Computational Structural Biology: Methods and Applications

✍ Scribed by Torsten Schwede, Torsten Schwede, Manuel C. Peitsch

• Publisher: World Scientific Publishing Company
• Year: 2008
• Tongue: English
• Leaves: 790
• Edition: 1
• Category: Library

✦ Synopsis

Computational structural biology has made tremendous progress over the last two decades, and this book provides a recent and broad overview of such computational methods in structural biology. It covers the impact of computational structural biology on protein structure prediction methods, macromolecular function and protein design, and key methods in drug discovery. It also addresses the computational challenges of experimental approaches in structural biology. In addition to reviewing the current state of computational structural biology, each chapter ends with a brief, visionary discussion on the future outlook, whereby the main challenges for the coming years are elucidated. Written by an international panel of expert contributors, this book can serve as a reference manual for students and practitioners alike.

Contents: Structure Prediction and Assessment Methods: Protein Fold Recognition and Threading (L J McGuffin); Assessment of Protein Structure Predictions (E Capriotti & M A Marti-Renom); From Structure to Function to Design: Evolution of Protein Folds (A N Lupas & K K Koretke); Atomistic Simulations of Reactions and Transition States (M Meuwly); Protein Protein Interactions and Aggregation Processes (R I Dima); Drug Discovery and Pharmacology: MD-Based Free Energy Simulations (M A Cuendet et al.); Structure-Based Computational Approaches to Drug Metabolism (M A Lill); New Frontiers in Experimental Methods: New Frontiers in X-ray Crystallography (C U Stirnimann & M G Grütter); New Frontiers in Characterizing Structure and Dynamics by NMR (M Nilges et al.); Selected Topics: Docking for Neglected Diseases as Community Efforts (M Podvinec et al.); Protein Structure Databases (M John et al.); Molecular Graphics in Structural Biology (A M Lesk et al.); and other chapters.

✦ Table of Contents

Contents......Page 8
Preface......Page 6
Section I STRUCTURE PREDICTION AND ASSESSMENT METHODS......Page 12
1.1 Introduction......Page 14
1.2.1 Comparative Protein Structure Modeling Techniques......Page 16
1.2.1.1 Identification of modeling templates and sequence alignments......Page 17
1.2.1.3 Model refinement......Page 18
1.2.2 Accuracy and Limitations of Comparative Protein Structure Modeling......Page 19
1.2.2.1 Template availability and structural diversity......Page 20
1.2.2.3 Membrane proteins......Page 21
1.2.3 De novo Modeling Techniques......Page 22
1.3 Protein Modeling and Structural Genomics......Page 24
1.4 Integrative (Hybrid) Modeling Techniques......Page 26
1.5 Assessment and Evaluation of Prediction Accuracy......Page 28
1.5.1 Critical Assessment of Techniques for Protein Structure Prediction (CASP)......Page 29
1.5.3 Model Quality Evaluation......Page 30
1.6.1 Typical Applications of Protein Models......Page 31
1.6.2 Modeling GPCRs......Page 32
1.7.1 Protein Modeling Servers and Software Tools......Page 33
1.7.2 Protein Model Databases......Page 35
1.8.1 Model Refinement......Page 37
1.8.2 Integrative (Hybrid) Modeling......Page 38
References......Page 39
2.1 Introduction......Page 48
2.2 Sequence-based Fold Recognition Beyond the Twilight Zone......Page 52
2.3 Structure-based Fold Recognition and Optimal Sequence Threading......Page 55
2.4 Hybrid Methods and Fully Automated Servers......Page 57
2.5 Meta-Servers......Page 60
2.6 Critical Assessment of Methods: CASP, CAFASP, Livebench and EVA......Page 62
2.7 Proteome Scale Fold Recognition......Page 64
2.8 Future Outlook......Page 65
References......Page 67
3.1 Introduction......Page 72
3.2 Structure and Components of Scoring Functions......Page 75
3.2.1 Contact Scoring Functions......Page 76
3.2.2 Distance-dependent Scoring Functions......Page 77
3.2.3 Accessible Surface Scoring Functions......Page 79
3.3 How is a Scoring Function Derived?......Page 80
