material by nonspecific adsorption to tubes; a practical lower limit is 5 to 10 µg of peptide. Sensitive amino acid residues During oxidative iodinations, the amino acid residues at greatest risk for damage are tryptophan, methionine, and cysteine. The problem is greatest for tryptophan, as an oxidized indole ring cannot be restored by subsequent treatment with a reducing agent such as dithiothreitol. When these residues are critical for peptide function, the lactoperoxidase or Bolton-Hunter methods may be preferred for 125I labeling. Iodination work-up Several options are available for the removal of unbound radioactive iodide from a peptide once the reaction is complete, including solidphase extraction (e.g., using Sep-Pak cartridges; Basic Protocol 1), anion-exchange chromatography (UNIT 8.2), gel filtration (UNIT 8.3), and dialysis (APPENDIX 3B). The choice of desalting procedure will depend on the peptide, particularly its size and charge. For small peptides the Sep-Pak method works well, because the peptide binds to the Sep-Pak cartridge, allowing extensive washing to remove all traces of free iodide (Tsomides and Eisen, 1993). For

larger peptides and proteins, anion-exchange chromatography (e.g., Dowex column) and gel filtration are popular, as the peptide is eluted immediately while iodide is retained on the column. To ensure that the removal of unbound iodide is complete, many investigators precipitate a radiolabeled protein with trichloroacetic acid (TCA) to determine the proportion of TCA-precipitable counts. Dialysis is a timeconsuming but effective method for removing traces of iodide, provided the peptide is large enough to be retained by the dialysis membrane used. Note that for highly negatively charged peptides, there may be some retention on anion-exchange columns. For peptides of 10 to 20 amino acids, separation of labeled peptide from free iodide may be poor on an anion-exchange or gel-filtration column because the peptide’s molecular mass is so close to the exclusion limit of the column. For such peptides the Sep-Pak method may be advantageous. Once unbound radioactive iodide has been removed, the labeled peptide mixture can be used as is, or the individual products can be separated from each other as well as from the unreacted peptide that is almost always present (see Support Protocol 2). If unlabeled peptide is removed by reversed-phase HPLC, the specific radioactivity of the labeled peptide will be maximized and will equal the specific radioactivity of carrier-free 125I (∼2000 Ci/mmol) multiplied by the number of 125I atoms incorporated per peptide molecule. Poor iodination results Table 3.3.2 gives potential causes and possible remedies for poor iodination results.

Anticipated Results In radioiodinating a purified protein, it is customary to aim for incorporation of up to one 125I atom per protein molecule, i.e., ∼20 µCi/µg for a protein of molecular mass 100,000 Da. For short peptides it is rare to label all the molecules, but specific radioactivities are typically on the same order (e.g., 20 µCi/µg for a peptide of mass 1000 Da, representing only 1% of the peptide labeled and 99% unlabeled). More labeled peptide is obtained when nonradioactive 127I is added during the iodination reaction (see Support Protocol 1). For example, Figure 3.3.2 shows the results of iodinating an eight-residue peptide with 20 mCi 125I and 5 µmol 127I, followed by separating the products from unlabeled peptide by HPLC.

Detection and Assay Methods

3.3.17 Current Protocols in Protein Science

Table 3.3.2

Troubleshooting the Iodination Reaction

Problem

Potential cause

Possible remedy

Poor iodine incorporation

Peptide contains reducing agent, e.g., from synthesis/cleavage Peptide contains detergent or other component that becomes iodinated Tyrosine/histidine residues are unreactive because of tertiary structure Iodo-Beads are unreactive

HPLC-purify or desalt peptide prior to use HPLC-purify peptide

Poor peptide recovery

Solubility problem

Change solvent system or adjust pH

Failed elution from Sep-Pak

Replace with ion exchange, gel filtration, or dialysis Increase amount of peptide or add carrier protein (after iodination reaction) Use chloramine T instead of Iodo-Beads

Nonspecific losses

Labeled peptide unreactive

Tyrosine is crucial for activity

Other peptide damage

Time Considerations Each of the listed protocols is easily completed within a few hours (not including HPLC purification if chosen), and with practice can be done in even less time. The resulting radioactive peptides will, of course, decay according to the half-life of the radioisotope used (or in some cases faster, if there is radiation damage). To calculate specific radioactivity after t days, simply multiply the initial specific radioactivity by 2 ( − t ⁄t ⁄2 ), where t1⁄2 is the half-life of the radioisotope (60 days for 125I). 1

Literature Cited Bolton, A.E. and Hunter, W.M. 1973. The labelling of proteins to high specific radioactivities by conjugation to a 125I-containing acylating agent. Biochem. J. 133:529-539. Chersi, A., Trinca, M.L., and Camera, M. 1988. 14 C-labeling of synthetic peptides. J. Immunol. Methods 110:271-273. In Vitro Radiolabeling of Peptides and Proteins

Treat peptide with denaturing agent Use alternate strategy, e.g., Bolton-Hunter reagent Test for color (see Support Protocol 1); use fresh lot

Crestfield, A.M., Moore, S., and Stein, W.H. 1963. The preparation and enzymatic hydrolysis of reduced and S-carboxymethylated proteins. J. Biol. Chem. 238:622-627.

Iodinate for shorter time and/or use less label Use alternate strategy, e.g., Bolton-Hunter reagent Use alternate strategy

Edelhoch, H. 1962. The properties of thyroglobulin. VIII. The iodination of thyroglobulin. J. Biol. Chem. 237:2778-2787. Eisen, H.N. and Keston, A.S. 1949. The immunologic reactivity of bovine serum albumin labeled with trace amounts of radioactive iodine (131I). J. Immunol. 63:71-80. Fraenkel-Conrat, H. and Colloms, M. 1967. Reactivity of tobacco mosaic virus and its protein toward acetic anhydride. Biochemistry 6:27402745. Fraker, P.J. and Speck, J.C., Jr. 1978. Protein and cell membrane iodinations with a sparingly soluble chloroamide, 1,3,4,6-tetrachloro-3α,6αdiphenylglycoluril. Biochem. Biophys. Res. Commun. 80:849-857. Gahmberg, C.G. and Andersson, L.C. 1977. Selective radioactive labeling of cell surface sialoglycoproteins by periodate-tritiated borohydride. J. Biol. Chem. 252:5888-5894. Greenwood, F.C., Hunter, W.M., and Glover, J.S. 1963. The preparation of 131I-labelled human growth hormone of high specific radioactivity. Biochem. J. 89:114-123.

3.3.18 Current Protocols in Protein Science

Hubbard, A.L. and Cohn, Z.A. 1975. Externally disposed plasma membrane proteins. I. Enzymatic iodination of mouse L cells. J. Cell Biol. 64:438-460.

Thean, E.T. 1990. Comparison of specific radioactivities of human α-lactalbumin iodinated by three different methods. Anal. Biochem. 188:330-334.

Hunter, W.M. and Greenwood, F.C. 1962. Preparation of iodine-131 labelled human growth hormone of high specific activity. Nature 194:495496.

Tsomides, T.J. and Eisen, H.N. 1993. Stoichiometric labeling of peptides by iodination on tyrosyl or histidyl residues. Anal. Biochem. 210:129-135.

Jentoft, N. and Dearborn, D.G. 1983. Protein labeling by reductive alkylation. Methods Enzymol. 91:570-579.

Wood, F.T., Wu, M.M., and Gerhart, J.C. 1975. The radioactive labeling of proteins with an iodinated amidination reagent. Anal. Biochem. 69:339349.

Marchalonis, J.J. 1969. An enzymatic method for the trace iodination of immunoglobulins and other proteins. Biochem. J. 113:299-305.

Wolff, J. and Covelli, I. 1969. Factors in the iodination of histidine in proteins. Eur. J. Biochem. 9:371-377.

Marchalonis, J.J., Cone, R.E., and Santer, V. 1971. Enzymic iodination: A probe for accessible surface proteins of normal and neoplastic lymphocytes. Biochem. J. 124:921-927. Markwell, M.A.K. 1982. A new solid-state reagent to iodinate proteins: Conditions for the efficient labeling of antiserum. Anal. Biochem. 125:427432. Rizzino, A. and Kazakoff, P. 1991. Iodination of peptide growth factors: Platelet-derived growth factor and fibroblast growth factor. Methods Enzymol. 198:467-479. Sarin, V.K., Kent, S.B.H., Tam, J.P., and Merrifield, R.B. 1981. Quantitative monitoring of solidphase peptide synthesis by the ninhydrin reaction. Anal. Biochem. 117:147-157. Stewart, J.M. and Young, J.D. 1984. Solid Phase Peptide Synthesis. Pierce Chemical Co., Rockville, Ill.

Key References Means, G.E. and Feeney, R.E. 1971. Chemical Modification of Proteins. Holden-Day, Inc., San Francisco, Calif. Excellent overview of side chain chemistries, including but not limited to radiolabeling procedures. Tsomides and Eisen, 1993. See above. Describes use of Sep-Pak to remove free iodine, HPLC to separate iodinated products, Edman degradation to identify products, and pH dependence of tyrosine vs. histidine iodination using Iodo-Beads.

Contributed by Ton N.M. Schumacher and Theodore J. Tsomides Massachusetts Institute of Technology Cambridge, Massachusetts

Tack, B.F. and Wilder, R.L. 1981. Tritiation of proteins to high specific activity: Application to radioimmunoassay. Methods Enzymol. 73:138147.

Detection and Assay Methods

3.3.19 Current Protocols in Protein Science

Assays for Determination of Protein Concentration

UNIT 3.4

Accurate measurement of protein concentration is critical since the results are used in other calculations, such as determination of enzyme activity. Errors in protein concentration determination tend to amplify overall errors in these calculations. Furthermore, protein assays are often purchased as kits from commercial suppliers, which result in a poor understanding of the underlying chemistry. This unit presents a survey of the common protein assay methods and highlights their usefulness and limitations. Additionally, the chemistry underlying each assay is explained to aid in troubleshooting and assay selection. There is no single protein assay method that yields absolutely accurate results. Each method has different advantages and limitations. The primary intention of this unit is to inform users how to select the most appropriate protein assay for a specific application. The Kjeldahl method (Ballentine, 1957) and the acid digestion–ninhydrin method (Lovrien and Matulis, 1995) are no longer in general use and are not included here. This unit discusses the following protein assay methods that are commonly used in biochemical laboratories: the Lowry assay (see Basic Protocol 1), the Bradford assay (see Basic Protocol 2 and Alternate Protocol 1), the BCA assay (see Basic Protocol 3 and Alternate Protocol 2), and UV spectroscopy to determine protein concentration (see Basic Protocol 4). Support Protocol 1 discusses standard curves and data processing in detail and is a good place for a novice to start before beginning any experimentation. Also included are two precipitation strategies for dealing with buffer incompatibility. Finally, many applications of SDS-PAGE require equal loading of samples based on total protein. Typically, this requires laborious extraction in a buffer compatible with a protein assay, before adding SDS-PAGE loading buffer. Alternate Protocol 3 details a method for alkylation of excess reducing agent in SDS-PAGE loading buffer for direct analysis in the BCA assay. The protocols in this unit should produce valid results for most applications. There are also kits available for protein determination from Sigma, BioRad, and Pierce. Prior to employing an assay kit or protocol, the user is advised to consult the Strategic Planning section because it describes how to select the most appropriate assay for a particular task. The assays have different strengths and weaknesses, particularly regarding buffer compatibility. Since some investigators may have limited experience with biochemical techniques, the Commentary presents a discussion of the critical parameters for protein assays, focusing on the standard curve and a brief review of spectrophotometry. Finally, a Support Protocol provides suggestions for processing data using a spreadsheet.

STRATEGIC PLANNING Assay choice Two common applications of the protein assay are enzyme activity assays and equal protein loading of SDS-PAGE gels. The strategies and considerations for these applications are considerably different. In enzyme activity calculations, the primary concern is the accuracy of the results, whereas for equal loading of SDS-PAGE gels, precision is more important. There are many other applications for protein assays, e.g., in nutrition and pharmacology, and it is strongly recommend that the literature of the particular field Detection and Assay Methods Contributed by Bradley J.S.C. Olson and John Markwell Current Protocols in Protein Science (2007) 3.4.1-3.4.29 C 2007 by John Wiley & Sons, Inc. Copyright


3.4.1 Supplement 48

be consulted before employing one of the assays presented in this unit to ensure that these assays are applicable in the field. It is particularly important to realize that these assays are estimation procedures unless the individual assay has been standardized to total amino acid analysis (UNIT 3.2). One of the most difficult aspects of assaying protein concentration is the selection of an assay compatible with the sample. All of the assays presented in this unit detect specific properties of a protein, not the entire protein itself; thus protein and buffer composition will be the primary determinants of the appropriate assay. Figure 3.4.1 presents a flow chart of the assay selection process. If selection of a routine estimation procedure for relatively pure protein samples is the goal, highest accuracy will be achieved by standardizing the assay to a total amino acid analysis of the unknown (UNIT 3.2). All of the presented methods have different sensitivities to different proteins such as bovine serum albumin and gamma-globulin (Stoscheck, 1990).

Figure 3.4.1 Flow chart for selecting a protein assay. To use the chart, begin with step A to find the protein assays that are most compatible with the sample. Assays for Determination of Protein Concentration

3.4.2 Supplement 48

Current Protocols in Protein Science

Figure 3.4.1 (continued) Then proceed to step B to obtain the list of assays compatible with the buffer system. Compare the results from each step to find the most compatible assay for the sample. Refer to the text for the assay and Table 3.4.1 to confirm assay compatibility before using a protein assay strategy. Abbreviations: 2-ME, 2-mercaptoethanol; DTT, dithiothreitol; SDS, sodium dodecyl sulfate. Detection and Assay Methods

3.4.3 Current Protocols in Protein Science

Supplement 48

Sample composition Sample composition is critical when choosing a protein assay. For example, a protein rich in arginine residues will produce an artificially high result using a Bradford assay, whereas the same protein will most likely produce a more accurate result using either the Lowry or BCA assay. Conversely, a protein high in cysteine would produce an artificially high result using the BCA assay, but would likely produce better results with the Lowry or Bradford assay. In general, the BCA and Lowry assays perform better with complex protein mixtures. Buffer composition Buffer composition is an important consideration when selecting a protein assay. The Lowry assay is very sensitive for determining protein concentration; however, common buffer components such as ethylenediaminetetraacetic acid (EDTA) interfere with chromophore production. The BCA assay is compatible with a wide range of detergents, including sodium dodecylsulfate (SDS), but does not tolerate reducing agents such as dithiothreitol (DTT). The Bradford assay will not tolerate high concentrations of detergents but will work in the presence of reducing agents, such as DTT or 2-ME. See Table 3.4.1 for a comprehensive table listing interfering compounds and the limiting concentration of each for the different assay methods. If it is not possible to find an assay that is compatible with the buffer system, then the best strategy is to employ a precipitation step (see Support Protocols 2 and 3). After precipitation, the sample is resuspended in a buffer compatible with one of the protein assays. High-throughput adaptations The Bradford and BCA assays are easily adapted for high-throughput analysis using a microtiter plate format, as presented in Alternate Protocols 1 and 2, respectively. These protocols are carried out in flat-bottom 96-well microtiter plates and are the methods of choice when processing a large number of unknowns. If microtiter plate–based assays are performed often, it is recommended to use a multichannel repeating pipettor to increase accuracy and decrease user fatigue as samples are dispensed. A convenient approach is to set up a master plate with pre-diluted standards and unknowns that can then be dispensed onto multiple replicate plates. Another significant advantage of this format is that a larger number of standard curve points can be processed, thus increasing the overall accuracy of the results.

Assays for Determination of Protein Concentration

Selection of microtiter plate and plate reader To adapt protein assays to high-throughput analyses, it is important to select a microtiter plate reader that will accommodate the rigors of analysis. An instrument that has two features: a tungsten and mercury or deuterium lamp as a light source, permitting assays at both UV and visible wavelengths is recommended. The instrument should also utilize interchangeable filter wheels or a diffraction grating to permit tuning of the light source. If a filter wheel is used, it is important to purchase a filter wheel with a fairly narrow bandpass (typically, a bandpass range of 10 to 20 nm; a wider bandpass may result in overlap with unbound Coomassie brilliant blue G-250 dye (CBBG) in the Bradford assay or the copper reagent in the BCA assay). Interference filters are more expensive than simple glass filters, but have a narrower bandpass. For the BCA assay, it is desirable to have a short time between each sample reading. Ideally, all 96 wells in the plate should be read in ≤2 min. Any polystyrene untreated flat-bottomed microplate should be acceptable. Suppliers include BD Falcon, Corning Costar, and Whatman.

3.4.4 Supplement 48

Current Protocols in Protein Science

Table 3.4.1 Concentration Limits of Chemicals in Protein Assaysa

Substanceb

Concentration limits Enhanced copperc

BCAd

Dyee

UVf 280 nm

205 nm

Acids and bases HCl

0.1 M

0.1 M

>1 M

0.5 M

NaOH

0.1 M

0.1 M

>1 M

25 mM

PCA

<1.25%

<1%

10%

1M

TCA

<1.25%

<1%

10%

<1%

Buffers Acetate Ammonium sulfate

>28 mM

Borate

0.2 M

0.6 M

0.1 M

10 mM

20%

1M

>50%

9% >100 mM

10 mM

Citrate

2.5 mM

<1 mM

50 mM

5%

<10 mM

Glycine

2.5 mM

1M

0.1 M

1M

5 mM

HEPES

2.5 µM

100 µM

100 mM

Phosphate

250 mM

250 mM

2M

1M

50 mM

Tris

250 mM

0.1 M

2M

0.5 M

40 mM

1%

1%

10%

<0.1%

0.30%

0.1%

<20 mM

Detergents Brij 35

1%

CHAPS Deoxycholate

1% 625 µg/ml

0.25%

Digitonin

10%

Lubrol PX

1%

10%

Octylglucoside

1%

10%

SDS

1.25%

1%

0.10%

0.10%

0.10%

Triton X-100

0.25%

1%

0.10%

0.02%

<0.01%

>10%

2%

0.30%

0.1%

Triton X-100(R) Tween 20

0.10%

1%

Dithiothreitol

50 µM

<1 mM

1M

3 mM

0.1 mM

2-Mercaptoethanol

1.8 µM

<1%

1M

10 mM

<10 mM

DNA/RNA

0.2 mg

0.1 mg

0.25 mg

1 µg

DMSO

>6.2%

5%

EDTA

125 µM

10 mM

Glycerol

25%

KCl

30 mM

Reductants

Miscellaneous 20%

<10%

0.1 M

30 mM

0.2 mM

10%

100%

40%

5%

<10 mM

1M

100 mM

50 mM continued

Detection and Assay Methods

3.4.5 Current Protocols in Protein Science

Supplement 48

Table 3.4.1 Concentration Limits of Chemicals in Protein Assaysa, continued

Substanceb

Concentration limits Enhanced copperc

BCAd

Dyee

UVf 280 nm

205 nm

NaCl

1.75 M

1M

5M

>1 M

0.6 M

Sucrose

50 mM

40%

1M

2M

0.5 M

Urea

>200 mM

3M

6M

>1 M

<0.1 M

a This table is a general guide. Figures preceded by (<) or (>) symbols indicate that the tolerable limit for the chemical is unknown

but is, respectively, less than or greater than the amount shown. Blank spaces indicate that data were unavailable. Reproduced with permission from Stoscheck (1990). b Abbreviations: PCA, perchloric acid; TCA, trichloroacetic acid; CHAPS, 3-[(3-cholamidopropyl)dimethylammonio]-1propanesulfonate; EDTA, ethylenediamine tetraacetic acid; HEPES, 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid; SDS, sodium dodecyl sulfate R, reduced; DMSO, dimethyl sulfoxide. c Figures indicate the final concentration of the chemical in the assay. d Figures indicate the concentration of the chemical in a 50-µl sample. e Figures indicate the concentration of the chemical in a 25-µl sample. f Figures indicate the concentration of the chemical that does not produce an absorbance of 0.5 over water.

BASIC PROTOCOL 1

Assays for Determination of Protein Concentration

THE LOWRY ASSAY The Lowry method (Lowry et al., 1951) relies on two different reactions. The first reaction is the formation of a copper ion complex with amide bonds, forming reduced copper in alkaline solutions. This is called a Biuret chromophore and is commonly stabilized by the addition of tartrate (Gornall et al., 1949). The second reaction is reduction of the Folin-Ciocalteu reagent (phosphomolybdate and phosphotungstate), primarily by the reduced copper-amide bond complex as well as by tyrosine and tryptophan residues. The reduced Folin-Ciocalteu reagent is blue and thus detectable with a spectrophotometer in the range of 500 to 750 nm. The Biuret reaction itself is not very sensitive. Using the Folin-Ciocalteu reagent to detect reduced copper makes the Lowry assay nearly 100 times more sensitive than the Biuret reaction alone. Several useful modifications of the original Lowry assay have been developed to increase the dynamic range of the assay over a wider protein concentration (Hartree, 1972), to make the assay less sensitive to interference by detergents (Dulley and Grieve, 1975), and to first precipitate the proteins to remove interfering contaminants (Bensadoun and Weinstein, 1976). The Lowry assay is relatively sensitive, but requires more time than other assays and is susceptible to many interfering compounds (Table 3.4.1). The following substances are known to interfere with the Lowry assay: detergents, carbohydrates, glycerol, Tricine, EDTA, Tris, potassium compounds, sulfhydryl compounds, disulfide compounds, most phenols, uric acid, guanine, xanthine, magnesium, and calcium. Many of these interfering substances are commonly used in buffers for preparing proteins or in cell extracts. This is one of the major limitations of the assay. The Lowry assay is also sensitive to variations in the content of tyrosine and tryptophan residues, a trait shared with the ultraviolet assay at 280 nm (see Basic Protool 4). The assay is linear over the range of 1 to 100 µg protein (Fig. 3.4.2). The absorbance can be read in the region of 500 to 750 nm, with 660 nm being the most commonly employed. Other wavelengths can also be used, however, and may reduce the effects of contamination (e.g., chlorophyll in plant samples interferes at 660 nm, but not at 750 nm). Also, if the A660 values are low, sensitivity can be increased by rereading the samples at 750 nm. A typical Lowry assay standard curve is depicted in Figure 3.4.2 and a typical assay spreadsheet is listed in Table 3.4.2.

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Figure 3.4.2 A sample Lowry protein assay standard curve produced using BSA at triplicate points of 0, 10, 20, 30, 40, and 50 µg. The data are fit with a linear regression by the line y = 153.06x + 0.179 with an R2 value of 0.992. The data table used to generate the figure and depiction of a typical Lowry assay is shown in Table 3.4.2.

Materials 1 mg/ml protein standard (e.g., BSA, albumin, or γ-globulin; see Support Protocol 1) Sample Lowry assay mix (see recipe) Freshly prepared diluted Folin-Ciocalteu reagent (see recipe) Test tubes (e.g., 16 × 125–mm) Spectrophotometer warmed up and set to 660 nm (or other appropriate wavelength) and cuvette 1. Prepare standards containing a range of 1 to 100 µg protein (e.g., albumin or γglobulin) to a volume of 1 ml (see Support Protocol 1 for information on standard curve preparation). 2. Add sample to water in a 16 × 125–mm test tube to yield a final volume of 1 ml. Also include two separate tubes containing water for water blanks. Additionally, use buffer blanks if necessary (see Spectrophotometry for more detail). 3. To each tube, add 5 ml of freshly prepared Lowry assay mix and thoroughly vortex. 4. Incubate tubes 10 min at room temperature. 5. Add 0.5 ml of diluted Folin-Ciocalteu reagent to each tube and vortex immediately. 6. Incubate 30 min at room temperature. Detection and Assay Methods

3.4.7 Current Protocols in Protein Science

Supplement 48

Table 3.4.2 Typical Layout of a Lowry Assay and Dataa

Standard 1 mg/ml standard (µl)

H2 O (µl)

Assay mix (ml)

Diluted Folin-Ciocalteu reagent (ml)

BSA (µg)

A660

0

1000

5

0.5

0

0

0

1000

5

0.5

0

0.009

0

1000

5

0.5

0

−0.003

10

990

5

0.5

10

0.062

10

990

5

0.5

10

0.064

10

990

5

0.5

10

0.077

20

980

5

0.5

20

0.125

20

980

5

0.5

20

0.127

20

980

5

0.5

20

0.136

30

970

5

0.5

30

0.172

30

970

5

0.5

30

0.193

30

970

5

0.5

30

0.201

40

960

5

0.5

40

0.25

40

960

5

0.5

40

0.265

40

960

5

0.5

40

0.242

50

950

5

0.5

50

0.344

50

950

5

0.5

50

0.321

50

950

5

0.5

50

0.334

A660

Protein (µg) from Eqn. 1

Equation 1: [µg protein] = (153.06 × A660 ) + 0.179 Unknowns Unknown (µl)

H2 O (µl)

Assay mix (ml)

Diluted Folin-Ciocalteu reagent (ml)

1

999

5

0.5

1

999

5

0.5

5

995

5

0.5

5

995

5

0.5

20

980

5

0.5

20

980

5

0.5

a Typical layout of a Lowry assay and data used to create the sample standard curve shown in Figures 3.4.2 and 3.4.6.

The volumes of unknown used are typical amounts required to be within the dynamic range of the assay.

7. Vortex the tubes, zero the spectrophotometer with the blank, and measure absorbance at 660 nm (or other appropriate wavelength).

Assays for Determination of Protein Concentration

The absorbance will not change significantly if all the samples are read at the same time. The Lowry assay is not an endpoint assay, so samples will change in absorbance if too much time elapses between sample readings. The typical time that elapses during the reading of samples (<10 min) does not usually result in significant changes in absorbance.

8. Refer to Support Protocol 1 for data analysis.

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THE BRADFORD ASSAY The Bradford assay (Bradford et al., 1976) is a popular protein assay because it is simple, rapid, inexpensive, and sensitive. The Bradford assay is based on the direct binding of Coomassie brilliant blue G-250 dye (CBBG) to proteins at arginine, tryptophan, tyrosine, histidine, and phenylalanine residues. The assay primarily responds to arginine residues (eight times as much as the other listed residues), therefore, if the sample is rich in arginines (e.g., histone), it may be necessary to use an arginine-rich standard as well. Anionic CBBG binds to these residues producing an absorbance maximum at 595 nm, whereas the free dye in solution has an absorbance maximum at 470 nm (Fig. 3.4.3). The assay is monitored at 595 nm in a spectrophotometer, and thus measures the CBBG complex with the protein, which results in an absorption peak shift.

BASIC PROTOCOL 2

The absorption spectra of the bound and free dye overlap and cause the Bradford assay to respond nonlinearly to protein concentration, conflicting with a common misconception that the response is linear with respect to protein concentration. However, when a standard curve is performed, a second-order curve will fit the data more accurately than a linear fit (Fig. 3.4.4). Since the data will be analyzed with software that is capable of fitting second-order curves, there is no reason not to use an appropriate curve fit to the standards. One crucial aspect of this assay is the buffer blank (see Spectrophotometry). Since the assay responds nonlinearly to protein concentration, it is important to be certain about the zero point. Because this point is so important to the curve fit, it is highly recommended that at least two buffer blanks be included. If it is determined that interference is not occurring, as is the usual case, proceed to use the water blank. The water and buffer blanks will have a relatively high absorbance, therefore, zeroing the spectrophotometer with air is recommended. This will produce an A595 of ∼0.5 for the blank. However, to zero the spectrophotometer with the blank would force the spectrophotometer to subtract

Figure 3.4.3 Absorbance spectra of the Bradford reagent and the Bradford reagent bound to 20 µg of BSA standard. The free reagent (solid line) has an absorbance peak of 470 nm whereas the Bradford reagent complexed with protein (dashed line) has an absorbance peak near 600 nm. Note that the unbound dye partially overlaps with the bound form of the reagent and thus leads to the nonlinear response of the Bradford assay.

Detection and Assay Methods

3.4.9 Current Protocols in Protein Science

Supplement 48

Figure 3.4.4 A sample micro-Bradford assay standard curve. BSA standard was added in triplicate at 0, 0.25, 1.25, 2.5, 5, 7.5, and 10 µg. Note that the assay does not respond linearly to the concentration of standard. The data is fit with the equation y = 4.5898x2 + 14.424x − 10.694 and has an R2 value of 0.9979. The data table used to generate the figure and depiction of a typical micro-Bradford assay is shown in Table 3.4.3.

a very large amount from every sample read. An absorbance of 0.5 is equivalent to only 31% of the light being transmitted by the reagent blank. This will tend to produce error and imprecision into the sample readings especially at high protein concentration. By zeroing against air, less noise occurs in the readings and A595 values stay below 2.0, a common upper limit for absorbance values (see Spectrophotometry). The choice of standard protein for this assay is important to the success of the assay. Many investigators have noted abnormalities of using various standards with the Bradford assay. Bovine serum albumin (BSA) was the original standard of choice, and is the standard that most likely comes with the assay if purchased as a kit. However, it has been noted that BSA has a significantly higher than “normal” response in the assay (e.g., Stoscheck, 1990) and may not always be suitable. Several researchers, therefore, use immunoglobulin G (IgG) or lysozyme as the preferred standard for the assay, but other standards may be appropriate depending on the particular sample protein. The CBBG dye used in the assay strongly binds to quartz cuvettes. Therefore, glass or plastic cuvettes should be utilized. It is convenient to use disposable plastic cuvettes, making cleanup much easier. Using a single cuvette for all the samples is recommended, as plastic cuvettes are not constructed with the same precision as quartz or glass cuvettes. By using a single microcuvette and maintaining its orientation in the sample holder, this eliminates one potential source error from the system. For the 1-ml micro-Bradford assay, after taking an A595 reading, the contents are aspirated and 0.1 ml of the next sample is used to wash the cuvette. The cuvette is again aspirated and the remaining 0.9 ml of the next sample is added to the cuvette for A595 determination. Assays for Determination of Protein Concentration

There are two major formats of this assay, each with a different detection range. The micro-assay format is designed for protein concentrations between 1 and 20 µg. The macro-assay format is designed for protein concentrations in the range of 20 to 100 µg.

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It is generally more convenient to use the micro-assay format because it uses less of the sample. The micro-assay format also results in less chemical waste and allows the concentrate to be used for more assays. The assay can also be carried out in a microwell plate, which is a very convenient way to process a large number of samples rapidly (see High-Throughput Adaptations). Some proteins, especially hydrophobic, membrane or “sticky” proteins tend to precipitate in the presence of CBBG and will appear as dark-blue clumps after the Bradford reagent has been added. Precipitation of the sample causes light scattering and incorrect reporting of absorbance. If there is formation of precipitate when the dye reagent is added to the sample, add a small amount of sodium hydroxide to help solubilize the protein (see steps 3a and 3b below). It is commonly stated that detergent or surfactant-containing samples are not compatible with the Bradford assay. It is common to lyse bacterial cultures in a proprietary surfactant solution such as BugBuster (Novagen). The authors have found that the amount of surfactant in extracts prepared with 100 µl BugBuster for pelleted cells from a 5-ml culture is sufficiently small that it does not interfere with the Bradford assay. Extracts containing 0.5 to 4 mg/ml protein require <10 µl of sample in a 1-ml total assay volume. A typical standard curve for the micro-Bradford assay is depicted in Figure 3.4.4 and a typical micro-Bradford assay spreadsheet is shown in Table 3.4.3.

Materials 1 mg/ml protein standard (e.g., BSA, albumin, or γ-globulin; see Support Protocol 1) Protein sample 1 M NaOH (optional) Bradford reagent (see recipe) Spectrophotometer warmed up at least 15 min before use and set to 595 nm, and cuvette Test tubes (e.g., 10 × 75–mm for the micro assay or 17 × 100–mm for the macro assay) Micro-Bradford assay 1a. Prepare standards containing a range of 1 to 20 µg protein (e.g., albumin or γglobulin) to a volume of 800 µl (to a volume of 700 µl if adding 1 M NaOH). See Support Protocol 1 for information on standard curve preparation. Table 3.4.3 contains detailed information for preparing a typical Bradford assay with the stand curve results depicted in Figure 3.4.4.

2a. Add sample to water in a final volume of 800 µl (700 µl if adding 1 M NaOH). 3a. Optional: Add 100 µl of 1 M NaOH to each sample and vortex. NaOH is added to ensure that the sample does not precipitate upon addition of Bradford reagent in step 4a. If a sample (not containing NaOH) precipitates upon addition of Bradford reagent then a new sample must be prepared since addition of NaOH after precipitation will not cause it to dissolve. NaOH should thus be added to the new sample prior to addition of Bradford reagent.

4a. Add 200 µl Bradford reagent, vortex, and incubate 5 min at room temperature. The Bradford assay is an endpoint assay, therefore, absorbance readings will not change significantly over time.

5a. Record blank, vortex samples again, and measure the absorbance at 595 nm. 6a. Refer to Support Protocol 1 for information on data processing. Current Protocols in Protein Science

Detection and Assay Methods

3.4.11 Supplement 48

Table 3.4.3 Typical Layout for a Micro-Bradford Assay and Dataa

Standard 1 mg/ml standard (µl)

H2 O (µl)

1 M NaOH (µl)

Bradford reagent (µl)

BSA (µg)

A595

0

700

100

200

0

0.603

0

700

100

200

0

0.603

0

700

100

200

0

0.613

1

699

100

200

1

0.675

1

699

100

200

1

0.664

1

699

100

200

1

0.662

5

695

100

200

5

0.9

5

695

100

200

5

0.878

5

695

100

200

5

0.883

10

690

100

200

10

1.069

10

690

100

200

10

1.062

10

690

100

200

10

1.062

15

685

100

200

15

1.254

15

685

100

200

15

1.257

15

685

100

200

15

1.279

20

680

100

200

20

1.443

20

680

100

200

20

1.444

20

680

100

200

20

1.43

25

675

100

200

25

1.651

25

675

100

200

25

1.631

25

675

100

200

25

1.638

Equation 2: [µg protein] = (4.5898 × A595 ) + (14.424 × A595 ) − 10.694 2

Unknowns Unknown (µl)

H2 O (µl)

1 M NaOH (µl)

Bradford reagent (µl)

1

699

100

200

1

699

100

200

5

695

100

200

5

695

100

200

20

680

100

200

20

680

100

200

A595

Protein (µg) from Eqn. 2

a Typical layout for a micro-Bradford assay and the data used to create the sample standard curve in Figure 3.4.4. The

volumes of unknown used are typical amounts required to be within the dynamic range of the assay.

Assays for Determination of Protein Concentration

Macro-Bradford assay 1b. Prepare standards containing a range of 20 to 200 µg protein (e.g., albumin or γglobulin) in a standard volume (generally ≤1 ml). Refer to Support Protocol 1 for information on standard curves. 2b. Dilute unknowns to an estimated concentration of 20 to 200 µg protein per tube in a same volume as the standards.

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3b. Optional: Add 0.25 ml of 1 M NaOH to each sample and vortex. NaOH is added to ensure that the sample does not precipitate upon addition of Bradford reagent in step 4a. If a sample (not containing NaOH) precipitates upon addition of Bradford reagent then a new sample must be prepared since addition of NaOH after precipitation will not cause it to dissolve. NaOH should thus be added to the new sample prior to addition of Bradford reagent.

4b. Add 5 ml Bradford reagent, vortex, and incubate 5 min at room temperature. 5b. Record blank, vortex samples again, and measure the absorbance at 595 nm. 6b. Refer to Support Protocol 1 for information on data processing.

MICROTITER PLATE BRADFORD For microtiter plate–based Bradford assays, it is recommended that a commercial reagent be considered. The authors find that, due to the small volume, any precipitates that may be in a laboratory prepared reagent will interfere with the determination. A laboratory prepared reagent must be filtered before use. The reagent should be pipetted from the top of the solution to avoid interference from precipitates that may be found at the bottom. The microtiter plate assay version is more prone to protein precipitation due to the higher concentration of dye. Even if the protein sample does not precipitate in the regular Bradford assay, it should again be verifed that the protein is not precipitating in this assay format. If protein sample precipitation is observed, add a small volume of 1 M NaOH (see step 2) to solubilize the protein.

ALTERNATE PROTOCOL 1

Materials Sample 1 mg/ml protein standard (e.g., BSA; see Support Protocol 1) Bradford reagent (see recipe) 1 M NaOH Microtiter plate (see Strategic Planning) Multichannel pipettor or repeating pipettor, optional Microtiter plate reader (see Strategic Planning) 1. Prepare unknowns and standards in a final volume of 160 µl. If protein solubility is an issue in the presence of the Bradford reagent reduce sample volume to 140 µl to accommodate addition of NaOH in step 2.

2. Optional: Add 20 µl of 1 M NaOH. NaOH is added to ensure that the sample does not precipitate upon addition of Bradford reagent in step 4a. If a sample (not containing NaOH) precipitates upon addition of Bradford reagent then a new sample must be prepared since addition of NaOH after precipitation will not cause it to dissolve. NaOH should thus be added to the new sample prior to addition of Bradford reagent.

3. Add 40 µl of Bradford reagent to each well of a microtiter plate and carefully mix. Using a multichannel pipettor facilitates mixing. If one is not available, carefully tap the side of the microtiter plate to avoid cross-contaminating wells or use a minishaker.

4. Incubate 5 min at room temperature. If this is the first time using the microtiter plate format, carefully inspect the microtiter plate wells on a lightbox for signs of precipitate formation. If protein precipitation is observed, add 20 µl of 1 M NaOH and reduce the final sample volume as indicated above.

