Kolinski Multiscale Approaches to Protein Modeling

1. Auflage 2010
ISBN: 978-1-4419-6889-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 355 Seiten

The book gives a comprehensive review of the most advanced multiscale methods for protein structure prediction, computational studies of protein dynamics, folding mechanisms and macromolecular interactions. It approaches span a wide range of the levels of coarse-grained representations, various sampling techniques and variety of applications to biomedical and biophysical problems. This book is intended to be used as a reference book for those who are just beginning their adventure with biomacromolecular modeling but also as a valuable source of detailed information for those who are already experts in the field of biomacromolecular modeling and in related areas of computational biology or biophysics.

Autoren/Hrsg.

Kolinski, Andrzej

Inhaltsverzeichnis

1;Preface;5
2;Contents;7
3;Contributors;9
4;1 Lattice Polymers and Protein Models;13
4.1;1.1 Reduced Models of Chain Molecules;13
4.2;1.2 Simple Lattice Polymers;16
4.3;1.3 Simple Lattice Polymers with Protein-Like Features;19
4.4;1.4 Minimal Protein-Like Models;21
4.5;1.5 High-Coordination Lattice Protein Models;24
4.6;1.6 Protein Folding and Structure Prediction with Lattice Models;28
4.7;References;29
5;2 Multiscale Protein and Peptide Docking;33
5.1;2.1 Introduction;33
5.2;2.2 Rigid Docking Procedures;35
5.3;2.3 Flexible Docking;35
5.4;2.4 Multiscale Flexible Docking with CABS;36
5.4.1;2.4.1 Treating of Flexibility;38
5.4.2;2.4.2 Example of Peptide Docking to Receptor Protein;39
5.4.3;2.4.3 Protein--Protein Docking;40
5.5;2.5 Perspectives;42
5.6;References;43
6;3 Coarse-Grained Models of Proteins: Theory and Applications;46
6.1;3.1 Introduction;46
6.2;3.2 History of Coarse-Grained Protein Models;48
6.3;3.3 Choice of Conformational Space Representation;54
6.4;3.4 Interaction Schemes;55
6.5;3.5 Derivation of Coarse-Grained Force Fields;56
6.5.1;3.5.1 Basic Formulations;57
6.5.2;3.5.2 Statistical Potentials (Boltzmann Principle);58
6.5.3;3.5.3 Factor Expansion of the PMF;62
6.5.4;3.5.4 Force-Matching Method;66
6.5.5;3.5.5 Optimization of an Effective Energy Function;68
6.5.6;3.5.6 ''Knowledge-Based'' and ''Physics-Based'' Potentials;71
6.6;3.6 Applications in Protein Structure Prediction;72
6.7;3.7 Applications to Study Protein Dynamics and Thermodynamics;75
6.8;3.8 Conclusions and Outlook;81
6.9;References;82
7;4 Conformational Sampling in Structure Prediction and Refinement with Atomistic and Coarse-Grained Models;95
7.1;4.1 Introduction;95
7.2;4.2 Iterative Structure Refinement Framework;97
7.2.1;4.2.1 Quantitative Measure of Sampling Efficiency;98
7.3;4.3 Protein Models at Different Resolutions;100
7.3.1;4.3.1 All-Atom Models of Proteins;100
7.3.1.1;4.3.1.1 Sampling with All-Atom Force Fields;102
7.3.2;4.3.2 Coarse-Grained Models of Proteins;102
7.3.2.1;4.3.2.1 PRIMO;103
7.3.2.2;4.3.2.2 SICHO;108
7.4;4.4 Iterative Refinement with Different Protein Models;110
7.4.1;4.4.1 Sampling Protocol;110
7.4.1.1;4.4.1.1 All-Atom Molecular Dynamics Simulations;111
7.4.1.2;4.4.1.2 PRIMO Molecular Dynamics Simulations;111
7.4.1.3;4.4.1.3 SICHO Lattice Monte Carlo Sampling;111
7.4.2;4.4.2 Refinement Toward the Native State;112
7.5;4.5 Summary and Outlook;115
7.6;References;116
