From Protein Structure to Function With Bioinformatics

by Rigden, Daniel John

• ISBN13: 9781402090578
• ISBN10: 1402090579
• Edition: 1st
• Format: Hardcover
• Copyright: 2009-05-01
• Publisher: SPRINGER

Summary

Proteins lie at the heart of almost all biological processes and have an incredibly wide range of activities. Central to the function of all proteins is their ability to adopt, stably or sometimes transiently, structures that allow for interaction with other molecules. An understanding of the structure of a protein can therefore lead us to a much improved picture of its molecular function. This realisation has been a prime motivation of recent Structural Genomics projects, involving large-scale experimental determination of protein structures, often those of proteins about which little is known of function. These initiatives have, in turn, stimulated the massive development of novel methods for prediction of protein function from structure. Since model structures may also take advantage of new function prediction algorithms, the first part of the book deals with the various ways in which protein structures may be predicted or inferred, including specific treatment of membrane and intrinsically disordered proteins. A detailed consideration of current structure-based function prediction methodologies forms the second part of this book, which concludes with two chapters, focusing specifically on case studies, designed to illustrate the real-world application of these methods. With bang up-to-date texts from world experts, and abundant links to publicly available resources, this book will be invaluable to anyone who studies proteins and the endlessly fascinating relationship between their structure and function.