5. Read samples at an appropriate wavelength (commonly 595 nm). 6. See Support Protocol 1 for information on data processing. Current Protocols in Protein Science

Detection and Assay Methods

3.4.13 Supplement 48

BASIC PROTOCOL 3

BCA ASSAY The BCA assay (Smith et al., 1985) measures the formation of Cu+ from Cu2+ by the Biuret complex in alkaline solutions of protein using bicinchoninic acid (BCA). Originally, the mechanism of the BCA assay was thought to be similar to the Lowry assay, but it is now known that there are two distinct reactions that take place with copper ions unique to the BCA assay (Wiechelman et al., 1988). The first reaction occurs at lower temperatures and is the result of the interaction of copper and BCA with cysteine, cystine, tryptophan, and tyrosine residues in the protein. At elevated temperatures, the peptide bond also is responsible for color development. Hence, performing the assay at 37◦ C or 60◦ C versus room temperature increases the sensitivity and reduces the variation in the response of the assay as a function of protein composition. When possible, the assay should be incubated at 60◦ C since, after the reaction is complete, the absorbance does not increase appreciably, whereas after cooling samples incubated at 37◦ C to room temperature, the blank continues to increase in absorbance at ∼2.3% every 10 min. The BCA reagent replaces the Folin-Ciocalteu reagent in the Lowry assay. The BCA reagent forms a complex with Cu+ , which has a strong absorbance at 562 nm. BCA is advantageous in that it does not interact with as many contaminants and buffer components as the Folin-Ciocalteu reagent, especially detergents. Components that interfere with the BCA assay either lead to reduction of Cu2+ (e.g., DTT) or copper chelators (e.g., EGTA). Generally, these are not critical components of buffers and can be easily removed or omitted prior to the assay. Note that there are two variations of the assay. The micro-based assay requires three reagents, whereas the test tube protocol requires only two reagents. The micro-based assay reagents are at a significantly higher concentration than the macro-based assay reagents. To prevent precipitation of BCA, it is prepared as a separate reagent in the micro-based format. It is important to use the correct reagent mixtures and ratios for the

Assays for Determination of Protein Concentration

Figure 3.4.5 A sample micro-BCA assay standard curve with triplicate BSA standard points of 0, 0.625, 3.125, 6.25, 12.5, and 18.75 µg. Note that the curve is linear across a wide range and is fit with the equation y = 10.571x − 2.0254 and has a R2 value of 0.9893. The data table used to generate the figure and depiction of a typical micro-BCA assay is shown in Table 3.4.4.

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chosen format. In Strategic Planning, a microtiter plate adaptation of the micro-based assay is described. A typical standard curve for the micro-BCA assay is depicted in Figure 3.4.5 and assay setup is listed in Table 3.4.4.

Table 3.4.4 Typical Layout of a Micro-BCA Assay and Dataa

Standard 1 mg/ml standard (µl)

H2 O (µl)

Micro-BCA solution (µl)

BSA total (µg)

A562

0

500

500

0

0.149

0

500

500

0

0.153

0

500

500

0

0.151

0.5

499.5

500

0.5

0.179

0.5

499.5

500

0.5

0.187

0.5

499.5

500

0.5

0.179

2.5

497.5

500

2.5

0.433

2.5

497.5

500

2.5

0.451

2.5

497.5

500

2.5

0.445

5

495

500

5

0.744

5

495

500

5

0.700

5

495

500

5

0.738

10

490

500

10

1.228

10

490

500

10

1.209

10

490

500

10

1.226

15

485

500

15

1.689

15

485

500

15

1.642

15

485

500

15

1.651

20

480

500

20

1.988

20

480

500

20

1.913

20

480

500

20

2.009

A562

Protein (µg) from Eqn. 3

Equation 3: [µg protein] = (10.571 × A562 ) − 2.0254 Unknowns Unknown (µl)

H2 O (µl)

Assay mix (ml)

1

499

500

1

499

500

5

495

500

5

495

500

20

480

500

20

480

500

a Typical layout of a micro-BCA assay and the data used to create the sample standard curve in Figure 3.4.5.

The volumes of unknown used are typical amounts required to be within the dynamic range of the assay.

Detection and Assay Methods

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Materials Macro-BCA or micro-BCA assay solution (see recipes) Samples 1 mg/ml standard proteins (e.g., BSA, albumin, or γ-globulin; see Support Protocol 1) Test tubes (e.g., 10 × 75–mm for micro-BCA assay or 13 × 100–mm for macro-BCA assay) Spectrophotometer warmed up and set to 562 nm, and cuvette 60◦ C (or 37◦ C) heating block or water bath Micro-BCA assay (1-ml) 1a. Prepare fresh micro-BCA assay solution. Set up test tubes (e.g., 10 × 75–mm) containing samples and known amounts of standard protein in the range of 0.5 to 20 µg with each final sample volume of 500 µl. Refer to Support Protocol 1 for information on preparation of a standard curve. 2a. Add 500 µl of micro-BCA assay solution to each tube, vortex, and incubate the sample 15 min at 60◦ C. Alternatively, incubate 1 hr at 37◦ C, although, in practice, a 60◦ C incubation will yield more consistent results.

3a. Cool samples to room temperature. Note that this is extremely important. The simplest method is to put the test-tube rack into a pan containing room temperature water.

4a. Vortex samples and read absorbance at 562 nm. 5a. Refer to Support Protocol 1 for information on data processing.

Macro-BCA assay (2.1-ml) 1b. Prepare fresh macro-BCA assay solution. 2b. Set up 13 × 100–mm test tubes containing samples and known amounts of standard protein in the range of 0 to 100 µg in a total tube volume of 0.1 ml. 3b. Add 2.0 ml macro-BCA assay solution to each tube and vortex. 4b. Incubate tubes 15 min at 60◦ C. Alternatively, incubate tubes 30 min at 37◦ C.

5b. Cool the tubes to room temperature. 6b. Vortex samples and read the absorbance at 562 nm. 7b. Refer to Support Protocol 1 for information on data processing. ALTERNATE PROTOCOL 2

Assays for Determination of Protein Concentration

MICROTITER PLATE BCA ASSAY The microtiter plate BCA assay is based on the micro-BCA assay (see Basic Protocol 3) and utilizes the same reagents. This protocol results in a final volume of 250 µl and in the authors’ experience, works with most commercially available microtiter plates, but occasionally, some plates cannot accommodate 250 µl without well-to-well contamination. In this case, reducing the volume of reagents may help, but may result in a narrowing of the detection range. For the microtiter plate–based assay, incubating for 15 min at 60◦ C and cooling the plate to room temperature prior to reading the plate is recommended. Some microtiter plate readers can do this entire operation automatically.

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Current Protocols in Protein Science

Materials Micro-BCA working solution (see recipe) 1 mg/ml protein standard (e.g., BSA; see Support Protocol 1) Microtiter plate(s) (BD Falcon 353915; Costar 3370; Whatman 7701-1350) Microtiter plate reader, warmed up and ready to read at 562 nm 60◦ C (or 37◦ C) oven or water bath 1. Prepare sufficient micro-BCA working solution for all assays—prepare ∼15% to 20% extra to account for waste and to avoid running out. 2. Prepare standards and unknowns each in a 125-µl final volume in individual wells of the microtiter plate. 3. Add 125 µl of micro-BCA working solution to each well and gently mix by tapping the side of the microtiter plate or using a minishaker 4. Incubate 15 min at 60◦ C. Alternatively, incubate 30 min at 37◦ C, but the absorbance will continue to increase at 2.3% per 10 min)

5. Cool plate to room temperature. 6. Gently mix by tapping the side of the plate or by using a minishaker and read absorbance at 562 nm.

UV ABSORBANCE TO MEASURE PROTEIN CONCENTRATION The use of ultraviolet absorbance to measure protein concentration is perhaps the most simple and quick method, but also the most likely to produce inaccurate results. Determination of protein concentration by ultraviolet absorption, usually at 280 nm, depends on the presence of aromatic amino acids in proteins. Tyrosine and tryptophan absorb at ∼280 nm, whereas phenylalanine has a peak of absorbance at ∼260 nm. Higher orders of protein structure may also modify the molar absorptivities of tyrosine and tryptophan and thus the UV detection is sensitive to pH and ionic strength.

BASIC PROTOCOL 4

Unless the protein sample is pure and its extinction coefficient is known, UV determination of protein concentration will invariably be significantly in error. This protocol is included here because it is extremely useful for making quick and rough estimates of protein concentration before proceeding to a more accurate assay such as those listed above. A useful equation (Layne, 1957) for approximate concentration determination using UV absorbance is as follows: [protein] (mg/ml) = (1.55 × A280 ) – (0.76 × A260 ) This equation is not sufficiently accurate for routine biochemical calculations. Many other cellular components, particularly nucleic acids, also absorb UV light. Although different proteins will have different amino acid compositions and thus different molar absorptivities, this method can be very accurate when comparing different solutions of the same protein. Glass and polystyrene cuvettes absorb UV light, therefore, quartz cuvettes should be used. Methacrylate plastic cuvettes are also available, which transmit ∼60% of 280-nm light and it is possible to zero spectrophotometers with them and acquire absorbance readings. Methacrylate cuvettes are significantly less expensive than quartz cuvettes and may be a good alternative in environments where cuvette breakage is a concern (e.g., teaching laboratories).

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This method is the least sensitive of the methods presented in this unit. For increased sensitivity, the wavelength can be altered to a range of 210 to 225 nm (Stoscheck, 1990). This measures the amide bonds in proteins and is often the wavelength range used for measuring absorbance of HPLC column fractions. However, it is much more subject to interference from many more biological components and compounds used to make buffer solutions. If using a pure protein with a known extinction coefficient (ε), it is possible to directly determine the concentration from the measured A280 . The extinction coefficients for many proteins have been published (Fasman, 1989). Because of the different mole fractions of tyrosine and tryptophan residues in different proteins, their extinction coefficients can be very different. An example of the difference in extinction coefficient at 280 nm between proteins having different amino acid compositions is as follows: consider the following ε1% values (Fasman, 1989) for horse liver alcohol dehydrogenase, 4.2 to 4.5; bovine serum albumin, 6.3 to 6.8; rabbit IgG, 13.5 to 14.6; and egg white lysozyme, 24.7 to 27.2. Most protein extinction coefficients are not expressed as εmolar (M−1 cm−1 ) as is usual for most biomolecules, but rather as ε1% (%−1 cm−1 ). The ε1% is defined as the A280 value of a 1% solution (1 g/100 ml) of the protein. It is necessary to be aware of this difference when calculating the amount of protein from A280 values. Using the molar absorptivity, A280 /εmolar produces a concentration in molar terms. Using the percent absorptivity, A280 /ε1% produces a concentration in percent. When using the percent absorptivity, it is common to use the equation (A280 /ε1% ) × 10 to yield the concentration in units of mg/ml. If the amino acid sequence of the sample protein is known, it is possible to calculate the theoretical extinction coefficient (Gill and von Hippel, 1989). A procedure for determining the concentration of an unknown using UV absorbance and a standard curve is presented here. In general, the results from this approach will also not be accurate enough to use in downstream calculations.

Materials Sample Protein standard prepared in the same buffer as unknown Sample buffer Spectrophotometer and quartz or UV-transparent cuvettes 1. To perform an ultraviolet reading at 280 nm, warm up the spectrophotometer and set it to 280 nm. 2. Zero the spectrophotometer with a water blank. 3. Construct a standard curve (0 to 3 mg/ml) using a protein standard of choice in the expected concentration range at 280 nm in a quartz or UV-transparent cuvette. 4. Measure the A280 of the buffer used to prepare the sample. 5. Measure the A280 of the sample. If the A280 is >2, dilute an aliquot of the sample in the sample buffer and remeasure. 6. Calculate the protein concentration from the standard curve. 7. Refer to Support Protocol 1 for information on data processing. Assays for Determination of Protein Concentration

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STANDARD CURVES AND DATA PROCESSING Standard Curves and Standard Selection Two details must be understood about standard curves. First, they are called standard “curves” because the response of absorbance to the amount of added protein is not necessarily linear. Second, the range of protein concentrations chosen for the standard curve (e.g., 0 to 25 µg or 0 to 100 µg) will depend on the assay employed. The A280 method (see Basic Protocol 4) is the least sensitive and requires the highest amounts of protein in the standard curve, whereas the Bradford method (see Basic Protocol 2) is sensitive, but not linear over a very wide range, necessitating the use of a relatively narrow range of standard protein.

SUPPORT PROTOCOL 1

Another factor to keep in mind is that the standard curve is based on one protein (usually BSA). The mixture of proteins in the sample may not yield identical results as the standard, in terms of a signal as a function of protein concentration directly corresponding to the standard chosen. While BSA, in general, is a suitable choice as a standard, it does have limitations as a standard (see Standard Selection) and this should be kept in mind when assaying protein samples. One of the most often overlooked assay parameters is the generation of a standard curve with each assay, which is known as an internal standard curve. Internal standard curves are essential for obtaining accurate results, especially for the Lowry (see Basic Protocol 1) and BCA (see Basic Protocol 3 and Alternate Protocol 2) assays, which are nonendpoint reactions. In the case of the Bradford assay, many procedures state that it is not necessary to run internal standard curves with each assay. However, analysis of years of compiled data for various Bradford assays has shown that standard curves can exhibit significant amounts of drift, concluding that neglecting to perform an internal standard curve may yield inaccurate results. Typically, standard curves should be run at least in duplicate.

Sample dilution series In addition to having unknown samples fall within the linear response range of the standard curve, it is good practice to perform a dilution series of the unknown(s). Expect to observe a change in the response of the unknown that follows the slope of the standard curve. For example, if 5 and 10 µl of the sample produce responses equivalent to 7.5 and 15 µg of protein, respectively, confidence may be placed in the results. If the unknowns do not change when diluted, or the response curve does not follow that of the standard curve, assay selection and buffer compatibility may need to re-evaluated. Standard selection Standard selection for protein assays is critical. If the selected standard does not respond to the assay in a manner similar to the unknowns, the magnitude of error in the assay may be significant. The most common standards are BSA or purified-IgG proteins. A review by Stoscheck (1990) describes how differently these two proteins respond in all of the assays presented in this unit. While these have become the de facto standards of choice, there are experiments where these may not be the most appropriate standards. Unfortunately, the only way to ensure total accuracy is by standardizing any assay procedure to quantitative amino acid analysis (UNIT 3.2). Bovine serum albumin (BSA) is the most commonly used standard for protein assays. BSA in no way is the perfect standard, but it has become the arbitrary standard of choice. Serum albumins are “sticky” proteins to the extent that drug adsorption to them is a critical factor in pharmacokinetic calculations (Mandula et al., 2006). This means that BSA tends to bind to other proteins and molecules and, thus, it is very difficult to purchase

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Figure 3.4.6 Data for a sample Lowry protein assay standard curve produced using BSA standard at triplicate points of 0, 10, 20, 30, 40, and 50 µg. The data are identical to those in Figure 3.4.2 except that BSA (µg) is plotted on the x axis and A660 is plotted on the y axis. The data are fit with a linear regression by the line y = 0.0065x + 0.0001 with an R2 value of 0.992. Note that to use the equation to return protein concentration, the equation must be rearranged.

in a 100% pure form. These contaminants can lead to problems with any of the listed assays due to side reactions or blocking interaction sites. Protein standards can be purchased from most chemical suppliers in liquid form or they may be prepared in the laboratory. Usually, a 1 to 5 mg/ml stock solution is prepared in bulk (50 to 100 ml) to avoid variation in the concentration of standards from day to day. When the solution is prepared, do not shake it because this will lead to denaturation of the protein (observed as bubbles on the top of the solution), but rather gently swirl to dissolve the BSA in water. This solution is then dispensed into smaller aliquots (1- to 5-ml) and stored at –20◦ C. As with any solution, it must be mixed upon thawing because melting solutions form an internal concentration gradient. The best strategy for preparing a standard curve dilution series is to prepare the standard as a 1 to 5 mg/ml stock in water and prior to the assay, dilute the stock in the same buffer as the unknowns—including a buffer blank. This strategy will provide the most accurate result, because the effects of the buffer are accounted for in the standard curve. It is also possible to prepare a standard curve dilution series in water and use a buffer blank, however, the buffer used in the unknowns should be fully compatible with the assay.

Assays for Determination of Protein Concentration

Placing absorbance on the x axis during data processing Presented here is a hypothetical example that illustrates why it is more convenient to use the y axis for protein concentration. Consider the data used to generate Figure 3.4.2 and listed in Table 3.4.2. If this data is plotted with the protein (µg) data on the x axis, the standard curve has a curve fit line with the equation y = 0.0065x + 0.0001 (Fig. 3.4.6). The x values are micrograms of protein and the y values are A590 . Thus, the equation can be rewritten as follows to be more intuitive: [A590 ] = 0.0065x[µg protein] + 0.0001

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The measurement taken is the absorbance of the sample, therefore, to calculate the micrograms of protein in the unknown sample, the equation is rearranged as follows: [µg protein] = ([A590 ] – 0.0001)/0.0065 While not a difficult rearrangement, it could introduce errors into the calculation of the amount of protein by an inexperienced laboratory worker. Now consider the same data, rearranged so that the protein (µg) data is on the y axis as in Figure 3.4.2. A linear fit of this data produces the equation y = 153.06x + 0.179. The x values this time are A590 , whereas the y values are micrograms of protein. Thus, the equation becomes [µg protein] = (153.06 × [A590 ]) + 0.179 Now the equation need not be rearranged before use. The advantage of plotting standard curves with the protein amounts on the y axis is especially apparent when using a nonlinear fit as with the Bradford assay (Fig. 3.4.4). In the case where a spreadsheet is being used to calculate standard curve and experimental data, the line-fit equation can be directly copied from the graph and pasted into the spreadsheet formula, thus simplifying the entire process of analyzing data.

Considerations for Processing Data with Spreadsheet Software There are numerous spreadsheet software packages that can be used for processing data from a standard curve. Some general considerations are presented here for preparing the data using these software packages. Standard curve generation Standard curves should be produced using a scatter plot. When possible, plot each data point individually (see examples in Figs. 3.4.2, 3.4.4, and 3.4.5). This will allow the curvefitting algorithm to fit each data point and for the curve fit to reflect the real variation in the data points. As mentioned above, the y axis should be concentration values and the x axis should be absorbance values, as this will allow the curve fit to directly return a function without having to rearrange the equation. Note that many spreadsheet programs allow calculation of a curve fit with a function in the spreadsheet from the set of standard data. For experienced users, this is a convenient shortcut and allows the production of template spreadsheets for data analysis. For inexperienced protein assay users, it is recommended to produce a graphical plot with a curve fit to avoid errors in data analysis and to have a visual check that there has not been an error in entering the data. If performing the Bradford assay, be sure to use a polynomial curve fit as the Bradford assay is a nonlinear assay (see Fig. 3.4.4). The BCA and Lowry assays both perform linearly, therefore, a linear curve fit is acceptable (Figs. 3.4.2 and 3.4.5).

Unknowns within standard curve range A common error made by novices is the utilization of sample data points that do not fit within their standard curves to extrapolate results. To make valid use of a standard curve, data points must be within the linear range of the spectrophotometer (see Spectrophotometry). When unknowns are compared to a standard curve in this way, it is termed interpolation. Avoid extrapolating results in cases where no standard curve data points are in the range of the unknown to support the value that has been determined. Furthermore, the response range of the assay (which is listed with each assay) is considerably narrower than the range of the spectrophotometer. It is generally best to have at least two standard curve data points on either side of the results for the unknowns. This

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may require pilot assays to ensure the addition of the proper amounts or to properly dilute the sample into the correct range. To find the proper range, Basic Protocol 4 describes a crude, but rapid, estimation of the protein concentration by UV spectrophotometry. SUPPORT PROTOCOL 2

ACETONE PRECIPITATION OF PROTEIN Often, the easiest way to remove interfering non-proteinacious substances from protein preparations is to precipitate the protein prior to the assay. In this case, two standard curves should be performed. The first standard curve is prepared from a dilution series of standards that are precipitated under similar conditions as the unknowns, and a second standard curve is prepared without precipitation to allow estimation of the amount of sample lost during the precipitation. It is best to precipitate identical points on the standard curve dilution, however, this is time consuming. Alternatively, a few select points covering the entire range of the standard curve can be precipitated and then compared to the results of the nonprecipitated standard points. For example, in the Lowry assay described in Table 3.4.2 points at 10, 30, and 50 µg of BSA would be precipitated and compared to nonprecipitated standards in a compatible buffer. Acetone precipitation is the easiest method and is the most compatible with downstream assays.

Materials 100% acetone, −20◦ C Protein sample Protein assay–compatible buffer −20◦ C freezer 1.5-ml microcentrifuge tubes (acetone-compatible, not polycarbonate) Microcentrifuge 1. Add cold 100% acetone to the protein sample to a final concentration of 80% (v/v) (e.g., 0.2 ml diluted protein and 0.8 ml acetone) in acetone-compatible microcentrifuge tubes. 2. Vortex the sample vigorously. 3. Incubate 1 hr at −20◦ C in an explosion-proof freezer. 4. Pellet the protein by centrifuging 20 min at 15,000 × g, 4◦ C. CAUTION: Acetone is flammable. Do not scale this precipitation up to volumes >1.5 ml.

5. Carefully decant the supernatant and dispose as organic waste, being careful not to dislodge the pellet. 6. Dry the pellet 20 to 30 min at room temperature by inverting the tube onto a clean paper towel or lint-free wipe. Care should be taken not to dislodge the pellet. Do not over-dry the pellet as this may make is difficult to resuspend. 7. Resuspend the pellet in a protein assay–compatible buffer by vortexing. SUPPORT PROTOCOL 3

Assays for Determination of Protein Concentration

TCA PRECIPITATION OF PROTEIN Acetone precipitation (see Support Protocol 2) is the easiest method and is most compatible with downstream assays, however, a large protein sample may require an impractically large volume to bring the concentration of acetone up to 80%. In these cases, a second protocol utilizing trichloroacetic acid (TCA) precipitation of protein is provided. In TCA precipitation, it is critical that excess TCA be removed before assaying the protein. CAUTION: TCA is caustic and will burn the skin, therefore, proper precautions must be taken when handling this reagent.

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Materials 80% (w/v) TCA Protein sample 80% acetone, −20◦ C Protein assay–compatible buffer −20◦ C freezer 1.5-ml centrifuge tubes (acetone-compatible, not polycarbonate) Microcentrifuge 1. Wearing gloves, add TCA to the protein sample to a final concentration of 4%. 2. Incubate 30 min on ice. 3. Centrifuge 10 min at 15,000 × g, 4◦ C. 4. Decant the supernatant and dispose as organic waste, being careful not to dislodge the pellet. 5. Add an equal volume of 80% cold acetone to the pellet to wash away residual TCA. Vortex vigorously to release the pellet from the bottom of the centrifuge tube. 6. Centrifuge 10 min at 15,000 × g, 4◦ C and repeat 80% acetone wash four additional times. 7. Dry the pellet inverted on a paper towel or lint-free wipe for 30 min being careful not to dislodge or over-dry the pellet. 8. Resuspend the pellet in a protein assay–compatible buffer by vigorous vortexing.

ALKYLATION OF REDUCTANT AND BCA ASSAY OF PROTEIN SAMPLES IN SDS-PAGE LOADING BUFFER

ALTERNATE PROTOCOL 3

One of the most common problems encountered when performing polyacrylamide gel electrophoresis (PAGE; Chapter 10) and immunoblot analysis (UNITS 10.7 & 10.8) of protein extracts is how to load equal amounts of samples for semi-quantitative comparison. All too often researchers load SDS-PAGE gels based on tissue weight before homogenization, but this can lead to significant sample-to-sample variation. Also, some researchers will stain a duplicate gel and compare one or two bands between the gels and state that they are equally loaded. When investigating changes in protein composition between two samples, it is preferable to load based on total protein concentration. Many vendors now offer detergent- and reductant-compatible protein assays to quantitate samples prior to loading of gels for electrophoretic fractionation. These assays are usually more expensive than normal assay kits and either rely on a method similar to what follows, or on protein precipitation prior to an assay such as the Lowry assay (e.g., BioRad RC/DC). One critical factor to this assay is that bromophenol blue should not be added to the SDS-PAGE sample buffer as it may interfere. It is preferable to prepare SDS-PAGE loading buffer as a 6× stock without bromophenol blue and then, if desired, add bromophenol blue from a 10 mg/ml stock to the sample prior to gel loading. Once the amount of sample that is required from each unknown for equal loading is determined, it is critical that all of the samples are diluted to the same volume. If this is not done, band broadening (for high volume samples) and band narrowing (for small volume samples) during the stacking phase of electrophoretic fractionation may be observed. This assay (Hill and Straka, 1988) is a two-step procedure and takes advantage of the fact that the BCA assay is compatible with SDS up to concentrations of 5%. The 6× SDS-PAGE loading buffer described in UNIT 10.1 results in a 1% final concentration of SDS

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in the sample. However, it also contains DTT or 2-mercaptoethanol (2-ME) at greater than millimolar concentrations, which is not compatible with the assay. Thus, prior to the assay, the thiol groups of DTT (or 2-ME) must be alkylated with iodoacetamide to form an S-carboxymethyl derivative of DTT or 2-ME. Iodoacetamide does not interfere with the BCA assay, nor does alkylated DTT or 2-ME, therefore, after the iodoacetamide treatment, samples may be assayed directly in the BCA assay (see Basic Protocol 3 or Alternate Protocol 2). Finally, two standard curves should be performed, especially when starting out with this procedure. Samples for one standard curve will be suspended in SDS-PAGE sample buffer while the others will be in water and will not be treated with iodoacetamide. This will ensure that the addition of the SDS-PAGE loading buffer has not interfered with the BCA assay, and it will also confirm a negligible difference in BCA assay response in the presence of SDS-PAGE loading buffer. In the authors’ experience, little detectable difference has been found between samples prepared in SDS-PAGE sample buffer compared to those in water. It is appropriate to include both water blanks and buffer blanks in this assay. The spectrophotometer should be allowed to warm up and then zeroed with the water blank before recording the A562 of the buffer blank so it can be subtracted from the standard curve containing SDS-PAGE loading buffer.

Materials Stock protein standards 6× SDS-PAGE loading dye without bromophenol blue (UNIT 10.1; see recipe) Unknowns 20 mM iodoacetamide solution (see recipe) Additional reagents and equipment for BCA assay (see Basic Protocol 3) 1. Dilute stock protein standard to 100 µl final volume of water and 100 µl final volume of 1× SDS-PAGE loading buffer (without bromophenol blue) 2. Prepare unknowns in a final volume of 100 µl with 1× SDS-PAGE loading buffer without bromophenol blue. Make sure samples have been homogenized, heated, and centrifuged prior to assaying for total protein.

3. Add 100 µl of 20 mM iodoacetamide solution to the sample. 4. Incubate 20 min at 37◦ C. 5. Proceed to Basic Protocol 3 by adding working reagent directly to the sample. 6. See Support Protocol 1 for data analysis.

REAGENTS AND SOLUTIONS Use Milli-Q-purified water or equivalent in all recipes and protocol steps. For common stock solutions, see APPENDIX 2E; for suppliers, see SUPPLIERS APPENDIX.

Bradford reagent To 50 ml of 95% ethanol, add 100 mg Coomassie Brilliant Blue G-250 (CBBG).

Assays for Determination of Protein Concentration

When the CBBG is partially dissolved, add 100 ml 85% (w/v) phosphoric acid and stir until CBBG is completely dissolved. Let the solution stir overnight to assure maximal dissolution. Dilute to 1 liter with water. Store up to 1 month at room temperature or 3 months at 4◦ C. continued

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Prior to use, filter enough reagent for the day’s work through Whatman no. 1 filter paper. While it is possible to make the reagent in the laboratory, due to the use of phosphoric acid, it more cost effective to purchase a commercial reagent. Commercial reagents also do not need to be filtered prior to use. Note that different brands of commercial reagent (e.g., BioRad or Sigma) have different concentrations and thus different volumes are required.

Bromophenol blue stock solution 10 mg/ml bromophenol blue in water Bromophenol blue is not very soluble in water, therefore, allow the solution to settle prior to use. Store up to 1 year at room temperature.

Folin-Ciocalteu reagent Dilute purchased reagent (e.g., Sigma F9252) with an equal volume of water to prepare the desired volume. Prepare diluted reagent fresh daily. Iodoacetamide solution 20 mM iodoacetamide (2-iodoacetamide) in 100 mM Tris·Cl, pH 8.0 Prepare fresh daily Any buffer compatible with the BCA assay may be used in lieu of Tris·Cl, pH 8.0, as long as the pH is near physiological pH.

Lowry alkaline reagent 2 g NaOH (0.1 M) 10 g Na2 CO3 2.5 g sodium tartrate (use of potassium salt will cause SDS to be insoluble) 12.5 g sodium dodecylsulfate Bring to a final volume of 500 ml with water Store in a plastic bottle up to 3 months at room temperature and check that it is optically clear before using. If the solution is cloudy or particulates are visible, prepare a new batch.

Lowry assay mix 50 ml Lowry alkaline reagent (see recipe) and 0.5 ml Lowry copper reagent (see recipe) Prepare fresh daily Lowry copper reagent 1 g CuSO4 ·5H2 O Bring to a final volume of 100 ml with water Store up to 1 year at room temperature Macro-BCA assay reagent A 1 g sodium bicinchoninate (BCA) 2 g sodium carbonate 0.16 g sodium tartrate 0.4 g NaOH 0.95 g sodium bicarbonate Adjust the pH to 11.25 with 10 M NaOH. Bring to 100 ml with distilled water Store up to 1 year at room temperature Detection and Assay Methods

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Macro-BCA assay reagent B 0.4 g CuSO4 ·5H2 O Bring to 10 ml with distilled water Store for 1 year at room temperature Macro-BCA assay solution 50:1 (v/v) macro-BCA assay reagent A/macro-BCA assay reagent B (see recipes) Make fresh daily Micro-BCA assay reagent A 8 g sodium carbonate monohydrate 1.6 g sodium tartrate Adjust the pH to 11.25 with 10 M NaOH Bring to 100 ml with distilled water Store up to 1 year at room temperature Micro-BCA assay reagent B 4 g BCA in 100 ml distilled water Store up to 1 year at room temperature Micro-BCA assay reagent C 0.4 g CuSO4 ·5 H2 O in 10 ml water. Store up to 1 year at room temperature Micro-BCA assay solution 25:25:1 (v/v/v) micro-assay reagent A/micro-assay reagent B/micro-assay reagent C (see recipes) Prepare fresh daily SDS-PAGE loading buffer without bromophenol blue, 6× 7 ml 0.5 M Tris·Cl, 0.4% SDS, pH 6.8 3.0 ml glycerol 1 g SDS 0.93 g DTT Add water to 10 ml Store in small aliquots at −70◦ C, do not re-freeze Do not add bromophenol blue until after the BCA assay has been performed.

COMMENTARY Critical Parameters and Troubleshooting

Assays for Determination of Protein Concentration

In addition to sample composition (see Strategic Planning) and proper standard curve production (see Support Protocol 1) there are several other elements for a successful protein assay. Be sure to use clean glassware since it is difficult to get accurate results using dirty glassware. For the methods described in this unit, it is recommended to use new glass tubes and discard them after use. High-quality detergents and distilled water should be used to clean glassware. In some cases, such as the Bradford assay, it may be more convenient

to use disposable plastic cuvettes, rather than those made from Pyrex or quartz, because the assay reagents may leave a residue. If a cuvette does not look clean, it should be first washed with soap and water. If the residue remains, a pipe cleaner works well for cleaning cuvettes. Bend the pipe cleaner in the middle and use the bent end to scrub the inside of the cuvette, being careful not to use the other end of the pipe cleaner as the wires will scratch the optical surfaces of the cuvette. If this is not sufficient to remove residues from cuvettes, try soaking in 50% nitric acid overnight. Also, a wash with ethanol or methanol often works well

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to remove contaminating materials. Cuvettes should be thoroughly washed with distilled water between determinations. While carrying out absorbance determinations, hold the filled cuvette up and look though it to determine that the solution is optically clear and without precipitates or floaters. Spectrophotometry Spectrophotometry used in the protein assays presented in this unit either relies on the presence of a metal (e.g., Cu2+ in the BCA and Lowry assays) or involves a system of conjugated double bonds (e.g., CBBG in the Bradford assay) to absorb photons produced by the spectrophotometer. Spectrophotometry is advantageous in that it is a nondestructive method. Because different molecules have different absorption characteristics, a particular compound in a mixture can often be singled out for observation. Most importantly, highly accurate measurements can be made in a short period of time. Lambert-Beer (or Beer-Lambert) equation The Beer-Lambert law of absorption states that the fraction of light absorbed is (1) proportional to the thickness of the absorbing solution, (2) proportional to the concentration of absorbing species, and (3) independent of the light intensity. Mathematically presented as follows: The Beer-Lambert equation: A = –log(I/I0 ) = εcl where ε is the molar extinction coefficient or molar absorptivity and usually has units of M−1 cm−1 , except as noted above (see Basic Protocol 4 introduction) for proteins based on a mass percent. If I0 = intensity of the light entering a colored solution; I = intensity of light leaving the solution; c = concentration; l = length of the light path (pathlength) through the solution; then –log(I/I0 ) is proportional to both the pathlength and the concentration. The term –log(I/I0 ) is called the absorbance (A) of the solution. Extinction coefficients The extinction coefficient is a proportionality constant that relates absorbance to the concentration and pathlength. ε = A/(cl) Absorbance is a dimensionless quantity (essentially a ratio). Pathlength is usually 1 cm. Notice that ε is a value true for only the wavelength at which it is defined. The same

molecule at different wavelengths has different molar absorptivities. Percent transmittance Many spectrophotometers are capable of measuring both absorbance, A, and percent transmittance, %T. Transmittance is defined as I/I0 and is not directly proportional to the absorbance. Absorbance is the –log of the transmittance. Values of transmittance normally vary from 0 to 1.0. Percent transmittance is defined as: %T = (I/I0 ) × 100. Percent transmittance is helpful for zeroing the spectrophotometer prior to use. With a buffer blank, the %T should read 100%, and with the light beam blocked, it should read 0%. There is a practical limit on the absorbance that can be measured in a spectrophotometer. An absorbance of 1 means that the transmittance is 0.10 and that only 10% of the light is reaching the light-measuring photodiode. An absorbance of 2 means that the transmittance is 0.01 and only 1% of the light is passing through the sample. An absorbance of 3 means a transmittance of 0.001 and 0.1% of the light is passing through the sample. Good spectrophotometers can measure absorbance values up to 3. Less sophisticated spectrophotometers that are more commonly found in laboratories can generally only measure absorbance to 2 or 2.5 before there is so little light being measured that the signal-to-noise ratio deteriorates. For the purposes of protein assays, keeping the absorbance range between 0.05 and 2.0 is recommended. Zeroing and blanks The use of the jargon “zero” and “blank” is often confusing and sometimes used interchangeably. Here, “zero” is used as a verb to mean the act of setting the absorbance of a spectrophotometer readout to a value of 0.0. Obviously, one could zero the spectrophotometer without a cuvette in the sample holder; this is not normally done and would be referred to as zeroing with air. It is much more common to zero the spectrophotometer with a cuvette in the sample holder. The cuvette used to zero the spectrophotometer is called a blank. If a cuvette containing only water is used, it is called a water blank. If a cuvette containing water instead of sample, plus the assay reagents, is used to zero the spectrophotometer, it is referred to as a reagent blank.

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If a cuvette containing the buffer components found in the sample to be measured plus the assay reagents is used, this is called a buffer blank. It is important to keep these differences in mind, since they are not equivalent and can lead to misinterpretation and different results.

easily automate both data acquisition and analysis. Typically, the high-throughput methods are the best strategies to use if the assay is compatible with the sample, as they are quick, reliable, and allow acquisition of more data points per sample.

Quantitative amino acid analysis The assays described in this unit have different sensitivities for proteins with different amino acid compositions. This is often not a significant issue for complex protein extracts, but becomes significant for purified proteins. Typically, amino acid analysis is performed by a service facility. An increasing number of companies will perform this service at a reasonable cost. Most service companies will be able to provide the precise protein concentration, and these results can be compared to results from one or all of the assays presented in this unit.

Literature Cited

Anticipated Results Tables 3.4.2 to 3.4.4 demonstrate typical results for the Lowry, Bradford, and BCA assays, respectively. These tables can also be used as assay and spreadsheet templates and are of particular value to new assay users.