3.3.1 Selection of Source Experimental Data......Page 82
3.3.2 Reference Systems......Page 84
3.4.1 Classical Overall Score Calculation......Page 85
3.4.1.2 Structure space reference frame......Page 86
3.4.2 Classical Detailed Score Calculation......Page 88
3.5 Typical Applications of Scoring Functions in Protein Structure Prediction......Page 89
3.5.2 Model Ranking......Page 90
3.5.4 Folding and Molecular Simulations......Page 91
3.6 Other Applications of Scoring Functions......Page 92
3.7.1 Reference Systems and Atom Type Definitions......Page 93
3.7.2 Solvation Models......Page 94
3.7.4 Multivariate Scoring Functions......Page 95
References......Page 96
4.1 Introduction......Page 100
4.2 Protein Structure Prediction......Page 102
4.3.1 Physics-based Energies......Page 104
4.3.2 Knowledge-based Potentials......Page 106
4.3.3 Combined Scoring Functions......Page 110
4.3.4 Clustering Approaches......Page 111
4.4 Evaluation of Model Quality Assessment Methods......Page 112
4.5 Future Outlook......Page 113
References......Page 115
5.1 Introduction......Page 122
5.2 The Expected Quality of a Model......Page 123
5.2.1 Some Useful Definitions......Page 126
5.3 Biological Applications......Page 127
5.3.1 Solving the Phase Problem in Crystallography by Molecular Replacement......Page 128
5.3.2 Prediction of Biological Function......Page 129
5.3.3 Redesigning Proteins......Page 130
5.3.4 Modifying the Biochemical Properties of Proteins......Page 132
5.4 Future Outlook......Page 133
References......Page 135
Section II FROM STRUCTURE TO FUNCTION TO DESIGN......Page 140
6.1 Introduction......Page 142
6.2 Protein Folding......Page 143
6.3 Homology and the Reconstruction of Evolutionary Events......Page 144
6.4 Stability of Folds Across Time......Page 145
6.5 Fold Change in Evolution......Page 150
6.6 Origin of Folds......Page 156
6.7 Future Outlook......Page 158
References......Page 159
7.1 Introduction......Page 164
7.2 Recognizing Domain Boundaries in Multi-domain Structures......Page 165
7.3.1 Structural Variation between related Protein Structures......Page 167
7.3.2 Rigid Body Superposition and Quantifying Structural Similarity......Page 168
7.3.3 Approaches for Comparing Secondary Structures between Proteins......Page 169
7.3.4 Residue-based Approaches for Comparing Secondary Structures......Page 171
7.4.1 Classifying Homologues Using Sequence Profile Methods......Page 173
7.5.1 The CATH Database......Page 174
7.5.2 The Structural Classification of Proteins (SCOP) Database......Page 178
7.6 Predicting Sequence Relatives in the Genomes and Sequence Databases......Page 179
7.7 Population Statistics from Domain Structure Classifications......Page 181
7.8 Structural Variation in Domain Superfamilies and Correlation with Functional Modifications......Page 184
7.9 Identifying Functional Relationships in Homologous Superfamilies......Page 190
7.10 The End of the Fold? Is There Evidence for a Structural Continuum?......Page 193
7.11 Future Outlook......Page 194
References......Page 195
8.1 Introduction......Page 200
8.2.2 Active Sites are in Deepest Cleft......Page 206
8.2.3 Binding Site Shapes are Complementary to Ligand Shapes......Page 207
8.2.4 Binding of the Ligand Induces Conformational Changes in the Binding Site......Page 209
8.2.5 Binding Site Residues are Highly Conserved......Page 210
8.2.6 Complementary Electrostatic Potentials Between Binding Sites and Ligands......Page 212
8.2.7 Catalytic Residues Destabilize the Enzyme Structure and Have Perturbed pKa-values......Page 214
8.2.8 Hydrophobic Interactions are Essential for Binding......Page 218
8.2.10 Potential Functions for Estimating Binding Energy......Page 219
8.2.12 Precautions with PDB Structures......Page 221
8.3.1 Comparing Catalytic Templates......Page 223
8.3.2 Comparing Atomic Coordinates......Page 224
8.3.3 Comparing Binding Surfaces......Page 225
8.4 Future Outlook......Page 226
References......Page 229
9.1 Introduction......Page 234
9.2 Potential Energy Functions for Biomolecular Simulations......Page 235
