J. Rigden From Protein Structure to Function with Bioinformatics

2. Auflage 2017
ISBN: 978-94-024-1069-3
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 503 Seiten

Reihe: Biomedical and Life Sciences (R0)

This book is about protein structural bioinformatics and how it can help understand and predict protein function. It covers structure-based methods that can assign and explain protein function based on overall folds, characteristics of protein surfaces, occurrence of small 3D motifs, protein-protein interactions and on dynamic properties. Such methods help extract maximum value from new experimental structures, but can often be applied to protein models. The book also, therefore, provides comprehensive coverage of methods for predicting or inferring protein structure, covering all structural classes from globular proteins and their membrane-resident counterparts to amyloid structures and intrinsically disordered proteins.

The book is split into two broad sections, the first covering methods to generate or infer protein structure, the second dealing with structure-based function annotation. Each chapter is written by world experts in the field. The first section covers methods ranging from traditional homology modelling and fold recognition to fragment-based ab initio methods, and includes a chapter, new for the second edition, on structure prediction using evolutionary covariance. Membrane proteins and intrinsically disordered proteins are each assigned chapters, while two new chapters deal with amyloid structures and means to predict modes of protein-protein interaction. The second section includes chapters covering functional diversity within protein folds and means to assign function based on surface properties and recurring motifs. Further chapters cover the key roles of protein dynamics in protein function and use of automated servers for function inference. The book concludes with two chapters covering case studies of structure prediction, based respectively on crystal structures and protein models, providing numerous examples of real-world usage of the methods mentioned previously.

This book is targeted at postgraduate students and academic researchers. It is most obviously of interest to protein bioinformaticians and structural biologists, but should also serve as a guide to biologists more broadly by highlighting the insights that structural bioinformatics can provide into proteins of their interest.

Autoren/Hrsg.