8;5 Effective All-Atom Potentials for Proteins;120
8.1;5.1 Introduction;120
8.2;5.2 Effective Potentials;122
8.3;5.3 Applications;125
8.3.1;5.3.1 Folding Thermodynamics;125
8.3.2;5.3.2 Mechanical Unfolding;128
8.3.3;5.3.3 Aggregation;130
8.4;5.4 Summary;132
8.5;References;132
9;6 Statistical Contact Potentials in Protein Coarse-Grained Modeling: From Pair to Multi-body Potentials;136
9.1;6.1 Introduction;136
9.2;6.2 History of Development of Knowledge-Based Potentials;138
9.2.1;6.2.1 Inverse Boltzmann Relationship;139
9.2.2;6.2.2 Quasi-chemical Approximation;142
9.3;6.3 Distant-Independent Potential Functions;143
9.3.1;6.3.1 Sample Weighing;144
9.4;6.4 Distance-Dependent Potential Functions;146
9.5;6.5 Geometric Potential Functions;148
9.6;6.6 Multi-body Potentials;148
9.6.1;6.6.1 Four-Body Contact Potentials;149
9.6.1.1;6.6.1.1 Construction of Four-Body Contacts;149
9.6.2;6.6.2 Four-Body Contact Potential Energy Function;151
9.7;6.7 Optimization Method;152
9.8;6.8 Comparative Analysis of Statistical Protein Contact Potentials to Infer Ideal Amino Acid Interaction Forms;153
9.9;6.9 Statistical Force Fields for Coarse-Grained Protein Models;155
9.10;6.10 Applications of Knowledge-Based Potential Functions;156
9.11;6.11 Future Developments;158
9.12;References;162
10;7 Bridging the Atomic and Coarse-Grained Descriptions of Collective Motions in Proteins;167
10.1;7.1 Introduction;167
10.2;7.2 Protein Internal Dynamics Observed over Different Timescales: Methods;170
10.2.1;7.2.1 Low-Energy Collective Excitations;171
10.2.2;7.2.2 Structural Substates;171
10.2.3;7.2.3 Inter-substate and Intra-substate Fluctuations;172
10.2.4;7.2.4 Comparison of Structural Fluctuations in Different Substates;173
10.2.5;7.2.5 Coarse-Grained Description and Modeling of Protein Internal Dynamics;174
10.2.5.1;7.2.5.1 Elastic Network Models;174
10.2.5.2;7.2.5.2 Identifying Protein Dynamical Domains;175
10.3;7.3 Protein Internal Dynamics Observed Over Different Timescales: The Case of Adenylate Kinase;175
10.3.1;7.3.1 Conformational Fluctuations in the Presence of a Nearly Flat Free-Energy Landscape: The Case of TAT;182
10.4;7.4 Concluding Remarks;183
10.5;References;184
11;8 Structure-Based Models of Biomolecules: Stretching of Proteins, Dynamics of Knots, Hydrodynamic Effects, and Indentation of Virus Capsids;187
11.1;8.1 Introduction;187
11.2;8.2 The Structure-Based Models of Proteins;191
11.3;8.3 The Structure-Based Models of the DNA and Dendrimers;196
11.4;8.4 Examples of Applications of the Structure-Based Models of Proteins;199
11.4.1;8.4.1 Mechanical Strength of 17,134 Proteins;199
11.4.2;8.4.2 Dynamics of Knots;202
11.4.3;8.4.3 Proteins in Membranes;206
11.4.4;8.4.4 Hydrodynamic Interactions;207
11.4.5;8.4.5 Nanoindentation of Virus Capsids;208
11.5;References;211
12;9 Sampling Protein Energy Landscapes -- The Quest for Efficient Algorithms;217
12.1;9.1 Introduction;217
12.2;9.2 Basic Simulation Techniques;218
12.2.1;9.2.1 Molecular Dynamics;218
12.2.2;9.2.2 Monte Carlo;219
12.2.3;9.2.3 Optimization Techniques;221
12.3;9.3 Advanced Simulation Techniques;222
12.3.1;9.3.1 Unfolding Simulations;222
12.3.2;9.3.2 Advanced Updates;223
12.3.3;9.3.3 Generalized-Ensemble Techniques;224
12.3.3.1;9.3.3.1 Random Walks in Order Parameter Space;225
12.3.3.2;9.3.3.2 Random Walks in Control Parameter Space;228