9.3.1 Potential Energy Functions......Page 240
9.3.2.1 Transition state theory......Page 241
9.3.2.2 Progression coordinates for locating transition states......Page 243
9.4.1 Transition Path Sampling for Protein Folding......Page 245
9.4.2.1 Ligand rebinding in MbCO......Page 247
9.4.2.2 Rebinding in MbNO......Page 249
9.4.3 Enzymatic Reactions......Page 252
9.5 Outlook and New Challenges......Page 254
9.5.2 Separation of Energetics and Nuclear Dynamics......Page 255
9.5.3 Application-Specific Force Fields and Predictive Simulations......Page 256
References......Page 257
10.1 Introduction......Page 264
10.2.1 Local Motions: Advanced Atomistic Molecular Dynamics......Page 266
10.2.2 Domain Motions: Normal Mode Analysis......Page 270
10.2.3 Coarse-Grained Models Beyond the Harmonic Limit......Page 273
10.3.1 Functional “Dynamics” of Ribonuclease A......Page 276
10.3.1.1 Atomic scale hypothesis for D121A effects......Page 277
10.3.1.2 Simulation studies......Page 278
10.3.2 Activation of a Signaling Protein: CheY......Page 279
10.3.2.1 “Y-T” Coupling versus population shift......Page 280
10.3.2.2 Simulation studies......Page 282
10.3.3.1 Mechanochemical coupling in myosin......Page 283
10.3.3.2 Simulation studies......Page 284
10.3.4.1 Gating transition of MscL......Page 288
10.3.4.2 Simulation studies......Page 289
10.4 Concluding Discussions and Future Outlook......Page 293
10.4.1.1 What are the roles of “slow (μs-ms) motions”
in enzyme catalysis?......Page 296
10.4.1.2 What are the bottlenecks for large-scale functional motions?......Page 297
10.4.1.3 Can functional motions be modulated in a predictive manner?......Page 298
10.4.1.4 Are there major differences between “functional motions” in vitro and those in vivo?......Page 299
References......Page 300
11.1 Introduction......Page 310
11.2.1 Formation of Aggregation Prone Conformations......Page 312
11.2.2 Mechanisms of Oligomerization......Page 315
11.2.3 Applications to the Kinetics of Fibril Formation
of Aβ Peptides......Page 316
11.2.4 Application to the Early Steps of Prion Proteins Fibril Formation......Page 319
11.2.5 Applications to the Study of Fibril Formation in Polyglutamine Disease-Related Peptides......Page 320
11.3 Self-Association Processes Under Various Cellular Conditions......Page 322
11.3.2 The Role of Water in the Oligomerization of Proteins......Page 323
11.4 Formation of Soluble Oligomers in the Early Steps of Fibril Formation......Page 325
11.5 Future Outlook......Page 329
References......Page 331
12.1 Introduction......Page 336
12.2.1 Available High Resolution Structures......Page 337
12.2.3 Modeling the Voltage-sensor of a Kv Channel......Page 340
12.3 Explicit Membrane System......Page 342
12.3.1 Assembling an Ion Channel and Membrane System......Page 343
12.3.3 Force Field Limitations......Page 345
12.4.1 Transmembrane Voltage as an Analytical Function......Page 348
12.4.2 Potential of Mean Force......Page 350
12.4.3 Hierarchal PMF/BD Framework......Page 353
12.5.1 Selectivity Concepts......Page 356
12.5.2 Selectivity Calculations by Free Energy Perturbation......Page 358
12.6 Gating......Page 360
12.7 Overview of Alternative Approaches......Page 361
12.7.1 Grand Canonical Brownian Dynamics......Page 362
12.8 Future Outlook......Page 363
12.8.2 Studying Macroscopic Conformational Changes Involved in Transduction Events......Page 364
12.8.3 Bridging the Gap between Atomic Simulations and Physiology......Page 365
References......Page 366
13.1 Introduction......Page 374
13.2 Pre-Ewald Times......Page 387
13.4 The Ewald Era......Page 388
13.4.2 Solvent and Ion Parameters......Page 390
13.4.3 tRNA and Modified Nucleotides......Page 391
13.4.4 Ribozymes......Page 392
13.4.6 Viral Particles......Page 393
13.5 Dynamic Models......Page 394
13.6 Future Outlook......Page 395
Acknowledgments......Page 397
References......Page 398
14.1 Introduction......Page 412
14.2 Methodology of Computational Protein Design......Page 415
14.3.1 Protein Re-engineering......Page 418
14.3.2 De novo Designed Proteins......Page 422
14.4 Future Outlook......Page 427