J. Rigden, Daniel

Inhaltsverzeichnis

1;Preface to the Second Edition;5
1.1;References;6
2;Contents;7
3;Generating and Inferring Structures;16
4;1 Ab Initio Protein Structure Prediction;17
4.1;Abstract;17
4.2;1.1 Introduction;18
4.3;1.2 Energy Functions;19
4.3.1;1.2.1 Physics-Based Energy Functions;21
4.3.2;1.2.2 Knowledge-Based Energy Function Combined with Fragments;25
4.4;1.3 Conformational Search Methods;32
4.4.1;1.3.1 Monte Carlo Simulations;32
4.4.2;1.3.2 Molecular Dynamics;33
4.4.3;1.3.3 Genetic Algorithm;34
4.4.4;1.3.4 Mathematical Optimization;35
4.5;1.4 Model Selection;35
4.5.1;1.4.1 Physics-Based Energy Function;36
4.5.2;1.4.2 Knowledge-Based Energy Function;37
4.5.3;1.4.3 Sequence-Structure Compatibility Function;38
4.5.4;1.4.4 Clustering of Decoy Structures;39
4.6;1.5 Remarks and Discussions;39
4.7;Acknowledgements;41
4.8;References;41
5;2 Protein Structures, Interactions and Function from Evolutionary Couplings;50
5.1;Abstract;50
5.2;2.1 Introduction;51
5.3;2.2 Evolutionary Couplings from Sequence Alignments;55
5.3.1;2.2.1 The Global Model;55
5.4;2.3 Three-Dimensional Protein Structures from Evolutionary Couplings;59
5.4.1;2.3.1 Transmembrane Proteins;61
5.4.2;2.3.2 Protein Interactions and Complexes;62
5.4.3;2.3.3 Conformational Plasticity and Disordered Proteins;64
5.5;2.4 Predicting the Effect of Mutations;65
5.6;2.5 Summary and Future Challenges;67
5.7;References;68
6;3 Fold Recognition;72
6.1;Abstract;72
6.2;3.1 Introduction;72
6.2.1;3.1.1 The Importance of Blind Trials: The CASP Competition;73
6.2.2;3.1.2 Ab Initio Structure Prediction Versus Homology Modelling;73
6.2.3;3.1.3 The Limits of Fold Space;75
6.3;3.2 Pushing Sequence Similarity to the Limits: The Power of Evolutionary Information;77
6.3.1;3.2.1 The Rise of Hidden Markov Models;80
6.3.2;3.2.2 Using Predicted Structural Features;81
6.3.3;3.2.3 Harnessing 3D Structure to Enhance Recognition;83
6.3.4;3.2.4 Knowledge-Based Potentials;83
6.3.5;3.2.5 Summary;85
6.4;3.3 CASP: The Great Filter;85
6.4.1;3.3.1 The Leaders;86
6.4.2;3.3.2 Individual Algorithms;86
6.4.3;3.3.3 Consensus Methods;88
6.5;3.4 Post-processing;89
6.5.1;3.4.1 Choosing and Combining Candidate Models;89
6.5.1.1;3.4.1.1 Clustering;90
6.5.1.2;3.4.1.2 Model Quality Assessment Programs (MQAPs);90
6.5.1.3;3.4.1.3 Combining Models Optimally—Multiple Template Modelling;92
6.5.2;3.4.2 Post-processing in Practice;92
6.5.3;3.4.3 Use of Contacts;95
6.5.3.1;3.4.3.1 From Sequence to Profiles to Contact Maps;97
6.6;3.5 Tools for Fold Recognition on the Web;98
6.7;3.6 The Future;99
6.8;References;101
7;4 Comparative Protein Structure Modelling;104
7.1;Abstract;104
7.2;4.1 Introduction;104
7.2.1;4.1.1 Structure Determines Function;104
7.2.2;4.1.2 Sequences, Structures, Structural Genomics;105
7.2.3;4.1.3 Approaches to Protein Structure Prediction;107
7.3;4.2 Steps in Comparative Protein Structure Modelling;109
7.3.1;4.2.1 Searching for Structures Related to the Target Sequence;111
7.3.2;4.2.2 Selecting Templates;113
7.3.3;4.2.3 Sequence to Structure Alignment;115
7.3.4;4.2.4 Model Building;116
7.3.4.1;4.2.4.1 Template Dependent Modelling;116
7.3.4.2;4.2.4.2 Template Independent Modelling: Modelling Loops, Insertions;119
7.3.4.3;4.2.4.3 Refining Models;123
7.3.4.4;4.2.4.4 Hybrid Modelling of Proteins and Complexes with Experimental Restraints;124
7.3.5;4.2.5 Model Evaluation;127
7.4;4.3 Performance of Comparative Modelling;129
7.4.1;4.3.1 Accuracy of Methods;129
7.4.2;4.3.2 Errors in Comparative Models;130
7.5;4.4 Applications of Comparative Modelling;132
7.5.1;4.4.1 Modelling of Individual Proteins;132
7.5.2;4.4.2 Comparative Modelling and the Protein Structure Initiative;132
7.6;4.5 Summary;133
7.7;References;134
8;5 Advances in Computational Methods for Transmembrane Protein Structure Prediction;148