Time Considerations

Assays for Determination of Protein Concentration

Basic Protocols 1 and 3 (the Lowry and BCA assays) can easily be completed and data collected in 1 to 1.5 hr. The Bradford assay (see Basic Protocol 2) typically can be completed in <1 hr. UV absorbance (see Basic Protocol 1) is one of the quickest methods and data can be acquired in <30 min. Users unfamiliar with data analysis of protein assay data can anticipate requiring ∼30 min to 1 hr to analyze data. Once familiar with the data analysis process, it is typically completed in 10 to 20 min, especially if using the spreadsheet templates in Tables 3.4.2 to 3.4.4. If sample preparation such as precipitation is required prior to assay, this will typically add 1 hr to analysis time. The high-throughput adaptations can be completed in a similar amount of time as the regular assays for approximately five microtiter plates. Additional microtiter plates only add a few minutes to analysis time. These times can be significantly shortened and the number of samples scaled up by the utilization of multichannel and repeating pipettors. For large-scale operations, these assays will scale nicely with robotics. Many microtiter plate readers also come with software that can

Ballentine, R. 1957. Determination of total nitrogen and ammonia. Methods Enzymol. 3:984-995. Bensadoun, A. and Weinstein, D. 1976. Assay of proteins in the presence of interfering materials. Anal. Biochem. 70:241-250. Bradford, M.M. 1976. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72:248-254. Dulley, J.R. and Grieve, P.A. 1975. A simple technique for eliminating interference by detergents in the Lowry method of protein determination. Anal. Biochem. 64:136-141. Fasman, G.D. 1989. Practical Handbook of Biochemistry and Molecular Biology. CRC Press, Boca Raton, Florida. Gill, S.C. and von Hippel, P.H. 1989. Calculation of protein extinction coefficients from amino acid sequence data. Anal. Biochem. 182:319-326. Gornall, A.G., Bardawill, C.S., and David, M.M. 1949. Determination of serum proteins by means of the biuret method. J. Biol. Chem. 177:751766. Hartree, E.E. 1972. Determination of protein: A modification of the Lowry method that gives a linear photometric response. Anal. Biochem. 48:422-427. Hill, H.D. and Straka, J.G. 1988. Protein determination using bicinchoninic acid in the presence of sulfhydryl reagents. Anal. Biochem. 170:203208. Layne, E. 1957. Spectrophotometric and turbidimetric methods for measuring proteins. Methods Enzymol. 3:447-455. Lovrien, R. and Matulis, D. 1995. Assays for total protein. Curr. Protoc. Protein Sci. 3.4.4-3.4.24. Lowry, O.H., Rosebrough, N.J., Farr, A.L., and Randall, R.J. 1951. Protein measurement with the Folin phenol reagent. J. Biol. Chem. 193:265-275. Mandula, H., Parepally, J.M., Feng, R., and Smith, O.R. 2006. Role of site-specific binding to plasma albumin in drug availability to brain. J. Pharmacol. Exp. Ther. 317:667-675. Smith, P.K., Krohn, R.I., Hermanson, G.T., Mallia, A.K., Gartner, F.H., Provenzano, M.D., Fujimoto, E.K., Goeke, N.M., Olson, B.J., and Klenk, D.C. 1985. Measurement of protein using bicinchoninic acid. Anal. Biochem. 150:7685. Stoscheck, C.M. 1990. Quantitation of protein. Methods Enzymol. 182:50-68.

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Wiechelman, K.J., Braun, R.D., and Fitzpatrick, J.D. 1988. Investigation of the bicinchoninic acid protein assay: Identification of the groups responsible for color formation. Anal. Biochem. 175:231-237.

Contributed by Bradley J.S.C. Olson Michigan State University East Lansing, Michigan John Markwell University of Nebraska Lincoln, Nebraska

Detection and Assay Methods

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Kinetic Assay Methods The design and use of a particular enzyme assay is dependent on the objectives of the particular investigation. If the focus is on enzyme purification or issues of clinical biochemistry, experimental protocols typically require optimizing conditions for all substrates and effectors—e.g., with respect to pH value, temperature, buffering agent, and ionic strength. However, optimal conditions may not be reasonable for all parameters and factors. For example, substrate instability may preclude studies at the enzyme’s optimal pH or at the physiological temperature. Thus, Silverstein and Sulebele (1969) examined the exchange rates of malate dehydrogenase at 1°C to avoid problems arising from the decomposition of oxaloacetate. Likewise, in clinical studies and enzyme purification, saturating concentrations of substrate(s) are typically required, although this may not be possible or even desirable in many instances, as limitations in substrate solubility or substrate inhibition of the enzyme may discourage the use of elevated concentrations. The focus in clinical studies and enzyme purification is on providing good estimates of the activities of a specific enzyme in a number of physiological samples with a rapid, straightforward, and reproducible assay method. However, for the kineticist the objective is to characterize the initial rates of the enzyme-catalyzed reaction under a wide set of conditions. It is through these detailed studies, often carried out at subsaturating substrate concentrations, that the investigator is able to determine the true optimal parameters to be used in protein purification and clinical studies, as well as to probe the dynamics and mechanism of the reaction and its modulation by other factors. The purpose of this unit is to provide a brief review of issues important in the design of initial-rate assay methods. In most instances, the investigator already has some information about the kinetic properties of the enzyme, obtained from preliminary experiments as well as from the literature. It is necessary to have an approximation of the magnitude of the Michaelis constants of all the substrates in order that detailed initial rate studies can be effectively designed. Certainly, information on the identity of the substrates and products is required, along with an effective procedure for monitoring substrate depletion or product accumulation. The nature of the reaction and the properties of the substrates help Contributed by R. Donald Allison Current Protocols in Protein Science (1996) 3.5.1-3.5.11 Copyright © 2000 by John Wiley & Sons, Inc.

UNIT 3.5 dictate the assay procedure used. Enzyme-assay methods include those based on spectrophotometry, radiometry, pH-stat, turbidometry, polarography, and fluorometry. Spectrophotometric and fluorometric assay protocols rely on the differences in spectral or fluorescent properties, respectively, of the product(s) relative to the substrate(s). With radiometric procedures, effective means for separating labeled substrate(s) from labeled product(s) must be available so that the amount of product formed at a given time can be accurately measured. Those reactions that utilize or generate protons (or hydrons) can be followed by pH-stat methods. If the reaction studied results in a change in solution turbidity (e.g., caused by aggregation of a macromolecular complex), then turbidometric protocols can effectively assay for the protein that causes the change in turbidity. All of these methods, as well as others, can be found in Bergmeyer (1983).

GENERAL ASPECTS OF ASSAY DESIGN An enzyme’s progress curve (i.e., a plot of product formation or substrate depletion as a function of time; see Fig. 3.5.1) consists of four phases that are of interest to the investigator. The duration of the pre-steady-state region is relatively short and is typically followed via rapid-flow or relaxation techniques. Discussion of this region of the progress curve is beyond the scope of this unit, but a review of these techniques has recently appeared (Fierke and Hammes, 1995). The next portion of the curve is the steady-state phase, a longer phase characterized by relatively constant concentrations of each of the individual enzyme forms (e.g., the enzyme-substrate binary complexes). During the third phase of the progress curve— the post-steady-state region—the levels of these enzyme complexes change rapidly, until the last phase—equilibrium—has been reached. Each of these phases contains information of use to the investigator. The steadystate portion of the curve typically exists for a few to several hundred seconds. It is this region of the curve that is most accessible to the majority of investigators. In addition, the relatively straightforward mathematical treatment of steady-state kinetics provides an excellent foundation for the subsequent characterization of the enzyme system. Because enzyme rate expressions can be quite complex, a number of

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Product concentration (nmo l/ m l)

15 d c 10

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Figure 3.5.1 Typical progress curve for an enzyme-catalyzed reaction, where a represents the pre-steady-state region, b the steady-state region, c the post-steady-state region, and d the equilibrium region.

assumptions are made to linearize the equations. The investigator should ensure that each of these factors are addressed when any assay protocol is being designed.

Maintaining Constant Reaction Conditions The status of each experimental parameter—e.g., temperature, ionic strength, and pH—should remain constant over the time course of the assay.

Kinetic Assay Methods

Temperature Prior to initiation of the reaction by addition of a small aliquot of enzyme or substrate, the reaction mixture should be thermally equilibrated with an accurately controlled constanttemperature water bath for several minutes. If the substrate is thermally stable, this incubation should be for ≥10 min or until the desired reaction temperature is reached. If the substrate is unstable at this temperature, it may be necessary either to assay at a different temperature, to correct for substrate decay with time, or to initiate the reaction by addition of a small aliquot of substrate solution that was freshly prepared and maintained in an ice bath. When the reaction is initiated by addition of either the enzyme or the substrate, the volume of the aliquot should be small enough to minimize any alterations in temperature upon addition. Typi-

cally, there is at least a hundred-fold difference in volume between the reaction mixture and the initiating aliquot. If the enzyme and substrate are stable under the assay conditions, then both solutions should be preincubated at this temperature. It should be emphasized that valid initial rate experiments are only obtained when good temperature control is maintained. The temperature-regulated water bath should have a variability of ≤0.1°C. When the reaction is initiated, the solution should be thoroughly mixed. If reaction progress is followed using an instrument such as a spectrophotometer, the sample compartment should be thermally isolated. Another issue of concern is choice of the temperature at which enzyme activity will be measured. In 1964, the International Union of Biochemistry (IUB) recommended a temperature of 30°C. Clinical chemists have often suggested a change to 37°C. However, the final choice of temperature will be dependent on the system being investigated. For example, the activity of glutamate dehydrogenase isolated from the hyperthermophile Pyrococcus furiosus is negligible at both 30°C and 37°C; the enzyme exhibits maximal activity at 97°C (Klump et al., 1992). Clearly, at some point in the investigation, the effect of temperature on the catalytic activity will have to be characterized.

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Buffer selection In setting up the assay protocol, care should also be exercised in selecting an appropriate buffer agent. Unfortunately, many investigators choose a buffer solely on the basis of the desired pH of the reaction medium. Many excellent buffers, however, have a significant oxidationreduction potential that can severely alter an enzyme’s activity. For example, cacodylate (pKa = 6.27) and other organoarsenicals have a potent oxidizing potential under acidic conditions. Borate (pKa = 9.23) and other boratebased buffers form complexes with many diols, polyols, carbohydrates, and ribonucleotides; for example, in the presence of borate ion, both L- and D-serine form a transition complex and strongly inhibit γ-glutamyl transpeptidase (Tate and Meister, 1978). Hence, care should be exercised in selecting a buffer. In determining which buffer is optimal for a given enzyme system, it is best to examine the enzyme’s activity with as many buffers as possible. Buffers in whose presence the enzyme exhibits high activity should then be examined to determine if the buffering agent interacts with the protein. If pH and ionic strength are held constant, the enzyme activity should be independent of the buffer concentration. Another factor of importance in selecting the appropriate buffer is the capacity of the buffering agent to bind metal ions. Ideally, a good buffer will not form any significant complexes with metal-ion cofactors. Metal-ion concentrations The role of metal ions is crucial with many enzymes, particularly with systems that require nucleotides—e.g., phosphotransferases. Care must be exercised in designing initial-rate assays to ensure that the concentration of free metal ions does not vary significantly with substrate concentration. Thus, when varying the concentrations of substrates such as ATP, the concentration of free, uncomplexed Mg2+ should remain relatively constant. One of two methods is commonly utilized to achieve this end: either (1) maintaining the total nucleotide concentration and the metal concentration at a constant stoichiometric ratio (e.g., 10:1) or (2) maintaining a constant excess of metal ion over the total nucleotide concentration (e.g., 1.0 mM free Mg2+). The second method, which has been demonstrated to be the preferable of the two (O’Sullivan and Smithers, 1979), requires knowledge of the stability constant for the metal-ion–nucleotide complex under the conditions of the assay.

Substrate Stability and Purity It is crucial to establish the degree of stability of the substrate as well as that of any enzyme effector under the assay conditions (also see discussion of Maintaining Constant Reaction Conditions). Substrate and effector stabilities can be tested by preparing a solution of the compound at a concentration that will be used experimentally with the other components of the assay mixture, but without the enzyme. Then, samples are removed at different periods of time, usually over a period of several hours, and assayed either chemically or enzymatically (Bergmeyer, 1983). A plot of concentration as a function of time will reveal if the substrate is stable or if there is a decay. When present, this decomposition is usually a first-order process and can be described by the equation [S]t = [S]0 e−kt, where [S]t and [S]0 are the substrate concentrations at time t and time 0, respectively, and k is the first-order rate constant. If the decomposition is greater than first-order or composed of more than one first-order process, then the decay must be evaluated by the appropriate expression, which can be found in any basic kinetics text. An issue related to substrate stability is substrate purity. Impurities in the substrate, effectors, or enzyme preparation can be potential sources of experimental error. Because alternative substrates and competitive inhibitors are often structurally similar to the substrate and frequently have similar chemical and physical properties, it is not surprising to find these compounds as impurities in commercial substrate preparations. Spectral and chromatographic analyses, as well as enzymatic analysis, are crucial in establishing the purity of a particular biochemical substance. If impurities are present in a reaction mixture, a kinetic analysis will give wrong estimates of the kinetic parameters (Allison and Purich, 1979). Heavymetal ions are often found as impurities in commercial substrates. To remove these, it may be necessary to recrystallize the substrates, possibly in the presence of a chelating agent such as EDTA. These issues of purity and stability are also pertinent for any modulators of enzyme activity, including the product(s). The products have to be stable over the time course of the assay. In some instances, the precise nature of the true product of the enzyme-catalyzed reaction may not be known. In these cases, the investigator may follow the reaction via substrate depletion. It is frequently useful to do a kinetic analysis of freshly prepared substrate in the absence and

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presence of enzymatically prepared product. A decent understanding of the possible presence of product inhibition in initial-rate experiments is always useful to the protein chemist.

Enzyme Stability and Purity

Kinetic Assay Methods

Enzymes often undergo a slow inactivation. Usually, the rate of this inactivation is accelerated by dilution of the stock enzyme preparation. Efforts have to be made to eliminate or lessen the loss of enzyme activity with time. Often, multisubstrate enzymes are stabilized by the presence of a substrate. For example, tubulin:tyrosine ligase is stabilized by the presence of its protein substrate (Deans et al., 1992) and the presence of Mg2+–ATP is known to stabilize a number of phosphotransferases. If the enzyme inactivation can be retarded by the presence of a substrate, the investigator should note the amount of that substrate that is present with the enzyme so that proper corrections can be made when the reaction is initiated with the full assay mixture. Other agents—e.g., glycerol, certain salts, high-ionic-strength solutions, and EDTA— have also been used to stabilize proteins. Often, inactivation is a result of surface denaturation or adsorption of the enzyme on glass surfaces following dilution. The presence of stabilizing agents and/or use of plastic containers can lessen these effects significantly. The presence of serum albumin or another protein will frequently minimize the effects of enzyme dilution; the investigator should test several proteins when this technique is used. The stabilizing protein may also bind the substrates or the enzyme’s cofactors; if this is the case, corrections have to be made. If a stabilizing agent cannot be identified, the inactivation should be minimized by using the smallest dilution possible. If practical, the reaction may be initiated by the addition of one of the substrates. Corrections for loss of enzyme activity can also be made by establishing an activity-decay curve. However, it should be verified that the loss of activity is due to total inactivation and not simply to a conformational change to a form having less activity (i.e., a form with different kinetic parameters). This possibility could be tested by kinetic assays at significantly different times following dilution or by two reference assays, one using a saturating substrate level and the other using a subsaturating substrate level. The presence of thiol reagents—e.g., 2-mercaptoethanol, dithiothreitol, or dithioerythritol—is routinely helpful in maintaining activity

for those enzymes that require reduced thiol groups for activity. Care must be exercised that the reducing agent does not produce other effects. For example, γ-glutamylcysteine synthetase is inhibited by the presence of thiol reagents even though cysteine is a substrate (Seelig and Meister, 1985). Enzyme purity is also an issue of concern. It is important in early initial-rate studies to eliminate contaminating activities and to demonstrate that any minor activity does not substantially affect the reaction being studied. For multisubstrate enzymes, this can be readily accomplished by observing the stability of each substrate, effector, and product separately in the presence of the enzyme preparation. Adenylate kinase is a common contaminant in preparations of a wide variety of phosphotransferases and other nucleotide-dependent enzymes. This contaminating enzyme can be inhibited by performing the assay at high ionic strength (Bowen and Kerwin, 1955). Addition of elevated levels of AMP will also inhibit adenylate kinase. However, many phosphotransferases are also inhibited by this mononucleotide or are themselves inhibited by high ionic strength. Purich and Fromm (1972) and Lienhard and Secemski (1973) have shown that the multisubstrate analogs P1,P4-di(adenosine-5′)tetraphosphate and P1,P5-di(adenosine-5′)pentaphosphate are potent and specific inhibitors of this activity. In some instances it may be impossible to completely eliminate side reactions. Enzymes that have branched reaction pathways (i.e., multifunctional enzymes) will exhibit two pathways. Most multifunctional enzymes are transferases having multiple acceptor specificity. Glucose-6-phosphatase catalyzes not only the hydrolysis of glucose-6-phosphate but also its synthesis; thus, it may effect the transfer of phosphoryl groups from pyrophosphate, carbamoyl phosphate, or other glucose-6-phosphate molecules (Nordlie, 1982). This synthetic role of glucose-6-phosphatase is not a minor activity; at a concentration of 100 mM D-glucose, the synthesis and hydrolysis activities are approximately equal. The enzyme γglutamyl transpeptidase catalyzes the hydrolysis of a number of γ-glutamyl-containing molecules (including glutathione and glutathione disulfide) and also exhibits a transferase activity in which the glutamyl moeity is transferred to any of a number of amino acid acceptors, particularly cysteine, glutamine, and methionine (Allison, 1985). The hydrolase and transferase activities of γ-glutamyl transpeptidase

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are approximately equal in the presence of a pool of acceptor substrates at their physiological concentrations (Allison and Meister, 1981). Asparagine synthetase catalyzes the glutamine-dependent synthesis of L-asparagine and also exhibits a significant glutaminase activity (Boehlein et al., 1994). In all of these cases, it is possible to use inactivation studies to observe the parallel loss of both (or all) activities with time.

Enzyme Concentration The duration of the steady-state phase of an enzyme-catalyzed reaction is dependent on a number of factors. Perhaps the most important of these is the relative concentration of the enzyme and substrate. The ratio of the initial substrate concentration to the total enzyme concentration should be ≥100:1. With lower values, the steady-state phase will be of very short duration. It should be pointed out that this relationship holds for enzyme effectors as well. Thus, when investigating the influences of an activator or competitive inhibitor on enzyme activity, the total enzyme concentration should be much less than the concentration of the effector. This may prove difficult if the effector has a very tight affinity with the enzyme. In these cases, the protocols outlined by Williams and Morrison (1979) will prove useful.

Initial Rate and Steady-State Condition In an experiment, the investigator typically measures the rate of an enzyme-catalyzed reaction by calculating the slope of the progress curve. This procedure is only valid if the plot of product concentration as a function of time has been demonstrated to be linear within the time frame of the assay. However, this is not the only criterion that determines initial rate and steady-state kinetics. Linearity has been observed in many non-steady-state systems (Allison and Purich, 1979). The general rule of thumb is that initial rate conditions prevail when the substrate concentration is still within 10% of its starting value. This value is clearly acceptable for reactions that are thermodynamically quite favorable and that do not exhibit any significant product inhibition. However, if the equilibrium constant for the reaction is not large, an estimate of the degree of substrate-to-product conversion should be made. With smaller equilibrium constants, the initial-rate assay protocol necessarily must be more sensitive. In addition, if the product has very tight affinity for the enzyme,

product accumulation can lead to significant errors in initial-rate determinations even in those cases where the degree of conversion is low. A preliminary test to assess this possibility is to look at initial rates in the absence and presence of a known amount of product. Where product inhibition is a problem, product can be depleted if an auxiliary enzyme system for doing this is present. Nevertheless, product inhibition can be an important regulatory scheme for an enzyme. Any kinetic characterization of an enzyme should include accurate measurements of the dissociation constants for all products. Product-inhibition studies can also assist the investigator in establishing mechanistic details of the protein undergoing evaluation (Rudolph, 1979). In addition to establishing that the progress curve is truly linear, demonstrating that the degree of substrate depletion in the initial time course of the assay is low, and assessing the degree of product inhibition that may be present, the investigator should also demonstrate in the early stages of the kinetic studies that the rates observed are linear with respect to the total enzyme concentration ([E]total). If true steadystate conditions are present, a plot of initial rates versus [E]total (in which the enzyme concentration is varied above and below the final value to be used in the initial-rate assays) should be linear.

Substrate Concentration Ranges A wide range of substrate concentrations should be tested in preliminary studies. Such trials will provide an estimate of the Michaelis constant (Km) and an assessment of whether substrate inhibition occurs at elevated substrate levels. These tests will also detect the possible existence of cooperativity. Once an estimate of the Michaelis constant has been made, a more detailed kinetic study can be made, in which the substrate concentration is typically varied from ∼0.2 to 5.0 times the value of the apparent constant. It should be emphasized that reasonable estimates of the Km value can only be made when initial rates have been obtained from substrate concentrations both below and above the true Km value. For a one-substrate enzymecatalyzed reaction: Km = [A](Vmax − v)/v

where [A] is the substrate concentration, Vmax is the maximal velocity of the reaction, and v is the velocity of the reaction at the substrate concentration. A series of substrate concentrations with their respective initial rate velocities

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will provide an estimate of the Michaelis constant. In a standard double-reciprocal plot, the horizontal intercept is numerically equal to −1/Km. For multisubstrate enzymes (see below), estimates of the Michaelis constants will be obtained from slope and intercept replots of the initial rate data. In addition, appropriate statistical treatment of the raw data should be provided (Wilkinson, 1961; Cleland, 1979a) for both Km and Vmax values (or kcat values, equal to Vmax/[E]total, if those are reported). Note that, regardless of whether Vmax or kcat values are reported, the total protein concentration, as well as the specific activity of the enzyme used, should be reported whenever possible. With enzyme systems utilizing two substrates, the concentrations of both substrates must be varied as described above. Typically, three solutions are prepared. One solution contains one of the substrates at its highest level. A second solution contains the second substrate at its highest level. The third solution contains the buffer, all cofactors, metal ions, auxiliary enzymes, and any other effectors, but no substrates. This third solution is used to prepare serial dilutions of the other two stock solutions. Thus, a matrix of initial rate measurements on both substrates can be evaluated from a single experimental trial. Figure 3.5.2 illustrates a typical double-reciprocal plot for a two-sub-

strate enzyme-catalyzed reaction (where the the two substrates are A and B). In this example, Michaelis constants for the two substrates can be obtained by constructing slope and/or intercept replots of the data. For example, plotting the slopes of the five lines as a function of the reciprocal of the concentration of B will yield a straight line with a horizontal intercept numerically equal to −1/KB, where KB is the Michaelis constant for B. For enzymes that utilize three or more substrates, the protocol described above can become unwieldy. Two procedures have been described to address the design of kinetic studies for these proteins. In the first method (Frieden, 1959), one of the three substrates is held constant at a concentration above its respective Michaelis constant that is still subsaturating. The other two are then varied as described above. This same procedure is then followed with each of the remaining substrates. Thus, multisubstrate enzymes are reduced to pseudo-two-substrate systems. One distinct advantage of this procedure is that standard graphical presentations of the data thus produced (e.g., via double-reciprocal plots) are identical to that shown in Figure 3.5.2. However, the concentration value for the third substrate have to be provided. In the second procedure, the concentration of one substrate is varied while the other substrates are held at a

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Figure 3.5.2 Double-reciprocal plot of a two-substrate enzyme-catalyzed reaction, where v = the velocity of the reaction and [A] and [B] represent the concentrations of substrates A and B respectively.

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constant ratio, although the absolute amounts are varied. This is then repeated for all remaining substrates until the concentration of each of the substrates has been varied (Fromm, 1967). One limitation with this approach is the difficulty in obtaining true Michaelis constants. The presence of substrate inhibition can be an annoyance to an investigator who is attempting to determine the kinetic parameters of an enzyme as well as identify the binding pattern. However, the pattern of such inhibition can serve as a tool in understanding the kinetic and possibly the regulatory scheme for the enzyme. Substrate inhibition can occur as a result of a number of different causes—i.e., the formation of a dead-end complex with a particular enzyme form, the appearance of a different binding mechanism at elevated levels of substrate, the presence of an allosteric site for the substrate, or nonspecific inhibition that is due to an elevated ionic strength or another factor. When substrate inhibition occurs, in the case of multisubstrate enzymes, estimates of the Ki value (i.e., the dissociation constant for inhibition) for the substrate can be obtained from slope and intercept replots of the kinetic data. For enzymes having a single substrate, the data can be fit to an enzyme-rate expression containing a term for substrate inhibition (v = Vmax[S]/{Km + [S] + [S]2/Ki}). In addition, estimates of Km, Ki, and Vmax can be obtained from double-reciprocal plots (Cleland, 1979b). Note that in such cases, double-reciprocal plots will be nonlinear, with the degree of curvature dependent on the magnitude of the Ki value. In these cases, it may be necessary to use a computer program to fit the data. Cleland (1979a) has provided a program to address substrate inhibition. In addition, one might consider utilizing a Marmasse plot (Marmasse, 1963). Although rarely used, this plot provides good estimates of Km and Ki. A Marmasse plot is a plot of 1/v versus (α + 1/α ), where α = [A]/[A]m and [A]m represents the concentration of substrate at which the velocity is highest (note that this is not Vmax, as substrate inhibition is present.) This plot, when combined with the observation that [A]m = 1 (Km/Ki) ⁄2, will provide values for the kinetic parameters (Cleland, 1979b).

CONTINUOUS VERSUS STOP-TIME ASSAYS As the best estimate for the initial rate of an enzyme-catalyzed reaction derives from the slope of the steady-state region of the progress curve, continuous assays are greatly preferable to single-point assays. If it is not possible to use

a continuous assay, the investigator must demonstrate that the single-point assay is a true measure of the steady-state velocity of the reaction. In the single-point method, an aliquot of the reaction mixture is removed from the sample at a given time point after the reaction has been initiated (after a “blank” aliquot has been removed from the reaction mixture prior to the initiation step). Aliquots from at least four different time points are removed from the reaction solution; then the reaction is terminated and the amount of product formed (or substrate depleted) is determined. It should be ascertained that the termination procedure truly quenches the reaction. Once the steady-state relationship has been established, replicate kinetic analyses at each substrate concentration will provide sufficient data for a statistical analysis. The number of replicate samples that are assayed should also be reported.

COUPLED ENZYME ASSAYS In many instances, continuous assays are made possible by the presence of auxiliary enzymes. Product formation is measured by having the product react further with another enzyme (or enzymes). Although such assay systems are convenient, precautions must be exercised to ensure that the initial rates observed reflect the enzyme that is undergoing investigation and not the auxiliary system. If the auxiliary system is influencing the initialrate results, the reported values for the kinetic parameters will be in error and the double-reciprocals may exhibit considerable nonlinearity. The product or products formed by the enzyme undergoing study clearly direct the choice of auxiliary enzyme(s). This coupling system should provide turnover in excess over that due to the enzyme being investigated. However, the degree of excess will vary depending upon the assay method, the nature of the coupling system, and the magnitude of the kinetic parameters for each auxiliary enzyme as measured under the same conditions being observed with the target enzyme. Once this information is in hand, the amount of auxiliary enzyme needed can be calculated (McClure, 1969; Storer and Cornish-Bowden, 1974). Test runs should then be undertaken using both the calculated and higher levels of auxiliary enzymes to insure that identical rates are obtained, indicating that a sufficient excess has been achieved. The auxiliary enzyme system should also have minimal effect on cofactors and effectors required by the target enzyme.

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Frequently, a small amount of product may be present in the reaction mixture as a contaminant. For example, commercial preparations of ATP often include a small amount of ADP. The contaminating product can be removed prior to initiation of the assay by incubating the reaction mixture with the auxiliary system. The system should also be checked to insure that no activity is observed in the absence of the auxiliary enzymes. Finally, care should be exercised when using commercial preparations for the auxiliary enzymes. Often these sources of proteins contain contaminating activities that may cause serious difficulties unless they are addressed.

BINDING STUDIES

Kinetic Assay Methods

Measurements of equilibrium ligand binding can frequently add to the characterization of an enzyme’s binding mechanism, and also act as a tool in such procedures as molecularreceptor binding studies and radioimmunoassays. A wide variety of methods have been utilized in characterizing binding phenomena—e.g., gel filtration and equilibrium dialysis. The theory underlying this valuable tool can be found in any physical biochemistry text. Commonly, the binding data are analyzed with Scatchard plots (Scatchard, 1949) in which the number of moles of ligand bound per mole of total protein (ν) are plotted versus the ratio of ν to the free molar concentration of the ligand. These plots are frequently used to obtain values for the ligand dissociation constant, to identify interacting sites, and to estimate the binding capacity of the protein. Care should be exercised in this last use of the binding data, as it is easy to obtain a poor estimate of the stoichiometry of binding from Scatchard plots (Klotz, 1982, 1983). For example, a Scatchard plot of the binding of diazepam to benzodiazepine receptors is reported to provide an incorrect total number of receptor sites. Often, such problems are due to having only a few data points in the Scatchard plot at which the ligand concentration is above the half-maximum binding point. Just as in kinetic data, concentrations of the binding ligand both above and below this half-maximum point should be investigated (Klotz, 1982). Feldman (1983) has recently presented a statistical analysis associated with ligand binding data and Scatchard plots. As this paper points out, estimates of molar binding capacity should always be reported with the appropriate degree of statistical certainty. Without such statistical analyses, reports of the number of binding sites can be meaningless.

PRESENTATION OF INITIAL-RATE DATA The description of the assay protocols used in connection with enzyme purifications, in clinical studies, and in all kinetic investigations should include a clear definition of the unit of enzyme activity. When possible, velocities should be expressed in terms of molarity changes with time, as molarity is an intensive variable. Other units, proportional to molarity, are in common use. In all cases, the conditions of the assay should be fully described, including the volume of the initiating aliquot, the specific activity, and the amount of enzyme used in each experiment. The international unit (U), defined as the conversion of 1 µmol of substrate to product per minute, is in common use. Another unit of catalytic activity, the katal, corresponding to the transformation of one mole of substrate per second, is also in use. It has been suggested that the latter unit be restricted to catalytic activities measured in a clinical context (Dybkaer, 1979). Initial-rate data is frequently best presented via graphical displays. These visual linearizations of kinetic information provide a straightforward evaluation of the binding sequence and mechanism, as well as a ready appraisal of the kinetic parameters, reversible inhibition, cooperativity (both allosteric and hysteretic), and many other steady-state parameters. Whenever possible, slope and/or intercept replots should also be provided in the graphical display, perhaps as insets of the initial rate data. There are three methods frequently employed for depicting kinetic data, with the double-reciprocal plot (i.e., 1/v versus 1/[A]; see Fig. 3.5.2) being the most common. The other common plots are [A]/v versus [A] and v versus v/[A]. Wilkinson (1961) has discussed each of these plots in terms of statistics. When the data are unweighted, the three graphical presentations are not statistically equivalent. However, this problem is considerably mitigated by proper analysis using computer methods and data weighting (Siano et al., 1975). As mentioned earlier, Cleland (1979a) has provided a number of computer programs for the analysis of data. Eisenthal and Cornish-Bowden (1974) have also introduced a novel method for graphical treatment of data that provides a useful tool for evaluating experimental error. An Eisenthal–Cornish-Bowden plot is based on the equation (Vmax/v) − (Km/[A]) = 1. Instead of a particular set of data (i.e., a single initial rate velocity with its respective substrate concentration) being represented by a point in a Car-

3.5.8 Supplement 5

Current Protocols in Protein Science

tesian coordinate system, the velocity (v) is denoted along the vertical axis and the substrate concentration ([A]) is denoted along the horizontal axis. The two points thus plotted are then connected by a line. Thus, whereas in the other graphical procedures the velocity and substrate-concentration data are represented by points, in the Eisenthal–Cornish-Bowden plot the same data are represented by lines. These lines, obtained from a set of initial-rate experiments, will intersect about a point equivalent to v = Vmax and [A] = Km. Because median values of intersection points are used, outlier (i.e., aberrant) data will have minimal effects on estimates of the kinetic parameters. In addition, this plot is very useful in that calculations are not required and data do not have to be weighted. However, it does have limitations (Cornish-Bowden and Endrenyi, 1981, 1986). For example, the plot can become quite unwieldly with multisubstrate enzymes and in inhibition studies. It has recently been suggested (Brand and Johnson, 1992; Johnson and Brand, 1994) that all of the graphical methods described above may be unnecessary now that personal computers and associated new methods of data analysis are available. For example, global analysis of biochemical data is a powerful tool that may prove to be quite useful in the analysis of enzyme kinetics (Beechem, 1992). However, these numerical methods have not yet been commonly applied to studies such as those of multisubstrate systems, equilibrium exchanges, or product inhibition. At this time, with proper statistical weighting of the data, multisubstrate systems are more readily appraised by one of the common graphical procedures. More importantly, these visual depictions of initial-rate data are a clear aid to any individual reviewing the kinetic experiments.

CONCLUDING REMARKS Initial-rate assays are extremely useful tools in the detailed characterization of an enzyme. The behavior, mechanism, and regulatory properties of an enzyme can be readily assessed by straightforward assays. Such assays can frequently be completed within a day, once preliminary runs have identified the magnitude of the kinetic parameters and revealed those factors that affect activity. However, the details of the kinetic mechanism and regulatory properties of the enzyme can easily be obscured by poor assay design. The structural/functional role of an enzyme, as well as its regulation and control, can only be assessed by a reliable

initial-rate assay. In addition, the data assembled from these rigorous studies permit the development of more reliable and sensitive assay protocols for protein purification and identification.

LITERATURE CITED

Allison, R.D. 1985. γ-Glutamyl transpeptidase: Kinetics and mechanism. Methods Enzymol. 113:419-437. Allison, R.D. and Meister, A. 1981. Evidence that transpeptidation is a significant function of γglutamyl transpeptidase. J. Bio l. Ch em. 256:2988-2992. Allison, R.D. and Purich, D.L. 1979. Practical considerations in the design of initial velocity enzyme rate assays. Methods Enzymol. 63:3-22. Beechem, J.M. 1992. Global analysis of biochemical and biophysical data. Methods Enzymol. 210:37-54. Bergmeyer, H.U. (ed.) 1983. Methods of Enzymatic Analysis, 3rd ed., Vols. 1-4. Verlag Chemie, Deerfield Beach, Florida. Boehlein, S.K., Richards, N.G.J., and Schuster, S.M. 1994. Glutamine-dependent nitrogen transfer in Escherichia coli asparagine synthetase B. J. Biol. Chem. 269:7450-7457. Bowen, W.J. and Kerwin, T.P. 1955. The purification of myokinase with an ion exchange resin. Arch. Biochem. Biophys. 57:522-524. Brand, L. and Johnson, M.L. (eds.). 1992. Numerical computer methods. Methods Enzymol. Vol. 210. Cleland, W.W. 1979a. Statistical analysis of enzyme kinetic data. Methods Enzymol. 63:103-138. Cleland, W.W. 1979b. Substrate inhibition. Methods Enzymol. 63:500-513. Cornish-Bowden, A. and Endrenyi, L. 1981. Fitting of enzyme kinetic data without prior knowledge of weights. Biochem. J. 193:1005-1008. Cornish-Bowden, A, and Endrenyi, L. 1986. Robust regression of enzyme kinetic data. Biochem. J. 234:21-29. Deans, N.L., Allison, R.D., and Purich, D.L. 1992. Steady-state kinetic mechanism of bovine brain tubulin:tyrosine ligase. Biochem. J. 286:243251. Dybkaer, R.. 1979. International Union of Pure and Applied Chemistry and International Federation of Clinical Chemistry. IUPAC Section of Clinical Chemistry; Commission on Quantities and Units in Clinical Chemistry and IFCC Committee on Standards, Expert Panel on Quantities and Units; Approved Recommendation (1978) Quantities and units in clinical chemistry. Clin. Chim. Acta 96:157F-183F. Eisenthal, R. and Cornish-Bowden, A. 1974. The direct linear plot. A new graphical procedure for estimating enzyme kinetic parameters. Biochem. J. 139:715-720.

Detection and Assay Methods

3.5.9 Current Protocols in Protein Science

Supplement 5

Feldman, H.A. 1983. Statistical limits in Scatchard analysis. J. Biol. Chem. 258:12865-12867.

hexokinase type III isozyme. Arch. Biochem. Biophys. 170:587-600.

Fierke, C.A. and Hammes, G. 1995. Transient kinetic approaches to enzyme mechanisms. Methods Enzymol. 249:1-32.

Silverstein, E. and Sulebele, G. 1969. Catalytic mechanism of pig heart mitochondrial malate dehydrogenase studied by kinetics at equilibrium. Biochemistry 8:2543-2550.

Frieden, C. 1959. Glutamate dehydrogenase III. The order of substrate addition in the enzymatic reaction. J. Biol. Chem. 234:2391-2396. Fromm, H.J. 1967. The use of competitive inhibitors in studying the mechanism of action of some enzyme systems utilizing three substrates. Biochim. Biophys. Acta 139:221-230. Johnson, M.L. and Brand, L. (eds.). 1994. Numerical computer methods, part B. Methods Enzymol. 240:xi-xiii, 1-36, 51-170, 181-198, 311-322, 781-816. Klotz, I.M. 1982. Numbers of receptor sites from Scatchard graphs: Facts and fantasies. Science 217:1247-1249. Klotz, I.M. 1983. Number of receptor sites from Scatchard and Klotz graphs: A constructive critique. Science 220:981. Klump, H., Di Ruggiero, J., Kessel, M., Park, J.-B., Adams, M.W.W., and Robb, F.T. 1992. Glutamate dehydrogenase from the hyperthermophile Pyrococcus furiosus. J. Biol. Chem. 267:2268122685.

Tate, S.S. and Meister, A. 1978. Serine-borate complex as a transition-state inhibitor of γ-glutamyl transpeptidase. Proc. Natl. Acad. Sci. U.S.A. 75:4806-4809. Wilkinson, G.N. 1961. Statistical estimations in enzyme kinetics. Biochem. J. 80:324-332. Williams, J.W., and Morrison, J.F. 1979. The kinetics of reversible tight-binding inhibition. Methods Enzymol. 63:437-467.

KEY REFERENCES Bergmeyer, 1983. See above. Detailed collection of enzyme assay protocols and methods for a wide variety of enzymes, including descriptions of many special techniques: e.g., fluorometry, turbidimetry, luminometry, and microtechniques.

Lienhard, G.E. and Secemski, I.I. 1973. P1, P5Di(adenosine-5′)pentaphosphate, a potent multisubstrate inhibitor of adenylate kinase. J. Biol. Chem. 248:1121-1123.

Dixon, M. and Webb, E.C. 1979. Enzymes, 3rd ed. Academic Press, New York.

Marmasse, C. 1963. Enzyme inhibition by excess substrate. Biochim. Biophys. Acta 77:530-535.