References......Page 428
15.1 Introduction......Page 436
15.2 B Cell Epitopes: Classification and Structural Characteristics......Page 437
15.3.1 Sequence-based Prediction of B Cell Epitopes......Page 440
15.3.2 Prediction of B Cell Epitopes Based on Protein Structure......Page 443
15.4 Future Outlook......Page 446
15.4.1 Vaccines Based on Linear Epitopes or Peptides......Page 448
15.4.2 Vaccines Based on Discontinuous Epitopes......Page 449
References......Page 450
16.1 Introduction......Page 456
16.1.1 Structural Elements of Antibodies......Page 457
16.2.1 Modeling of the Framework Regions......Page 459
16.2.2 Modeling of the Hypervariable Regions......Page 461
16.2.3 Side Chain Modeling and Refining of the Model......Page 464
16.3.1 Classifying the Antibody Binding Sites......Page 466
16.3.2 Construction of Models......Page 467
16.4 Utilization of Antibody Modeling......Page 468
References......Page 470
Section III DRUG DISCOVERY AND PHARMACOLOGY......Page 478
17.1 Introduction......Page 480
17.2.1 Docking Programs......Page 485
17.2.2 Docking Accuracy: Self-Docking......Page 488
17.2.3 Docking accuracy: Cross-docking......Page 490
17.3 Flexible Receptor Docking: Treatment of Induced Fit Effects......Page 492
17.4.1 Standard Empirical Scoring Functions......Page 495
17.4.4 Applications......Page 502
17.4.2 Improved Representation of the Hydrophobic Effect......Page 497
17.4.3 Enrichment Studies......Page 500
17.5 Future Outlook......Page 504
References......Page 507
18.1 Introduction......Page 512
18.2 Overview......Page 513
18.3 Ligand-based Pharmacophore Model Generation......Page 514
18.4 Structure-based Pharmacophore Perception......Page 518
18.4.2 Identifying and Ranking the Pharmacophore Models......Page 519
18.5 Future Outlook......Page 521
References......Page 522
19.1 Introduction......Page 524
19.2 Exact Methods......Page 526
19.2.1 Exact Statistical Mechanics Methods for Free Energy Differences......Page 527
19.2.1.2 Thermodynamic integration......Page 530
19.2.2 Relative Free Energy Differences from Thermodynamic Cycles......Page 531
19.2.3 Absolute Binding Free Energy Differences Using the Double Decoupling Method......Page 532
19.2.4 Potentials of Mean Force from Configurational Transformations......Page 533
19.2.4.1 The umbrella sampling method......Page 535
19.2.4.2 Constraint-based methods......Page 538
19.2.4.3 Advanced methods......Page 539
19.2.5 Nonequilibrium Methods......Page 540
19.3.1 LIE......Page 543
19.3.2 MM-PBSA......Page 545
References......Page 551
20.1 Introduction......Page 560
20.2 Multi-Dimensional QSAR......Page 563
20.3 Computational Pharmacology: Modeling GPCRs (Neurokinin-1, CCR-3, Bradykinin B2 receptor)......Page 570
20.4 Computational Toxicology: Modeling Nuclear Receptors (Aryl Hydrocarbon, Estrogen α/β, Androgen, Thyroid α/β, PPAR γ, Glucocorticoid Receptor)......Page 572
20.5 Modeling Toxicity — The VirtualToxLab Concept......Page 575
20.6 Future Outlook......Page 577
References......Page 579
21.1 Introduction......Page 584
21.2.1 Structural Data for P450 Enzymes......Page 586
21.2.2 Prediction of Binding Modes......Page 590
21.2.3 Prediction of Binding Affinities......Page 591
21.2.4 Modeling of Access/Exit Channels......Page 594
21.2.5 Reaction Mechanism......Page 597
21.3 Induction of Drug Metabolism......Page 600
21.4 Future Outlook......Page 603
References......Page 605
Section IV NEW FRONTIERS IN EXPERIMENTAL METHODS......Page 610
22.1 Introduction......Page 612
22.2 The Methods......Page 613
22.2.1 Protein Production......Page 614
22.2.2 Crystallization......Page 615
22.2.3.1 Diffraction data collection......Page 616
22.2.3.2 X-ray sources......Page 617
22.2.4.1 Single and multiple anomalous sispersion methods......Page 618
22.2.4.2 Molecular replacement......Page 621
22.2.5.2 Refinement......Page 622
22.2.5.3 Automation of model building and refinement......Page 624
22.3.1 Structure-based Drug Design......Page 625
22.3.3 Membrane Proteins......Page 626
References......Page 627
23.1 Introduction......Page 634