8.1;Abstract;148
8.2;5.1 Introduction;149
8.3;5.2 Membrane Protein Structural Classes;149
8.3.1;5.2.1 ?-Helical Bundles;150
8.3.2;5.2.2 Transmembrane ?-Barrels;150
8.4;5.3 Databases;152
8.5;5.4 Multiple Sequence Alignments;153
8.6;5.5 Transmembrane Protein Topology Prediction;154
8.6.1;5.5.1 Early ?-Helical Topology Prediction Approaches;155
8.6.2;5.5.2 Machine Learning Approaches for ?-Helical Topology Prediction;155
8.6.3;5.5.3 Signal Peptides and Re-entrant Helices;157
8.6.4;5.5.4 Consensus Approaches for ?-Helical Topology Prediction;158
8.6.5;5.5.5 Transmembrane ?-Barrel Topology Prediction;159
8.6.6;5.5.6 Empirical Approaches for ?-Barrel Topology Prediction;160
8.6.7;5.5.7 Machine Learning Approaches for ?-Barrel Topology Prediction;161
8.6.8;5.5.8 Consensus Approaches for ?-Barrel Topology Prediction;162
8.7;5.6 3D Structure Prediction;163
8.7.1;5.6.1 Homology Modelling of ?-Helical Transmembrane Proteins;163
8.7.2;5.6.2 Homology Modelling of Transmembrane ?-Barrel Proteins;164
8.7.3;5.6.3 De Novo Modelling of ?-Helical Transmembrane Proteins;165
8.7.4;5.6.4 De Novo Modelling of Transmembrane ?-Barrels;167
8.7.5;5.6.5 Covariation-Based Approaches;167
8.7.6;5.6.6 Evolutionary Covariation-Based Methods for De Novo Modelling of ?-Helical Membrane Proteins;168
8.7.7;5.6.7 Evolutionary Covariation-Based Methods for Transmembrane ?-Barrel Structure Prediction;170
8.8;5.7 Future Directions;171
8.9;Competing Interests;171
8.10;References;171
9;6 Bioinformatics Approaches to the Structure and Function of Intrinsically Disordered Proteins;179
9.1;Abstract;179
9.2;6.1 The Concept of Protein Disorder;180
9.3;6.2 Sequence Features of IDPs;181
9.3.1;6.2.1 The Unusual Amino Acid Composition of IDPs;181
9.3.2;6.2.2 Low Sequence Complexity and Disorder;181
9.3.3;6.2.3 Flavours of Disorder;182
9.4;6.3 Prediction of Disorder;183
9.4.1;6.3.1 Charge-Hydropathyhydrophobicity Plot;183
9.4.2;6.3.2 Propensity-Based Predictors;183
9.4.3;6.3.3 Prediction Based on Simplified Biophysical Models;186
9.4.4;6.3.4 Machine Learning Algorithms;187
9.4.5;6.3.5 Related Approaches for the Prediction of Protein Disorder;189
9.4.6;6.3.6 Comparison of Disorder Prediction Methods;190
9.5;6.4 Databases of IDPs;191
9.6;6.5 Structural Features of IDPs;192
9.7;6.6 Functional Classification of IDPs;193
9.7.1;6.6.1 Gene Ontology-Based Functional Classification of IDPs;194
9.7.2;6.6.2 Classification of IDPs Based on Their Mechanism of Action;195
9.7.2.1;6.6.2.1 Entropic Chains;196
9.7.2.2;6.6.2.2 Function by Transient Binding;196
9.7.2.3;6.6.2.3 Functions by Permanent Binding;197
9.7.3;6.6.3 Functional Features of IDPs;197
9.7.3.1;6.6.3.1 Short Linear motifs;198
9.7.3.2;6.6.3.2 Disordered Binding Regions/Molecular Recognition Features;199
9.7.3.3;6.6.3.3 Intrinsically Disordered Domains;199
9.8;6.7 Prediction of the Function of IDPs;200
9.8.1;6.7.1 Predicting Short Recognition Motifs in IDRs;202
9.8.2;6.7.2 Prediction of Disordered Binding Regions/MoRFs;203
9.8.3;6.7.3 Combination of Information on Sequence and Disorder: Phosphorylation Sites and CaM Binding Motifs;204
9.8.4;6.7.4 Correlation of Disorder Pattern and Function;205
9.9;6.8 Evolution of IDPs;206
9.10;6.9 Conclusions;207
9.11;Acknowledgements;207
9.12;References;207
10;7 Prediction of Protein Aggregation and Amyloid Formation;216
10.1;Abstract;216
10.2;7.1 Introduction;217
10.3;7.2 The Physico-chemical and Structural Basis of Protein Aggregation;217
10.3.1;7.2.1 Intrinsic Determinants of Protein Aggregation;224
10.3.2;7.2.2 Extrinsic Determinants of Protein Aggregation;225
10.3.3;7.2.3 Specific Sequence Stretches Drive Aggregation;225
10.3.4;7.2.4 Structural Determinants of Amyloid-like Aggregation;226
10.4;7.3 Prediction of Protein Aggregation from the Primary Sequence;227
10.4.1;7.3.1 Phenomenological Approaches;232