12.3.3.3;9.3.3.3 Random Walks in Model Space;229
12.3.3.4;9.3.3.4 Optimizing the Efficiency of Generalized-Ensemble Sampling;230
12.4;9.4 Recent Applications;232
12.5;9.5 Conclusion;235
12.6;References;235
13;10 Protein Structure Prediction: From Recognition of Matches with Known Structures to Recombination of Fragments;239
13.1;10.1 Introduction;239
13.2;10.2 Protein Structure Prediction Methods: Classification and Critical Evaluation;240
13.3;10.3 Meta Approaches to Template-Based Prediction;245
13.4;10.4 From Multiple Template-Based Models to Hybrids;247
13.5;10.5 Fragment Assembly: A New Trend in De Novo Protein Structure Prediction;250
13.5.1;10.5.1 De Novo Modeling by Fragment Assembly (and Subsequent Refinement);251
13.5.2;10.5.2 Hybrid Methods Involving Fragment Assembly and Folding Simulations;254
13.5.3;10.5.3 Other Methods Based on Fragment Prediction;255
13.6;10.6 Why Are the Fragments-Assembly Methods So Successful?;256
13.7;10.7 Conclusions and Outlook;257
13.8;References;258
14;11 Genome-Wide Protein Structure Prediction;263
14.1;11.1 Introduction;264
14.2;11.2 Pioneering Efforts in Genome-Scale Structure Predictions;266
14.3;11.3 TASSER Methods;268
14.4;11.4 I-TASSER Methods;269
14.5;11.5 TASSER/I-TASSER Structure Prediction on Large-Scale Benchmarks;272
14.6;11.6 Prediction of All Medium-Sized ORFs in the E. coli Genome;274
14.7;11.7 Structural Modeling of All 907 Putative GPCRs in the Human Genome;275
14.8;11.8 Application of I-TASSER to the Chlamydia trachomatis Genome;280
14.9;11.9 Concluding Remarks;281
14.10;References;282
15;12 Multiscale Approach to Protein Folding Dynamics;288
15.1;12.1 Introduction;288
15.2;12.2 Structural Dynamics from Combination of Experiment and Simulation;289
15.3;12.3 Protein Dynamics by a High-Resolution Reduced Modeling;292
15.3.1;12.3.1 Paradigm Systems of Protein Folding Studies by a High-Resolution De Novo Modeling;292
15.4;12.4 Summary;296
15.5;References;297
16;13 Error Estimation of Template-Based Protein Structure Models;301
16.1;13.1 Introduction;301
16.2;13.2 Overview of Quality Assessment Measures;304
16.2.1;13.2.1 Physics-Based Score;305
16.2.2;13.2.2 Knowledge-Based Potential;305
16.2.3;13.2.3 Assessing Alignment Quality;306
16.3;13.3 The SPAD Score;306
16.3.1;13.3.1 Definition of the SPAD Score;306
16.3.2;13.3.2 Correlation of SPAD to RMSD of Models;308
16.3.3;13.3.3 Correlation to the Local Quality of Models;308
16.4;13.4 Real-Value Quality Assessment of Structure Models;309
16.4.1;13.4.1 Tondel's Method;309
16.4.2;13.4.2 ProQ;310
16.4.3;13.4.3 TVSMod;310
16.4.4;13.4.4 The SubAqua Method;311
16.4.4.1;13.4.4.1 Correlation of Quality Assessment Terms to RMSD;311
16.4.4.2;13.4.4.2 Variable Selection for Constructing Regression Models;312
16.4.4.3;13.4.4.3 Two-Step Procedure to Predict Local Quality;315
16.5;13.5 Summary;316
16.6;References;317
17;14 Evaluation of Protein Structure Prediction Methods: Issues and Strategies;321
17.1;14.1 Introduction;321
17.2;14.2 Numerical Evaluation of Model Quality;324
17.3;14.3 The Identification of Successful Strategies;327
17.4;14.4 Recognition of Progress in Protein Structure Prediction;329
17.5;14.5 A Priori Estimates of Model Quality;332
17.6;14.6 Applications of Protein Models to Biomedical Research;335
17.7;14.7 Conclusions and Outlook;339
17.8;References;340
18;Index;346