Fromm, H.J. 1975. Initial Rate Enzyme Kinetics. Springer-Verlag, New York.

McClure, W.R. 1969. A kinetic analysis of coupled enzyme assays. Biochem. 8:2782-2786.

Valuable introductory text with considerable details on experimental design.

Nordlie, R.C. 1982. Kinetic examination of enzyme mechanisms involving branched reaction pathways: A detailed consideration of multifunctional glucose-6-phosphatase. Methods Enzymol. 87:319-353. O’Sullivan, W.J. and Smithers, G.W. 1979. Stability constants for biologically important metal-ligand complexes. Methods Enzymol. 63:294-336.

Classic text on the basics of enzymology; Chapter 4 deals specifically with enzyme kinetics.

Kuby, S.A. 1990. A Study of Enzymes, vol. I. CRC Press, Boca Raton, Florida Detailed text on steady-state kinetics. Contains useful chapters on the effects of metal cofactors and transient phase kinetics. Purich, D.L. (ed.) 1979. Enzyme kinetics and mechanism, part A. Methods Enzymol. Vol. 63.

Purich, D.L. and Fromm, H.J. 1972. Inhibition of rabbit muscle adenylate kinase by the transition state analogue, P1,P4-di(adenosine-5′)tetraphosphate. Biochim. Biophys. Acta 276:563-567.

Describes a variety of initial rate methods and inhibitor studies for characterizing enzyme-catalyzed reactions.

Rudolph, F.B. 1979. Product inhibition and abortive complex formation. Methods Enzymol. 63: 411436.

Purich, D.L. (ed.) 1980. Enzyme kinetics and mechanism, part B. Methods Enzymol. Vol. 64.

Scatchard, G. 1949. The attraction of proteins for small molecules and ions. Ann. N.Y. Acad. Sci. 51:660-673. Seelig, G.F. and Meister, A. 1985. Glutathione biosynthesis: γ-Glutamylcysteine synthetase from rat kidney. Methods Enzymol. 113:379-390. Siano, D.B., Zyskind, J.W., and Fromm, H.J. 1975. A computer program for fitting and statistically analyzing initial rate data applied to bovine Kinetic Assay Methods

Storer, A.C. and Cornish-Bowden, A. 1974. The kinetics of coupled enzyme reactions. Biochem. J. 141:205-209.

Describes a number of isotopic probes for studying enzyme mechanisms and contains valuable chapters on allosterism, hysteresis, immobilized systems, and processivity. Purich, D. L. (ed.) 1982. Enzyme kinetics and mechanism, part C. Methods Enzymol. Vol. 87. Describes methods for characterizing intermediates in enzyme-catalyzed reactions and using stereochemical probes for enzyme mechanisms; includes additional initial-rate and inhibitor methods and discusses further uses of isotopic probes.

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Purich, D.L. (ed.) 1995. Enzyme kinetics and mechanism, part D. Methods in Enzymol. 249:3662. Covers in detail specialized topics in enzyme kinetics including transition-state approaches, kinetic probes with site-directed mutagenesis, partition analysis, positional isotope exchange, interfacial catalysis, and hydrogen tunneling.

Contributed by R. Donald Allison University of Florida Gainesville, Florida

Detection and Assay Methods

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Supplement 5

Biotinylation of Proteins in Solution and on Cell Surfaces

UNIT 3.6

A protein is typically labeled with the water-soluble vitamin biotin to tag it for later recovery or for detection with biotin-binding proteins, such as avidin or streptavidin. Applications of this approach include identification and purification of cell surface–exposed proteins, measurement of ligand internalization kinetics, and temporal changes in protein distributions inside cells using light or electron microscopy. Many modified biotins, capable of reacting with protein lysyls, cysteinyls, or carbohydrates, are commercially available (see Table 3.6.1). It is usually possible to find at least one biotin reagent that can derivatize a given protein without affecting the function of interest. For instance, most enzyme activities are independent of protein modification on carbohydrate residues. Also, when used with enzyme-linked or radiolabeled avidin or streptavidin, biotinylation can be an effective technique for visualizing proteins that do not bind well to dyes after separation by SDS–polyacrylamide gel electrophoresis (SDS-PAGE; UNIT 10.1). This unit contains three protocols for biotinylation of isolated proteins: attaching biotin to primary amines (e.g., ε-amino groups of lysyl residues; see Basic Protocol 1); attaching biotin to sulfhydryls (i.e., thiol groups of cysteinyl residues; see Basic Protocol 3); and attaching biotin to carbohydrate residues on proteins (see Basic Protocol 4). As biotinylation of lysyl and cysteinyl residues may alter protein function, modification of protein carbohydrates, which is usually innocuous, may be preferable if intact protein function is required (e.g., for activity assays or affinity purification). Biotinylation of cysteinyl thiols requires that disulfide bonds in isolated proteins be reduced before labeling (see Support Protocol 1). Mild reaction conditions (e.g., in aqueous buffers at pH 7.5 to 8.5) and the small size of the biotin moiety also make biotinylation ideal for labeling surface proteins on living cells (see Basic Protocol 2). Such site-specific labeling enables investigators to monitor and/or effect subcellular purifications. Finally, this unit includes a brief description of methods for detecting biotinylated proteins (see Support Protocol 2). COVALENT ATTACHMENT OF BIOTIN TO LYSINES This protocol is a specialized version of the succinimidyl ester amidation reaction described in Basic Protocol 1 of UNIT 15.2. The reagent used, NHS-LC-biotin (see Fig. 3.6.1), has an N-hydroxysuccinimide (NHS) ester group to increase reactivity with primary amines and a long-chain (LC) spacer arm to extend the biotin from the protein surface, thus improving binding of subsequent reagents (e.g., avidin). The protein of interest is incubated with NHS-LC-biotin. Glycine is added to stop the reaction, and biotinylated proteins are purified by dialysis. This protocol works well over the range of 50 µl to 50 ml (and possibly for much larger volumes), so the volume of the reaction mixture should be chosen to suit the investigator’s needs.

BASIC PROTOCOL 1

If biotinylation of lysines results in loss of protein function, biotinylation of thiols (see Basic Protocol 3) or carbohydrate groups (see Basic Protocol 4) may prove useful. NOTE: NHS-biotin should be stored in the presence of desiccant at −20°C. When the reagent is thawed, it should be warmed thoroughly to room temperature before the container is opened. Enzymatic Manipulation of DNA and RNA Contributed by Elizabeth J. Luna Current Protocols in Protein Science (1996) 3.6.1-3.6.15 Copyright © 1996 by John Wiley & Sons, Inc.

3.6.1 Supplement 6

Table 3.6.1

Commercially Available Biotinylating Reagents

Suppliera

Reagent

Molecular weight

Comments

Specific for reaction with primary amines (see Basic Protocols 1 and 2, Support Protocol 2) NHS-biotin Pierce, Sigma, Vector Labs Soluble in DMF or DMSO; store all NHS 341.4 reagents at −20°C in presence of desiccant Sulfo-NHS-biotin Pierce, Sigma, Vector Labs 443.4 Water soluble NHS-LC-biotin Pierce, Sigma, Vector Labs 454.5 Soluble in DMF or DMSO; spacer arm improves accessibility of protein-bound biotin to affinity matrices NHS-SS-biotin Pierce 606.7 Soluble in DMF; cleavable by disulfide reduction 2-Iminobiotin-NHS Calbiochem, Pierce 421.3 Soluble in DMF; exhibits pH-sensitive binding to avidin Specific for reaction with sulfhydryls (see Basic Protocol 3, Support Protocols 1 and 2) Iodoacetyl-LC-biotin Pierce 510.4 Soluble in DMF or DMSO; reacts with amino groups above pH 7.5 Biotin-LC maleimide Sigma 479.6 Soluble in DMF; reacts with amino groups above pH 7.5 Maleimido-propionyl Molecular Probes, Pierce 523.6 Soluble in DMF; reacts with amino groups biocytin above pH 7.5 Biotin-HPDP Pierce 539.8 Soluble in DMSO; cleavable by disulfide reduction, regenerating initial cysteinyl Specific for reaction with (oxidized) carbohydrates (see Basic Protocol 4, Support Protocol 2) Biotin-LC-hydrazide Calbiochem, Pierce, Vector 258.3 Soluble in water (to ∼5 mM) and DMSO Labs (to ∼50 mM) Biotin-hydrazide Molecular Probes, Pierce, 371.5 Soluble in water (to ∼5 mM) and DMSO Sigma (to ∼50 mM) aSee SUPPLIERS APPENDIX for addresses and phone numbers of manufacturers.

Materials ∼1 mg/ml purified protein Coupling buffer (see recipe), ice cold 2 mg/ml NHS-LC-biotin (Pierce, Vector Labs) in N,N-dimethylformamide (DMF), room temperature, prepared just before use 50 mM glycine in coupling buffer 0.22-µm filter: e.g., Spin-X centrifuge filter (Costar) or syringe filter (Nalgene) Additional reagents and equipment for dialysis (UNIT 4.4 and APPENDIX 3B) 1. If necessary, dialyze the purified protein into coupling buffer to remove traces of interfering buffer components that may have been introduced during purification. Complete exchange of buffer components can be assumed after a 48-hr dialysis against 100 vol of coupling buffer with three to four buffer changes. However, if the initial concentration of the interfering agent is relatively low, a 16- to 24-hr dialysis with two buffer changes is generally sufficient.

Biotinylation of Proteins in Solution and on Cell Surfaces

Azide, glycine, ammonium ions, and Tris are common buffer components that interfere with the biotin labeling reaction. Although these reagents should be avoided, reagents without primary or secondary amino groups can be included to stabilize the protein or to prevent labeling at functional sites. For instance, NaCl, CaCl2, sucrose, glycerol, Triton X-100, and HEPES all can be used in the coupling buffer, if required.

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Current Protocols in Protein Science

O HN NaO3S NH2

+

O

NH

O

N O C (CH2)5 NH C (CH2)4

protein

S

NHS-LC-biotin

O HN

O

NH

O

NH C (CH2)5 NH C (CH2)4 protein

S biotinylated protein

+ NaO3S

O N OH O

Figure 3.6.1 Reaction of N-hydroxysuccinimide (NHS)-LC-biotin with protein.

2. Rapidly add the freshly prepared suspension of NHS-LC-biotin in DMF to the protein in coupling buffer, using 1 vol biotin in DMF to 20 vol protein solution. The biotin/protein mixture may become turbid because NHS-LC-biotin forms a fine suspension in water, rather than a true solution. If the protein is sensitive to organic solvents or if the highest affinity binding to avidin afforded by the LC spacer is not necessary, sulfo-NHS-biotin (Pierce, Sigma, or Vector Labs) can be substituted for NHS-LC-biotin. Sulfo-NHS-biotin is very water soluble and can be added at the same final biotin/protein molar ratio (∼10:1) from a freshly prepared aqueous stock solution without precipitate formation.

3. Incubate ∼1 hr on ice. Mix occasionally. Optimal extents of biotinylation must be determined empirically for each protein and are a compromise between high levels of incorporated biotin and retention of protein function. The biotin/protein ratio during labeling and the length of labeling time recommended here have worked well for a number of proteins. However, biotin-to-protein ratios as low as 1:1 and as high as 60:1 have been reported. Similarly, optimal labeling times may vary between 1 and 18 hr.

4. Add 1 vol of 50 mM glycine to bind unreacted biotin. 5. Remove glycine and free biotin by exhaustive dialysis against coupling buffer, or another buffer of choice. Dialysis for ≥36 hr against three or more changes of 500 vol each is recommended. Once the labeling step is complete, any buffer, including Tris⋅HCl, can be used for dialysis (UNIT 4.4 & APPENDIX 3B). Unincorporated sulfo-NHS-biotin can be removed quickly by gel-filtration on an Econo-Pac 10DG desalting column (Bio-Rad) or a Sephadex G-25 column (Pharmacia Biotech; see UNIT 8.3). However, because any suspended micelles of unreacted NHS-LC-biotin will coelute in the void volume with the protein, proteins labeled with NHS-LC-biotin should be dialyzed overnight against one change of buffer before gel filtration.

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6. For long-term storage, sterilize biotinylated proteins by passing the solution through a 0.22-µm filter. Spin-X centrifuge filters (Costar) work well for small volumes (<1 ml); syringe filters (Nalgene) are recommended for much larger volumes. Sodium azide also may be added up to 0.02% (w/v) to inhibit bacterial growth, but it is most effective below pH 7.0. Thimerosal (0.1% [w/v]) is more useful in solutions with higher pH values. CAUTION: Sodium azide is toxic; wear gloves when handling.

7. Store biotinylated proteins on ice or at 4°C. Some biotinylated proteins can be frozen and stored in aliquots at −20° or −70°C; others are reported to aggregate or lose function under these conditions. Generally, the biotinylated protein should be stored under the conditions recommended for the unlabeled protein. If no information is available about storage conditions for the protein of interest, it is wise to test small aliquots for relative stability under the following sets of conditions: (1) fast frozen in sterile cryogenic vials (Corning) or in test tubes with O-ring caps (Sarstedt) using a dry ice/ethanol slurry, followed by storage of aliquots (1a) at −20°C, (1b) at −70°C, and (1c) in a liquid nitrogen Dewar flask. Other aliquots should be (2) mixed well with an equal volume of glycerol (to make a 50% glycerol solution) and stored at −20°C, (3) stored on ice, and (4) kept at 4°C. Shortly after the initial storage and at periodic intervals thereafter, aliquots should be removed from each of the storage conditions and tested for biological, binding, or enzymatic activity. Once the most efficient storage condition has been determined, future preparations of the biotinylated protein should be stored accordingly in aliquots sufficient for one, or a few, experiments. Repeated cycles of freezing and thawing should be avoided. CAUTION: Tubes stored under liquid nitrogen can explode on warming if liquid nitrogen has gotten inside the tube. Loosen caps on tubes immediately after taking them out of a liquid nitrogen Dewar flask. The concentration of the biotinylated protein can be determined using any of the assays described in UNIT 3.4, and the concentration of protein-associated biotin can be measured in a variety of solid-phase competition assays with labeled avidin and known amounts of free biotin (Savage et al., 1992). However, because most assays with biotinylated protein are either qualitative or comparative, many investigators merely verify that biotinylation has occurred, i.e., by analysis of electrotransferred protein after SDS-PAGE (UNIT 10.10). BASIC PROTOCOL 2

SIDE-SPECIFIC BIOTINYLATION OF PROTEINS ON CELL SURFACES Biotinylation of proteins at the extracellular surface of living cells is a fast, nonradioactive means of labeling and tracking the plasma membrane during cell fractionation (Ingalls et al., 1986; UNIT 4.2). The biotinylated proteins also are easily isolated after membrane solubilization and affinity chromatography on columns containing bound avidin or streptavidin (Chia and Luna, 1989). Biotinylation of living cells requires the water-soluble biotin derivative sulfo-NHS-biotin. The starting material is intact cells, either freshly isolated or from suspension or adherent cell cultures; control experiments starting with lysed cells or isolated membranes are, however, recommended to verify that cell integrity is maintained during the procedure. Cells are reacted with freshly dissolved sulfo-N-hydroxysuccinimide (NHS)-biotin for 30 min with mixing. Glycine is added to bind unreacted biotin, and cells are washed and lysed to prepare plasma membranes or solubilized proteins.

Biotinylation of Proteins in Solution and on Cell Surfaces

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Current Protocols in Protein Science

NOTE: NHS-biotin should be stored in the presence of desiccant at −20°C. When the reagent is thawed, it should be warmed thoroughly to room temperature before the container is opened. Materials 107 to 1010 cells of interest, in suspension (e.g., erythrocytes, leukocytes, or HeLa cells), or cultures of adherent cells Physiological buffer, pH 7.4 (see recipe), ice cold Sulfo-NHS-biotin (Pierce or Vector Labs) 10 mM glycine in physiological buffer Additional reagents and equipment for lysing cells and isolating plasma membranes (UNIT 4.2 and UNIT 4.3) or for solubilizing proteins for SDS gel electrophoresis (UNIT 10.1) NOTE: Carry out all steps in this protocol at 4°C or on ice. 1. Remove serum proteins by washing the cells of interest at least three times with ice-cold physiological buffer, centrifuging cells in suspension for 5 min at ∼500 × g, 4°C, between washes. Adherent cells are most easily washed and labeled while still attached to tissue culture flasks. To avoid labeling cytoplasmic proteins, the biotinylating agent should be thoroughly blocked and removed before dissociating attached cells from the flask (i.e., detach the cells after step 5). The buffer can be varied, depending on the preferences of the cells and the investigator. Dulbecco’s PBS or Earle’s balanced salts (e.g., Sigma) also work well. In contrast, PBS containing only ∼5 mM phosphate (APPENDIX 2E) may not have sufficient buffering capacity. As in Basic Protocol 1, the principal consideration is to omit reagents containing primary amino groups that could compete for binding to the sulfo-NHS-biotin.

2. During the final cell wash, dissolve the sulfo-NHS-biotin in 0.1 vol ice-cold physiological buffer for a final concentration of 2 mg/ml (∼5 mM). For instance, for ∼1010 cells in a volume of 9 ml, dissolve 20 mg sulfo-NHS-biotin in 1 ml physiological buffer. If labeling attached cells, mix the sulfo-NHS-biotin with the entire amount of buffer immediately before adding the mixture to the cells. Minimize the amount of hydrolysis of the succinimide ester by limiting its exposure to water in the absence of cells.

3. Gently suspend the cells by adding ice-cold physiological buffer first. Then add 1⁄10 vol of dissolved sulfo-NHS-biotin. Mix gently without lysing the cells. Although labeling of cell-surface lysines proceeds faster as the pH of the buffer is raised from pH 7.4 to 9.0, so does the hydrolysis of the sulfo-NHS-biotin. In addition, many cells are lysed in higher-pH buffers.

4. Incubate 30 min at 4°C with gentle rotation or swirling. This procedure should label only surface-exposed proteins. Higher temperatures will result in increased biotinylation of membrane proteins, but much of the increase may be due to endocytic membrane turnover.

5. Block unreacted biotinylating reagent for ≥10 min by adding an equal volume of 10 mM glycine in physiological buffer. Wash cells with buffer three times as in step 1. Monitor the cells for lysis during centrifugation, using phase-contrast or differential interference contrast (DIC) light microscopy. If the cells are too fragile to withstand repeated washes, incubate with the glycine at 4°C for 15 to 30 min, then wash only one or two times. Blocking is faster at elevated pH, but pH 7.4 is adequate as long as the cells are not permeabilized during the washes.

Enzymatic Manipulation of DNA and RNA

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6. Lyse the cells and isolate plasma membranes (see UNIT 4.2) or solubilize proteins for SDS-PAGE (see UNIT 10.1, Basic Protocol 1). Store biotinylated membranes in aliquots at −20°C. It is usually a good idea to perform a parallel, control experiment in which cells are biotinylated after lysis or in which isolated membranes are biotinylated. The control procedure should label both surface-exposed and intracellular polypeptides, providing evidence for cellular integrity during the initial labeling procedure. The control can also demonstrate that if a protein of particular interest remains unlabeled under surface-labeling conditions but is labeled in isolated membranes or lysates, it is inherently capable of reacting with the sulfo-NHS-biotin reagent and, thus, is either a cytosolic protein or a membrane protein without accessible external lysines. BASIC PROTOCOL 3

COVALENT ATTACHMENT OF BIOTIN TO THIOLS An alternative to biotinylating lysyl groups (see Basic Protocol 1) is biotinylation of thiols or carbohydrate groups (see Basic Protocol 4). Several modified biotins react specifically with sulfhydryls (see Table 3.6.1). This protocol is described for iodoacetyl-LC-biotin in N,N-dimethylformamide (DMF). However, guidelines for using biotin-maleimide and N-(6-[biotinamido]hexyl)-3′-(2′-pyridyldithio)propionamide (biotin-HPDP) reagents are given in step annotations. Purified protein or membrane fractions are first subjected to disulfide reduction (see Support Protocol 1). The reduced material is mixed with iodoacetyl-LC-biotin. Unreacted components are then separated from labeled proteins by gel filtration or dialysis before the proteins are filter sterilized and stored. Materials 0.5 mg/ml protein or cell fraction of interest, e.g., purified membranes 100 mM sodium phosphate/5 mM EDTA, pH 8.3 (APPENDIX 2E) 4 mM iodoacetyl-LC-biotin (Pierce) in DMF, freshly prepared or thawed from storage at −20°C Additional reagents and equipment for reducing protein disulfides (see Support Protocol 1), dialysis (UNIT 4.4 and APPENDIX 3B), gel-filtration chromatography (UNIT 8.3), or centrifugal filtration (UNIT 15.1, Support Protocol 3) 1. If necessary, reduce protein disulfides in the protein or cell fraction of interest (see Support Protocol 1) and remove the thiol-reducing agent by dialysis (UNIT 4.4 and APPENDIX 3B), gel-filtration chromatography (UNIT 8.3), or centrifugal filtration (UNIT 15.1). Use the sodium phosphate/EDTA buffer to make up the solution of reduced protein. If samples must be dialyzed for prolonged periods, nitrogen gas should be bubbled through the dialysis buffer to help exclude the atmospheric oxygen that will slowly (1 to 7 days) oxidize the reduced disulfides. EDTA chelates free metal ions that can catalyze this oxidation.

2. Add 20 µl of 4 mM iodoacetyl-LC-biotin in DMF for each milliliter of protein or cell fraction in the sodium phosphate/EDTA buffer. For proteins with an “average” number of cysteines, i.e., 1.7 mol % (Table A.1A.1), this provides an equimolar ratio of iodoacetyl-LC-biotin. The cost and relative insolubility of biotinylated alkylating reagents contraindicate their use in a 5- to 10-fold molar excess over total thiols, as described in Basic Protocol 1.

Biotinylation of Proteins in Solution and on Cell Surfaces

Depending on the protein and activity to be studied, another thiol-directed biotinylating reagent may be preferable. At pH 8.3, iodoacetyl-LC-biotin reacts irreversibly and specifically with free sulfhydryls. Maleimide reagents (biotin-LC-maleimide and maleimidopropionyl biocytin) also irreversibly derivatize sulfhydryls but will react with some primary amines unless the pH is lowered to between 6.5 and 7.5. Biotin-

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HPDP reacts rapidly and specifically through disulfide exchange with free protein sulfhydryls; consequently, proteins derivatized with biotin-HPDP should not be exposed to reduced thiol agents unless reversal of the labeling procedure is desired.

3. Incubate ∼2 hr at room temperature or overnight at 0° to 4°C. 4. Remove unreacted reagent by gel-filtration chromatography (UNIT (UNIT 4.4 and APPENDIX 3B).

8.3)

or dialysis

5. Filter the solution through an 0.22-µm filter and store biotinylated proteins at 0° to 4°C or biotinylated membranes in aliquots at −20°C. REDUCTION OF DISULFIDE BONDS Reducing disulfide bonds may be necessary to create the free sulfhydryls on protein cysteines needed for biotinylation with thiol-directed biotin reagents (see Basic Protocol 3). Disulfide reduction can also be used to elute proteins tagged with the amine-directed reagent N-hydroxysuccinimide (NHS)-SS-biotin (see Basic Protocol 1) from avidin affinity columns. This protocol gives an effective method for reducing a thiol-sensitive disulfide bond that is not essential for the quaternary structure, e.g., that in immunoglobulins. The protein of interest is dialyzed into reducing buffer and mixed with reducing agent at elevated temperature (i.e., 37°C). Excess reductant is removed by gel-filtration chromatography (UNIT 8.3). The reduced protein is then ready for immediate biotinylation. In general, disulfide reduction can be assumed to be innocuous, if not advantageous, for proteins that reside in the cell cytoplasm, but it may perturb function in extracellular proteins or in the extracellular domains of transmembrane proteins.

SUPPORT PROTOCOL 1

Materials 1 to 10 mg/ml purified protein Reducing buffer: 100 mM sodium phosphate/5 mM EDTA, pH 6.0 (APPENDIX 2) Reducing agent: 50 mM dithiothreitol (DTT),100 mM 2-mercaptoethanol (2-ME), or 100 mM mercaptoethylamine⋅HCl (Sigma) Econo-Pac 10DG desalting column (Bio-Rad) or Sephadex G-25 column (Pharmacia Biotech) Additional reagents and equipment for dialysis (UNIT 4.4 and APPENDIX 3B) and gel-filtration chromatography (UNIT 8.3) 1. If necessary, dialyze purified protein of interest into reducing buffer. 2. Add 1 vol of 1 to 10 mg/ml purified protein in reducing buffer to an equal volume of reducing agent. Incubate 1 hr at 37°C with occasional mixing. Each mole of DTT contains two thiols, so only half the concentration (50 mM) is required for the same concentration of reducing equivalents used for other reducing agents (100 mM 2-ME or 100 mM mercaptoethanolamine⋅HCl).

3. During the incubation in step 2, equilibrate an Econo-Pac 10DG desalting column or a Sephadex G-25 column with reducing buffer. The column should contain ≥10 vol of gel-filtration matrix (i.e., at least five times more volume than that of the reaction in step 2).

4. Cool the reaction to room temperature. Remove excess reducing agent by gel-filtration chromatography (UNIT 8.3) using the equilibrated column. Collect at least ten fractions, each containing ∼1 vol (i.e., the same volume as the initial volume of protein solution). Alternatively, excess reducing agent may be removed by centrifugal filtration (UNIT 15.1, Support Protocol 3) or dialysis (UNIT 4.4 and APPENDIX 3B). If prolonged dialysis is required,

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reoxidation of protein disulfides can be slowed by including 5 mM EDTA and/or by bubbling nitrogen gas through the dialysis buffer.

5. Read the absorbance of each fraction at 280 nm (A280). Pool fractions that correspond to the first peak of absorbance. 6. As soon as possible after removing excess reducing agent, use the pooled fractions in cysteinyl-directed biotinylations (see Basic Protocol 3). BASIC PROTOCOL 4

COVALENT ATTACHMENT OF BIOTIN TO CARBOHYDRATES Biotinylation of protein carbohydrates requires oxidation of vicinal carbohydrate hydroxyl groups to aldehydes that can bind biotin-LC-hydrazide. Sodium m-periodate is used as the oxidizing agent for glycoproteins in solution, but enzyme-mediated oxidations can be used to oxidize glycoproteins of cells or membrane fractions. Briefly, glycoproteins are oxidized, excess oxidizing agent is removed by gel-filtration, biotin reagent is added, and the biotinylated glycoprotein is purified in a microconcentrator. Materials 0.1 to 0.2 mg/ml glycoprotein Labeling buffer: 100 mM sodium acetate, pH 5.5 (APPENDIX 2E) 30 mM and 3 mM sodium m-periodate (Sigma, mol. wt. 213.9) in water, freshly prepared and stored in the dark 5 mM biotin-LC-hydrazide (Calbiochem, Pierce, or Vector Labs) in labeling buffer (store ≤2 months at 4°C) Stop solution: 100 mM Tris⋅Cl, pH 7.5 (APPENDIX 2E) Econo-Pac 10DG desalting column (Bio-Rad) or Sephadex G-25 column (Pharmacia Biotech) Microconcentrator Additional reagents and equipment for dialysis (UNIT 4.4 and APPENDIX 3B) and gel-filtration chromatography (UNIT 8.3) 1. If needed, dialyze the glycoprotein into labeling buffer. 2. Add 1 vol of 30 mM or 3 mM sodium m-periodate to 2 vol of 0.1 to 0.2 mg/ml glycoprotein in labeling buffer for a final periodate concentration of either 10 mM or 1 mM. Incubate ∼30 min at room temperature in the dark. Because proteins differ in sensitivity to periodate, it is a good idea to try at least two different concentrations of the oxidant. For antibodies, O’Shannessy et al. (1984) report excellent results with 10 mM sodium m-periodate. For labeling a transmembrane glycoprotein that requires a detergent for solubilization, avoid the use of octylglucoside, which also reacts with periodate. Cell-surface carbohydrates on erythrocytes and on purified erythrocyte membranes have been biotinylated by labeling cells or purified membranes for 2 hr at room temperature with a solution containing 0.03 U/ml Vibrio cholerae neuraminidase (Boehringer Mannheim), 3 U/ml Dactylium dendroides galactose oxidase, and 0.4 mM biotin-LC-hydrazide in physiological buffer (Bayer et al., 1988). With many cells and cell membranes, however, those conditions will promote cell lysis and/or proteolysis.

3. During the incubation period (step 2), equilibrate an Econo-Pac 10DG desalting column or Sephadex G-25 column with labeling buffer. Biotinylation of Proteins in Solution and on Cell Surfaces

The column should contain ≥15 vol of gel-filtration matrix (i.e., at least five times more volume than that of the reaction in step 2).

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4. Remove unincorporated sodium m-periodate by gel-filtration chromatography (UNIT 8.3) using the equilibrated column. Collect at least ten fractions, each containing ∼1 vol (i.e., the same volume as the initial volume of sodium m-periodate). 5. Read the absorbance of each fraction at 280 nm (A280). Pool fractions that correspond to the first peak of absorbance. 6. Add 1 vol of 5 mM biotin-LC-hydrazide in labeling buffer. Incubate 1 hr at room temperature with occasional mixing. The amount of dilution of protein on the gel-filtration column can vary without significantly affecting the efficiency of the reaction.

7. Terminate the reaction by adding 5 vol stop solution. Tris, glycine, ammonium ions, and other buffer components containing primary amines should be avoided during the oxidation and labeling steps because they form reversible Schiff bonds with the carbohydrate aldehydes. These Schiff bonds will inhibit hydrazone bond formation. Primary amines are, however, good for stopping the labeling reaction. If the biotinylated protein is expected to be less than ∼0.5 mg/ml after concentration, a carrier protein, such as BSA, can be added after the stop solution. Carrier protein helps to eliminate protein loss during concentration but will complicate quantification of the percent recovery of the biotinylated glycoprotein and of the number of biotins incorporated per glycoprotein molecule.

8. Remove unreacted biotin-LC-hydrazide by centrifugation in a microconcentrator with a molecular weight cutoff (MWCO) that is less than half that of the molecular weight of the protein in solution (UNIT 4.4). After concentrating the protein, suspend the sample to the initial volume with labeling buffer and reconcentrate. Repeat until the buffer has been changed a total of three times and the glycoprotein has reached the desired concentration. Any buffer in which the glycoprotein is stable can be substituted for labeling buffer at this step. To estimate the final glycoprotein concentration, assume no more than 50% loss during the entire procedure, as long as the protein is known to be well-behaved in solution. If precision is necessary, the molar ratio of biotin to glycoprotein can be calculated after determining protein concentrations (UNIT 3.4) and biotin concentrations (e.g., by solidphase binding assays with avidin; Savage et al., 1992), after taking into account any added carrier protein. However, the estimate of 50% loss during experimental manipulation generally provides a reasonable starting point for most qualitative experiments (and is seldom off by more than a factor of 2).

9. Store the concentrated glycoprotein in aliquots at −70° or −20°C. For discussion of storage conditions, see Basic Protocol 1 (step 7 annotation).

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SUPPORT PROTOCOL 2

DETECTION OF BIOTINYLATED PROTEINS Once proteins or cells are biotinylated (see Basic Protocols 1 to 4), the next step is often detection (e.g., in the course of performing fluorescence or electron microscopy, assaying enzyme activity, or sizing proteins). Experimental samples and biotinylated standards can be analyzed simultaneously. This protocol gives brief instructions for separating proteins and visualizing them with 125I-labeled avidin or avidin-based chromophores. Avidin-biotin complex (ABC) detection kits are available for isolated proteins (described in this protocol) as well as for proteins on fixed tissue, tissue sections, and microplate wells. Materials Biotinylated experimental protein (see Basic Protocols 1 to 4) Biotinylated protein molecular weight standards (Bio-Rad or Vector Labs) 3% (w/v) BSA in PBS (APPENDIX 2E) Nitrocellulose membrane 0.5 µCi 125I-Bolton-Hunter-labeled avidin (DuPont NEN) in 5 ml avidin buffer (see recipe) or Vectastain ABC kit (Vector Labs) or ImmunoPure ABC kit (Pierce) Additional reagents and equipment for SDS-PAGE (UNIT 10.1), electroblotting (UNIT 10.7), and detecting proteins in immunoblots (UNIT 10.10) 1. Electrophorese biotinylated experimental protein and protein molecular weight standards in separate lanes on a one-dimensional SDS–polyacrylamide gel (UNIT 10.1) 2. Electroblot proteins onto a nitrocellulose membrane (UNIT 10.7) and block ∼1 hr at 22°C with 3% BSA in PBS. Note that the milk-based blocking solution described in UNIT 10.10 should not be used because milk contains free biotin that can lead to unacceptably high backgrounds.

3a. To visualize biotinylated proteins by labeling with radioactive avidin: Incubate the blot in 0.5 µCi of 125I-Bolton-Hunter-labeled avidin (Mock, 1990) in 5 ml avidin buffer for 2 hr at 22°C. Wash the membrane extensively in the same buffer without avidin or BSA, air dry, and expose to film (Goodloe-Holland and Luna, 1987). 3b. To visualize biotinylated proteins with avidin-linked chromophores in the Vectastain ABC kit or ImmunoPure ABC kit: Follow the manufacturer’s instructions. The ABC procedure is essentially identical to that described in UNIT 10.10, except that steps 5 and 6 (incubation with a biotinylated secondary antibody) are omitted.

REAGENTS AND SOLUTIONS Use Milli-Q-purified water or equivalent for the preparation of all buffers. For common stock solutions, see APPENDIX 2E; for suppliers, see SUPPLIERS APPENDIX.

Avidin buffer 150 mM NaCl 4% (w/v) BSA 0.02% (v/v) Triton X-100 50 mM sodium borate Adjust to pH 10.0 to 10.5 with 1 M NaOH Store ≤2 months at 4°C Biotinylation of Proteins in Solution and on Cell Surfaces

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Coupling buffer Select one of the following buffers (see Critical Parameters for discussion of practical considerations and APPENDIX 2E for recipes): 100 mM NaHCO3, pH 8.5 100 mM Na2CO3, pH 8.5 100 mM sodium phosphate buffer, pH 7.5 100 mM sodium phosphate buffer, pH 8.0 100 mM sodium phosphate buffer/5 mM EDTA, pH 8.3 Physiological buffer, pH 7.4 7 g NaCl (120 mM final) 22 ml 0.2 M KH2PO4 (APPENDIX 2E; 4.4 mM final) 103 ml 0.2 M Na2HPO4 (APPENDIX 2E; 20.6 mM final) Add H2O to 1 liter Check pH (it should be ∼7.4; if not, check concentrations of stock solutions) Store ≤2 months at 4°C or ≤2 weeks at room temperature COMMENTARY Background Information The extraordinarily high affinity of avidin for biotin was first discovered as the explanation for a deficiency disease caused by ingestion of large numbers of raw eggs. In this disease, which is characterized by skin lesions and growth retardation, the essential vitamin biotin is depleted because it is sequestered by avidin in egg whites (György et al., 1941). The dissociation constant at each of the four biotinbinding sites of avidin is ∼10−15 M (Green, 1975), making the avidin-biotin interaction one of the strongest known noncovalent biological interactions. Retention of high-affinity binding after derivatization of the biotin carboxyl group with a wide variety of agents has led to the development of numerous assays involving the recovery or detection of biotintagged moieties with avidin or other high-affinity biotin-binding proteins described below (reviewed by Wilchek and Bayer, 1990 and Savage et al., 1992). Selection of biotinylation reagent There are a number of reagents that can be used for biotinylation (see Table 3.6.1). Selection of reagent depends on the intended uses for the biotinylated product. For instance, most enzyme activities are independent of protein modification on carbohydrate residues, which generally can be assumed to be innocuous; labeling of cysteinyls, however, which generally can be assumed to be innocuous, may disrupt disulfide bonds that are important for structure or activity, so biotinylation of cysteinyls is not recommended for extracellular proteins. Biotinylation of lysyl groups works well

with many proteins, including those on surfaces of intact cells, but it also may lead to loss of protein function. N-Hydroxysuccinimide (NHS) esters of biotin react readily at alkaline pH with any accessible, unprotonated primary amine, such as ε-amino groups of lysyl side chains in proteins (see Basic Protocol 1 and Basic Protocol 2). Because lysine is abundant in proteins, accounting for ∼7% of total amino acid residues in an average protein, NHS-biotin and its derivatives (see Table 3.6.1) are the most commonly used biotinylating agents. Modifications of NHS-biotin include addition of a sulfonate group to improve solubility in water (sulfoNHS-biotin), insertion of a long-chain (LC) spacer arm between the biotin moiety and the reactive ester to increase the availability of the biotin at the protein surface (NHS-LC-biotin), and addition of a cleavable site, such as a disulfide bond (NHS-SS-biotin), to permit release of the protein from biotin using reducing agents such as 2-mercaptoethanol (2-ME) or dithiothreitol (DTT). A cleavable disulfide bond is particularly useful for eluting biotinylated proteins from an affinity matrix containing covalently bound avidin because the exceptionally high affinity (Kd ∼10−15 M) and correspondingly low off-rate of the biotin-avidin interaction complicates elution with chaotropes (but see below). Several commercially available reagents (see Table 3.6.1) permit biotinylation of proteins on cysteinyl residues. At physiological pH (pH 7.5 to 8.0), iodoacetyl-LC-biotin, biotinLC-maleimide, maleimidopropionyl biocytin, biotin-HPDP, and biotin-maleimide all react

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primarily with free sulfhydryl groups on reduced cysteines. For cytoplasmic proteins, in particular, labeling with cysteine-directed reagents may permit retention of functions lost after derivatization of lysines. However, these reagents may not be advisable for labeling extracellular or membrane proteins with conformations stabilized by disulfide bonds because reduction of at least some disulfides with thiol-reducing agents (e.g., 50 mM DTT or 100 mM 2-ME) is generally necessary for efficient labeling (see Support Protocol 1). Biotin hydrazides (see Table 3.6.1), especially those with a six-carbon spacer (biotinLC-hydrazide), can be excellent reagents for biotinylating galactose and N-acetylgalactosamine residues on extracellular and transmembrane proteins (see Basic Protocol 4). The biotin hydrazide reagents covalently bind to aldehydes that have been generated from vicinal carbohydrate hydroxyls (cis-diols) by mild chemical or enzyme oxidation. The resulting hydrazone bonds are stable between pH 2 and 10. Sodium m-periodate provides a fast, effective means for oxidizing cis-diols on many soluble proteins, including immunoglobulins. However, the reagent also can react with serines, threonines, and tyrosines. Consequently, periodate oxidation sometimes affects protein biological activity and often alters migration on SDS–polyacrylamide gels. If these side effects cannot be tolerated (e.g., when identifying cellsurface proteins by biotinylation), then galactose and N-acetylgalactosamine residues should be exposed and oxidized by treating the cells on membranes with a combination of neuraminidase and galactose oxidase.