10.4.2;7.3.2 Structure-Based Approaches;236
10.4.3;7.3.3 Consensus Methods;241
10.4.4;7.3.4 Applications of Sequence-Based Predictors;243
10.4.4.1;7.3.4.1 Proteome-Wide Analyses;243
10.4.4.2;7.3.4.2 Prediction of in vivo Protein Aggregation;252
10.5;7.4 Prediction of Aggregation Propensity from the Tertiary Structure;253
10.6;7.5 Concluding Remarks;264
10.7;References;265
11;8 Prediction of Biomolecular Complexes;275
11.1;Abstract;275
11.2;8.1 Introduction;276
11.3;8.2 Docking;278
11.3.1;8.2.1 Step 1: Searching;279
11.3.2;8.2.2 Step 2: Scoring;280
11.3.3;8.2.3 Data-Driven Docking;284
11.4;8.3 The Challenges of Docking: Flexibility and Binding Affinity;285
11.4.1;8.3.1 Changes upon Binding: The Flexible Docking Challenge;285
11.4.2;8.3.2 The ‘Perfect’ Scoring Function and the Binding Affinity Problem;286
11.5;8.4 Protein-Peptide Docking;288
11.6;8.5 Post-docking: Interface Prediction from Docking Results and Use of Docking-Derived Contacts for Clustering and Ranking;289
11.6.1;8.5.1 Web Tools for the Post-docking Processing;291
11.7;8.6 Concluding Remarks;293
11.8;Acknowledgements;293
11.9;References;294
12;From Structures to Functions;303
13;9 Function Diversity Within Folds and Superfamilies;304
13.1;Abstract;304
13.2;9.1 Defining Function;305
13.3;9.2 From Fold to Function;306
13.3.1;9.2.1 Definition of a Fold;306
13.3.1.1;9.2.1.1 General Understanding;306
13.3.1.2;9.2.1.2 Practical Definitions;307
13.3.1.3;9.2.1.3 Paradigm Shift;308
13.3.2;9.2.2 Prediction of Function Using Fold Relationships;309
13.3.2.1;9.2.2.1 Folds with a Single Function;309
13.3.2.2;9.2.2.2 Supersites;310
13.3.2.3;9.2.2.3 Superfolds;312
13.4;9.3 Function Diversity Between Homologous Proteins;312
13.4.1;9.3.1 Definitions;312
13.4.1.1;9.3.1.1 General Understanding;312
13.4.1.2;9.3.1.2 Practical Definitions;313
13.4.2;9.3.2 Evolution of Protein Superfamilies;316
13.4.3;9.3.3 Function Divergence During Protein Evolution;317
13.4.3.1;9.3.3.1 Function Diversity at the Superfamily Level;318
13.4.3.2;9.3.3.2 Function Diversity Between Close Homologues;324
13.5;9.4 Conclusion;329
13.6;Bibliography;329
14;10 Function Prediction Using Patches, Pockets and Other Surface Properties;335
14.1;Abstract;335
14.2;10.1 Definitions of Protein Surfaces;336
14.3;10.2 Surface Patches;337
14.3.1;10.2.1 Hydrophobic Patches;337
14.3.2;10.2.2 Electrostatics;344
14.3.3;10.2.3 Sequence Conservation;346
14.3.4;10.2.4 Surface Atom Triplet Propensities;347
14.3.5;10.2.5 Multiple Properties;348
14.4;10.3 Pockets;348
14.4.1;10.3.1 Geometric Descriptions of Pockets;350
14.4.2;10.3.2 Channels and Tunnels;351
14.4.3;10.3.3 Distinguishing Functional Pockets;352
14.4.4;10.3.4 Predicting Ligands for Pockets;353
14.4.4.1;10.3.4.1 Pocket Matching;353
14.4.4.2;10.3.4.2 Docking for Function Prediction;354
14.5;10.4 Prediction of Catalytic Residues;355
14.6;10.5 Protein-Protein Interfaces;357
14.7;10.6 Other Specialised Binding Site Predictors;358
14.8;10.7 Medicinal Applications;360
14.9;10.8 Conclusions;361
14.10;References;362
15;11 3D Motifs;369
15.1;Abstract;369
15.2;11.1 Background: Functional Annotation;370
15.2.1;11.1.1 What Is Function?;371
15.2.2;11.1.2 Genomics and Functional Annotation;371
15.2.3;11.1.3 The Need for Structure-Based Methods;373
15.3;11.2 3D Motif Matching Techniques;374
15.3.1;11.2.1 What Is a 3D Motif?;374
15.3.2;11.2.2 Historical Development of Motif Matching Methods;377
15.4;11.3 Algorithmic Approaches to Motif Matching;381
15.4.1;11.3.1 Methods Using 3D Motifs;382
15.4.2;11.3.2 Efficiency Considerations for 3D Motifs;383
15.4.3;11.3.3 Methods with Nonstandard Motif Information;384
15.4.4;11.3.4 Interpretation of Results;385
15.5;11.4 Methods for Deriving Motifs;386
15.5.1;11.4.1 Literature Search and Manual Curation;387
15.5.2;11.4.2 Annotated Sites in PDB Structures;387
