Multiscale Approaches to Protein Modeling (eBook)

Andrzej Kolinski (Herausgeber)

eBook Download: PDF

2010 | 2011
XII, 355 Seiten
Springer New York (Verlag)
978-1-4419-6889-0 (ISBN)

Infos

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.

Inhalt

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

Produkt-Details

• Erscheint lt. Verlag: 13.10.2010
• Zusatzinfo: XII, 355 p.
• Verlagsort: New York
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik
• Themenwelt: Studium ► 1. Studienabschnitt (Vorklinik) ► Biochemie / Molekularbiologie
• Themenwelt: Naturwissenschaften ► Biologie ► Biochemie
• Themenwelt: Naturwissenschaften ► Biologie ► Genetik / Molekularbiologie
• Themenwelt: Naturwissenschaften ► Biologie ► Mikrobiologie / Immunologie
• Themenwelt: Technik
• Schlagworte: Protein Structure
• ISBN-10: 1-4419-6889-X / 144196889X
• ISBN-13: 978-1-4419-6889-0 / 9781441968890