Biotinylation of Proteins in Solution and on Cell Surfaces

Detection of biotinylated proteins A large number of biotinylated proteins and biotin-detection reagents are commercially available, either as individual compounds or in the form of kits. For instance, biotinylated dipalmitoyl-L-α-phosphatidylethanolamine (Pierce) can be used as a phospholipid tracer in membrane reconstitution studies. Also, biotinylated insulin (Sigma), testosterone (Sigma), and lectins (Pierce or Vector Labs) can be used to identify and/or purify their cellular receptors. Avidin-biotin complex (ABC) detection kits (e.g., Vectastain ABC kit, Vector Labs, and ImmunoPure ABC kit, Pierce; see Support Protocol 2) amplify even weak signals by taking advantage of the multivalency of avidin for biotin and the incorporation of multiple biotins into most biotinylated antibodies and enzymes. Other, similar kits from Vector Labs and Pierce

permit visualization of biotinylated proteins on fixed tissue, thin microscopic sections, and microtiter plate wells, allowing use of the biotinavidin high-affinity interaction in immunofluorescence microscopy, electron microscopy, and enzymatic assays, as well as for protein size determinations. The molecular masses of experimentally obtained biotinylated proteins are best estimated by comparison with the sizes of biotinylated protein molecular weight markers (Bio-Rad or Vector Labs). Because the addition of one or a few of the small biotin molecules does not significantly increase the mass of most proteins, the sizes of biotinylated protein standards are considered identical to those of prestained or unstained markers. Additionally, the markers are visualized with the same detection methods as the proteins of interest, permitting direct size comparisons. Nonspecific, biotin-independent binding to avidin can usually be controlled by employing buffers of pH 10.0 to 10.5, which is near the avidin isoelectric point (pI 10.0). Use of such buffers minimizes electrostatic interactions but causes little or no diminution of the avidin-biotin affinity. When high-pH buffers are undesirable, avidin should be replaced either by a neutralized, deglycosylated form of avidin (Neutralite Avidin from Molecular Probes or Southern Biotechnology, or NeutrAvidin from Pierce) or by streptavidin (Pierce, Sigma, and Vector Labs). Streptavidin is a tetrameric protein from the culture broth of Streptomyces avidinii; it binds four biotin molecules per mole with an affinity similar to that of avidin. Both neutralized avidin (pI 6.3) and streptavidin (pI 5.0) exhibit fewer nonspecific electrostatic interactions at physiological pH and ionic strength. Streptavidin does, however, contain a sequence that causes biotin-independent binding to integrins and related cell-surface fibronectin receptors under certain conditions (Alon et al., 1990). Many commercially available derivatives of streptavidin (Pierce, Sigma, and Vector Labs) and neutralized avidin (Pierce and Southern Biotechnology) may be used in microscopic and enzymatic assays as a replacement for underivatized avidin. Recovery of biotinylated proteins Paradoxically, the high affinity of the biotin-avidin interaction can be a disadvantage during affinity purification of biotinylated proteins. Biotinylated proteins bind tightly to avidin affinity matrices (Molecular Probes, Pierce, Sigma, or Vector Labs) under almost

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any physiological conditions, but the high-affinity binding is reversed only by harsh denaturants, such as 8 M guanidine⋅HCl, pH 1.5, or by boiling with 2% (w/v) SDS. Further, the large positive charge on avidin (pI 10.0) and its carbohydrate residues promote nonspecific binding of many negatively charged proteins and some lectins to avidin affinity columns. For this reason, matrices containing bound NeutrAvidin (Pierce) or streptavidin (Molecular Probes, Pierce, Sigma, and Vector Labs) are often used for the affinity purification of biotinylated proteins, despite the significantly greater expense of these matrices. For preparative purification of biotinylated proteins by affinity chromatography, whether it be on columns containing bound avidin, streptavidin, or neutralized avidin, there are two approaches. The first approach is to accept that the biotin-avidin interaction is essentially irreversible and to attach the biotin label to a dispensable moiety that is left on the column after eluting the protein of interest. For instance, biotinylated monoclonal antibodies can be used to purify individual membrane antigens (Updyke and Nicolson, 1984), and biotinylated insulin and other ligands have been used to purify their specific receptors by avidin or streptavidin affinity chromatography (Kohanski and Lane, 1985/1986; Kraehenbuhl and Bonnard, 1990). Similarly, disulfide bond reduction (see Support Protocol 1) will release proteins that have been biotinylated with NHSSS-biotin (Chia and Luna, 1989) or with biotin-HPDP (see Table 3.6.1). The second approach to purifying native proteins is to moderate the avidin-biotin binding affinity by modifying either the bound avidin or the biotin. For example, “monomeric” avidin columns (Pierce or Sigma), generated by treating immobilized avidin with 0.1 M guanidine⋅HCl, pH 1.5, contain one lower-affinity biotin-binding site (Kd ∼5 × 10−8 M) per bound avidin molecule. After presaturating residual high-affinity sites on the column with excess free biotin, biotin-containing proteins can be bound to the columns and then eluted with either 0.1 M glycine, pH 2.0 to 2.8, or 2 mM free biotin (Henrikson et al., 1979; Kohanski and Lane, 1990). A converse strategy is to derivatize protein lysines with 2-iminobiotin-NHS (see Table 3.6.1), a guanido analog of NHS-biotin that exhibits a strong pH dependence in interactions with avidin and streptavidin (Heney and Orr, 1 98 1) . At p H 9.5, imin ob iotin binds avidin/streptavidin with characteristically high

affinity. However, proteins labeled with iminobiotin can be eluted from avidin columns with either 0.1 M acetic acid or 50 mM ammonium acetate/0.5 M NaCl, pH 4.0. Similarly, columns containing bound 2-iminobiotin (Molecular Probes, Pierce, or Sigma) can be used to purify avidin, streptavidin, and their derivatives. For further information on affinity chromatography, see Chapter 9.

Critical Parameters and Troubleshooting The most critical aspects of protein biotinylation are the storage and solubilization conditions for the biotinylating reagent. This is particularly critical with NHS derivatives, which contain succinimide ester groups that are very reactive with water. The NHS-biotins should be stored in the presence of desiccant at −20°C, as recommended by the suppliers, and the reagent bottles should be warmed completely to room temperature before opening to minimize condensation. Similarly, it is critical to mix the compounds with the protein to be labeled as soon as possible after dissolving the reagents in aqueous solutions. Most other parameters are less critical. Although the rate of protein biotinylation is influenced by temperature, short incubations (e.g., 30 min to 1 hr) at either 0° to 4°C or room temperature are usually satisfactory. The extent of biotinylation is determined by the amounts of reagents mixed and the reaction time. Biotin-to-protein molar ratios from 1:1 to 60:1 and reaction times up to 18 hr have been described. Because biotinylation is directed at selected groups in proteins (e.g., labeling ranges from a single cysteinyl or carbohydrate to some or all lysyls, which make up 7 of 100 residues in a typical protein), the choice of biotinylating reagent is often more critical than the reaction conditions. If an enzyme loses activity during biotinylation and if a change of biotinylating reagent is contraindicated, one strategy is to protect the enzyme’s active site by adding excess substrate during the biotinylation reaction. The medium in which biotinylation takes place must be free of components that will interfere with the reaction chemistry. Coupling buffers for NHS-biotin reagents (see Basic Protocols 1 and 2) must be free of ingredients with primary amines. Excess thiol-reducing agent (see Support Protocol 1) must be removed for efficient use of sulfhydryl-directed reagents (see Basic Protocol 3), and excess cis-diol-oxidizing agent must be eliminated for labeling of protein carbohydrates (see Basic Protocol 4).

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Biotinylation of Proteins in Solution and on Cell Surfaces

Finally, pH is an important factor. Lysyl-directed reagents require unprotonated amines (e.g., pH 7.5 to 8.5), whereas maleimide-based thiol-directed reagents undergo side reactions with amines at pH 7.5 or above and thus need more acid conditions (e.g., pH 6.5 to 7.5) if specificity for cysteines is required. Carbohydrate-directed reagents also require acid conditions (e.g., pH 5.5) for specificity. Reagent solubility may also be a factor. If the protein of interest is sensitive to organic solvents, such as DMF and DMSO, use the water-soluble sulfo-NHS-biotin for labeling lysyl groups. If highest affinity binding is not needed, biotinylating reagents lacking the LC spacer may be more water soluble. When biotinylating a subset of proteins in a complex mixture—for example, when determining which plasma membrane proteins are accessible to a cell-surface biotinylating reagent—it is useful to include two controls in each experiment. First, some cells should always be carried through the labeling and fractionation procedure in the absence of biotinylating reagent, in order to identify endogenously biotinylated proteins, which are involved in many intracellular carboxylation reactions. Second, to help identify proteins that may be exposed by cell breakage during manipulation, cytoplasmically oriented proteins should be labeled with biotinylating reagent either after deliberate mechanical lysis in the presence of 0.2% (v/v) Triton X-100 or after plasma membrane purification. The group of biotinylated cytoplasmically oriented proteins generated with this control should differ dramatically from that observed after biotinylation of unbroken cells. If the two SDS-PAGE profiles appear similar, then the biotinylating procedure should be repeated under conditions that are less conducive to cell lysis (e.g., biotinylate cells that are still attached to tissue culture plastic). Because proteins differ dramatically in the number of surface-accessible reactive groups and how they are affected by labeling with a foreign tag such as biotin, it is hard to predict which biotinylating compound will work best for a particular protein. However, biotinylation chemistries are similar to those used for a variety of other compounds, including fluorescent labels. Thus, if a literature reference exists for fluorescently labeling a protein of choice, the most expeditious strategy is to obtain a biotinylating compound with the same reactive group (i.e., succinimide ester, maleimide, or hydraz-

ide moiety) and to follow the published reaction conditions for labeling the protein. If no information is available about accessible groups or about the functional consequences of a particular derivatization strategy, it is a good idea to try different types of biotinylating reagents at different reagent-to-protein ratios. Reagent/protein molar ratios of 1:1 and 20:1 are usually good starting points. Preliminary characterization of protein functionality should suggest which of the reaction chemistries is superior and whether a higher or lower stoichiometry yields the most efficient labeling.

Anticipated Results The biotinylation protocols in this unit should biotinylate most proteins. The extent of reaction and the degree to which the protein retains its endogenous structure and activity will vary but may be predictable if other investigators have used a similar chemistry to derivatize the protein with a “tag” other than biotin. Soluble proteins biotinylated by these procedures have been used as ligands in binding assays in vitro and as tracers for cellular processes including endocytosis, membrane trafficking, and cytoskeletal remodeling (reviewed in Savage et al., 1992, and Wilchek and Bayer, 1990). Cell surface–biotinylated membrane proteins can be used similarly as markers for plasma membrane proteins in biochemical fractionations and in membrane recycling assays. In addition, biotinylated protein(s) can be purified, either alone or in combination with receptors or other binding partners, on avidin or streptavidin affinity columns.

Time Considerations Labeling reactions involving succinimide esters (NHS-biotin and analogs) and those involving biotin-hydrazides are relatively rapid and usually complete within 1 to 2 hr at 4°C. Reactions of sulfhydryls with maleimides and iodoacetamides are slower, particularly when carried out in lower-pH buffers that optimize reaction selectivity of protein sulfhydryls over amino groups. Although these reactions can be terminated after only 2 to 4 hr, reaction extents are usually increased by longer incubations (e.g., overnight at 4°C). Similarly, depending on the choice of separation technique, removal of unincorporated biotinylating reagent from labeled protein can be either rapid or slow, with gel-filtration chromatography and centrifugal filtration requiring

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no more than 1 to 2 hr, whereas dialysis may take 1 to 3 days. Separation steps also are often dictated more by the properties of the protein being labeled than by investigator preference. Some proteins become irreversibly aggregated if subjected to centrifugal filtration; other proteins are denatured or proteolyzed during prolonged dialysis. Here again, it helps to know what other investigators have done successfully with the particular protein under investigation.

Literature Cited Alon, R., Bayer, E.A., and Wilchek, M. 1990. Streptavidin contains an RYD sequence which mimics the RGD receptor domain of fibronectin. Biochem. Biophys. Res. Commun. 170:1236-1241. Bayer, E.A., Ben-Hur, H., and Wilchek, M. 1988. Biocytin hydrazide—A selective label for sialic acids, galactose, and other sugars in glycoconjugates using avidin-biotin technology. Anal. Biochem. 170:271-281. Chia, C.P. and Luna, E.J. 1989. Phagocytosis in Dictyostelium discoideum is inhibited by antibodies directed primarily against common carbohydrate epitopes of a major cell-surface plasma membrane glycoprotein. Exp. Cell Res. 181:11-26. Goodloe-Holland, C.M. and Luna, E.J. 1987. Purification and characterization of Dictyostelium discoideum plasma membranes. Methods Cell Biol. 28:103-128. Green, N.M. 1975. Avidin. Adv. Protein Chem. 29:85-133. Gyöargy, P., Rose, C.S., Eakin, R.E., Snell, E.E., and Williams, R.J. 1941. Egg-white injury as the result of nonabsorption or inactivation of biotin. Science 93:477-478. Heney, G. and Orr, G.A. 1981. The purification of avidin and its derivatives on 2-iminobiotin-6aminohexyl-Sepharose 4B. Anal. Biochem. 114:92-96.

Henrikson, K.P., Allen, S.H.G., and Maloy, W.L. 1979. An avidin monomer affinity column for the purification of biotin-containing enzymes. Anal. Biochem. 94:366-370. Ingalls, H.M., Goodloe-Holland, C.M., and Luna, E.J. 1986. Junctional plasma membrane domains isolated from aggregating Dictyostelium discoideum amebae. Proc. Natl. Acad. Sci. U.S.A. 83:4779-4783. Kohanski, R.A. and Lane, M.D. 1985/1986. Receptor affinity chromatography. Ann. N.Y. Acad. Sci. 447:373-385. Kohanski, R.A. and Lane, M.D. 1990. Monovalent avidin affinity columns. Methods Enzymol. 184:194-200. Kraehenbuhl, J.-P. and Bonnard, C. 1990. Purification and characterization of membrane proteins. Methods Enzymol. 184:629-641. Mock, D.M. 1990. Sequential solid-phase assay for biotin based on 125I-labeled avidin. Methods Enzymol. 184:224-233. O’Shannessy, D.J., Doberson, M.J., and Quarles, R.H. 1984. A novel procedure for labeling immunoglobulins by conjugation to oligosaccharide moieties. Immunol. Lett. 8:273-277. Savage, M.D., Mattson, G., Desai, S., Nielander, G.W., Morgensen, S., and Conklin, E.J. 1992. Avidin-Biotin Chemistry: A Handbook. Pierce Chemical Co., Rockford, Ill. Updyke, T.V. and Nicolson, G.L. 1984. Immunoaffinity isolation of membrane antigens with biotinylated monoclonal antibodies and immobilized streptavidin matrices. J. Immunol. Methods 73:83-95. Wilchek, M. and Bayer, E.A. (eds.) 1990. Avidinbiotin technology. Methods Enzymol. 184:1-746.

Contributed by Elizabeth J. Luna Worcester Foundation for Biomedical Research Shrewsbury, Massachusetts

Enzymatic Manipulation of DNA and RNA

3.6.15 Current Protocols in Protein Science

Supplement 6

Metabolic Labeling with Amino Acids

UNIT 3.7

Metabolic labeling techniques are used to study the biosynthesis, processing, intracellular transport, secretion, degradation and physical-chemical properties of proteins. In this unit, protocols are described for metabolically labeling mammalian cells with radiolabeled amino acids (Table 3.7.1). Cells labeled using these procedures are suitable for analysis by immunoprecipitation, characterization of cellular proteins, analysis of protein trafficking, and one- and two-dimensional gel electrophoresis (UNITS 10.1-10.4). The first protocols describe pulse-labeling (10 to 30 min) of mammalian cells in suspension with [35S]methionine (see Basic Protocol), and necessary modifications for adherent mammalian cells (see Alternate Protocol 1). Alternate protocols are also presented for pulse-chase analysis (see Alternate Protocol 2) and long-term (“steady state”) labeling (6 to 32 hr; see Alternate Protocol 3). This is followed by conditions for metabolic labeling of cells with radiolabeled amino acids other than [35S]methionine (see Alternate Protocol 4). The degree of label incorporation can be determined by precipitation with trichloroacetic acid (TCA; see Support Protocol). NOTE: All solutions and equipment coming into contact with living cells must be sterile, and aseptic technique should be used accordingly. NOTE: All culture incubations should be performed in a humidified 37°C, 5% CO2 incubator unless otherwise specified. Some media (e.g., DMEM) may require altered levels of CO2 to maintain pH 7.4. Table 3.7.1

Radiolabeled Amino Acids Used in Metabolic Labeling of Proteins

Amino acida Leucine Serine Glutamic acid Lysine Alanine Valine Glycine Threonine Arginine Aspartic acid Proline Glutamine Phenylalanine Tyrosine Asparagined Cysteine Isoleucine Histidine Methionine Tryptophan

Frequency (%)b

Radioisotope

10.4 8.1 7.3 7.0 7.0 6.2 5.7 5.6 5.0 4.9 4.9 4.5 4.5 3.6 3.5 3.4 2.9 2.5 1.8 1.3

3H 3H 3H 3H 3H 3H 3H 3H 3H 3H 3H 3H 3H 3H

— 35S 3H 3H 35S 3H

Specific activity (Ci/mmol)

Commentsc

5-190 5-40 15-80 40-110 10-85 10-65 10-60 5-25 30-70 10-50 15-130 20-60 15-140 15-60 — >800 30-140 30-70 >800 20-30

E T, I T, I E T E T, I E E T, I — T, I E — — — E E E E

aAll amino acids in this table are in the L configuration. bFrequency of amino acid residues in proteins, taken from Lathe (1985). cE, essential amino acids; T, amino acids that are modified by transamination (Coligan et al., 1983);

I, amino acids that are converted by cells to other amino acids. See Critical Parameters for more details. dAsparagine is difficult to label (Coligan et al., 1983).

Detection and Assay Methods Contributed by Juan S. Bonifacino Current Protocols in Protein Science (1999) 3.7.1-3.7.10 Copyright © 1999 by John Wiley & Sons, Inc.

3.7.1 Supplement 17

SAFETY PRECAUTIONS FOR WORKING WITH 35S-LABELED COMPOUNDS When working with radioactive materials, take appropriate precautions to avoid contamination of the experimenter and the surroundings. Carry out the experiment and dispose of wastes in an appropriately designated area, following guidelines provided by the local radiation safety officer (also see APPENDIX 2B). Solutions containing 35S-labeled compounds have been found to release volatile radioactive substances (Meisenhelder and Hunter, 1988). In addition to the usual safety practices followed when handling radioactive materials (see APPENDIX 2B), some extra precautions should be taken when using 35S-labeled amino acids: 1. Vials containing 35S-labeled compounds should always be handled in a designated fume hood equipped with an activated charcoal filter. This includes thawing the solution, opening the vial, and adding the radiolabeled amino acid to the labeling medium. Before opening, vials should be vented with a needle attached to a syringe packed with activated charcoal. Tissue culture hoods should not be used for this purpose, as they are likely to become contaminated. 2. Minimize exposure of 35S-containing solutions to the air. 3. Use baths, incubators, and centrifuges designated for work with radioactive materials. Place a tray containing a layer of activated charcoal in the CO2 incubator to reduce the amount of 35S-labeled compounds released to the air during cell labeling. Alternatively, filters impregnated with activated carbon (β-Safe, Schleicher & Schuell) can be attached to the covers of tissue culture dishes. 4. Monitor areas used during labeling by conducting wipe tests. 5. Dispose of solid and liquid 35S waste quickly and with appropriate precautions. BASIC PROTOCOL

Metabolic Labeling with Amino Acids

PULSE-LABELING OF CELLS IN SUSPENSION WITH [35S]METHIONINE Pulse-labeling of proteins is performed by incubating cells for short periods (≤30 min) in culture medium containing a radiolabeled amino acid. The conditions described below are optimized for labeling times of 10 to 30 min, but the same procedure can be used for periods up to 6 hr, although fewer cells and/or more labeling medium should be used for optimal results. Very short pulses (<10 min) may require special conditions (see Critical Parameters). For labeling times >6 hr, see Alternate Protocol 3. [35S]methionine is the radiolabeled amino acid of choice for metabolic labeling of proteins because of its high specific activity (>800 Ci/mmol) and ease of detection. A potential disadvantage of [35S]methionine is its low abundance (∼1.8% of the average amino acid composition; see Table 3.7.1). For proteins that contain little (e.g., only one) or no methionine, other radiolabeled amino acids should be used (see Alternate Protocol 4). Materials [35S]L-Methionine (>800 Ci/mmol) or 35S-labeled protein hydrolyzate (>1000 Ci/mmol) Pulse-labeling medium (see recipe), warmed to 37°C Cell suspension (e.g., Jurkat, RBL, K562, BW5147, or T or B cell hybridomas), grown in a humidified 37°C, 5% CO2 incubator or prepared from tissues (e.g., lymphocytes) PBS (APPENDIX 2E), ice cold Vacuum aspirator with trap for liquid radioactive waste

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Additional reagents and equipment for TCA precipitation (optional; see Support Protocol) 1. Thaw [35S]methionine at room temperature and prepare a 0.1 to 0.2 mCi/ml working solution in prewarmed (37°C) pulse-labeling medium. CAUTION: Volatile 35S-containing compounds can be released during the labeling procedure (refer to Safety Precautions and APPENDIX 2B). Keep [35S]methionine-containing medium in a tightly capped tube in a 37°C water bath until use. Do not let it sit >60 min at 37°C.

2. Harvest 0.5–2 × 107 cells in suspension by centrifuging 5 min at 300 × g, room temperature. 3. Aspirate supernatant and resuspend cells by gently tapping the bottom of the tube. Add 10 ml prewarmed pulse-labeling medium. Mix by inversion of the tube and centrifuge 5 min at 300 × g, room temperature. Repeat this step once. 4. Resuspend cells at 5 × 106 cells/ml in prewarmed pulse-labeling medium and incubate 15 min in a 37°C water bath to deplete intracellular pools of methionine. Invert tube periodically to resuspend cells. 5. Centrifuge cells 5 min at 300 × g, room temperature, and aspirate supernatant. 6. Resuspend cells in 2 ml [35S]methionine working solution (step 1). Cap tube tightly. Incubate cells 10 to 30 min (see Critical Parameters) in a 37°C water bath, resuspending frequently by gentle inversion of the tube. Alternatively, use a rotator placed in a 37°C incubator.

7. Centrifuge cells 5 min at 300 × g, 4°C, and aspirate supernatant. Resuspend with gentle swirling in 10 ml ice-cold PBS and repeat centrifugation. CAUTION: The medium and wash are radioactive—follow applicable safety regulations for disposal. Because of the short times and high concentrations of radiolabeled amino acids employed, pulse-labeling may not result in complete depletion of label from the medium. If this is the case, the labeling mixture can be reused. Collect the labeling medium containing [35S]methionine or 35S-labeled protein hydrolyzate and filter carefully through a 0.45-ìm filter unit. Estimate the percentage of unincorporated radioactivity by scintillation counting aliquots of the labeling mixture before and after labeling the cells. Store the labeling mixture up to 2 months frozen at −20°C.

8. Optional: Determine amount of label incorporation by trichloroacetic acid (TCA) precipitation (see Support Protocol 1). Labeled cells are now ready for the desired processing and analysis. If cell pellets cannot be processed immediately, they can be kept on ice for a few hours or frozen at −80°C for several days. Thaw frozen cell pellets on ice before analysis. In most cases, freezing cells will not affect the biochemical properties of the proteins, but freezing and thawing cell extracts after solubilization with detergents (APPENDIX 1B) can cause dissociation of multisubunit complexes or proteolysis of labeled proteins.

Detection and Assay Methods

3.7.3 Current Protocols in Protein Science

Supplement 17

ALTERNATE PROTOCOL 1

PULSE-LABELING OF ADHERENT CELLS WITH [35S]METHIONINE Labeling of adherent cells is essentially the same as described for cells in suspension except that cells are labeled while attached to a dish. As with suspended cells, adherent cells are pulse-labeled 10 to 30 min (see Basic Protocol introduction). Alternatively, some adherent cells can be detached from plates by incubation for 10 min at 37°C with 10 mM EDTA in PBS (APPENDIX 2E) and labeled as a suspension (see Basic Protocol). This is particularly advantageous when labeling cells for pulse-chase experiments, because it simplifies handling of multiple samples and allows more uniform labeling of the cells. Additional Materials (also see Basic Protocol) Adherent cells (e.g., HeLa, NRK, M1, COS-1, CV-1, or fibroblasts or endothelial cells in primary culture) 100-mm tissue culture dishes 1. Grow adherent cells to 80% to 90% confluency in 100-mm tissue culture dishes. Depending on the cell type, a confluent 100-mm dish will contain 0.5–2 × 107 cells.

2. Prepare [35S]methionine working solution (see Basic Protocol, step 1). 3. Aspirate culture medium from the dishes and wash twice by gently swirling with 10 ml prewarmed (37°C) pulse-labeling medium, aspirating medium after each wash. 4. Add 5 ml prewarmed pulse-labeling medium and incubate 15 min in a humidified 37°C, 5% CO2 incubator to deplete intracellular pools of methionine. 5. Remove medium from cells, add 2 ml [35S]methionine working solution (from step 2), and incubate 10 to 30 min in a CO2 incubator. If necessary, as little as 1 ml of labeling medium per plate can be used. It is critical, however, that the plates sit on a perfectly horizontal surface during incubation to avoid drying of the cell monolayer.

6. Remove medium from cells. Wash once with 10 ml ice-cold PBS and remove PBS. CAUTION: The medium and wash are radioactive—follow applicable safety regulations for disposal. If significant detachment of cells occurs during labeling, it may be necessary to scrape the cells carefully in the [35S]methionine-containing medium and transfer the suspension to a 15-ml centrifuge tube before centrifuging and washing with PBS.

7. Add 10 ml ice-cold PBS and scrape cells carefully with either a disposable plastic scraper or a rubber policeman. 8. Transfer the suspension to a 15-ml centrifuge tube, centrifuge 5 min at 300 × g, 4°C, and discard supernatant. 9. Optional: Determine amount of label incorporation by trichloroacetic acid (TCA) precipitation (see Support Protocol 1). Labeled cells are now ready for the desired processing and analysis (see Basic Protocol, step 8 annotation, if cells are not to be used immediately).

Metabolic Labeling with Amino Acids

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PULSE-CHASE LABELING OF CELLS WITH [35S]METHIONINE Pulse-chase protocols are used to analyze time-dependent processes, such as posttranslational modification, transport, secretion, or degradation of newly synthesized proteins. Cells in suspension or adherent cells are pulse-labeled with [35S]methionine (see Basic Protocol and Alternate Protocol 1), after which they are incubated (chased) in complete medium containing excess unlabeled methionine, as described below.

ALTERNATE PROTOCOL 2

Additional Materials (also see Basic Protocol and Alternate Protocol 1) Chase medium (see recipe), 37°C 1. Prepare 0.5–2 × 107 cells per sample per time point, and pulse-label 10 to 30 min with 2 ml of 0.1 to 0.2 mCi/ml [35S]methionine per 0.5–2 × 107 cells (see Basic Protocol, steps 1 to 6, or see Alternate Protocol 1, steps 1 to 5). 2. Remove the [35S]methionine working solution, wash once with 10 ml of 37°C chase medium, and add 10 ml of 37°C chase medium. Rapid termination of the labeling reaction can be achieved by adding two times the volume of chase medium containing excess unlabeled methionine (15 mg/liter) directly to the labeling mixture. Final concentration of cells in suspension should be 2 × 106 cells/ml for chases ≤2 hr, or 0.5 × 106 cells/ml for chases >2 hr. For adherent cells, add 10 ml/100-mm dish.

3. Incubate for the desired time at 37°C. Incubate cell suspensions with rotation in tightly capped tubes. Incubate adherent cells in a CO2 incubator. 4a. For cells in suspension: Collect cells by centrifuging 5 min at 300 × g, 4°C. The supernatant can be discarded or can be collected for analysis of proteins that are secreted or shed into the medium.

4b. For adherent cells: Scrape off adherent cells and transfer to 15-ml centrifuge tubes. Centrifuge 5 min at 300 × g, 4°C, and either save or discard the supernatant as in step 4a. 5. Optional: Determine amount of label incorporation by trichloroacetic acid (TCA) precipitation (see Support Protocol 1). Labeled cells are now ready for the desired processing and analysis (see Basic Protocol, step 8 annotation, if cells are not to be used immediately).

LONG-TERM LABELING OF CELLS WITH [35S]METHIONINE Long-term labeling refers to continuous metabolic labeling of cells for periods of 6 to 32 hr. Long-term labeling is particularly advantageous when studying proteins that are synthesized at low rates. It is also used to accumulate mature labeled proteins, rather than biosynthetic precursors, for characterization of their properties. Steady-state labeling is a form of long-term labeling in which cells are incubated with the radiolabeled amino acid until the rates of synthesis and degradation of the radiolabeled proteins are equal. Steady-state labeling allows calculation of the stoichiometry of subunits within a protein complex, provided that the primary structure of the subunits is known. In these procedures, unlabeled methionine is added to the medium to maintain cell viability and to sustain incorporation of label for the duration of the experiment. The amount of unlabeled methionine added depends on factors such as the length of the labeling period and the cell density. Media containing between 5% and 20% the normal amount of unlabeled methionine are generally used. The conditions described below are suitable for overnight (∼16 hr) labeling of cells in suspension or adherent cells.

ALTERNATE PROTOCOL 3

Detection and Assay Methods

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Additional Materials (also see Basic Protocol and Alternate Protocol 1) Long-term labeling medium (see recipe), warmed to 37°C 75-cm2 tissue culture flask 1. Prepare a 0.02 to 0.1 mCi/ml [35S]methionine working solution in prewarmed (37°C) long-term labeling medium (see Basic Protocol, step 1). For cell suspensions: 2a. Prepare and wash cells once with prewarmed long-term labeling medium (see Basic Protocol, steps 2 and 3). 3a. Resuspend 0.5–2 × 107 cells in 25 ml of 0.02 to 0.1 mCi/ml [35S]methionine working solution and transfer to a 75-cm2 tissue culture flask. For adherent cells: 2b. Grow 0.5–2 × 107 adherent cells in a 75-cm2 tissue culture flask (80% to 90% confluency) in a CO2 incubator. Wash once with prewarmed long-term labeling medium (see Alternate Protocol 1, step 3). 3b. Add 25 ml of 0.02 to 0.1 mCi/ml [35S]methionine working solution to each 75-cm2 flask. 4. Tighten caps to prevent release of volatile 35S-labeled compounds. Incubate 16 hr in a CO2 incubator. 5. Wash cells and determine incorporation (see Basic Protocol, steps 7 and 8, or Alternate Protocol 1, steps 6 to 9). Labeled cells are now ready for the desired processing and analysis (see Basic Protocol, step 8 annotation, if cells are not to be used immediately). ALTERNATE PROTOCOL 4

METABOLIC LABELING WITH OTHER RADIOLABELED AMINO ACIDS In some instances, it may be necessary to label proteins with radiolabeled amino acids other than [35S]methionine—e.g., when the proteins have a low methionine content or have no methionine residues at all. In these cases, the best choices are [35S]cysteine or [3H]leucine. Cysteine residues are more abundant in proteins than methionine residues (3.4% versus 1.8%; Table 3.7.1), although cysteine is less stable. Formulations of [35S]cysteine that have high specific activity (>800 Ci/mmol) can be obtained from several companies. Leucine has the advantage of being the most frequent amino acid in proteins (10.4%; Table 3.7.1) and specific activities of [3H]leucine are the highest among 3H-labeled amino acids (up to 190 Ci/mmol). If proteins are known to be rich in a particular amino acid or if special methods are to be performed (e.g., radiochemical sequencing or multiple labeling), 3H-labeled amino acids other than [3H]leucine can be used. Label cells with these amino acids as described for [35S]methionine in the previous protocols. Substitute [35S]cysteine or 3H-labeled amino acids in the labeling medium. Use labeling medium lacking cysteine or any other respective amino acid. CAUTION: Solutions containing [35S]cysteine release volatile 35S-labeled compounds (refer to Safety Precautions).

Metabolic Labeling with Amino Acids

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Current Protocols in Protein Science

TCA PRECIPITATION TO DETERMINE LABEL INCORPORATION In metabolic labeling experiments, it is often useful to monitor the incorporation of radioactivity into total cellular proteins. This can easily be achieved by precipitation with trichloroacetic acid (TCA) using BSA as a carrier protein. This protocol can be used at the end of the labeling procedures in each protocol.

SUPPORT PROTOCOL

Materials Labeled cell suspension (see Basic Protocol or Alternate Protocols 1 to 4) BSA/NaN3: 1 mg/ml BSA containing 0.02% (w/v) sodium azide (NaN3) 10% (w/v) TCA solution (see recipe), ice cold Ethanol Filtration apparatus attached to a vacuum line 2.5-cm glass microfiber filter disks (Whatman GF/C) CAUTION: TCA is extremely caustic. Protect eyes and avoid contact with skin when preparing and handling TCA solutions. 1. Add 10 to 20 µl of a labeled cell suspension to 0.1 ml BSA/NaN3. Place on ice. 2. Add 1 ml ice-cold 10% (w/v) TCA solution. Vortex vigorously and incubate 30 min on ice. 3. Filter the suspension onto 2.5-cm glass microfiber filter disks in a filtration apparatus under vacuum. 4. Wash the disks twice with 5 ml ice-cold 10% (w/v) TCA solution and twice with ethanol. Air dry 30 min. CAUTION: The wash fluids should be handled as mixed chemical/radioactive waste—follow applicable safety regulations for disposal (see APPENDIX 2B).

5. Spot the same volume of the radiolabeled cell suspension used in step 1 (10 to 20 µl) on a glass microfiber disk. Air dry. This disk will be used to measure the total amount of radiolabeled amino acid in the cell suspension.

6. Transfer disks from steps 4 and 5 to 20-ml scintillation vials, add 5 ml scintillation fluid, and measure the radioactivity in a scintillation counter. 7. Calculate the ratio of TCA-precipitable label to total radioactivity (i.e., the ratio of sample radioactivity in step 4 to the total radioactivity in step 5). REAGENTS AND SOLUTIONS Use deionized or distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2E; for suppliers, see SUPPLIERS APPENDIX.

NOTE: Use sterile tissue culture technique to prepare these reagents. Chase medium Pulse-labeling medium (see recipe) containing 15 mg/liter unlabeled methionine or equivalent excess amount of other amino acid used for radiolabeling. Store up to 2 weeks at 4°C. Long-term labeling medium (90% methionine-free medium) Mix 9 vol pulse-labeling medium (see recipe), lacking methionine or other appropriate amino acid, with 1 vol chase medium (see recipe) containing the same amino acid. Store up to 2 weeks at 4°C.

Detection and Assay Methods

3.7.7 Current Protocols in Protein Science

Supplement 24

Pulse-labeling medium Use supplemented Dulbecco’s modified Eagle medium (DMEM; see APPENDIX 3C but omit nonessential amino acids) or RPMI 1640, each lacking methionine or other specific amino acid, but containing 10% (v/v) FBS (dialyzed overnight against saline solution to remove unlabeled amino acids). Add 25 mM HEPES buffer (with pH adjusted to 7.4 with NaOH). Store up to 2 weeks at 4°C. The amino acid used to label cells is omitted from this medium (e.g., if [35S]methionine is employed, methionine-free DMEM or RPMI 1640 must be used). Specific amino acid–free media can be obtained from several tissue culture medium suppliers, or can be prepared (see APPENDIX 3C) from amino acid–free medium by adding individual amino acid components, omitting the one that will be used to label. A kit for the preparation of such media is available (Select-Amine from Life Technologies).

Trichloroacetic acid (TCA) solution, 10% (w/v) Prepare a 100% (w/v) TCA stock solution by dissolving the entire contents of a newly opened TCA bottle in water (e.g., dissolve the contents of a 500-g bottle of TCA in sufficient water to yield a final volume of 500 ml). Store up to 1 year at 4°C. Prepare 10% (w/v) TCA by dilution and store up to 3 months at 4°C. CAUTION: TCA is extremely caustic. Protect eyes and avoid contact with skin when preparing and handling TCA solutions.

