From protein structure to function with bioinformatics:
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| Format: | Book |
| Language: | English |
| Published: |
Dordrecht
Springer
[2017]
|
| Edition: | Second edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xv, 503 Seiten Illustrationen, Diagramme |
| ISBN: | 9789402410679 |
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| 245 | 1 | 0 | |a From protein structure to function with bioinformatics |c Daniel J. Rigden, editor |
| 250 | |a Second edition | ||
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Record in the Search Index
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| adam_text | Contents
Part I Generating and Inferring Structures
1 Ab Initio Protein Structure Prediction 3
Jooyoung Lee, Peter L Freddolino and Yang Zhang
1 1 Introduction 4
1 2 Energy Functions 5
121 Physics-Based Energy Functions 7
122 Knowledge-Based Energy Function Combined
with Fragments 11
1 3 Conformational Search Methods 18
131 Monte Carlo Simulations 18
132 Molecular Dynamics 19
133 Genetic Algorithm 20
134 Mathematical Optimization 21
1 4 Model Selection 21
141 Physics-Based Energy Function 22
142 Knowledge-Based Energy Function 23
143 Sequence-Structure Compatibility Function 24
144 Clustering of Decoy Structures 25
1 5 Remarks and Discussions 25
References 27
2 Protein Structures, Interactions and Function from Evolutionary
Couplings 37
Thomas A Hopf and Debora S Marks
2 1 Introduction 38
2 2 Evolutionary Couplings from Sequence Alignments 42
221 The Global Model 42
2 3 Three-Dimensional Protein Structures from Evolutionary
Couplings 46
Contents
viii
231 Transmembrane Proteins 48
232 Protein Interactions and Complexes 49
233 Conformational Plasticity and Disordered Proteins 51
2 4 Predicting the Effect of Mutations 52
2 5 Summary and Future Challenges 54
References 55
3 Fold Recognition 59
Lawrence A Kelley
3 1 Introduction 59
311 The Importance of Blind Trials: The CASP
Competition 60
312 Ab Initio Structure Prediction Versus Homology
Modelling 60
313 The Limits of Fold Space 62
3 2 Pushing Sequence Similarity to the Limits: The Power
of Evolutionary Information 64
321 The Rise of Hidden Markov Models 67
322 Using Predicted Structural Features 68
323 Harnessing 3D Structure to Enhance Recognition 70
324 Knowledge-Based Potentials 70
325 Summary 72
3 3 CASP: The Great Filter 72
331 The Leaders 73
332 Individual Algorithms 73
333 Consensus Methods 75
3 4 Post-processing 76
341 Choosing and Combining Candidate Models 76
342 Post-processing in Practice 79
343 Use of Contacts 82
3 5 Tools for Fold Recognition on the Web 85
3 6 The Future 86
References 88
4 Comparative Protein Structure Modelling 91
Andräs Fiser
4 1 Introduction 91
411 Structure Determines Function 91
412 Sequences, Structures, Structural Genomics 92
413 Approaches to Protein Structure Prediction 94
4 2 Steps in Comparative Protein Structure Modelling 96
421 Searching for Structures Related to the Target
Sequence 98
422 Selecting Templates 100
Contents
ix
423 Sequence to Structure Alignment 102
424 Model Building 103
425 Model Evaluation 114
4 3 Performance of Comparative Modelling 116
431 Accuracy of Methods 116
432 Errors in Comparative Models 117
4 4 Applications of Comparative Modelling 119
441 Modelling of Individual Proteins 119
442 Comparative Modelling and the Protein Structure
Initiative 119
4 5 Summary 120
References 121
5 Advances in Computational Methods for Transmembrane
Protein Structure Prediction 135
Tim Nugent, David Jones and Sikander Hayat
5 1 Introduction 136