Table of Contents

Generatin and Inferring Structures
Ab Initio Protein Structure Prediction	p. 3
Introduction	p. 3
Energy Functions	p. 5
Physics-Based Energy Functions	p. 5
Knowledge-Based Energy Function Combined with Fragments	p. 9
Conformational Search Methods	p. 13
Monte Carlo Simulations	p. 14
Molecular Dynamics	p. 15
Genetic Algorithm	p. 15
Mathematical Optimization	p. 16
Model Selection	p. 16
Physics-Based Energy Function	p. 17
Knowledge-Based Energy Function	p. 17
Sequence-Structure Compatibility Function	p. 18
Clustering of Decoy Structures	p. 19
Remarks and discussions	p. 19
Fold Recognition	p. 27
Introduction	p. 27
The Importance of Blind Trials: The CASP Competition	p. 28
Ab Initio Structure Prediction Versus Homology Modelling	p. 28
The Limits of Fold Space	p. 30
A Note on Terminology: 'Threading' and 'Fold Recognition'	p. 31
Threading	p. 31
Knowledge-Based Potentials	p. 32
Finding an Alignment	p. 34
Heuristics for Alignment	p. 35
Remote Homology Detection Without Threading	p. 38
Using Predicted Structural Features	p. 39
Sequence Profiles and Hidden Markov Models	p. 41
Fold Classification and Support Vector Machines	p. 43
Consensus Approaches	p. 45
Traversing the Homology Network	p. 45
Alignment Accuracy, Model Quality and Statistical Significance	p. 47
Algorithms for Alignment Generation and Assessment	p. 47
Estimation of Statistical Significance	p. 48
Tools for Fold Recognition on the Web	p. 49
The Future	p. 50
Comparative Protein Structure Modelling	p. 57
Introduction	p. 57
Structure Determines Function	p. 57
Sequences, Structures, Structural Genomics	p. 58
Approaches to Protein Structure Prediction	p. 58
Steps in Comparative Protein Structure Modelling	p. 60
Searching for Structures Related to the Target Sequence	p. 62
Selecting Templates	p. 64
Sequence to Structure Alignment	p. 65
Model Building	p. 67
Model Evaluation	p. 76
Performance of Comparative Modelling	p. 77
Accuracy of Methods	p. 77
Errors in Comparative Models	p. 78
Applications of Comparative Modelling	p. 80
Modelling of Individual Proteins	p. 80
Comparative Modelling and the Protein Structure Initiative	p. 80
Summary	p. 81
Membrane Protein Structure Prediction	p. 91
Introduction	p. 91
Structural Classes	p. 92
Alpha-Helical Bundles	p. 92
Beta-Barrels	p. 92
Membrane Proteins Are Difficult to Crystallise	p. 94
Databases	p. 94
Multiple Sequence Alignments	p. 96
Transmembrane Protein Topology Prediction	p. 98
Alpha-Helical Proteins	p. 98
Beta-Barrel Proteins	p. 102
Whole Genome Analysis	p. 102
Data Sets, Homology, Accuracy and Cross-Validation	p. 103
3D Structure Prediction	p. 105
Future Developments	p. 107
Bioinformatics Approaches to the Structure and Function of Intrinsically Disordered Proteins	p. 113
The Concept of Protein Disorder	p. 113
Sequence Features of IDPs	p. 115
The Unusual Amino Acid Composition of IDPs	p. 115
Sequence Patterns of IDPs	p. 115
Low Sequence Complexity and Disorder	p. 116
Prediction of Disorder	p. 116
Prediction of Low-Complexity Regions	p. 116
Charge-Hydropathy Plot	p. 117
Propensity-Based Predictors	p. 117
Predictors Based on the Lack of Secondary Structure	p. 118
Machine Learning Algorithms	p. 119
Prediction Based on Contact Potentials	p. 120
A Reduced Alphabet Suffices to Predict Disorder	p. 121
Comparison of Disorder Prediction Methods	p. 122
Functional classification of IDPs	p. 122
Gene Ontology-Based Functional Classification of IDPs	p. 122
Classification of IDPs Based on Their Mechanism of Action	p. 123
Function-Related Structural Elements in IDPs	p. 126
Prediction of the Function of IDPs	p. 128
Correlation of Disorder Pattern and Function	p. 128
Predicting Short Recognition Motifs in IDRs	p. 128
Prediction of MoRFs	p. 129
Combination of Information on Sequence and Disorder: Phosphorylation Sites and CaM Binding Motifs	p. 131
Flavours of Disorder	p. 131
Limitations of IDP Function Prediction	p. 132
Rapid Evolution of IDPs	p. 132
Sequence Independence of Function and Fuzziness	p. 133
Good News: Conservation and Disorder	p. 134
Conclusions	p. 135
From Structures to Functions
Function Diversity Within Folds and Superfamilies	p. 143
Defining Function	p. 143
From Fold to Function	p. 145
Definition of a Fold	p. 145
Prediction of Function Using Fold Relationships	p. 148
Function Diversity Between Homologous Proteins	p. 151
Definitions	p. 151
Evolution of Protein Superfamilies	p. 152
Function Divergence During Protein Evolution	p. 154
Conclusion	p. 162
Predicting Protein Function from Surface Properties	p. 167
Surface Descriptions	p. 167
The van Der Waals Surface	p. 167
Molecular Surface (Solvent Excluded Surface)	p. 168
The Solvent Accessible Surface	p. 168
Surface Properties	p. 169
Hydrophobicity	p. 169
Electrostatics Properties	p. 170
Surface Conservation	p. 171
Function Predictions Using Surface Properties	p. 171
Hydrophobic Surface	p. 172
Electrostatic Surface	p. 172
Surface Conservation	p. 173
Combining Surface Properties for Function Prediction	p. 174
Protein-Ligand Interactions	p. 174
Properties of Protein-Ligand Interactions	p. 174
Predicting Binding Site Locations	p. 175
Predictions of Druggability	p. 178
Annotation of Ligand Binding Sites	p. 178
Protein-Protein Interfaces	p. 180
Properties of Protein-Protein Interfaces	p. 180
Hot-Spot Regions in Protein Interfaces	p. 181
Predictions of Interface Location	p. 182
Summary	p. 184
3D Motifs	p. 187
Background and Significance	p. 188
What Is Function?	p. 189
Three-Dimensional Motifs: Definition and Scope	p. 190
Overview of Methods	p. 190
Motif Discovery	p. 190
Motif Description and Matching	p. 191
Interpretation of Results	p. 193
Specific Methods	p. 196
User-Defined Motifs	p. 197
Motif Discovery	p. 201
Related Methods	p. 208
Hybrid (Point-Surface) Descriptions	p. 208
Single-Point-Centred Descriptions	p. 208
Docking for Functional Annotation	p. 210
Discussion	p. 212
Conclusions	p. 212
Protein Dynamics: From Structure to Function	p. 217
Molecular Dynamics Simulations	p. 217
Principles and Approximations	p. 218
Applications	p. 220
Limitations - Enhanced Sampling Algorithms	p. 226
Principal Component Analysis	p. 230
Collective Coordinate Sampling Algorithms	p. 233
Essential Dynamics	p. 233
TEE-REX	p. 234
Methods for Functional Mode Prediction	p. 237
Normal Mode Analysis	p. 237
Elastic Network Models	p. 238
Concoord	p. 239
Summary and Outlook	p. 242
Integrated Servers for Structure-Informed Function Prediction	p. 251
Introduction	p. 251
The Problem of Predicting Function from Structure	p. 252
Structure-Function Prediction Methods	p. 253
ProKnow	p. 254
Fold Matching	p. 254
3D Motifs	p. 256
Sequence Homology	p. 257
Sequence Motifs	p. 257
Protein Interactions	p. 258
Combining the Predictions	p. 258
Prediction Success	p. 258
ProFunc	p. 259
ProFunc's Structure-Based Methods	p. 259
Assessment of the Structural Methods	p. 267
Conclusion	p. 269
Case Studies: Function Predictions of Structural Genomics Results	p. 273
Introduction	p. 273
Large Scale Function Prediction Case Studies	p. 275
Some Specific Examples	p. 281
Community Annotation	p. 287
Conclusions	p. 288
Prediction of Protein Function from Theoretical Models	p. 293
Background	p. 293
Protein Models as a Community Resource	p. 295
Model Quality	p. 296
Databases of Models	p. 297
Accuracy and Added Value of Model-Derived Properties	p. 298
Implementation	p. 300
Practical Application	p. 302
Plasticity of Catalytic Site Residues	p. 302
Mutation Mapping	p. 304
Protein Complexes	p. 305
Function Predictions from Template-Free Models	p. 306
Prediction of Ligand Specificity	p. 309
Structure Modelling of Alternatively Spliced Isoforms	p. 310
From Broad Function to Molecular Details	p. 312
What Next?	p. 314
Index	p. 319
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