15.5.3;11.4.3 Mining for Emergent Properties;388
15.5.3.1;11.4.3.1 Undirected Mining;388
15.5.3.2;11.4.3.2 Directed Mining;389
15.5.3.3;11.4.3.3 Directed Mining with Positive and Negative Examples;390
15.6;11.5 Molecular Docking for Functional Annotation;391
15.7;11.6 Discussion and Conclusions;393
15.8;Acknowledgements;393
15.9;References;394
16;12 Protein Dynamics: From Structure to Function;401
16.1;Abstract;401
16.2;12.1 Molecular Dynamics Simulations;401
16.2.1;12.1.1 Principles and Approximations;402
16.2.2;12.1.2 Applications;404
16.2.2.1;12.1.2.1 Nuclear Transport Receptors;405
16.2.2.2;12.1.2.2 Lysozyme;406
16.2.2.3;12.1.2.3 Aquaporins;408
16.2.3;12.1.3 Limitations—Enhanced Sampling Algorithms;410
16.2.3.1;12.1.3.1 Replica Exchange;411
16.3;12.2 Principal Component Analysis;414
16.4;12.3 Collective Coordinate Sampling Algorithms;417
16.4.1;12.3.1 Essential Dynamics;417
16.4.2;12.3.2 TEE-REX;418
16.4.2.1;12.3.2.1 Applications: Finding Transition Pathways in Adenylate Kinase;419
16.5;12.4 Methods for Functional Mode Prediction;421
16.5.1;12.4.1 Normal Mode Analysis;421
16.5.2;12.4.2 Elastic Network Models;422
16.5.3;12.4.3 CONCOORD;423
16.5.3.1;12.4.3.1 Applications;423
16.6;12.5 Summary and Outlook;427
16.7;References;428
17;13 Integrated Servers for Structure-Informed Function Prediction;434
17.1;Abstract;434
17.2;13.1 Introduction;434
17.2.1;13.1.1 The Problem of Predicting Function from Structure;435
17.2.2;13.1.2 Structure-Function Prediction Methods;437
17.3;13.2 ProKnow;438
17.3.1;13.2.1 Fold Matching;439
17.3.2;13.2.2 3D Motifs;441
17.3.3;13.2.3 Sequence Homology;441
17.3.4;13.2.4 Sequence Motifs;441
17.3.5;13.2.5 Protein Interactions;441
17.3.6;13.2.6 Combining the Predictions;442
17.3.7;13.2.7 Prediction Success;442
17.4;13.3 ProFunc;443
17.4.1;13.3.1 ProFunc’s Structure-Based Methods;444
17.4.1.1;13.3.1.1 Fold-Matching;444
17.4.1.2;13.3.1.2 Surface Clefts;445
17.4.1.3;13.3.1.3 Nests;445
17.4.1.4;13.3.1.4 Template Methods;446
17.4.1.5;13.3.1.5 PDBsum Structural Analyses;449
17.4.2;13.3.2 Assessment of the Structural Methods;449
17.5;13.4 Conclusion;451
17.6;Acknowledgements;451
17.7;References;452
18;14 Case Studies: Function Predictions of Structural Genomics Results;456
18.1;Abstract;456
18.2;14.1 Introduction;456
18.3;14.2 Function Prediction Case Studies;458
18.3.1;14.2.1 Teichman et al. (2001);458
18.3.2;14.2.2 Kim et al. (2003);458
18.3.3;14.2.3 Watson et al. (2007);460
18.3.4;14.2.4 Lee et al. (2011);463
18.4;14.3 Some Specific Examples;463
18.4.1;14.3.1 Adams et al. (2007);463
18.4.2;14.3.2 AF0491 Protein;464
18.4.3;14.3.3 The GxGYxYP Family;466
18.5;14.4 Community Annotation;467
18.6;14.5 Conclusions;468
18.7;Acknowledgements;469
18.8;References;469
19;15 Prediction of Protein Function from Theoretical Models;473
19.1;Abstract;473
19.2;15.1 Background;473
19.3;15.2 Suitability of Protein 3D Models for Structure-Based Predictions;475
19.3.1;15.2.1 Surface Properties;476
19.3.2;15.2.2 Functional Sites;478
19.3.3;15.2.3 Specific Binding Predictions;479
19.3.4;15.2.4 Small Molecule Binding;480
19.3.5;15.2.5 Protein-Protein Interactions;482
19.3.6;15.2.6 Protein Model Databases;483
19.4;15.3 Function Prediction Examples;484
19.4.1;15.3.1 Fold Prediction with Fragment-Based Ab Initio Models;484
19.4.2;15.3.2 Fold Prediction with Contact-Based Models;487
19.4.3;15.3.3 Plasticity of Catalytic Site Residues;489
19.4.4;15.3.4 Prediction of Ligand Specificity;490
19.4.5;15.3.5 Prediction of Cofactor Specificity Using an Entry from a Database of Models;491
19.4.6;15.3.6 Mutation Mapping;494
19.4.7;15.3.7 Protein Complexes;495
19.4.8;15.3.8 Structure Modelling of Alternatively Spliced Isoforms;496
19.4.9;15.3.9 From Broad Function to Molecular Details;497
19.5;15.4 Conclusions;499
19.6;References;499
20;Index;505