5 2 Membrane Protein Structural Classes 136
521 a-Helical Bundles 137
522 Transmembrane ß-Barrels 137
5 3 Databases 139
5 4 Multiple Sequence Alignments 140
5 5 Transmembrane Protein Topology Prediction 141
551 Early a-Helical Topology Prediction Approaches 142
552 Machine Learning Approaches for a-Helical
Topology Prediction 142
553 Signal Peptides and Re-entrant Helices 144
554 Consensus Approaches for a-Helical Topology
Prediction 145
555 Transmembrane ß-Barrel Topology Prediction 146
556 Empirical Approaches for ß-Barrel Topology
Prediction 147
557 Machine Learning Approaches for ß-Barrel Topology
Prediction 148
558 Consensus Approaches for ß-Barrel Topology
Prediction 149
5 6 3D Structure Prediction 150
561 Homology Modelling of a-Helical Transmembrane
Proteins 150
562 Homology Modelling of Transmembrane ß-Barrel
Proteins 151
563 De Novo Modelling of a-Helical Transmembrane
Proteins 152
564 De Novo Modelling of Transmembrane ß-Barrels 154
X
Contents
565 Covariation-Based Approaches 154
566 Evolutionary Covariation-Based Methods for De
Novo Modelling of ot-Helical Membrane Proteins 155
567 Evolutionary Covariation-Based Methods for
Transmembrane ß-Barrel Structure Prediction 157
5 7 Future Directions 158
References 158
6 Bioinformatics Approaches to the Structure and Function
of Intrinsically Disordered Proteins 167
Zsuzsanna Dosztänyi and Peter Tompa
6 1 The Concept of Protein Disorder 168
6 2 Sequence Features of IDPs 169
621 The Unusual Amino Acid Composition of IDPs 169
622 Low Sequence Complexity and Disorder 169
623 Flavours of Disorder 170
6 3 Prediction of Disorder 171
631 Charge-Hydropathy Plot 171
632 Propensity-Based Predictors 171
633 Prediction Based on Simplified Biophysical
Models 174
634 Machine Learning Algorithms 175
635 Related Approaches for the Prediction of Protein
Disorder 177
636 Comparison of Disorder Prediction Methods 178
6 4 Databases of IDPs 179
6 5 Structural Features of IDPs 180
6 6 Functional Classification of IDPs 181
661 Gene Ontology-Based Functional Classification
of IDPs 182
662 Classification of IDPs Based on Their Mechanism
of Action 183
663 Functional Features of IDPs 185
6 7 Prediction of the Function of IDPs 188
671 Predicting Short Recognition Motifs in IDRs 190
672 Prediction of Disordered Binding Regions/MoRFs 191
673 Combination of Information on Sequence and
Disorder: Phosphorylation Sites and CaM Binding
Motifs 192
674 Correlation of Disorder Pattern and Function 193
6 8 Evolution of IDPs 194
6 9 Conclusions 195
References 195
Contents
xi
7 Prediction of Protein Aggregation and Amyloid Formation 205
Ricardo Grana-Montes, Jordi Pujols-Pujol, Carlota Gömez-Picanyol
and Salvador Ventura
7 1 Introduction 206
7 2 The Physico-chemical and Structural Basis of Protein
Aggregation 206
721 Intrinsic Determinants of Protein Aggregation 213
722 Extrinsic Determinants of Protein Aggregation 214
723 Specific Sequence Stretches Drive Aggregation 214
724 Structural Determinants of Amyloid-like
Aggregation 215
7 3 Prediction of Protein Aggregation from the Primary
Sequence 216
731 Phenomenological Approaches 221
732 Structure-Based Approaches 225
733 Consensus Methods 230
734 Applications of Sequence-Based Predictors 232
7 4 Prediction of Aggregation Propensity from the Tertiary
Structure 242
7 5 Concluding Remarks 253
References 254
8 Prediction of Biomolecular Complexes 265
Anna Vangone, Romina Oliva, Luigi Cavallo
and Alexandre MJ J Bonvin
8 1 Introduction 266
8 2 Docking 268
821 Step 1: Searching 269
822 Step 2: Scoring 270
823 Data-Driven Docking 274