COMMENTARY Background Information Metabolic labeling of cellular proteins is achieved by placing cells in a nutritional medium containing all components necessary for growth of cells in culture, except for one amino acid that is substituted by its radiolabeled form. The radiolabeled amino acids are transported across the plasma membrane by carrier-mediated systems and, once in the cytosol, are loaded onto tRNA molecules before being incorporated into newly synthesized proteins. Because metabolic labeling techniques use the metabolic machinery of the cell to incorporate radiolabeled amino acids, there are limitations on the type of radiolabeled amino acids that can be employed. The list of potential precursors is restricted to L-amino acids normally found in proteins, in which one or more atoms are substituted by a radioisotope (Table 3.7.1). Sulfuric amino acids, such as methionine and cysteine, are conveniently labeled with 35S. Most other amino acids can be obtained labeled with 3H.

Critical Parameters

Metabolic Labeling with Amino Acids

The protocols in this unit should be considered as models from which more specific methods can be designed. The conditions described here have been optimized for radiolabeling proteins that are expressed at low to moderate levels (104 to 105 copies per cell) and assume that the labeled cells will be used for immuno-

precipitation. This level of expression is characteristic of most endogenous membrane proteins, lumenal organellar proteins, signal transduction proteins, and transcription factors. More abundant proteins, such as cytoskeletal proteins or proteins expressed by infection or transfection, can be labeled with less radiolabeled amino acid and/or fewer cells. The choice of a particular labeling protocol and its modification will be aided by careful consideration of a number of parameters that influence the incorporation of radiolabeled amino acids into proteins, as discussed below. Selection of amino acid label Purified [35S]methionine of high specific activity (>800 Ci/mmol) can be obtained from several suppliers of radiolabeled amino acids. Protein hydrolyzates of Escherichia coli grown in the presence of [35SO4]2− (e.g., Tran35S-label from ICN Biomedicals or Expre35S35S from NEN Life Sciences) can be used as substitutes for [35S]methionine in metabolic labeling techniques. These preparations contain 35S distributed among several different compounds. In a typical batch, ∼70% of the radioactivity will be present in the form of [35S]methionine and ∼15% as [35S]cysteine; the remainder is other 35S-labeled compounds. Labeling with these radioactive protein hydrolyzates in methionine-free medium will result in incorporation of label only in methionine residues; use of

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Current Protocols in Protein Science

methionine- and cysteine-free medium will result in labeling of both methionine and cysteine residues. Purified [35S]methionine should be used whenever certainty of labeling with only [35S]methionine is required. Another reason for using purified [35S]methionine is that it tends to be more stable and emit less volatile decomposition products than 35S-labeled protein hydrolyzates. In most cases, however, the relatively inexpensive radioactive protein hydroly zates (about one-third the cost of [35S]methionine) can be used instead of purified [35S]methionine. [35S]methionine preparations should be stored frozen at −80°C. Under these conditions, they are stable for at least 1 month; the half-life of 35S is 88 days. For proteins with one or no methionine residues but several cysteine residues, labeling with [35S]cysteine (available with specific activities >800 Ci/mmol) is a good option. 35Slabeled protein hydrolyzates are not good sources of [35S]cysteine for radiolabeling because only ∼15% of the 35S-labeled compounds are [35S]cysteine. Purified [3H]leucine (available with specific activities of up to 190 Ci/mmol) is a good alternative to 35S-labeled amino acids. The halflife of 3H is ∼12 years. The specific activities of other 3H-labeled amino acids range between 5 and 140 Ci/mmol (Table 3.7.1). Several problems can arise when using certain 3H-labeled amino acids due to their participation in metabolic pathways. The following problems must be considered when using tritiated amino acids for metabolic labeling of proteins. Nonessential versus essential amino acids. Cells are able to synthesize nonessential amino acids from other compounds. If the radiolabeled amino acids used are nonessential, their specific activity will be reduced by dilution with the endogenously synthesized amino acids. Therefore, essential amino acids (E in Table 3.7.1) should be favored for metabolic labeling. Transamination. The α-amino groups of many amino acids can be removed in reactions catalyzed by transaminases (T in Table 3.7.1). Deamination of the radiolabeled amino acids can be prevented by addition of the transaminase inhibitor (aminooxy)acetic acid during starvation and labeling of the cells (Coligan et al., 1983). Interconversion. Certain radiolabeled amino acids can be converted by cells into other amino acids (I in Table 3.7.1). This problem is of particular importance when one is attempt-

ing to determine the amino acid composition or sequence of radiolabeled proteins. Labeling time The experimental purpose, the turnover rate of the protein of interest, and the viability of the cells should all be considered when determining the length of time for labeling. If the purpose of the experiment is to identify or characterize a protein precursor, pulse-labeling (10 to 30 min) must be used (see Basic Protocol and Alternate Protocol 1). If the protein of interest has a low turnover rate or if it is necessary to accumulate a labeled mature product, a protocol for long-term labeling (6 to 32 hr) is more appropriate (see Alternate Protocol 3). If the biosynthesis, post-translational modification, intracellular transport, or fate of newly synthesized proteins is being analyzed, a pulsechase protocol should be used (see Alternate Protocol 2). The turnover rate of the protein of interest is an important parameter to be considered when determining the labeling time. Proteins with a high turnover rate are optimally labeled for short times. Longer times will result in increased labeling of other cellular proteins, causing a decrease in the relative abundance of the labeled protein of interest in the cell lysate. This could result in increased detection of nonspecific proteins in immunoprecipitates (see UNIT 3.8). Conversely, proteins that turn over slowly should be labeled for longer times. Incorporation of radiolabeled amino acids into proteins is directly proportional to the labeling time for a certain period, after which it tends to plateau. When all the limiting amino acid is consumed, protein synthesis ceases. The length of the initial phase of linear incorporation will vary with the concentration of the labeling amino acid and the density and metabolic activity of the cells. Concentration and specific activity of the radiolabeled amino acid Because radiolabeled amino acids are used in limiting amounts, their incorporation into proteins is directly proportional to their concentration in the labeling medium. If very short pulses are required, the concentration of labeled amino acid can be increased to compensate for the shorter labeling time. The incorporation of radiolabeled amino acids is also directly proportional to their specific activity. Addition of unlabeled amino acids, as is required in long-term labeling protocols, will result in reduced incorporation over short periods.

Detection and Assay Methods

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Cell density At low cell densities, the amount of labeled protein synthesized is directly proportional to the cell concentration. At high densities, however, incorporation will increase nonlinearly. Very concentrated cell samples (>5 × 107 cells/ml) can only be labeled for short periods (≤5 min) as rapid loss of metabolic activity and cell viability will occur due to acidification of the medium and accumulation of toxic metabolites. Conditions for very short pulses Very short pulses (2 to 10 min) are required to study post-translational modifications that occur shortly after synthesis. These include folding, disulfide bond formation, and early carbohydrate modifications of newly synthesized proteins. Proteins that are rapidly degraded after synthesis are also best studied with very short pulses. The protocol for very short pulses is similar to the Basic Protocol for pulselabeling, with the following modifications: (1) the pulse-labeling medium contains higher concentration of radiolabeled amino acid (up to 1 mCi/ml), (2) labeling is stopped by addition of ice-cold PBS containing 20 mM freshly prepared N-ethylmaleimide (NEM) to prevent oxidation of free sulfhydryls, and (3) if a chase is necessary, 1 mM cycloheximide is added to the chase medium to quickly stop the elongation of nascent polypeptide chains. For additional details on very short pulses, see Braakman et al. (1991). Temperature and pH Unless otherwise required for special conditions (e.g., heat shock, temperature-sensitive mutants), metabolic labeling should be performed at 37°C. For this reason, it is important that the labeling medium be warmed to 37°C before adding it to the cells. A dramatic reduction in incorporation occurs at room temperature. It is also important that the pH of the labeling medium be ∼7.4.

Anticipated Results A typical incorporation of labeled amino acid precursor after a 30-min pulse under the conditions described in this unit (see Basic Protocol and Alternate Protocols 1 and 2) is 5% to 20%. In the case of long-term labeling, the incorporation typically reaches 30% to 60%. If the labeled cells are used for immunoprecipitation (UNIT 3.8), followed by SDS-PAGE (UNIT 10.1) and autoradiography (UNIT 10.11), specific bands should be visible within 2 hr to 2 months of exposure.

Time Considerations It takes 1 to 2 hr to prepare cells and materials for labeling. The actual labeling time depends upon the protocol chosen—pulse-labeling takes 10 to 30 min and long-term labeling takes 6 to 32 hr. Washing and processing cells may take an additional 1.5 hr.

Literature Cited Braakman, I., Hoover-Litty, H., Wagner, K.R., and Helenius, A. 1991. Folding of influenza hemagglutinin in the endoplasmic reticulum. J. Cell Biol. 114:401-411. Coligan, J.E., Gates, F.T. III, Kimball, E.S., and Maloy, W.L. 1983. Radiochemical sequence analysis of metabolically labeled proteins. Methods Enzymol. 91:413-434. Lathe, R. 1985. Synthetic oligonucleotide probes deduced from amino acid sequence data. Theoretical and practical considerations. J. Mol. Biol. 183:1-12. Meisenhelder, J. and Hunter, T. 1988. Radioactive protein labelling techniques. Nature 335:120.

Key Reference Coligan et al., 1983. See above. Contains a detailed description of conditions used to metabolically label proteins with different radiolabeled amino acids.

Contributed by Juan S. Bonifacino National Institute of Child Health and Human Development, NIH Bethesda, Maryland

Metabolic Labeling with Amino Acids

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Analysis of Selenocysteine-Containing Proteins The presence of selenium in protein as a component in the diet of mammals was not anticipated from its early history. For example, in the 1930s, selenium was recognized as a toxin in livestock that caused severe illness and, if left untreated, death (Franke, 1934a,b). Furthermore, this element was later thought to cause cancer in laboratory animals (reviewed in Combs and Combs, 1986). The overabundance of selenium in soil in certain regions of the United States was therefore considered dangerous, as various “selenium-accumulating” plants that concentrate this element in high levels were being ingested by livestock and other animals. A variety of procedures employing both chemical and biological methods was developed to extract selenium from the soil or to convert it to a form that was not readily available for living organisms. The image of selenium changed completely in the mid-1950s. It was quite surprising when this element was shown to be essential for anaerobic growth of Escherichia coli (Pinset, 1954) and for prevention of liver necrosis in rats (Schwarz and Foltz, 1957). In fact, selenium is now recognized as a micronutrient that is capable of preventing certain forms of heart disease and cancer, decreasing mortality from AIDS in the HIV-infected population, playing a role in sperm maturation, and participating in immunological response and in suppression of viral expression (reviewed in Gladyshev et al., 2000). Perhaps the most recognized role of selenium in human nutrition is its ability to serve as a cancer-preventive agent. Recent doubleblind clinical trials have demonstrated that supplementation of the human diet with a daily dose of 200 µg of selenium resulted in 63%, 53%, and 48% reduction in the incidence of prostate, colon, and lung cancers, respectively (Clark et al., 1996). The majority of these biological and biomedical effects of selenium are most certainly mediated by selenium-containing proteins. Selenium was initially found in protein as a natural and essential component of mammalian glutathione peroxidase (Flohe et al., 1973; Rotruck et al., 1973). This element is incorporated into glutathione peroxidase and most other natural selenoproteins as the amino acid selenocysteine (Sec) in response to specific UGA

codons. Selenocysteine is normally present at the active center of enzymes (Stadtman, 1996), and this critical location of selenium in selenoproteins has generated considerable interest in their characterization. This interest in selenoproteins has resulted in the development of specific methods for their detection and characterization. Moreover, methods have been developed to incorporate selenium into specific sites in proteins, and these artificially derived selenoproteins have been used to obtain unique structural, functional, and mechanistic information.

CHEMICAL FORMS OF SELENIUM IN PROTEINS Selenium may be incorporated into protein as either selenocysteine or selenomethionine, or post-translationally as a cofactor. Both forms may occur nonspecifically when selenium replaces sulfur in sulfur metabolic pathways or when selenium is introduced into a protein by biochemical manipulations. However, both forms may also occur at specific sites in protein if selenium is incorporated by biological processes that are specialized for using this element.

Nonspecific Occurrence of Selenium in Proteins Selenium and sulfur have a similar affinity for sulfur pathways. The ratio of sulfur to selenium in proteins is generally determined by the ratio of sulfur to selenium in the environment or in the biological medium. Such a ratio is normally on the order of 1000:1, so that the proportion of selenium in sulfur-containing compounds and proteins is insignificant. However, when the selenium concentration approaches that of sulfur, the incorporation of selenium may be significant, which may result in selenium toxicity. Selenium toxicity may also result from interactions of low-molecularweight selenium compounds and various cellular metabolic pathways. The ability of selenium to replace sulfur in metabolic pathways has been used extensively to study the role of sulfur in proteins and metabolites (Table 3.8.1). Selenium-containing analogs of sulfur-containing cofactors and metabolites were used to characterize the active center structure and the kinetic properties of several proteins. For example, the nature of the

Contributed by Vadim N. Gladyshev and Dolph L. Hatfield Current Protocols in Protein Science (2000) 3.8.1-3.8.18 Copyright © 2000 by John Wiley & Sons, Inc.

UNIT 3.8

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Table 3.8.1 Overview of Methods to Obtain and Characterize Selenium Analogs of Sulfur-Containing Biological Compounds and Proteins

Method to replace S with Se

Method of analysis

Purpose

Replacement of all Met with Se-Met

X-ray crystallography Kinetic analyses

Replacement of all Cys with Se-Cys

Kinetic analyses Mass spectrometry

Targeted biosynthetic incorporation of Se-Cys in place of any other amino acid

Kinetic analyses Structural studies X-ray absorption spectroscopy EPR spectrometrya

To incorporate heavy atom into protein To solve a phase problem To characterize function of all Met To analyze disulfide bonds To test catalytic roles of all Cys To test redox roles of all Cys To analyze a particular Cys residue (redox and catalytic functions, participation in disulfide bond formation) To identify its direct ligands and to study its coordination to paramagnetic species To test catalytic functions of Ser or Cys To test redox functions of Ser or Cys To analyze coordination spheres of Ser or Cys

Chemical replacement of S (or O) EPR spectrometry with Se NMR spectrometry X-ray absorption spectroscopy Kinetic analyses Chemical synthesis of Kinetic analyses Se-containing compounds and Electrochemical analyses peptides NMR spectrometry X-ray crystallography

To test biological activity of a peptide, compound, cofactor, or biofactor To characterize its catalytic, redox, structural, and functional properties

aEPR spectrometry, electron paramagnetic resonance spectrometry.

Analysis of SelenocysteineContaining Proteins

thiol-binding site within the metal cluster active site of nitrogenase, the iron-molybdenum cofactor, was determined by the Se K-edge X-ray absorption spectroscopy of the selenol-substituted cofactor (Conradson et al., 1994). In addition, a selenium analog of lipoic acid was used to determine the kinetic parameters of selenolipoated proteins in a glycine cleavage system (Fujiwara et al., 1997). The electronic structure of iron-sulfur clusters in ferredoxins was also characterized by nuclear magnetic resonance (NMR) spectroscopy after replacement of sulfur with selenium (Bertini et al., 1993). One of the most advantageous applications of replacing sulfur with selenium has been the use of E. coli methionine auxotrophs grown in media in which selenomethionine replaced methionine. In this system, the methionine residues are quantitatively replaced in overexpressed proteins with selenomethionine residues. The presence of multiple selenium atoms in selenomethionine-containing proteins eliminates the step of obtaining heavy-atom derivatives that are otherwise required to solve a protein crystal structure. The developed multiwavelength anomalous diffraction (MAD) phasing method is now a common technique for determination of X-ray structures of proteins (Hendrickson et al., 1990; Hendrickson, 1991). This

technique, however, cannot be applied to natural selenoproteins, as these proteins usually contain only a single selenium atom. Incorporation of selenomethionine in place of methionine in protein is not very toxic, because methionines rarely occupy positions that are important to protein function. However, similar strategies that attempt to obtain proteins in which all cysteines are replaced with selenocysteines are usually not successful. This may be due to the fact that cysteines are often involved in redox reactions, and the presence of selenium dramatically changes the chemical properties of the protein. Nevertheless, several reports have described the expression of selenocysteine-containing analogs of proteins (Muller et al., 1994; Boschi-Muller et al., 1998). In one report (Muller et al., 1994), thioredoxin was expressed in an E. coli cysteine auxotroph that was grown in the presence of selenocysteine. Selenocysteine was clearly toxic to bacterial cells, and only after carefully adjusting growth conditions did the expression of limited amounts of the selenocysteine-containing protein occur. Interestingly, this selenothioredoxin formed a diselenide bond in place of the normal disulfide bond. However, the diselenide bond was characterized by a decreased redox potential, and thioredoxin reductase failed to reduce selenothioredoxin.

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In addition to the methods that replace either all methionines or all cysteines in proteins with the corresponding selenium-containing amino acids, a technique has been developed for sitespecific incorporation of selenocysteine. Since UGA dictates the incorporation of selenocysteine into selenoproteins (see below), site-directed mutagenesis to generate a TGA codon at a particular site in an open reading frame may be used to signal selenocysteine incorporation. Such a mutant TGA codon must be coupled with an additional signal that is necessary in the resulting mRNA for distinguishing between signals for selenocysteine incorporation and termination of protein synthesis. The latter signal is a stem-loop structure, designated as the selenocysteine insertion sequence (SECIS) element, and it is located in selenoprotein mRNA downstream of the UGA codon. It should be noted that SECIS elements (and systems for selenocysteine incorporation) are different in bacteria and mammals (see below), and therefore SECIS elements that are specific for the host organism must be utilized for selenoprotein expression. An example of an unnatural site-specific incorporation of selenocysteine into proteins is the expression of Methanobacterium formicicum formate dehydrogenase in E. coli (Heider and Bock, 1992). The natural cysteine residue was replaced in the expressed protein with selenocysteine by generating an in-frame TGA codon and a downstream E. coli–type SECIS element. A similar method has also been used to express eukaryotic selenoproteins in E. coli (Arner et al., 1999; Gladyshev et al., 1999b). Another example is the generation of unnatural selenocysteine residues in glutathione peroxidase in various sites in the open reading frame of the gene (Wen et al., 1998). In this case, TGA codons were individually generated in various locations within the open reading frame, but a single eukaryotic SECIS element was present in the 3′ untranslated region (3′-UTR).

Occurrence of Selenium in Natural Selenoproteins In natural selenoproteins, selenium is present either as a dissociable cofactor or as a selenocysteine residue. The dissociable cofactor has only been found in molybdenum-containing enzymes (Dilworth, 1982) in which selenium is coordinated to molybdenum in the enzyme active center (Gladyshev et al., 1994a). For example, in the seleno-molybdoenzyme form of nicotinic acid hydroxylase from Clos-

tridium barkeri, selenium is essential for catalytic activity, as evidenced by comparison of kinetic parameters of enzyme forms isolated from selenium-supplemented and seleniumdeficient cells (Gladyshev et al., 1994a). Other enzymes of this class are clostridial xanthine dehydrogenases and, perhaps, bacterial molybdenum-containing carbon dioxide dehydrogenases (Dobbek et al., 1999). The existence of selenium as a cofactor has not been described in eukaryotic and archaeal proteins. The occurrence of selenium as a selenocysteine residue is by far the most common form of this element in natural proteins. The means by which selenocysteine is incorporated into protein, the known selenoproteins, and the methods to identify and characterize these proteins are discussed below.

COTRANSLATIONAL INCORPORATION OF SELENOCYSTEINE INTO PROTEINS Selenocysteine is the 21st naturally occurring amino acid in protein. It is incorporated into protein cotranslationally in response to the codon UGA (Fig. 3.8.1). In addition to UGA, selenocysteine has its own tRNA, a specific elongation factor that is designated SELB, and the SECIS element that is located in mRNA downstream of UGA. As noted above, the SECIS element is essential for recognizing UGA as a signal for selenocysteine incorporation rather than as a signal for termination.

UGA as a Selenocysteine Codon When the genetic code was deciphered in the mid-1960s, it was thought to be universal (see Gladyshev et al., 2000, and references therein). However, subsequent studies revealed that many changes in the genetic code occurred in evolution. The code therefore has been referred to as the “almost universal genetic code” (Gesteland et al., 1992). In spite of variations in the meaning of codons in different organisms and organelles, only 20 amino acids were thought to be encoded in the gene sequences of organisms until the code was expanded to include selenocysteine in the late 1980s (reviewed in Bock et al., 1991; Hatfield and Diamond, 1993). Addition of selenocysteine marks the 21st amino acid in the genetic code, and is the only addition to the code since it was deciphered. UGA is most often used as a stop signal in nature, but because it also dictates selenocysteine insertion into protein, it has a dual function

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A

initiation of translation

selenocysteine codon

termination of translation SECIS

5′

AUG

UGA

3′ AAAA

UAA UAG UGA

B SECIS 5′

AUG

UGA

UAA UAG UGA

3′

Figure 3.8.1 Structure of selenoprotein mRNA in mammals (A) and E. coli (B). The UGA codon that is present in the open reading frame is decoded as selenocysteine. The stem-loop structure (the SECIS element) is present in the 3′-UTR in mammals and immediately downstream of the UGA selenocysteine codon in E. coli.

(reviewed in Kumaraswamy et al., 1999). AUG also has a dual function in the genetic code, serving as an initiation codon for protein synthesis and as a methionine codon at internal positions of protein. The dual role of AUG, however, has been known since the time the code was deciphered. The presence of UGA in an open reading frame is considered to be the most characteristic feature of selenoprotein genes. However, the occurrence of UGA in an open reading frame does not by itself mean that it codes for selenocysteine, as certain other criteria in gene sequences must also be met (see below).

The SECIS Element

Analysis of SelenocysteineContaining Proteins

The SECIS element is essential for signaling selenocysteine incorporation into protein. Only E. coli and mammalian SECIS elements have been studied in detail, and the stem-loop structures from these organisms clearly differ in terms of conserved sequences and overall structural organization (Low and Berry, 1996). Moreover, the SECIS elements in E. coli occur immediately downstream of the UGA selenocysteine codon, while those in mammals occur further downstream in the 3′-UTR. The distance between the SECIS element and UGA has been determined in mammals and may be as far as 5.4 kb (Buettner et al., 1998) or as close as 51 to 111 bp (Martin et al., 1996). Several archaeal selenoprotein sequences have also been analyzed (Wilting et al., 1997). It

was concluded from these analyses that archaeal SECIS-like structures are different from both mammalian and bacterial SECIS elements, and are likely located in archaea either upstream or downstream of UGA in the 5′- or 3′-UTR (Wilting et al., 1997). The largest number of SECIS elements has been characterized in mammals (GrundnerCulemann et al., 1999). Their structure and function have been probed by site-directed mutagenesis and by chemical and enzymatic methods (Martin et al., 1998; Walczak et al., 1998). These observations have led investigators to draw various conclusions about the conservation of primary and secondary structures of mammalian SECIS elements. A composite structure of the mammalian SECIS element is shown in Figure 3.8.2. It consists of an apical loop and two stems separated by an internal loop. A conserved motif, AUGA (or GTGA), is present at the base of the upper stem. The first nucleotide of this conserved motif is unpaired, while UGA and a following nonconserved nucleotide participate in the quartet of non-Watson-Crick interacting nucleotides. Other conserved nucleotides are an unpaired AA motif in the apical loop and GA in the 3′ portion of the quartet. The distance between the quartet (also designated as the SECIS core) and the conserved AA in the apical loop is 11 or 12 nucleotides. These characteristics of SECIS elements were used to design an algorithm for searching

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Current Protocols in Protein Science

apical loop

AA helix II

N A G U A

• • • •

N G A N

quartet

internal loop

helix I 5′

3′

Figure 3.8.2 Mammalian SECIS element. The conserved nucleotides are indicated by letters and the overall features of the consensus sequence are discussed in the text.

large nucleotide sequence databases and identifying mammalian SECIS elements. This algorithm, designated SECISearch, conducts searches in three steps: (1) an initial primary sequence search for conserved SECIS motifs, (2) a secondary structure search of the motifs of SECIS elements, and (3) determination of the free energy in a candidate SECIS element. Application of SECISearch to analyze the human expressed sequence tag (EST) database resulted in identification of new selenoprotein genes (Kryukov et al., 1999). Two versions of SECISearch are currently available: a database search version and an on-line version. These programs use slightly different parameters and are optimized for different purposes. The former version can be used to search nucleotide databases for the smallest number of sequences that satisfy primary and secondary structure and free energy parameters of the SECISearch algorithm. This version is thus optimized for efficiency of searches, although it may miss SECIS elements that deviate slightly from the consensus SECIS structure. This version was designed to minimize the probability of finding pseudo-SECIS structures. In contrast, the on-line version can be used to analyze a query nucleotide sequence for

the presence of a potential SECIS element. This version is optimized to find all potential SECIS elements in a sequence, which of course means that it may predict a pseudo-SECIS structure. The on-line version was designed to minimize the probability of missing the actual SECIS element. This version should be most helpful to investigators who would like to test a sequence of interest for the presence of a SECIS element and for rapid identification and visualization of potential SECIS elements. The online version is currently available at http:// bio-selenium.unl.edu/SECIS.htm (Kryukov et al., 1999).

Selenocysteine tRNA Selenocysteine, unlike the other 20 amino acids utilized in mammalian protein synthesis, is biosynthesized on its tRNA (Gladyshev et al., 2000). Thus, selenocysteine tRNA has a dual function of serving as both a carrier molecule for the synthesis of its amino acid as well as the molecule that donates selenocysteine to the growing selenopolypeptide in response to specific UGA codons. The tRNA is first aminoacylated with serine, which serves as the backbone for selenocysteine biosynthesis (see below). Therefore, this tRNA has been designated as tRNA[Ser]Sec. Selenocysteine tRNA[Ser]Sec is 90 nucleotides in length and is the longest eukaryotic tRNA sequenced to date (reviewed in Hatfield et al., 1999). The tRNA [Ser]Sec population consists of two major isoacceptors in mammalian cells that differ from each other by a single 2′-O-methylribose in the wobble position (position 34) of the anticodon. One isoacceptor has 5′-methylcarboxymethyluridine (mcmU) in position 34, and the other h a s 5′-methylcarboxymethyluridine-2′-Omethylribose (mcmUm). The degree of methylation of tRNA[Ser]Sec at position 34 is influenced by selenium status (Hatfield et al., 1991; Diamond et al., 1993; Choi et al., 1994; Chittum et al., 1997), and the presence of this methyl group affects tertiary structure (Diamond et al., 1993). However, the biological significance of this methyl group is not known. Biosynthesis of mcmU and mcmUm, as well as the other modified bases in tRNA[Ser]Sec, h av e b ee n e s t ab l i s h e d i n Xenopus oocytes (Choi et al., 1994; Sturchler et al., 1994; Kim et al., unpub. observ.). Selenocysteine tRNA[Ser]Sec is transcribed unlike any other known tRNA in that it begins transcription at the first base within the gene and thus lacks a 5′ leader sequence (Lee et

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al., 1987). The regulatory regions within the tRNA[Ser]Sec gene and the transcription factors and mechanisms governing its expression have been reviewed recently (Hatfield et al., 1999). Subsequent studies involving the regulatory elements of this tRNA have prim ari ly d eal t w ith th e selen ocysteine tRNA[Ser]Sec gene transcription-activating factor (Staf), which specifically enhances transcription of this tRNA gene (Adachi et al., 1998, 1999; Myslinski et al., 1998; Schuster et al., 1998; Schaub et al., 1999).

Selenocysteine Biosynthesis

Analysis of SelenocysteineContaining Proteins

As noted above, the biosynthesis of selenocysteine begins with the attachment of serine to tRNA[Ser]Sec, which is mediated by seryl-tRNA synthetase. The identity elements in tRNA[Ser]Sec for its aminoacylation are therefore governed by seryl-tRNA synthetase and they reside in the discriminator base and the long extra arm (Wu and Gross, 1993; Ohama et al., 1994). The acceptor, T, and D stems also play a role in the identity process (Amberg et al., 1996). The metabolic pathway involving synthesis of selenocysteine on tRNA[Ser]Sec (with serine serving as the backbone; Sunde and Evenson, 1987; Lee et al., 1989; Leinfelder et al., 1989) has been fully characterized in E. coli (see Commans and Bock, 1999, and references therein). In bacteria, a single enzyme, selenocysteine synthase, catalyses the removal of water from serine to form an aminoacrylyltRNA[Ser]Sec intermediate, which in turn catalyzes the acceptance of selenium from the activated form, monoselenophosphate, to yield selenocysteyl-tRNA[Ser]Sec and inorganic phosphate. The synthesis of monoselenophosphate is mediated by selenophosphate synthetase (Glass et al., 1993; Stadtman, 1996). In mammals, further work is required to establish the pathway of selenocysteine synthesis. Selenocysteine tRNA[Ser]Sec was originally discovered as a minor seryl-tRNA that decoded UGA (Hatfield and Portugal, 1970) and formed phosphoseryl-tRNA (Maenpaa and Bernfield, 1970), and was later shown to be selenocysteyltRNA[Ser]Sec (Lee et al., 1989). The role of phosphoseryl-tRNA[Ser]Sec in selenocysteine biosynthesis has not been resolved (Hatfield et al., 1994), but some investigators have proposed that it is “an active storage form” that can be regenerated for selenocysteine biosynthesis after its dephosphorylation (Amberg et al., 1996). It should be noted that the serylated form of tRNA[Ser]Sec has a role of its own in protein

biosynthesis. Seryl-tRNA[Ser]Sec is recognized by the normal elongation factor eEF-1 (Jung et al., 1994) and serves as an in vivo suppressor of UGA stop codons, inserting serine and extending protein synthesis (Chittum et al., 1998).

Elongation Factor SELB and Selenocysteine Incorporation Once selenocysteyl-tRNA[Ser]Sec is synthesized, selenocysteine may be donated to the nascent selenopolypeptide in response to the appropriate UGA codon. In addition to UGA and the SECIS element, another factor that is essential for selenocysteine incorporation into protein is the selenocysteine tRNA[Ser]Sec specific elongation factor SELB. This factor has so far only been described in bacteria (Forchhammer et al., 1990). Bacterial SELB binds to both selenocysteyl-tRNA[Ser]Sec and the SECIS element. The presence of selenocysteine attached to tRNA[Ser]Sec is critical in this interaction, as it avoids recognition of the serylated form (see Commans and Bock, 1999, and references therein). Studies with mammalian systems also suggest the existence of an analogous SELB factor (reviewed in Gladyshev et al., 2000). Several potential SELB candidates have been identified, and one of them appears to specifically recognize the quartet sequence of the SECIS element (Copeland and Driscoll, 1999). This protein also enhanced selenocysteine incorporation into protein from selenocysteyltRNA[Ser]Sec (Copeland et al., 2000). However, this SECIS-binding protein does not appear to bind selenocysteine tRNA and is likely a stimulatory factor. This suggests that the role of bacterial SELB is served by two or more proteins in mammals.

SELENOCYSTEINE-CONTAINING PROTEINS Selenocysteine-containing proteins have been characterized from both prokaryotes and eukaryotes. The number of selenoproteins differs among various organisms. For example, E. coli contains three selenoproteins and all three are formate dehydrogenases (Heider and Bock, 1993). Archaeon M. jannaschii contains up to seven selenoproteins (Wilting et al., 1997). Nematode C. elegans appears to have only a single selenoprotein (Gladyshev et al., 1999b; Buettner et al., 1999), and no selenoproteins are present in S. cerevisiae. To date, the largest number of selenoproteins, sixteen, has been identified in mammals, including humans (Gladyshev and Hatfield, 1999).

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Selenocysteine-containing proteins that have been characterized thus far are shown in Table 3.8.2. Selenocysteine appears to serve different functions in prokaryotic and eukaryotic proteins. In several prokaryotic selenoproteins, selenium is coordinated to a metal such as nickel, molybdenum, or tungsten. The selenium moiety participates in proton transfer in these proteins and also fine tunes the coordination sphere of a metal in the enzyme active center (Boyington et al., 1997). In contrast, selenoproteins in eukaryotes are often associated with disulfide oxidoreductase redox pathways, such as thioredoxin- and glutathione-dependent systems. However, the two common features among all selenoproteins with established function are that these proteins participate in redox processes and that selenocysteine is located at the enzyme active center. Further information on properties and functions of selenium-containing proteins can be found in recent reviews (Low and Berry, 1996; Stadtman, 1996; Gladyshev and Hatfield, 1999; Hatfield et al., 1999; Gladyshev et al., 2000).

SPECIFIC METHODS FOR IDENTIFYING AND CHARACTERIZING SELENOPROTEINS Several methods have been used extensively for the analysis of selenium-containing proteins. These methods have utilized the unique chemical and physical properties of selenium and its isotopes (see Table 3.8.3). For example, 75Se-enriched proteins can be easily detected in protein fractions with γ counters and on polyacrylamide gels by PhosphorImager analyses. In addition, 77Se-enriched proteins can be analyzed by NMR and electron paramagnetic resonance (EPR) spectroscopies. Selenoproteins have also been analyzed with methods such as X-ray absorption spectroscopy, atomic absorption, and mass spectrometry that detect natural selenium species or their derivatives. These procedures are discussed below, and their characteristics, advantages, and limitations are summarized in Table 3.8.3. The use of these methods generally implies that an investigator has sufficient quantities of a homogeneous selenoprotein. However, several methods, most notably metabolic labeling with 75Se, do not require pure protein for analyses. In addition, several techniques, such as NMR, EPR, and X-ray spectroscopies, require sophisticated equipment and extensive expertise, and therefore are often used on a collaborative basis. Finally, identification of new selenoprote-

ins through bioinformatics approaches are discussed.

Methods Utilizing the 75Se Isotope Selenium-containing proteins are most easily detected if they are labeled with 75Se. This γ emitter has a convenient half-life of 121 days and is available from several sources (e.g., University of Missouri Research Reactor Center). Selenoproteins are labeled with 75Se by cotranslational incorporation of selenium when cells are grown in the presence of [75Se]selenite (state of oxidation, +4). The use of selenate (state of oxidation, +6) should be avoided, as this molecule is not easily reduced to biologically active selenide (state of oxidation, −2). To label cells, 75Se, which is normally provided by commercial suppliers in a 35% nitric acid solution, is neutralized with sodium hydroxide and supplied directly to the growth medium (Gladyshev et al., 1999a). Depending on the type of experiment, between 5 and 500 µCi of 75Se are added to 50 ml of a bacterial or mammalian growth medium. To label selenoproteins in animals, the animals are injected peritoneally with freshly neutralized [75Se]selenite (Gladyshev et al., 1998) and then sacrificed at an appropriate time for extraction of proteins from the tissue of interest. It should be noted that [75Se]selenocysteine is not a good source of 75Se for biosynthetic radioactive labeling of selenoproteins. As discussed above, free selenocysteine cannot be incorporated into selenoproteins because this amino acid must be synthesized on tRNA[Ser]Sec from serine and monoselenophosphate. In contrast, [75Se]selenocysteine will enter sulfur pathways and will be incorporated into proteins in place of cysteine, resulting in a higher background during PhosphorImager or γ-counter analyses. Labeling of selenoproteins with 75Se provides an easy means of detecting these proteins in cellular fractions during protein isolation. 75Se may be quantitatively and directly detected in a γ counter. Multidetector counters allow analyses of 100 fractions in only 10 minutes with no loss of material. Therefore, this method is generally superior to spectrophotometric and kinetic assays in terms of sensitivity and time required. Selenoproteins that are labeled with 75Se may also be detected by PhosphorImager analyses on polyacrylamide gels. Under appropriate labeling conditions, this method is more sensitive than immunoblot assays. Growth of mammalian cells (APPENDIX 3C) in the presence

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Table 3.8.2 Selenocysteine-Containing Proteins in Bacteria, Archea, and Eukaryotes

Protein Bacteria

Archaea

Eukaryotes

Formate dehydrogenases Formate dehydrogenase H Formate dehydrogenase N Formate dehydrogenase O Hydrogenases

Organism

Anaerobic bacteria E. coli E. coli E. coli Sulfate-reducing bacteria Glycine reductase protein A Anaerobic bacteria Glycine reductase protein B Anaerobic bacteria Proline reductase protein B Anaerobic bacteria Betaine reductase protein B Anaerobic bacteria Sarcosine reductase protein Anaerobic bacteria B Selenophosphate synthetase Bacteria Formate dehydrogenases Methanogenic archaea Formylmethanofuran Methanogenic archaea dehydrogenase Hydrogenases Methanogenic archaea Selenophosphate synthetase M. jannaschii Cytosolic glutathione Vertebrates peroxidase (GPx1) Gastrointestinal glutathione Vertebrates peroxidase (GPx2) Vertebrates Plasma glutathione peroxidase (GPx3) Vertebrates, S. Phospholipid hydroperoxide glutathione mansoni peroxidase (GPx4) Plasma selenoprotein P Vertebrates (SelP) Selenoprotein W (SelW) Vertebrates Selenoprotein T (SelT) Vertebrates, S. mansoni Selenoprotein R (SelR) Vertebrates 15 kDa selenoprotein Vertebrates Selenophosphate synthetase Vertebrates 2 (SPS2) Thioredoxin reductase 1 Vertebrates, C. elegans (TR1) Thioredoxin reductase 2 Vertebrates (TR2) Thioredoxin reductase 3 Vertebrates (TR3) Thyroid hormone Vertebrates deiodinase 1 (DI1) Thyroid hormone Vertebrates deiodinase 2 (DI2) Thyroid hormone Vertebrates deiodinase 3 (DI3)

Properties; representative reference Contain Mo, FeS; Sawers (1994) 80 kDa; Mo, FeS; Boyington et al. (1997) 110 kDa; Mo, FeS, FAD; Benoit et al. (1998) 110 kDa; Mo, FeS, FAD; Berg et al. (1991) Ni, FeS, FAD; Eidsness et al. (1989) 18 kDa; Kreimer and Andreesen (1995) 47 kDa; Kreimer and Andreesen (1995) 47 kDa; Kabisch et al. (1999) 47 kDa; Andreesen et al. (1999) 47 kDa; Andreesen et al. (1999) ATP dependent; Guimaraes et al. (1996) Mo, FeS, FAD; Wilting et al. (1997) FeS, Mo or W; Vorholt et al. (1997) Ni, FeS, FAD; Wilting et al. (1997) ATP dependent; Guimaraes et al. (1996) Homotetramer of 23-kDa subunits; Chambers et al. (1986) Homotetramer of 23-kDa subunits; Chu et al. (1993) Homotetramer of 25-kDa subunits, glycoprotein; Maser et al. (1994) 20 kDa; Schuckelt et al. (1991)

55 kDa protein with 10-12 Sec; Burk and Hill (1999) 10 kDa protein subunit; Vendeland et al. (1995) 18 kDa protein subunit; Kryukov et al. (1999) 14 kDa protein subunit; Kryukov et al. (1999) 15 kDa protein subunit; Gladyshev et al. (1998) 50 kDa; Guimaraes et al. (1996) Homodimer of 57- kDa subunits; Gladyshev et al. (1996c) Homodimer of 65- kDa subunits; Sun et al. (1999) Homodimer of 57- kDa subunits; Lee et al. (1999) Homodimer of 29- kDa subunits; Berry et al. (1991) 30 kDa; Davey et al. (1995) 31 kDa; Croteau et al. (1995)

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Table 3.8.3

Specific Methods for Analyses of Selenium-Containing Proteins

Methoda

Descriptiona

Radioactive labeling

75Se isotope is commonly used More sensitive than immunoblot assay to detect, isolate, and characterize selenoproteins. Half-life of this γ emitter is 121 days.