23.2 Sample Preparation Methods......Page 635
23.3.1 Image Formation......Page 636
23.3.3 Scanning Transmission Electron Microscopy......Page 643
23.4.1 Different Methods for 2D Crystallization......Page 645
23.4.2 Data Acquisition......Page 648
23.4.3 Data Processing......Page 649
23.5 3D Electron Microscopy of Protein Complexes......Page 652
23.6 Electron Tomography......Page 658
23.7 Future Outlook......Page 660
References......Page 661
24.1 Introduction......Page 666
24.2 Determination of Structure......Page 667
24.2.1 The Hybrid Energy Function and Bayes’s Rule......Page 668
24.2.2 Obtaining Coordinates and Their Precision......Page 671
24.2.3 Treatment of Additional Parameters......Page 672
24.2.4 Sampling the Posterior Probability Distribution......Page 673
24.2.5 Data Statistics and Restraint Potentials......Page 674
24.2.6 Data Quality and the Weight on Edata(X)......Page 678
24.3 Probing Structural Dynamics by NMR......Page 679
24.3.1 Experimental Approaches......Page 681
24.3.2 Interpreting Experimental Measures of Dynamics......Page 682
24.3.3 Simultaneous Calculation of Structure and Dynamics......Page 684
24.4 Future Outlook......Page 685
References......Page 686
Section V SELECTED TOPICS......Page 692
25.1 Introduction......Page 694
25.2 Grid Computing......Page 696
25.3 Grid Computing in Biomedical Research......Page 698
25.4 Grid-based Computation to Discover Drug Candidates Against Targets of Public Interest......Page 701
25.5 Public-Private Partnerships: A Model for Drug Discovery Against Neglected Diseases......Page 703
25.6 Discussion......Page 708
25.7 Future Outlook......Page 710
References......Page 712
26.1 Introduction......Page 716
26.2 PDB Data Deposition and Processing......Page 718
26.3 The Electron Microscopy Databank (EMDB)......Page 720
26.4 The MSD Relational Database......Page 721
26.4.5.5 External cross-references/taxonomy......Page 729
26.4.5.2 Structure......Page 727
26.4.5.3 Secondary structure......Page 728
26.4.2 Storing Data in Databases......Page 722
26.4.2.1 Data integrity......Page 723
26.4.3 The MSDSD Production Line......Page 724
26.4.4 Characteristics of MSDSD......Page 725
26.4.5 MSDSD Data Architecture......Page 726
26.5 MSD Search and Analysis Services......Page 730
26.5.3 MSDchem......Page 731
26.5.4 MSDfold......Page 732
26.5.5 MSDPISA......Page 733
26.5.7 MSDtemplate......Page 734
26.6 The Future Outlook......Page 735
References......Page 736
27.1 Introduction......Page 740
27.2.1 Biology: Taxonomy by Morphology to Molecular Biology......Page 742
27.2.2 Chemistry: Atoms to Bonds to Quantum Mechanics......Page 743
27.2.4 Molecular Graphics by Artists......Page 745
27.2.4.3 Simplified representations or cartoons......Page 746
27.2.6 Contours and Surfaces......Page 748
27.2.6.1 Applying the techniques of topographic mapping and weather mapping......Page 749
27.2.6.2 Patterson maps......Page 752
27.2.6.3 Gaussian atoms and Kendrew models......Page 753
27.2.6.5 Wireframe models......Page 754
27.2.6.6 Model building and fitting to density......Page 755
27.2.6.7 Kendrew models......Page 756
27.2.6.9 Interactive graphics . Diamond, Katz/Levinthal, Jones......Page 757
27.2.6.10 Surface models, rolling ball models, potentials......Page 758
27.2.7 Higher Order Structure — Schematic Diagrams......Page 759
27.3 Computational Techniques......Page 761
27.3.1 Interactive Control of What is Displayed and How it is Displayed......Page 762
27.3.2.1 Color and pseudocolor......Page 764
27.3.2.2 Light sources, shadows, shading and depth cueing, texture, transparency......Page 765
27.3.2.3 Stereo......Page 766
27.3.2.5 Computing surfaces, contours, isosurfaces, and tesselations......Page 768
27.4.1 ORTEP......Page 769
27.4.3 Insight and VMD, SWISS-MODEL and DeepView......Page 770
27.4.5 Kinemage, Movie-Making......Page 771
27.4.6 RasMol, Chime, Jmol, PyMol, and ccp4mg......Page 773
27.5 Current Choices in Hardware and Software......Page 774
27.5.1 Current Applications......Page 775
27.6 The Future......Page 776
References......Page 777
Index......Page 782