8 3 The Challenges of Docking: Flexibility and Binding
Affinity 275
831 Changes upon Binding: The Flexible Docking
Challenge 275
832 The ‘Perfect’ Scoring Function and the Binding
Affinity Problem 276
8 4 Protein-Peptide Docking 278
8 5 Post-docking: Interface Prediction from Docking Results
and Use of Docking-Derived Contacts for Clustering
and Ranking 279
851 Web Tools for the Post-docking Processing 281
8 6 Concluding Remarks 283
References 284
Contents
xii
Part II From Structures to Functions
9 Function Diversity Within Folds and Superfamilies 295
Benoit H Dessailly, Natalie L Dawson, Sayoni Das
and Christine A Orengo
9 1 Defining Function 296
9 2 From Fold to Function 297
921 Definition of a Fold 297
922 Prediction of Function Using Fold Relationships 300
9 3 Function Diversity Between Homologous Proteins 303
931 Definitions 303
932 Evolution of Protein Superfamilies 307
933 Function Divergence During Protein Evolution 308
9 4 Conclusion 320
Bibliography 320
10 Function Prediction Using Patches, Pockets and Other Surface
Properties 327
Daniel J Rigden
10 1 Definitions of Protein Surfaces 328
10 2 Surface Patches 329
10 2 1 Hydrophobic Patches 329
10 2 2 Electrostatics 336
10 2 3 Sequence Conservation 338
10 2 4 Surface Atom Triplet Propensities 339
10 2 5 Multiple Properties 340
10 3 Pockets 340
10 3 1 Geometric Descriptions of Pockets 342
10 3 2 Channels and Tunnels 343
10 3 3 Distinguishing Functional Pockets 344
10 3 4 Predicting Ligands for Pockets 345
10 4 Prediction of Catalytic Residues 347
10 5 Protein-Protein Interfaces 349
10 6 Other Specialised Binding Site Predictors 350
10 7 Medicinal Applications 352
10 8 Conclusions 353
References 354
11 3D Motifs 361
Jerome P Nilmeier, Elaine C Meng, Benjamin J Polacco
and Patricia C Babbitt
11 1 Background: Functional Annotation 362
11 1 1 What Is Function? 363
11 1 2 Genomics and Functional Annotation 363
11 1 3 The Need for Structure-Based Methods 365
Contents xui
11 2 3D Motif Matching Techniques 366
11 2 1 What Is a 3D Motif? 366
11 2 2 Historical Development of Motif Matching
Methods 369
11 3 Algorithmic Approaches to Motif Matching 373
11 3 1 Methods Using 3D Motifs 374
11 3 2 Efficiency Considerations for 3D Motifs 375
11 3 3 Methods with Nonstandard Motif Information 376
11 3 4 Interpretation of Results 377
11 4 Methods for Deriving Motifs 378
11 4 1 Literature Search and Manual Curation 379
11 4 2 Annotated Sites in PDB Structures 379
11 4 3 Mining for Emergent Properties 380
11 5 Molecular Docking for Functional Annotation 383
11 6 Discussion and Conclusions 385
References 386
12 Protein Dynamics: From Structure to Function 393
Marcus B Kubitzki, Bert L de Groot and Daniel Seeliger
12 1 Molecular Dynamics Simulations 393
12 1 1 Principles and Approximations 394
12 1 2 Applications 396
12 1 3 Limitations—Enhanced Sampling Algorithms 402
12 2 Principal Component Analysis 406
12 3 Collective Coordinate Sampling Algorithms 409
12 3 1 Essential Dynamics 409
12 3 2 TEE-REX 410
12 4 Methods for Functional Mode Prediction 413
12 4 1 Normal Mode Analysis 413
12 4 2 Elastic Network Models 414
12 4 3 CONCOORD 415
12 5 Summary and Outlook 419
References 420
13 Integrated Servers for Structure-Informed Function Prediction 427
Roman A Laskowski
13 1 Introduction 427
13 1 1 The Problem of Predicting Function
from Structure 428
13 1 2 Structure-Function Prediction Methods 430
13 2 ProKnow 431
13 2 1 Fold Matching 432
13 2 2 3D Motifs 434
13 2 3 Sequence Homology 434
XIV
Contents
13 2 4 Sequence Motifs 434
13 2 5 Protein Interactions 434
13 2 6 Combining the Predictions 435