NMR spectroscopy

77Se

EPR spectroscopy

Atomic absorption spectroscopy Mass spectrometry

X-ray absorption spectroscopy

Sensitivity

Requires 0.1 to 1 mM protein concentration

may be detected in protein; method may reveal information about coordination of Se and enzyme reaction mechanisms 77Se is used to replace natural Se in a protein by growing cells in presence of [77Se]selenite. If 77Se is in vicinity of a paramagnetic group, method may reveal information about coordination of Se, structure of active center, and reaction mechanism of enzyme Se is directly detected in proteins and biological samples

Advantages Simultaneous detection of many proteins by PhopshorImager analysis; no loss of material in γ-counter assays; detection of selenoproteins in crude extracts Employs 77Se as a nonperturbing probe for protein structure and function

Requires 20 to 200 µM protein Provides unique structural and concentration mechanistic information

nM to µM range depending on sample composition Very sensitive, nM range

ICP-MS: analytical technique for determination of Se in proteins and biological samples; ESI-MS: may detect Se-containing peptides due to a unique isotopic composition of Se Se is directly detected in a protein; EXAFS analysis reveals coordination sphere of Se in a protein and may also determine oxidation state of environmental Se

Not very sensitive; requires ∼1 mM concentration of Se in sample

Reliable method for determination of Se content of a protein Sensitivity; utilizes unique isotopic composition of Se

No interference from contaminants; analysis of intermediates trapped by freezing

aAbbreviations: EPR, electron paramagnetic resonance; ESI-MS, electrospray ionization mass spectrometry; EXAFS, extended X-ray absorption

fine structure; ICP-MS, inductively coupled plasma mass spectrometry; NMR, nuclear magnetic resonance.

of 75Se followed by SDS-PAGE analysis of protein crude extracts (UNIT 10.1) allows simultaneous detection of several major selenoproteins including thioredoxin reductase 1, glutathione peroxidase 1, glutathione peroxidase 4, and the 15 kDa selenoprotein (Gladyshev et al., 1999a). These proteins may be detected simultaneously because they have different mobilities on SDS-PAGE (and nondenaturing) gels (UNITS 10.1 & 10.3). To obtain a selenoprotein signal, gels or immunoblot membranes (UNIT 10.11) are incubated overnight in a Phos-

phorImager cassette. Alternatively, an 75Se signal may be detected by exposing gels or membranes to an X-ray film for two to seven days (UNIT 10.11).

Methods Utilizing the 77Se Isotope Proteins enriched with 77Se (nuclear spin ; 2 natural abundance ∼7.5%) may be analyzed by NMR spectroscopy (for overview, see UNIT 17.5). In addition, if selenium is located in the vicinity of the paramagnetic group, these proteins may be probed with EPR spectroscopy. 1⁄

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77Se-enriched proteins are obtained by growing

cells in the presence of [77Se]selenite. In contrast to labeling of cells with 75Se, which does not require quantitative incorporation of [75Se]selenocysteine into a protein, 77Se must be a major form of selenium in a selenoprotein for subsequent analyses by NMR and EPR spectroscopies. Therefore, this isotope is normally used at concentrations as high as 1 µM to restrict incorporation of other selenium isotopes present in the medium. 77Se-NMR and 77Se-EPR spectroscopies provide information about electronic properties, coordination geometry, and reaction mechanisms of selenium sites in proteins. For example, EPR spectroscopy of 77Se-enriched enzyme preparations has been used to demonstrate the coordination of Se to Mo and to characterize reaction mechanisms of formate dehydrogenase (Gladyshev et al., 1994b; Khangulov et al., 1998) and nicotinic acid hydroxylase (Gladyshev et al., 1994a, 1996b). In addition, coordination of Se to Ni in hydrogenases has been demonstrated by EPR spectroscopy (He et al., 1989; Sorgenfrei et al., 1997). 77Se-NMR spectroscopy has been used in studies involving mammalian glutathione peroxidase (Gettins and Crews, 1991), bacterial selenomethionyl dihydrofolate reductase (Boles et al., 1992), and other enzymes.

Other Methods Atomic absorption spectroscopy This is the most commonly used analytical method for direct quantitation of selenium in biological samples. Conditions have been developed for determination of this element in proteins and biological media. Selenium in proteins and serum may be analyzed with Pd-Mg or Ag-Cu-Mg modifiers by graphite furnace atomic absorption spectroscopy with Zeeman background correction as described by Daher and Van Lente (1994). This method, however, can determine only one chemical element at a time. An additional common technique for quantitative selenium analysis in biological samples is neutron activation analysis (discussed in Chan et al., 1998a).

Analysis of SelenocysteineContaining Proteins

Mass spectrometry The capabilities of another analytical method for determination of selenium in proteins and biological samples, inductively coupled plasma mass spectrometry (ICP-MS), exceeds the slow, single-element analysis by atomic absorption spectroscopy in that it can

be used to simultaneously determine multiple trace elements. This method has been applied to determine selenium concentration in serum, and details of the technique can be found in Chan et al. (1998b). A variation of this technique, electrospray ionization mass spectrometry (ESI-MS), may potentially be used for identification of selenium-containing proteins and peptides due to the unique isotopic composition of selenium. For example, fragmentation of a selenium-containing peptide from human thioredoxin reductase produced a spectrum of masses in which selenium-containing fragments could be detected by a characteristic signature of masses (Fig. 3.2.2; Sun et al., 1999, and figures within). X-ray absorption spectroscopy Selenium in selenoproteins also may be probed with X-ray absorption spectroscopy. Extended X-ray absorption fine structure (EXAFS) may then reveal information about the oxidation state and coordination sphere of selenium. Limitations of this technique are the necessity to use synchrotron radiation and requirements for large amounts of samples. In addition, selenium concentration in the sample should be ≥1 mM, which cannot be reached for many proteins because of protein solubility problems. EXAFS analyses probed selenium ligands in formate dehydrogenase H from E. coli (George et al., 1998), hydrogenase from Desulfovibrio baculatus (Eidsness et al., 1989), selenomethionine-substituted cytochrome ba3 from Thermus thermophilus (Blackburn et al., 1999), selenium-substituted nitrogenase (Conradson et al., 1994), and other proteins.

Cautions to be Taken in Selenoprotein Isolation Selenium-containing proteins are generally very labile due to the high reactivity of selenium in proteins. Selenium may be lost from selenoproteins by hydrolysis in the presence of oxygen, and this process is enhanced in bright light. To obtain selenoproteins of the highest activity, it is recommended that they be isolated as quickly as possible at 4°C in relative darkness. The selenium moiety in natural selenoproteins often serves a redox function, and therefore selenoproteins may be additionally protected during isolation by adding a reducing agent such as dithiothreitol or 2-mercaptoethanol. A number of selenoproteins are quickly inactivated in the presence of oxygen. To isolate such proteins, anaerobic conditions may be required. In isolating most selenoproteins, de-

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oxygenated solutions may be used throughout the isolation procedure. In case of extreme oxygen sensitivity, anaerobic chambers may be necessary (Axley et al., 1990; Gladyshev et al., 1994b). Anaerobic chambers are normally operated at 1 ppm of oxygen or less. Selenoprotein formate dehydrogenase H from E. coli is extremely sensitive to traces of oxygen, but the enzyme can be isolated and crystallized in its active state at <1 ppm of oxygen in an anaerobic chamber (Gladyshev et al., 1996a).

Identification of Selenoprotein Genes in Nucleotide Sequence Databases Until recently, selenoproteins were identified by isolating and characterizing selenocysteine in alkylated protein hydrolysates. This is a labor-intensive method, requiring considerable effort to detect and purify a protein and requiring extensive experience in amino acid analyses. Furthermore, selenoproteins are generally present in low amounts in cells and tissues, and therefore any given selenoprotein may be a limiting factor for analysis. The accumulation of known selenoprotein gene sequences has provided an important tool for making certain generalizations about selenoprotein gene structure that have aided in identifying new selenoproteins. From these generalizations, it initially became apparent that the presence of in-frame TGA is not sufficient information to identify a selenoprotein. Misinterpretations of TGA codons as both selenocysteine and stop signals are known. A more rigorous procedure had to be developed to distinguish between in-frame TGA codons for selenocysteine and those for stop signals, and to identify selenoprotein genes. Three criteria have been defined recently for specifically identifying mammalian selenoprotein genes. The Mammalian Selenoprotein Gene Signature (MSGS) predicts that a protein gene contains a selenocysteine codon if the following criteria are satisfied: 1. Sec is conserved and is flanked by conserved regions in mammalian sequences for this protein. 2. The SECIS element sequence and structure are conserved and are located in the 3′UTRs of mammalian genes for this protein. 3. Genes occur (usually in lower eukaryotes) that contain a Cys codon in place of TGA in the gene for the putative selenoprotein (or distinct homologous genes occur that conserve the TGA codon), and that meet an additional criterion of encoding the sequences with

sufficiently long homologous regions on both sides of the Cys/Sec (or Sec/Sec) pair. MSGS therefore predicts that a selenoprotein will have a conserved Sec and its flanking regions, a conserved SECIS element in 3′-UTR of the corresponding mRNA, and distinct homologs that contain either Sec or Cys in place of selenocysteine in the protein of interest. MSGS has been used to analyze several databases, including the entire genomes of S. cerevisiae, E. coli, and C. elegans, and various EST and nonredundant databases. These searches found all known mammalian selenoprotein genes and, in addition, by satisfying MSGS criteria, they have identified several new selenoproteins (Kryukov et al., 1999; Gladyshev and Kryukov, unpub. data). A similar strategy may be applied to identify selenoprotein genes in bacterial, archaeal, and eukaryotic sequences. In fact, SECISearch and MSGS correctly identified selenoprotein genes in many eukaryotic organisms, such as nematodes, trematodes, flies, amoebae, fish, and birds. On the other hand, the search of the S. cerevsiae genome, the only eukaryotic organism that is known to lack selenoproteins, found no selenoprotein genes. This suggests that MSGS and SECISearch may be used to analyze any eukaryotic genome. However, at this point, it is not known whether deviations from MSGS may occur in eukaryotic genomes that falsely identify selenoproteins, or if sequences selected by MSGS should be further tested before making a final decision.

STRATEGIES FOR IDENTIFYING SELENOCYSTEINE-ENCODING GENES AND SELENOCYSTEINECONTAINING PROTEINS There are several approaches that may be used to determine whether a newly isolated mammalian (or other animal) gene encodes selenocysteine or whether the protein contains a selenocysteine moiety, outlined in Figures 3.8.3 and 3.8.4. The investigator may ask several questions regarding the presence of conserved features observed in known selenoprotein genes that should reside in the newly isolated gene (see Fig. 3.8.3). Does this gene have an in-frame TGA codon? Can a TGA codon that is thought to terminate protein synthesis potentially encode a selenocysteine residue? Does this gene have a putative SECIS element in the 3′-UTR? If the gene has an in-frame TGA codon and a putative SECIS-like structure, are these features conserved in orthologous genes in other mammals? Are there distinct homolo-

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1. Putative Mammalian Selenoprotein Gene Isolated

no

It is unlikely to be a selenoprotein gene

no

It is unlikely to be a selenoprotein gene

4. Do orthologous genes from other mammals conserve the in-frame TGA codon and SECIS element? yes

no

It is unlikely to be a selenoprotein gene

5. Are there distinct homologous proteins that either conserve the in-frame TGA codon or replace it with TGC or TGT (cysteine codons)?

no

It is unlikely to be a selenoprotein gene

2. Does it have an in-frame TGA codon? Is TGA a termination codon?

yes 3. Does it have a putative SECIS element in 3′-untranslated region?

yes

6. Candidate Selenoprotein Gene Identified

7. When this gene is expressed in mammalian cells, is the gene product labeled with 75Se?

8. Optional: Is expression of the protein dependent on levels of Se in cell culture or in the diet? How many equivalents of Se are present in the protein? Does this protein contain Se in the form of Sec?

9. New Selenoprotein Identified

Figure 3.8.3 Strategies to test whether a newly isolated gene encodes a selenoprotein. See text for details.

Analysis of SelenocysteineContaining Proteins

gous proteins that have a cysteine codon (TGC or TGT) in place of the TGA selenocysteine codon? If the answer to any of these questions is no, it is unlikely that the new gene encodes a selenoprotein. It should be noted, however, that some of these questions may not be answered if insufficient information is available for the new gene or its homologs. For example, SECIS elements are often located at the very 3′ ends of mRNAs. If this portion of cDNA is lacking, a SECIS element cannot be easily identified. In addition, variations from conserved features found in known SECIS ele-

ments may be anticipated to occur in at least some new selenoprotein genes. This is because certain point mutations in conserved features of previously characterized SECIS elements do not always correlate with decreased selenocysteine incorporation. Additional difficulties in answering questions in blocks 2 to 5 of Figure 3.8.3 may arise if a new gene does not have known homologs in other species. Therefore, further experimental verification for a new selenoprotein gene may be required. The experimental design in determining if a new gene may encode a selenocysteine may

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Method

1. Putative Selenoprotein Purified

Comments

atomic absorption spectroscopy

2. Does the protein contain Se? How many equivalents of Se are present?

If Se is not present in the protein then it is not a selenoprotein.

γ counter and PhosphorImager analyses

3. When the protein is isolated from cells grown in the presence of 75Se, is it 75Se-labeled?

If 75Se is not present in the protein then it is not a selenoprotein.

native and SDS/PAGE analysis

4. Is Se lost when protein is analyzed by gel electrophoresis in the presence of a reducing agent?

If Se is not present then it is not a Sec-containing protein.

amino acid composition analysis

5. Does amino acid analysis of 75Se-labeled protein show the presence of Sec?

If Sec is not present then it is not a selenoprotein.

amino acid sequencing (Edman degradation)

6. In the isolated 75Se-labeled peptide from a protease digest of an alkylated protein, what is the location of the 75Se-containing amino acid?

Location of Sec is identified by determining a cycle where 75Se is released.

tandem mass spectrometry (MS/MS) analysis

7. In the isolated Se-containing peptide obtained by a protease digest, what is the mass of the alkylated amino acid?

Sec has a unique mass. Se is also detected due to its unique natural isotope abundance.

computational screen (see Fig. 3.8.3)

8. Does the gene for this protein contain an in-frame UGA and/or a SECIS element?

The presence of UGA and SECIS element are expected.

9. New Selenoprotein Identified

Figure 3.8.4 Strategies to test whether a newly isolated protein is a selenoprotein. See text for details.

also include a question of whether the gene product incorporates selenium when it is expressed in mammalian cells. One technique to address this question is to use 75Se labeling of cells transfected with a construct that expresses the new gene. The molecular mass for a new selenoprotein should not correspond to the known masses for major natural selenoproteins expressed in host cells, such as thioredoxin reductase (57 kDa), glutathione peroxidase 1 (25 kDa), glutathione peroxidase 4 (20 kDa), and the 15 kDa protein. This can be achieved

by expressing the new gene as a fusion with a tag protein, such as green fluorescent protein. If all four questions in the computational screen are answered as yes, and if the overexpressed protein is labeled with 75Se, the data may be considered proof for identification of a new selenoprotein gene. Additional (optional) questions may be asked to further resolve the identity of the new putative selenoprotein gene. For example, is the expression of the gene product dependent on levels of selenium in cell culture or in the diet? What is the chemical form of

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Analysis of SelenocysteineContaining Proteins

selenium in the selenoprotein and how many equivalents of selenium are present in the protein? These questions may be addressed using the techniques summarized in Table 3.8.3 and discussed above. In contrast to the experimental design used in identifying new selenoprotein genes, a different set of questions may be asked to determine if an isolated protein is a selenoprotein. The first question to resolve is whether the protein contains selenium. The assay for selenium is straightforward and, if an atomic absorption spectrometer is not available, several companies are available that can determine selenium content in a protein. It is important to determine how many equivalents of selenium are present in a protein. It should be noted that a partial loss of selenium may occur during isolation due to its lability. Therefore, a value of 0.5 equivalents of selenium per protein subunit is not unusual, and a value of 0.9 equivalents is rarely achieved unless specific precautions are taken during isolation (e.g., avoiding direct sunlight, maintaining a minimum time for isolation, using deoxygenated or anaerobic conditions, and using reducing agents and metal chelators). Additional studies may be employed to determine whether the protein is labeled when host cells are grown in the presence of 75Se, and whether 75Se is lost when the protein is analyzed by SDS-PAGE in the presence of a strong reducing agent. Selenium may easily form selenosulfide bonds with reactive protein thiols, and these thioselenides show lower redox potentials compared to corresponding disulfides. Therefore, proteins with reactive cysteines may appear as selenoproteins during isolation, but lose selenium during SDS-PAGE in the presence of excess reducing agent. If possible, the identity of the chemical form of selenium in a protein should be determined. For example, the presence of selenocysteine in a protein can be determined by amino acid analysis, which involves coelution of the isotope from an acid hydrolysate of alkylated 75Se-labeled protein with an alkylated selenocysteine standard. The location of selenocysteine in a protein may also be determined. This can be accomplished, for example, by isolating a 75Se-labeled peptide from a protease digest of the putative selenoprotein and subsequent direct sequencing of the peptide. Although selenocysteine is chemically destroyed during Edman degradation, eluates from each sequencing cycle may be analyzed for the presence of 75Se. Alternatively, a selenium-containing peptide

from a protease digest may be analyzed by tandem mass spectrometry, and the location of the selenocysteine residue determined from the molecular masses of degradation products (Fig. 3.8.4). The location of selenocysteine in a protein may be also deduced by aligning the gene sequence encoding a TGA codon with the expected gene product. The gene must, of course, have been identified as a selenoprotein gene according to the conserved features in Figure 3.8.3.

CONCLUSIONS Selenocysteine has been identified as the 21st amino acid in protein, and it is encoded in genomes of representatives in the three primary life domains: bacteria, archaea, and eukaryotes. As a result, a dramatic increase in research activities related to natural and artificial selenoproteins has occurred in the last several years. The largest number of selenoproteins appears to be present in vertebrate genomes. Methods that are specific for the expression, detection, and characterization of these proteins have been developed and successfully applied. Likewise, significant progress has been made in developing methods for incorporating selenium into a protein in order to obtain useful information on protein structure and properties, particularly on the environment of selenium in selenium-containing proteins. Perhaps the most extensive application of artificial selenoproteins is the use of selenomethionine-enriched proteins in Xray crystallography. Bioinformatics approaches have provided alternative and rapid procedures for identifying and characterizing new selenocysteine-containing proteins. With accelerated discoveries of new natural selenoproteins and the availability of unique methods for selenoprotein analyses, this area of research is expected to be very fruitful in the future.

LITERATURE CITED Adachi, K., Saito, H., Tanaka, T., and Oka, T. 1998. Molecular cloning and characterization of the murine staf cDNA encoding a transcription activating factor for the selenocysteine tRNA gene in mouse mammary gland. J. Biol. Chem. 273:8598-8606. Adachi, K., Saito, H., Tanaka, T., and Oka, T. 1999. Hormonal induction of mouse selenocysteine transfer ribonucleic acid (tRNA) gene transcription-activating factor and its functional importance in the selenocysteine tRNA gene transcription in mouse mammary gland. Endocrinology 140:618623. Amberg, R., Mizutani, T., Wu, X.-Q., and Gross, H.J. 1996. Selenocysteine synthesis in mammalia: An identity switch from tRNA(Ser) to tRNA(Sec). J. Mol. Biol. 263:8-19.

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Andreesen, J.R., Wagner, M., Sonntag, D., Kohlstock, M., Harms, C., Gursinsky, T., Jager, J., Parther, T., Kabisch, U., Grantzdorffer, A., Pich, A., and Sohling, B. 1999. Various functions of selenols and thiols in anaerobic gram-positive, amino acids-utilizing bacteria. Biofactors 10:263-270. Arner, E.S., Sarioglu, H., Lottspeich, F., Holmgren, A., and Bock, A. 1999. High-level expression in Escherichia coli of selenocysteine-containing rat thioredoxin reductase utilizing gene fusions with engineered bacterial-type SECIS elements and coexpression with the selA, selB and selC genes. J. Mol. Biol. 292:1003-1016. Axley, M.J., Grahame, D.A., and Stadtman, T.C. 1990. Escherichia coli formate-hydrogen lyase. Purification and properties of the selenium-dependent formate dehydrogenase component. J. Biol. Chem. 265:18213-18218. Benoit, S., Abaibou, H., and Mandrand-Berthelot, M.A. 1998. Topological analysis of the aerobic membrane-bound formate dehydrogenase of Escherichia coli. J. Bacteriol. 180:6625-6634. Berg, B.L., Li, J., Heider, J., and Stewart, V. 1991. Nitrate-inducible formate dehydrogenase in Escherichia coli K-12. I. Nucleotide sequence of the fdnGHI operon and evidence that opal (UGA) encodes selenocysteine. J. Biol. Chem. 266:2238022385. Berry, M.J., Banu, L., and Larsen, P.R. 1991. Type I iodothyronine deiodinase is a selenocysteine-containing enzyme. Nature 349:438-440. Bertini, I., Ciurli, S., Dikiy, A., Luchinat, C. 1993. The electronic structure of the [4Fe-4Se]3+ clusters in C. vinosum HiPIP and E. halophila HiPIP II through NMR and EPR studies. J. Am. Chem. Soc. 115:12020-12028. Blackburn, N.J., Ralle, M., Gomez, E., Hill, M.G., Pastuszyn, A., Sanders, D., and Fee, J.A. 1999. Selenomethionine-substituted Thermus thermophilus cytochrome ba3: Characterization of the CuA site by Se and Cu K-EXAFS. Biochemistry 38:7075-7084. Bock, A., Forchhammer, K., Heider, J., Leinfelder, W., Sawers, G., Veprek, B., and Zinoni, F. 1991. Selenocysteine: The 21st amino acid. Mol. Microbiol. 5:515-20. Boles, J.O., Tolleson, W.H., Schmidt, J.C., Dunlap, R.B., and Odom, J.D. 1992. Selenomethionyl dihydrofolate reductase from Escherichia coli. Comparative biochemistry and 77Se nuclear magnetic resonance spectroscopy. J. Biol. Chem. 267:2221722223. Boschi-Muller, S., Muller, S., Van Dorsselaer, A., Bock, A., and Branlant, G. 1998. Substituting selenocysteine for active site cysteine 149 of phosphorylating glyceraldehyde 3-phosphate dehydrogenase reveals a peroxidase activity. FEBS Lett. 439:241-245. Boyington, J.C., Gladyshev, V.N., Khangulov, S.V., Stadtman, T.C., and Sun, P.D. 1997. Crystal structure of formate dehydrogenase H: Catalysis involving Mo, molybdopterin, selenocysteine, and an Fe4S4 cluster. Science 275:1305-1308.

Buettner, C., Harney, J.W., and Larsen, P.R. 1998. The 3′-untranslated region of human type 2 iodothyronine deiodinase mRNA contains a functional selenocysteine insertion sequence element. J. Biol. Chem. 273:33374-33378. Buettner, C., Harney, J.W., and Berry, M.J. 1999. The Caenorhabditis elegans homologue of thioredoxin reductase contains a selenocysteine insertion sequence (SECIS) element that differs from mammalian SECIS elements but directs selenocysteine incorporation. J. Biol. Chem. 274:21598-21602. Burk, R.F., and Hill, K.E. 1999. Orphan selenoproteins. Bioessays 21:231-237. Chambers, I., Frampton, J., Goldfarb, P., Affara, N., McBain, W., and Harrison, P.R. 1986. The structure of the mouse glutathione peroxidase gene: The selenocysteine in the active site is encoded by the “termination” codon, TGA. EMBO J. 5:12211227. Chan, S., Gerson, B., and Subramaniam, S. 1998a. The role of copper, molybdenum, selenium, and zinc in nutrition and health. Clin. Lab. Med. 18:673-685. Chan, S., Gerson, B., Reitz, R.E., and Sadjadi, S.A. 1998b. Technical and clinical aspects of spectrometric analysis of trace elements in clinical samples. Clin. Lab. Med. 18:615-629. Chittum, H.S., Hill, K.E., Carlson, B.A., Lee, B.J., Burk, R.F., and Hatfield, D.L. 1997. Replenishment of selenium deficient rats with selenium results in redistribution of the selenocysteine tRNA population in a tissue specific manner. Biochim. Biophys. Acta 1359:25-34. Chittum, H.S., Lane, W.S., Carlson, B.A., Roller, P.P., Lung, F.T., Lee, B.J., and Hatfield, D.L. 1998. Rabbit β-globin is extended beyond its UGA stop codon by multiple suppressions and translational reading gaps. Biochemistry 37:10866-10870. Choi, I.S., Diamond, A.M., Crain, P.F., Kolker, J.D., McCloskey, J.A., and Hatfield, D.L. 1994. Reconstitution of the biosynthetic pathway of selenocysteine tRNAs in Xenopus oocytes. Biochemistry 33:601-605. Chu, F.F., Doroshow, J.H., and Esworthy, R.S. 1993. Expression, characterization, and tissue distribution of a new cellular selenium-dependent glutathione peroxidase, GSHPx-GI. J. Biol. Chem. 268:2571-2576. Clark, L.C., Combs, G.F. Jr., Turnbull, B.W., Slate, E.H., Chalker, D.K., Chow, J., Davis, L.S., Glover, R.A., Graham, G.F., Gross, E.G., Krongrad, A., Lesher, J.L. Jr., Park, H.K., Sanders, B.B. Jr., Smith, C.L., and Taylor, J.R. 1996. Effects of selenium supplementation for cancer prevention in patients with carcinoma of the skin. A randomized controlled trial. Nutritional Prevention of Cancer Study Group. J. Am. Med. Assoc. 276:1957-1963. Combs, G.F. and Combs, S.B. 1986. The Role of Selenium in Nutrition. Academic Press, New York. Commans, S. and Bock, A. 1999. Selenocysteine inserting tRNAs: An overview. FEMS Microbiol. Rev. 23:335. Conradson, S.D., Burgess, B.K., Newton, W.E., Di Cicco, A., Filipponi, A., Wu, Z.Y., Natoli, C.R.,

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Hedman, B., and Hodgson, K.O. 1994. Selenol binds to iron in nitrogenase iron-molybdenum cofactor: An extended x-ray absorption fine structure study. Proc. Natl. Acad. Sci. U.S.A. 91:1290-1293. Copeland, P.R. and Driscoll, D.M. 1999. Purification, redox sensitivity, and RNA binding properties of SECIS-binding protein 2, a protein involved in selenoprotein biosynthesis. J. Biol. Chem. 274:25447-25454. Copeland, P.R., Fletcher, J.E., Carlson, B.A., Hatfield, D.L., and Driscoll, D.M. 2000. A novel RNA binding protein, SBP2, is required for the translation of mammalian selenoprotein mRNAs. EMBO J. 19:306-314. Croteau, W., Whittemore, S.L., Schneider, M.J., and St. Germain, D.L. 1995. Cloning and expression of a cDNA for a mammalian type III iodothyronine deiodinase. J. Biol. Chem. 270:16569-16575. Daher, R. and Van Lente, F. 1994. Concanavalin Abound selenoprotein in human serum analyzed by graphite furnace atomic absorption spectrometry. Clin. Chem. 40:62-70. Davey, J.C., Becker, K.B., Schneider, M.J., St. Germain, D.L., and Galton, V.A. 1995. Cloning of a cDNA for the type II iodothyronine deiodinase. J. Biol. Chem. 270:26786-26789. Diamond, A.M., Choi, I.S., Crain, P.F., Hashizume, T, Pomerantz, S.C., Cruz, R., Steer, C.J., Hill, K.E., Burk, R.F., McCloskey, J.A., and Hatfield, D.L. 1993. Dietary selenium affects methylation of the wobble nucleoside in the anticodon of selenocysteine tRNA[Ser]Sec. J. Biol. Chem. 268:14215-14223. Dilworth, G.L. 1982. Properties of the selenium-containing moiety of nicotinic acid hydroxylase from Clostridium barkeri. Arch. Biochem. Biophys. 219:30-38. Dobbek, H., Gremer, L., Meyer, O., and Huber, R. 1999. Crystal structure and mechanism of CO dehydrogenase, a molybdo iron-sulfur flavoprotein containing S-selanylcysteine. Proc. Natl. Acad. Sci. U.S.A. 96:8884-8889. Eidsness, M.K., Scott, R.A., Prickril, B.C., DerVartanian, D.V., Legall, J., Moura, I., Moura, J.J., and Peck, H.D. Jr. 1989. Evidence for selenocysteine coordination to the active site nickel in the [NiFeSe]hydrogenases from Desulfovibrio baculatus. Proc. Natl. Acad. Sci. U.S.A. 86:147-151. Flohe, L., Gunzler, W.A., and Schock, H.H. 1973. Glutathione peroxidase: A selenoenzyme. FEBS Lett. 32:132-134. Forchhammer, K., Rucknagel, K.P., and Bock, A. 1990. Purification and biochemical characterization of SELB, a translation factor involved in selenoprotein synthesis. J. Biol. Chem. 265:9346-9350. Franke, K.W. 1934a. A new toxicant occurring naturally in certain samples of plant foodstuffs. I. Results obtained in preliminary feeding trials. J. Nutr. 8:597-608. Analysis of SelenocysteineContaining Proteins

Franke, K.W. 1934b. A new toxicant occurring naturally in certain samples of plant foodstuffs. II. The occurrence of the toxican in the protein fraction. J. Nutr. 8:609-613.

Fujiwara, K., Okamura-Ikeda, K., Packer, L., and Motokawa, Y. 1997. Synthesis and characterization of selenolipoylated H-protein of the glycine cleavage system. J. Biol. Chem. 272:19880-19883. George, G.N., Colangelo, C.M., Dong, J., Scott, R.A., Khangulov, S.V., Gladyshev, V.N., and Stadtman, T.C. 1998. X-ray absorption spectroscopy of the molybdenum site of Escherichia coli formate dehydrogenase. J. Am. Chem. Soc. 120:1267-1273. Gesteland, R.F., Weiss, R.B., and Atkins, J.F. 1992. Recoding: Reprogrammed genetic decoding. Science 257:1640-1641. Gettins, P. and Crews, B.C. 1991. 77Se NMR characterization of 77Se-labeled ovine erythrocyte glutathione peroxidase. J. Biol. Chem. 266:4804-4809. Gladyshev, V.N. and Hatfield, D.L. 1999. Selenocysteine-containing proteins in mammals. J. Biomed. Sci. 6:151-160. Gladyshev, V.N., Khangulov, S.V., and Stadtman, T.C. 1994a. Nicotinic acid hydroxylase from Clostridium barkeri: Electron paramagnetic resonance studies show that selenium is coordinated with molybdenum in the catalytically active seleniumdependent enzyme. Proc. Natl. Acad. Sci. U.S.A. 91:232-236. Gladyshev, V.N., Khangulov, S.V., Axley, M.J., and Stadtman, T.C. 1994b. Coordination of selenium to molybdenum in formate dehydrogenase H from Escherichia coli. Proc. Natl. Acad. Sci. U.S.A. 91:7708-7711. Gladyshev, V.N., Boyington, J.C., Khangulov, S.V., Grahame, D.A., Stadtman, T.C., and Sun, P.D. 1996a. Characterization of crystalline formate dehydrogenase H from Escherichia coli. Stabilization, EPR spectroscopy, and preliminary crystallographic analysis. J. Biol. Chem. 271:8095-8100. Gladyshev, V.N., Khangulov, S.V., and Stadtman, T.C. 1996b. Properties of the selenium- and molybdenum-containing nicotinic acid hydroxylase from Clostridium barkeri. Biochemistry 35:212-223. Gladyshev, V.N., Jeang, K.-T., and Stadtman, T.C. 1996c. Selenocysteine, identified as the penultimate C-terminal residue in human T-cell thioredoxin reductase, corresponds to TGA in the human placental gene. Proc. Natl. Acad. Sci. USA 93:61466151. Gladyshev, V.N., Factor, V.M., Housseau, F., and Hatfield, D.L. 1998. Contrasting patterns of regulation of the antioxidant selenoproteins, thioredoxin reductase, and glutathione peroxidase, in cancer cells. Biochem. Biophys. Res. Commun. 251:488-493. Gladyshev, V.N., Stadtman, T.C., Hatfield, D.L., and Jeang, K.T. 1999a. Levels of major selenoproteins in T cells decrease during HIV infection and low molecular mass selenium compounds increase. Proc. Natl. Acad. Sci. U.S.A. 96:835-839. Gladyshev, V.N., Krause, M., Xu, X.M., Korotkov, K.V., Kryukov, G.V., Sun, Q.A., Lee, B.J., Wootton, J.C., and Hatfield, D.L. 1999b. Selenocysteinecontaining thioredoxin reductase in C. elegans. Biochem. Biophys. Res. Commun. 259:244-249. Gladyshev, V.N., Martin-Romero, F.J., Xu, X.-M., Kumaraswamy, E., Carlson, B.A., Hatfield, D.L., and

3.8.16 Supplement 20

Current Protocols in Protein Science

Lee, B.J. 2000. Molecular biology of selenium and its role in cancer, AIDS and other human diseases. Recent Develop. Biochem. In press.

(MAD): A vehicle for direct determination of three-dimensional structure. EMBO J. 9:16651672.

Glass, R.S., Singh, W.P., Jung, W., Veres, Z., Scholz, T.D., and Stadtman, T.C. 1993. Monoselenophosphate: Synthesis, characterization, and identity with the prokaryotic biological selenium donor, compound SePX. Biochemistry 32:12555-12559.

Jung, J.-E., Karoor, V., Sandbacken, M.G., Lee, B.J., Ohama, T., Gesteland, R.F., Atkins, J.F., Mullenbach, G.T., Hill, K.E., Wahba, A.J., and Hatfield, D.L. 1994. Utilization of selenocysteyltRNA[Ser]Sec and seryl-tRNA[Ser]Sec in protein synthesis. J. Biol. Chem. 269:29739-29745.

Grundner-Culemann, E., Martin, G.W. III, Harney, J.W., and Berry, M.J. 1999. Two distinct SECIS structures capable of directing selenocysteine incorporation in eukaryotes. RNA 5:625-635. Guimaraes, M.J., Peterson, D., Vicari, A., Cocks, B.G., Copeland, N.G., Gilbert, D.J., Jenkins, N.A., Ferrick, D.A., Kastelein, R.A., Bazan, J.F., and Zlotnik, A. 1996. Identification of a novel selD homolog from eukaryotes, bacteria, and archaea: Is there an autoregulatory mechanism in selenocysteine metabolism? Proc. Natl. Acad. Sci. U.S.A. 93:1508615091. Hatfield, D. and Diamond, A. 1993. UGA: A split personality in the universal genetic code. Trends Genet. 9:69-70. Hatfield, D.L. and Portugal, F.H. 1970. Seryl-tRNA in mammalian tissues: Chromatographic differences in brain and liver and a specific response to the codon, UGA. Proc. Natl. Acad. Sci. U.S.A., 67:1200-1206. Hatfield, D., Lee, B.J., Hampton, L., and Diamond, A.M. 1991. Selenium induces changes in the selenocysteine tRNA[Ser]Sec population in mammalian cells. Nucl. Acids Res. 19:939-943. Hatfield, D.L., Choi, I.S., Ohama, T., Jung, J.-E., and Diamond, A.M. 1994. Selenocysteine tRNA[Ser]Sec isoacceptors as central components in selenoprotein biosynthesis in eukaryotes. In Selenium in Biology and Human Health (R.F. Burk, ed.) pp. 25-44. Springer-Verlag, New York. Hatfield, D.L., Gladyshev, V.N., Park, S.I., Chittum, H.S., Carlson, B.A., Moustafa, M., Park, J.M., Huh, J.R., Kim, M., and Lee, B.J. 1999. Biosynthesis of selenocysteine and its incorporation into proteins as the 21st amino acid. Comprehensive Natural Products Chemistry 4:353-381. He, S.H., Teixeira, M., LeGall, J., Patil, D.S., Moura, I., Moura, J.J., DerVartanian, D.V., Huynh, B.H., and Peck, H.D. Jr. 1989. EPR studies with 77Se-enriched (NiFeSe) hydrogenase of Desulfovibrio baculatus. Evidence for a selenium ligand to the active site nickel. J. Biol. Chem. 264:2678-2682. Heider, J. and Bock, A. 1992. Targeted insertion of selenocysteine into the alpha subunit of formate dehydrogenase from Methanobacterium formicicum. J. Bacteriol. 174:659-663.