13 2 7 Prediction Success 435
13 3 ProFunc 436
13 3 1 ProFunc’s Structure-Based Methods 437
13 3 2 Assessment of the Structural Methods 442
13 4 Conclusion 444
References 445
14 Case Studies: Function Predictions of Structural
Genomics Results 449
James D Watson, Roman A Laskowski and Janet M Thornton
14 1 Introduction 449
14 2 Function Prediction Case Studies 451
14 2 1 Teichman et al (2001) 451
14 2 2 Kim et al (2003) i 451
14 2 3 Watson et al (2007) 453
14 2 4 Lee et al (2011) 456
14 3 Some Specific Examples 456
14 3 1 Adams et al (2007) 456
14 3 2 AF0491 Protein 457
14 3 3 The GxGYxYP Family 459
14 4 Community Annotation 460
14 5 Conclusions 461
References 462
15 Prediction of Protein Function from Theoretical Models 467
Daniel J Rigden, Iwona A Cymerman and Janusz M Bujnicki
15 1 Background 467
15 2 Suitability of Protein 3D Models for Structure-Based
Predictions 469
15 2 1 Surface Properties 470
15 2 2 Functional Sites 472
15 2 3 Specific Binding Predictions 473
15 2 4 Small Molecule Binding 474
15 2 5 Protein-Protein Interactions 476
15 2 6 Protein Model Databases 477
15 3 Function Prediction Examples 478
15 3 1 Fold Prediction with Fragment-Based Ab Initio
Models 478
15 3 2 Fold Prediction with Contact-Based Models 481
15 3 3 Plasticity of Catalytic Site Residues 483
15 3 4 Prediction of Ligand Specificity 484
Contents xv
15 3 5 Prediction of Cofactor Specificity Using an Entry
from a Database of Models 485
15 3 6 Mutation Mapping 488
15 3 7 Protein Complexes 489
15 3 8 Structure Modelling of Alternatively Spliced
Isoforms 490
15 3 9 From Broad Function to Molecular Details 491
15 4 Conclusions 493
References 493
|
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| isbn | 9789402410679 |
| language | English |
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| spelling | From protein structure to function with bioinformatics Daniel J. Rigden, editor Second edition Dordrecht Springer [2017] © 2017 xv, 503 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Strukturanalyse (DE-588)4183787-3 gnd rswk-swf Proteine (DE-588)4076388-2 gnd rswk-swf Bioinformatik (DE-588)4611085-9 gnd rswk-swf Proteine (DE-588)4076388-2 s Strukturanalyse (DE-588)4183787-3 s Bioinformatik (DE-588)4611085-9 s DE-604 Rigden, Daniel J. edt Erscheint auch als Online Ressource 978-94-024-1069-3 HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029754668&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | From protein structure to function with bioinformatics Strukturanalyse (DE-588)4183787-3 gnd Proteine (DE-588)4076388-2 gnd Bioinformatik (DE-588)4611085-9 gnd |
| subject_GND | (DE-588)4183787-3 (DE-588)4076388-2 (DE-588)4611085-9 |
| title | From protein structure to function with bioinformatics |
| title_auth | From protein structure to function with bioinformatics |
| title_exact_search | From protein structure to function with bioinformatics |
| title_full | From protein structure to function with bioinformatics Daniel J. Rigden, editor |
| title_fullStr | From protein structure to function with bioinformatics Daniel J. Rigden, editor |
| title_full_unstemmed | From protein structure to function with bioinformatics Daniel J. Rigden, editor |
| title_short | From protein structure to function with bioinformatics |
| title_sort | from protein structure to function with bioinformatics |
| topic | Strukturanalyse (DE-588)4183787-3 gnd Proteine (DE-588)4076388-2 gnd Bioinformatik (DE-588)4611085-9 gnd |
| topic_facet | Strukturanalyse Proteine Bioinformatik |
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