Kabisch, U.C., Grantzdorffer, A., Schierhorn, A., Rucknagel, K.P., Andreesen, J.R., and Pich, A. 1999. Identification of D-proline reductase from Clostridium sticklandii as a selenoenzyme and indications for a catalytically active pyruvoyl group derived from a cysteine residue by cleavage of a proprotein. J. Biol. Chem. 274:8445-8454. Khangulov, S.V., Gladyshev, V.N., Dismukes, G.C., and Stadtman, T.C. 1998. Selenium-containing formate dehydrogenase H from Escherichia coli: A molybdopterin enzyme that catalyzes formate oxidation without oxygen transfer. Biochemistry 37:3518-3528. Kreimer, S. and Andreesen, J.R. 1995. Glycine reductase of Clostridium litorale. Cloning, sequencing, and molecular analysis of the grdAB operon that contains two in-frame TGA codons for selenium incorporation. Eur. J. Biochem. 234:192-199. Kryukov, G.V., Kryukov, V.M., and Gladyshev, V.N. 1999. New mammalian selenocysteine-containing proteins identified with an algorithm that searches for selenocysteine insertion sequence elements. J. Biol. Chem. 274:33888-33897. Kumaraswamy, E., Xu, X.-M., Martin-Romero, F.J., Carlson, B.A., and Hatfield, D.L. 1999. Role of UGA in mammalian protein synthesis: Selenocysteine, stop, suppression and reading gaps. Curr. Top. Biomed. Res. 1:113-123. Lee, B.J., de la Pena, P., Tobian, J.A., Zasloff, M., and Hatfield, D. 1987. Unique pathway of expression of an opal suppressor phosphoserine tRNA. Proc. Natl. Acad. Sci. U.S.A. 84:6384-6388. Lee, B.J., Worland, P.J., Davis, J.N., Stadtman, T.C., and Hatfield, D.L. 1989. Identification of a selenocysteyl-tRNASer in mammalian cells that recognizes the nonsense codon, UGA. J. Biol. Chem. 264:9724-9727. Lee, S.R., Kim, J.R., Kwon, K.S., Yoon, H.W., Levine, R.L., Ginsburg, A., and Rhee, S.G. 1999. Molecular cloning and characterization of a mitochondrial selenocysteine-containing thioredoxin reductase from rat liver. J. Biol. Chem. 274:4722-4734.

Heider, J. and Bock, A. 1993. Selenium metabolism in micro-organisms. Adv. Microb. Physiol. 35:71-109.

Leinfelder, W., Stadtman, T.C., and Bock, A. 1989. Occurrence in vivo of selenocysteyltRNA(SerUCA) in Escherichia coli. Effect of sel mutations. J. Biol. Chem. 264:9720-9723.

Hendrickson, W.A. 1991. Determination of macromolecular structures from anomalous diffraction of synchrotron radiation. Science 254:51-58.

Low, S.C. and Berry, M.J. 1996. Knowing when not to stop: Selenocysteine incorporation in eukaryotes. Trends Biochem. Sci. 21:203-208.

Hendrickson, W.A., Horton, J.R., and LeMaster, D.M. 1990. Selenomethionyl proteins produced for analysis by multiwavelength anomalous diffraction

Maenpaa, P.H. and Bernfield, M.R. 1970. A specific hepatic transfer RNA for phosphoserine. Proc. Natl. Acad. Sci. U.S.A. 67:688-695.

Detection and Assay Methods

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Martin, G.W., Harney, J.W., and Berry, M.J. 1996. Selenocysteine incorporation in eukaryotes: Insights into mechanism and efficiency from sequence, structure, and spacing proximity studies of the type 1 deiodinase SECIS element. RNA 2: 171-182.

Sorgenfrei, O., Duin, E.C., Klein, A., and Albracht, S.P. 1997. Changes in the electronic structure around Ni in oxidized and reduced selenium-containing hydrogenases from Methanococcus voltae. Eur. J. Biochem. 247:681-687. Stadtman, T.C. 1996. Selenocysteine. Annu. Rev. Biochem. 65:83-100.

Martin, G.W. III, Harney, J.W., and Berry, M.J. 1998. Functionality of mutations at conserved nucleotides in eukaryotic SECIS elements is determined by the identity of a single nonconserved nucleotide. RNA 4:65-73.

Sturchler, C., Lescure, A., Keith, G., Carbon, P., and Krol, A. 1994. Base modification pattern at the wobble position of Xenopus selenocysteine tRNA(Sec). Nucl. Acids Res. 22:1354-1358.

Maser, R.L., Magenheimer, B.S., and Calvet, J.P. 1994. Mouse plasma glutathione peroxidase. cDNA sequence analysis and renal proximal tubular expression and secretion. J. Biol. Chem. 269:2706627073.

Sun, Q.A., Wu, Y., Zappacosta, F., Jeang, K.T., Lee, B.J., Hatfield, D.L., and Gladyshev, V.N. 1999. Redox regulation of cell signaling by selenocysteine in mammalian thioredoxin reductases. J. Biol. Chem. 274:24522-24530.

Muller, S., Senn, H., Gsell, B., Vetter, W., Baron, C., and Bock, A. 1994. The formation of diselenide bridges in proteins by incorporation of selenocysteine residues: Biosynthesis and characterization of (Se)2-thioredoxin. Biochemistry 33:3404-3412.

Sunde, R.A. and Evenson, J.K. 1987. Serine incorporation into the selenocysteine moiety of glutathione peroxidase. J. Biol. Chem. 262:933-937.

Myslinski, E., Krol, A., and Carbon, P. 1998. ZNF76 and ZNF143 are two human homologs of transcriptional activator Staf. J. Biol. Chem. 273:2199822006.

Vendeland, S.C., Beilstein, M.A., Yeh, J.Y., Ream, W., and Whanger, P.D. 1995. Rat skeletal muscle selenoprotein W: cDNA clone and mRNA modulation by dietary selenium. Proc. Natl. Acad. Sci. U.S.A. 92:8749-8753.

Ohama, T., Yang, D., and Hatfield, D.L. 1994. Selenocysteine tRNA and serine tRNA are aminoacylated by the same synthetase, but may manifest different identities with respect to the long extra arm. Arch. Biochem. Biophys. 315:293-301.

Vorholt, J.A., Vaupel, M., and Thauer, R.K. 1997. A selenium-dependent and a selenium-independent formylmethanofuran dehydrogenase and their transcriptional regulation in the hyperthermophilic Methanopyrus kandleri. Mol. Microbiol. 23:10331042.

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Walczak, R., Carbon, P., and Krol, A. 1998. An essential non-Watson-Crick base pair motif in 3′-UTR to mediate selenoprotein translation. RNA 4:74-84.

Rotruck, J.T., Pope, A.L., Ganther, H.E., Swanson, A.B., Hafeman, D.G., and Hoekstra, W.G. 1973. Selenium: Biochemical role as a component of glutathione peroxidase. Science 179:588-590. Sawers, G. 1994. The hydrogenases and formate dehydrogenases of Escherichia coli. Antonie Van Leeuwenhoek 66:57-88. Schaub, M., Myslinski, E., Krol, A., and Carbon, P. 1999. Maximization of selenocysteine tRNA and U6 small nuclear RNA transcriptional activation achieved by flexible utilization of a Staf zinc finger. J. Biol. Chem. 274:25042-25050. Schuckelt, R., Brigelius-Flohe, R., Maiorino, M., Roveri, A., Reumkens, J., Strassburger, W., Ursini, F., Wolf, B., and Flohe, L. 1991. Phospholipid hydroperoxide glutathione peroxidase is a selenoenzyme distinct from the classical glutathione peroxidase as evident from cDNA and amino acid sequencing. Free Radical Res. Commun. 14:343-361. Schuster, C., Krol, A., and Carbon, P. 1998. Two distinct domains in Staf to selectively activate small nuclear RNA-type and mRNA promoters. Mol. Cell. Biol. 18:2650-2658.

Wen, W., Weiss, S.L., and Sunde, R.A. 1998. UGA codon position affects the efficiency of selenocysteine incorporation into glutathione peroxidase-1. J. Biol. Chem. 273:28533-28541. Wilting, R., Schorling, S., Persson, B.C., and Bock, A. 1997. Selenoprotein synthesis in archaea: Identification of an mRNA element of Methanococcus jannaschii probably directing selenocysteine insertion. J. Mol. Biol. 266:637-641. Wu, X.G. and Gross, H.J. 1993. The long extra arms of human tRNA((Ser)Sec) and tRNA(Ser) function as major identity elements for serylation in an orientation-dependent, but not sequence-specific manner. Nucl. Acids Res. 21:5589-5594.

Contributed by Vadim N. Gladyshev University of Nebraska Lincoln, Nebraska Dolph L. Hatfield National Cancer Institute Bethesda, Maryland

Schwarz, K. and Foltz, C.M. 1957. Selenium as an integral part of factor 3 against dietary necrotic liver degeneration. J. Am. Chem. Soc. 79:3292-3293. Analysis of SelenocysteineContaining Proteins

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Solid-Phase Profiling of Proteins

UNIT 3.9

Recent advances in DNA sequencing and nucleic acid microarray technologies have led to the exponential growth of information on genome structure and function. Studies involving the human genome and the publication of almost its entire sequence in the latest special issues of Nature (2001) and Science (HGP, 2001) revealed several puzzling facts. First of all, the number of functional genes in humans is much smaller than was expected. Second, the difference in the number of genes between humans and baker’s yeast is only five-fold. The difference is even less between human and mouse. These facts, in turn, raise fundamental questions—e.g., how is the diversity of the species achieved and what makes human beings human? One of the main answers to this puzzle may lay in the much higher diversity of proteins as compared to the genes encoding them, due to such processes as differential splicing and post-translational processing and modification. At the present moment our knowledge about the protein complement of the genome is quite limited, and progress in this field is directly connected to the development of new approaches and technologies. Major advances in high-throughput technologies, such as protein array technologies (also see UNIT 19.6), are needed to analyze and quantify post-translational and structural protein changes in a large collection of proteins in a relatively short period of time. The eventual objective of the post-genome era is to obtain a comprehensive understanding of the organization and dynamics of the metabolic signaling and regulatory networks of life. Protein array technologies will definitely increase our pace towards this objective. Protein array technologies can be defined as any fabrication and use of arrays containing multiple proteins bound to solid surfaces. The term “protein array” may refer to several different forms of the technology, each differing in the method of protein immobilization and detection, as well as aspects of application. Researchers are encouraged to consult this section in consideration of which type of array technologies best suit their objective of study. Three kinds of arrays are described in this introduction and are noted in Table 3.9.1. The first category of array presented is the ProteinChip (Ciphergen), which is employed to discover new biomarkers or expressed proteins as well as protein-protein interactions (see Basic Protocols 1 and 2). This is retentate chromatography on a chip. Different proteins with different surface characteristics (e.g., charge, hydrophobicity) are retained on the chip surface, which is coated with different chemical groups. The second category of array is employed for large-scale protein-protein interaction studies. Antigens or proteins are immobilized or printed on membrane or modified microscopic slides. Specific interacting proteins or antibodies from a mixture that bind to the printed proteins will then generate a signal, which is detected either by immunoassay, fluorescence imaging, or mass spectroscopy (MS; Chapter 16). These two categories are aimed at screening multiple proteins in a single sample. The third type of chip array, the tissue array, is used to screen multiple samples for changes of a single protein. This requires the researcher to have a prior knowledge of the target protein, such as the availability of specific antibody. It is important to notice (with regret) that most of the technologies described in this unit are in the process of development and are therefore not commercially available at the present time. The only exception is ProteinChip technology (Ciphergen); therefore, we concentrate below on two of the most popular applications of ProteinChip technology, Contributed by Hon-chiu Eastwood Leung, Sau-mei Leung, and Alex Karavanov Current Protocols in Protein Science (2001) 3.9.1-3.9.16 Copyright © 2001 by John Wiley & Sons, Inc.

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Table 3.9.1

Applications and Types of Arrays

Array name and type of solid surface

Applications Biomarker discovery using functional groups

Protein-protein interactions with immobilized proteins

ProteinChip: aluminum chip coated with different chemical groups (retentate chromatography on chips) Membrane immobilized with multiple proteins

Detection method

Website/Reference

Mass spectrometry

http://www.ciphergen.com

ELISA (enzyme linked immunosorbent assay)

Lueking et al. (1999)

Multiple species of proteins printed on glass slides

Screening multiple samples for changes of single biomarker

ELISA, fluorescence Haab et al. (2000), scanning, or radioactivity MacBeath and Schreiber (2000) Rolling circle amplification ELISA or fluorescence Lizardi et al. (1998), technology (RCAT): Microtiter scanning Schweitzer et al. (2000), plates immobilized with Wiltshire et al. (2000) antigen-DNA hybrid molecules ProteinChip Mass spectrometry http://www.ciphergen.com MAGIChips Fluorescence or color http://www.ipd.anl.gov/ reaction biotech/programs/biochip PROfusion technology and Fluorescence or mass http://www.phylos.com HIP chips spectrometry Tissue array: Cylinders of Immunohistochemistry Kononen et al. (1998), tissues on a glass slide or fluorescence Moch et al. (1999), microscopy Sallinen et al. (2000) PROfusion technology and Fluorescence or mass http://www.phylos.com HIP chips spectrometry ProteinChip Mass spectrometry http://www.ciphergen.com

namely, protein profiling (see Basic Protocol 1) and protein-protein interactions (see Basic Protocol 2). Other procedures will be added as the technology becomes more developed (Merchant and Weinberger, 2000; Wright et al., 2000). Chromatography on a Chip: ProteinChip Arrays The ProteinChip arrays (Ciphergen) are aluminum strips that contain 8 or 24 active spots that are 2 mm in diameter. The surface of each spot is coated with a monolayer of chemically active molecules. All spots on the array carry the same kind of active surface, which permits the study of one sample under eight different conditions or eight samples under one particular condition. These molecules can be hydrophobic (C16 aliphatic hydrocarbon chain), ionic (quaternary amine for positively charged surface or carboxylate for negatively charged surface), metal binding (nitrilotriacetate), or hydrophilic material (silicon dioxide), as depicted in Figure 3.9.1. A series of ProteinChip arrays are arranged in parallel. The raw sample solution is deposited on every spot of each array. After binding, the spots on each chip are washed with appropriate buffers of different stringency. Proteins with higher affinity to a particular surface are captured, while proteins with weak affinity are washed away; therefore, a variety of proteins are retained based on the hydrophobicity, surface charge, phosphorylation, glycosylation, or primary sequence.

Solid-Phase Profiling of Proteins

To investigate interactions with particular proteins or DNA segments, subsets of ProteinChip arrays are coated with carbonyldiimidazole or epoxy groups. These chemical groups will attack the primary amine on proteins or nucleic acids, thus forming covalent linkages with the biomolecules. Proteins interacting with these covalently bound bio-

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Current Protocols in Protein Science

hydrophobic C16

hydrophilic

SiO2

C16

SiO2

SiO2

metal binding

anionic

metal ions metal metal

– CO2– – CO2–

cationic

– CO2–

–N+R 3 –N+R 3 + –N R 3

Figure 3.9.1 Types of ProteinChip array surfaces. From top to bottom, chromatographic surfaces include hydrophobic surfaces (C16 aliphatic hydrocarbon chain), hydrophilic surfaces (silicon dioxide), metal binding surfaces, anionic (carboxylate) surfaces, and cationic surfaces (quaternary amine).

molecules are pulled down and nonspecific binding proteins are removed with a washing step. Molecular masses of interacting proteins are detected by time-of-flight mass spectrometry. Applications of this technology include: biomarker discovery, protein-protein interactions, on-chip protein modification analysis, epitope mapping, tryptic peptide mapping, and identification of proteins, to name just few (for review see Merchant and Weinberger, 2000). Additional information can be obtained at http://www.ciphergen.com. OTHER PROTEIN ARRAY TECHNOLOGIES Glass surface immobilized with multiple proteins A glass surface is preferred over nitrocellulose or PVDF membrane (see below), as it has greater durability and lower intrinsic fluorescence, producing an increased signal/background ratio; however, choice of technology will depend on a balance of effort, quality, and price. Fluorescence is also more sensitive and gives a more discrete signal than that generated by enzyme-amplified chemiluminescence. Depending on the fabrication process, there are two categories of glass-surface protein microarray. The first category is a glass plate in a 96-well format formed by an enclosing Teflon mask. Each well contains four identical sets of 6 × 6 arrays (144 elements per well). The individual array element has a diameter of ∼275 µm, with a center-to-center spacing of 300 µm. The glass surface

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Supplement 26

is activated sequentially by aminopropyltrimethoxysilane and N-hydroxysuccinimide. Protein or antigens are printed onto the activated glass surface by a high-speed printing robot. Specific antibodies conjugated with biotin are added to each well to detect the presence of the target protein. Streptavidin–alkaline phosphatase in the presence of alkaline phosphatase substrate generates a detectable signal. Arrays are quantified using a high-resolution scanning CCD detector. This kind of protein microarray demonstrates simultaneous detection of eight different antigens and a marker protein at concentrations higher than 6.25 µg/ml (Mendoza et al., 1999). Another category of protein microarray is essentially a comparative fluorescence assay on a microscopic slide. Hundreds of specific antibodies or antigens are printed on the surface of derivatized microscopic slides. Protein solutions to be measured are labeled separately by covalent linkage of different fluorescent dyes to the amine group on the proteins. Two differentially labeled protein solutions are mixed together and then incubated with the array, so that the fluorescence ratio at each spot corresponds to the concentration ratio of each protein in the two solutions. Nonoverlapping excitation and emission spectra give the ability to detect and measure interactions simultaneously. Experimentally, 115 pairs of antibody/antigen in an array of approximately 1100 spots were studied, and target protein as low as 1 ng/ml was successfully detected using a fluorescence scanner (Haab et al., 2000; MacBeath and Schreiber, 2000). Membrane surface immobilized with multiple proteins Polyvinylidene difluoride (PVDF) or nitrocellulose filters are loaded with specific antigens (i.e., any protein for which a specific antibody exists) at 250 nmol using an automatic picking/spotting robot. A density of approximately 300 protein samples per square centimeter is spotted onto a membrane filter. The spacing between spots is 250 µm. Filters with immobilized antigens are incubated with monoclonal antibodies and followed by secondary antibodies. Positive reactions are detected as colored spots using a chemiluminescence method. Images are acquired with a cooled charge-coupled device (CCD) detector. Ninety-two different crude lysates of human fetal brain cDNA library are detected with a high level of reproducibility (Lueking et al., 1999). The detection limit is 10 fmol/µl. Rolling circle amplification technology The goal of this technology is to increase the sensitivity of the antigen-antibody assay using in situ amplification of DNA conjugated to the antibody so that a specific antigen or combination of antigens can be detected in complex biological mixtures (e.g., cell extracts, nuclear extracts, bodily fluids). In this technique, antigens are printed on microscopic glass slides or deposited on microtiter plates as described above, and an oligonucleotide primer is covalently attached to an antibody (DNA-antibody conjugate). After the binding of the DNA-antibody conjugate molecules to the specific antigen, amplification of DNA occurs in the presence of circular DNA, DNA polymerase, and nucleotide substrates. Amplification results in a long DNA molecule containing hundreds of copies of the circular DNA sequence that remain attached to the antibody. Detection of the amplified DNA can be carried out in a variety of ways, including direct incorporation of hapten-labeled or fluorescence labeled nucleotides, or by hybridization of fluorescence-labeled or enzymatically labeled complementary oligonucleotide probes (Lizardi et al., 1998; Schweitzer et al., 2000; Wiltshire et al., 2000). Tissue Arrays Solid-Phase Profiling of Proteins

Small tissue core biopsies are punched from selected regions of donor blocks. Tissue cores are transferred into defined array coordinates in the recipient block. Tissue microarrays

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consist of as many as 1000 cylindrical tissue biopsies from individual tumors. Sections of the microarrays provide targets for parallel in situ detection of DNA, RNA, and protein targets in each specimen. Consecutive sections allow the rapid analysis of hundreds of molecular markers in the same set of specimens (Moch et al., 1999). MAGIChips This type of biochip has been developed by Biochip Technology Center at Argonne National Laboratory (http://www.ipd.anl.gov/biotech/programs/biochip) and is based on micro-acrylamide blocks. Protein (or DNA) of interest is immobilized in these blocks, divided by hydrophobic spacers. In this way, each block represents an individual protein and can be manipulated independently of a large number of other individual proteins on the same chip, or the whole array may be manipulated in the same way. The main advantage of this type of array is three-dimensional gel support for individual protein (or DNA) samples, which increases greatly the capacity for immobilized molecules. Two recent publications describe in detail the procedure for preparation of such arrays (Vasiliskov et al., 1999; Arenkov et al., 2000) and present the results of immuno- and enzymatic assays for individual proteins on these chips (Arenkov et al., 2000). This group has developed quantitative fluorescence measurement and color analysis for data collection. MS analysis is under development (Stomakhin et al., 2000). These arrays can be used for protein-protein interaction screening and functional characterization of proteins; however, the disadvantage of this approach at the present stage of development, as well as of some of the other arrays mentioned above, is the need for purified proteins to be immobilized in gel pads. PROfusion technology and HIP chips General information about this very promising technology from Phylos can be found on the company web site (http://www.phylos.com). It is based on RNA-protein fusions where a covalent link is formed between the C-terminus of the protein and the 3′ end of the mRNA that encodes it. Initially mRNA molecules carrying puromycin at the 3′ end are produced. When the ribosome reaches the 3′ end of mRNA during in vitro translation reaction, puromycin enters the peptidyl transferase site and forms a covalent mRNA-protein fusion. A light-induced psoralen cross-linking reaction to attach a puromycin containing nucleotide to the 3′ end of mRNA has been recently described (Kurz et al., 2000). This approach can be used to produce natural and artificial mRNA-protein fusion libraries. At the same time, chemical synthesis and mutagenesis are means to produce such libraries with extremely high complexities (i.e., up to 1 × 1013). Such a library printed on the chip in an addressable manner can be screened for proteins or peptides with the preferred function. The presence of mRNA on one end of the fusion permits amplification and identification of the desired target. Drug target screenings and specific protein-protein interactions are obvious applications for this type of technology. One can envision several other applications for libraries with such enormous diversity. This approach has been extensively reviewed by Roberts (1999). A detailed description of the technology (Liu et al., 2000) has also been published. STRATEGIC PLANNING Success in protein profiling for biomarker discovery requires a consideration of sample components and complexity, the surface chemistry of the ProteinChip arrays, and binding/washing conditions. Different samples may require different preparation techniques before addition to the ProteinChip array. In general, samples can be divided into two major groups: tissue and cells that require protein extraction, pretreatment, and/or fractionation (e.g., bacteria, yeast, laser capture microdissection–procured cells, cultured cells, tissue,

Detection and Assay Methods

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plant), and fluids, either biological (e.g., serum, urine, cerebral spinal fluid, saliva) or other (e.g., cell culture medium, broth), that require pretreatment and/or fractionation. The choice between protein extraction or prefractionation is defined by the goal of the experiment. Here it should be mentioned that most conventionally used protein extraction methods are compatible with ProteinChip arrays; however, some modifications of the extraction solutions might be needed to improve the detection of proteins in final extracts. Preference should be given to nonionic detergents—e.g., Triton X-100, NP-40, n-octyl β-D-glucopyranoside (OGP)—over ionic detergents (e.g., SDS), and 2-mercaptoethanol is preferred over dithiothreitol (DTT). The use of solutions containing diethylpyrocarbonate should be avoided. A full but very short list of compounds that may interfere with protein detection on ProteinChip arrays can be found in the ProteinChip Users Guide supplied with a system. Choosing the proper control samples is extremely important for correct data interpretation and evaluation of protein profiling results. As far as protein profiling is concerned, controls are especially important for comparisons of normal versus diseased samples. A diseased state is usually accompanied by changes in protein profiles, which might not be specific for the given disease, but rather reflect the general reaction of an organism (e.g., in the case of serum, urine, cerebrospinal fluid), or the tissue microenvironment, to a harmful agent or process (e.g., laser capture microdissected samples, tumor tissue extracts); therefore, control samples should include not only adequate normal extracts, but also extracts from patients with an unrelated disease to discriminate between specific and general disease markers. BASIC PROTOCOL 1

Solid-Phase Profiling of Proteins

SERUM PROTEIN PROFILING USING PROTEINCHIP ARRAY Protein profiling uses different ProteinChip arrays and binding/washing conditions to capture different subsets of proteins from complex biological samples for comparison. Please note that, as described above (see Strategic Planning), it may be important to pretreat or fractionate samples before protein profiling to remove major proteins from samples so the minor ones can be detected (e.g., removing albumin from human serum), to fractionate complex samples into subfractions based on mass or charge in order to detect more peaks (especially high-mass proteins), and to decrease signal suppression. In addition, the proteins must be extracted and solubilized, and the samples must be prepared so that they are compatible with the chip chemistry. Finally, the samples must be diluted into the appropriate binding buffer. Materials 8 M electrophoresis grade urea (Sigma) in 1% CHAPS/1× phosphate buffered saline (PBS), pH 7.2 to 7.4 (APPENDIX 2E): prepare fresh or use aliquot stored at −70°C (do not freeze and thaw the buffer more than once) Serum sample, human Cibacron blue 3GA immobilized on 4% beaded agarose Type 3000 (Sigma) 1× PBS, pH 7.2 (APPENDIX 2E) 1 M urea/0.125% 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS)/1× PBS (APPENDIX 2E): prepare fresh or use aliquot stored at −70°C (do not freeze and thaw the buffer more than once) Buffers: pH 4: 50 mM sodium acetate: adjust pH with acetic acid; store up to 3 months at room temperature pH 6: 50 mM sodium phosphate: adjust pH with 1/7.3 (v/v) 50 mM Na2HPO4/50 mM NaH2PO4; store up to 3 months at room temperature

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pH 8: 50 mM Tris⋅HCl: adjust pH with HCl; store up to 3 months at room temperature pH 10: 100 mM 3-cyclohexamino-1-propanesulfonic acid (CAPS): adjust pH according to manufacturers’ recommendations; store up to 3 months at room temperature Saturated sinapinic acid (SPA) in 0.5% trifluoroacetic acid (TFA)/50% acetonitrile; prepare fresh daily Spin column with 0.45-µm cellulose acetate micro-spin filter (E & K Scientific) SAX2, WCX2 ProteinChip Arrays (Ciphergen Biosystems) 8-well bioprocessor (Ciphergen Biosystems) Adhesive tape Pap pen (i.e., a hydrophobic pen; Zymed) PBSII chip reader (Ciphergen Biosystems) NOTE: HPLC-quality reagents are recommended for preparation of all buffers. Dilute the sample 1. In an appropriate container, add 60 µl 8 M urea buffer to each of two 40-µl human serum samples (samples I and II; see Table 3.9.2) to be compared. 2. Vortex 10 min at 4°C. An automatic vortexer is useful for this purpose.

Prepare filter 3. Wash Cibacron blue 3GA beads (i.e., Cibacron blue 3GA immobilized on 4% beaded agarose Type 3000) five times with 1× PBS, pH 7.2. Adjust to 50% (v/v) beads/buffer with 1× PBS, pH 7.2. The Cibacron blue 3GA beads are shipped and stored in 0.5 M NaCl and 0.02% thimerosal and are used to remove albumin.

4. Add 150 µl of 50% (v/v) bead suspension into a spin column with a 0.45-µm cellulose acetate micro-spin filter. 5. Equilibrate bead suspension by adding 300 µl 1 M urea buffer and microcentrifuging to dryness at 1000 × g for 30 sec. Repeat two more times. Filter sample 6. Add 100 µl diluted serum sample (from step 2) to the filter. 7. Wash the original serum tube with 100 µl 1 M urea/0.125% CHAPS/1× PBS. Add the wash to the micro-spin filter. 8. Put the filter into a fresh collection tube. Vortex at 4°C for 15 min. 9. Microcentrifuge the filter at 1000 × g for 30 sec. Save the filtrate in the collection tube. 10. Add 100 µl of 1 M urea/0.125% CHAPS/1× PBS buffer to the column. 11. Put the filter into a fresh collection tube. Vortex 15 min at 4°C. 12. Put the filter back into the first collection tube and microcentrifuge at 1000 × g for 30 sec. The volume should be ∼300 ìl.

Load samples on the ProteinChip arrays 13. Put SAX2 (strong anionic exchange) and WCX2 (weak cationic exchange) ProteinChip arrays into an 8-well bioprocessor. Attach the top securely. 14. To each well of the arrays, add 125 µl appropriate buffer according to the profiling strategy detailed in Table 3.9.2. Seal wells with adhesive tape. Equilibrate by mixing

Detection and Assay Methods

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Table 3.9.2 ProteinChip Serum Profiling Strategy

Spot

Samplea

Buffer

A B C D E F G H

I II I II I II I II

pH 4 pH 4 pH 6 pH 6 pH 8 pH 8 pH 10 pH 10

aSamples I and II might be any two samples used for

comparison (e.g., diseased versus normal, drug treated versus control).

at 250 rpm on a platform shaker for 5 min at room temperature. Discard the wash and repeat once with fresh buffer. 15. Dilute serum samples 1:5 with appropriate pH buffer (Table 3.9.2) yielding a final protein concentration of ∼0.5 to 1 mg/ml. For example, for a pH 4 spot, dilute the serum sample with pH 4 buffer.

16. Apply 50 µl diluted serum sample to each appropriate spot. Seal the wells with adhesive tape and mix at 250 rpm on a platform shaker for 20 min at room temperature. 17. Remove the samples from both SAX2 and WCX2 chips by aspiration. Remove unbound protein 18. Wash the chips three times, each time for 5 min, with 125 µl appropriate pH buffer (Table 3.9.2) on a platform shaker at 250 rpm. 19. Wash the chips two times with water by filling and emptying the bioprocessor wells. 20. Remove the chips from the bioprocessor and air-dry. Outline the spots with a Pap Pen. Analyze ProteinChip arrays 21. Add 0.5 µl saturated sinapinic acid (SPA) in 0.5% trifluoroacetic acid (TFA)/50% acetonitrile twice per spot, air drying between additions. 22. Air dry and read the ProteinChip arrays in a PBSII ProteinChip reader (i.e., a time-of-flight mass spectrometer specifically designed to handle ProteinChip arrays). A typical result of serum profiling using a WCX chip with four pH buffer conditions is shown in Fig. 3.9.2. The difference (indicated by arrow) of the two serum samples (with or without spiked known protein) can be clearly seen in pH 6 (spectrum C versus D) and pH 8 (spectrum E versus F) buffers.

Solid-Phase Profiling of Proteins

Other cell lysis and profiling protocols can be found in Wright et al. (2000) and von Eggeling et al. (2000). Figure 3.9.3 is an example of protein profiles from crude rat liver lysate that were obtained using three different ProteinChip arrays and wash conditions (hydrophobic surface H4 chip with 10% acetonitrile, strong anionic exchange SAX chip with pH 8.5 buffer wash, and weak cationic exchange WCX chip with pH 4.5 buffer wash). Each set of conditions was able to generate a different protein profile (e.g., H4 with 10% acetonitrile wash).

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A 75 50 25 0

B

75 50 25

C

0 75 50 25

Intensity (relative units)

D

0 75 50 25

E

0 75 50 25

F

0 75 50 25

G

0 75 50 25

H

0 75 50 25 0 1,000

12,500

15,000

17,500

Mass/charge

Figure 3.9.2 Protein profiles of serum using WCX ProteinChip array and four different pH buffer conditions. Spectrum (A), (C), (E), and (G) are serum spiked with a known protein and spectrum (B), (D), (F), and (H) are serum without the spiked protein. The difference (indicated by the arrow) of the two samples can be clearly seen in pH 6 (spectrum C versus D) and pH 8 (spectrum E versus F) buffer conditions. Detection and Assay Methods

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H4 (10% ACN wash)

20 15 10 5

SAX (pH 8.5 wash)

60 40 20 0 WCX (pH 4.5 wash)

Intensity (relative units)

0

10

5

0 5,000

10,000

15,000

Mass/charge Figure 3.9.3 Protein profiles of crude rat liver lysate in three different ProteinChip arrays and wash conditions. The upper panel depicts hydrophobic surface H4 chip using a 10% acetonitrile wash. The middle panel is a strong anionic exchanger surface (SAX) chip with a pH 8.5 buffer wash. The lower panel is a weak cationic exchange (WCX) chip with a pH 4.5 buffer wash. Arrows indicate the most prominent proteins retained on one surface but not the other.

BASIC PROTOCOL 2

PROTEIN-PROTEIN INTERACTION ANALYSIS USING PROTEINCHIP ARRAYS Protein-protein interaction studies use PS1 or PS2 ProteinChip arrays, both with preactivated surfaces. Both covalently bind any protein of interest to the surface; however, due to the different chemistry used to produce them, they have different properties. The PS-1 surface has a higher capacity, but at the same time, higher nonspecific background binding due to the hydrophobicity of the proteins from cell or nuclear lysates. The PS-2 surface binds less of the protein of interest and, as a consequence, less of the interacting partner; however, nonspecific background binding is greatly diminished as compared to PS1. Most often, the amounts of possible interacting partners in cell lysates are not known; therefore at the beginning of a study one should use both to see which chip gives better results.

Solid-Phase Profiling of Proteins

An absolute requirement for this type of experiment is that one of the interacting partners (i.e., the one to be bound) be purified or at least semipurified by conventional methods. The final preparation should not contain reagents such as Tris, glycine, or sodium azide, as these substances interfere with covalent binding to the chip surface.

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The protocol described below has been used to prove binding specificity of the orphan ligand growth factor pleiotrophin to the orphan receptor anaplastic lymphoma kinase (ALK; Stoica et al., 2001). Materials 1× phosphate buffered saline (PBS), pH 7.2 (APPENDIX 2E) 1 M ethanolamine, pH 8.2 1× PBS, pH 7.2/0.5% and 0.1% Triton X-100 5 M NaCl (APPENDIX 2E) 25% (v/v) ethylene glycol (PS1 array) Cell extract, lysate, or conditioned media (≥1 mg/ml total protein) with or without a molar excess of either the purified protein of interest or a control protein in 1× PBS, pH 7.2 Saturated α-cyano-4-hydroxycinnamic acid (CHCA) or sinapinic acid (SPA) in 0.5% TFA/50% acetonitrile PS1 or PS2 ProteinChip array (Ciphergen Biosystems) PAP pen (PS1 array) Humidified chamber: empty pipet-tip box with damp filter paper fixed on top of the stand 15-ml plastic screw-cap centrifuge tube (Corning) Platform rocker PBSII chip reader (Ciphergen Biosystems) Add probe to the ProteinChip arrays 1. For PS1 ProteinChip array: Outline each spot on a PS1 ProteinChip array with a Pap (hydrophobic) pen and air dry. The PS2 chip does not need this treatment as it has a hydrophobic coating.

2. Prewet each spot with 3 µl of 1× phosphate buffered saline (PBS), pH 7.2, for 5 min in a humidified chamber. 3. Remove PBS from the spots and replace with 1 to 2 µl purified protein of interest or control protein. 4. Place the chip into a humidified chamber and incubate 1 hr at room temperature or overnight at 4°C. The latter is preferable as it ensures complete saturation.

5. Remove the chip from the humidified chamber and remove protein solution from spots by aspiration with a micropipettor. Inactivate surface and remove unbound protein 6. Add 5 µl of 1 M ethanolamine, pH 8.2 per spot, and incubate in a humidified chamber for 30 min at room temperature. 7. Remove ethanolamine solution by aspiration with a micropipettor and wash each spot twice with 1× PBS, pH 7.2/0.5% Triton X-100. IMPORTANT NOTE: Perform this step spot-by-spot without allowing spots to become dry at any time.

8. Place the chip into a 15-ml plastic screw-cap centrifuge tube and add 10 ml 1× PBS, pH 7.2/0.5% Triton X-100. Place the tube on a platform rocker and wash for 10 to 15 min. Repeat the wash one more time. Detection and Assay Methods

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A

2

15868.5+

1

Intensity (relative units)

0

B

2 15874.4+ 1 0

C

2 1 15849.7+ 0

D

2 1

15852.5+

0 25,000

50,000

75,000

100,000

Mass/charge

Figure 3.9.4 (A) Profile of PTN fraction purified on heparin-Sepharose using a normal phase chip (NP-2). (B to D) ALK ECD covalently bound to active spots on preactivated surface (PS1) chip and overlaid with: (B) PTN, (C) PTN+ALK ECD ∼1/1.66 molar ratio, and (D) PTN+TGFβ receptor ∼1/1.7 molar ratio.

9. Transfer the chip into a fresh 15-ml plastic screw-cap centrifuge tube. Add 10 ml 1× PBS, pH 7.2/0.1% Triton X-100 and incubate 5 min. Repeat wash one more time on a platform rocker. Add sample to the ProteinChip arrays 10. Adjust the salt concentration of cell extracts or lysates to 0.15 to 0.2 M NaCl/0.1% Triton X-100 by diluting with the appropriate buffer or adding 5 M NaCl. 11. Remove the chip from the centrifuge tube. To each spot, add 3 to 5 µl cell extract, lysate, or conditioned medium (≥1 mg/ml total protein) with or without a molar excess of either the purified protein of interest or a control protein in 1× PBS, pH 7.2, relative to the protein covalently bound to the surface. 12. Incu