Computational Approaches for Understanding Dynamical Systems Protein Folding and Assembly

Copyright......Page 1
Contributors......Page 2
Preface......Page 5
Pairwise-additive and polarizable atomistic force fields for molecular dynamics simulations of proteins......Page 8
Introduction......Page 9
Bonded interactions......Page 10
Nonbonded interactions......Page 13
Pairwise-additive force fields......Page 16
AMBER......Page 17
AMBER-derived implicitly polarized force fields......Page 27
CHARMM......Page 31
OPLS......Page 36
GROMOS......Page 43
Polarizable force fields......Page 49
Fluctuating charge models......Page 50
Induced dipole and multipole models......Page 53
The classical Drude oscillator......Page 59
Conclusions and future directions......Page 70
References......Page 71
Scale-consistent approach to the derivation of coarse-grained force fields for simulating structure, dynamics, and thermod .........Page 79
Introduction......Page 80
Potential of mean force of a coarse-grained system as a prototype of the effective energy function......Page 83
Steps toward a scale-consistent coarse-grained energy function......Page 84
Factorization of the PMF into Kubo cluster-cumulant functions......Page 85
Analytical scale-consistent approximations to the coarse-grained energy terms......Page 91
Parameterization of the effective energy expressions......Page 95
Force field calibration......Page 96
The UNIfied COarse-gRaiNed (UNICORN) model of biological macromolecules......Page 97
Long-range Upipjel and UBiBjel terms......Page 101
Torsional terms......Page 104
Backbone-local-correlation (Ucorr(3)) terms......Page 106
Molecular dynamics and other conformational-search engine implementation with UNICORN......Page 108
Applications of UNICORN......Page 109
Protein structure prediction......Page 110
Effect of hydrodynamic interactions on folding kinetics......Page 112
Investigation of Hsp70 chaperone cycle......Page 114
Investigation of telomere stability......Page 116
Conclusions......Page 119
References......Page 120
How to learn from inconsistencies: Integrating molecular simulations with experimental data......Page 129
Introduction......Page 130
Reweighting strategies......Page 132
Maximum entropy......Page 134
Maximum parsimony......Page 137
Bayesian inference or MaxPrior......Page 138
Comparing MaxEnt, MaxPars, and MaxPrior reweighting......Page 140
General applicability......Page 141
Imperfect force fields......Page 142
Numerical challenges......Page 143
Maximum entropy......Page 144
Empirical energy terms......Page 145
Bayesian inference......Page 146
Forward models......Page 147
Force field optimization......Page 148
Background on force field parametrization......Page 149
Proteins......Page 151
RNA......Page 154
Matching time-dependent and time-resolved data......Page 156
Maximum entropy and likelihood in dynamical systems......Page 158
Maximum Caliber......Page 160
Average Block Selection......Page 164
Challenges......Page 165
Balance between simulations and experimental data......Page 166
Interplay between reweighting and force field corrections......Page 167
Using kinetic data to reweight equilibrium ensembles......Page 168
A new generation of force fields......Page 169
Conclusions......Page 171
References......Page 172
Enhanced sampling and free energy calculations for protein simulations......Page 183
Introduction......Page 184
Collective variable and free energy......Page 185
Umbrella sampling......Page 189
Metadynamics......Page 192
Steered molecular dynamics......Page 197
CV-free sampling......Page 198
Replica exchange molecular dynamics......Page 199
Accelerated molecular dynamics......Page 202
Combination of enhanced sampling approaches......Page 205
Programs and tutorials......Page 207
Conclusion and outlook......Page 208
References......Page 210
Long-time methods for molecular dynamics simulations: Markov State Models and Milestoning......Page 220
Introduction......Page 221
Markovian approach: Markov state models for MD simulations......Page 223
Relative RMSD for state assignment......Page 228
Applications of MSMs to enhance sampling in simulations of folding and binding of amyloid peptides......Page 230
Milestoning......Page 233
Conclusions......Page 238
Acknowledgments......Page 239
References......Page 240
Introduction......Page 243
Thermal stability in silico......Page 245
Thermophilic proteins......Page 250
Protein stability in crowded environments......Page 260
Conclusion......Page 269
References......Page 270
Computer simulations of protein-membrane systems......Page 277
Introduction......Page 281
Lipid diversity: The scaffold of biological membranes......Page 284
Membrane proteins: The complexity of biological membranes......Page 292
Lipid rafts and hydrophobic mismatch: The regulation and organization of biological membranes......Page 294
Role of MD simulations in investigating protein-membrane systems......Page 295
Lipid force fields......Page 298
General description of classical force fields......Page 300
Area per lipid......Page 302
Membrane thickness and electron density profile......Page 305
Acyl chain order parameters......Page 307
Lateral diffusion coefficient......Page 309
CHARMM......Page 310
AMBER......Page 313
Slipids......Page 314
GROMOS......Page 315
Limitations of atomistic lipid force fields......Page 316
The HMMM model......Page 317
Coarse-grained force fields for lipids......Page 318
The all-atom to coarse-grained mapping......Page 321
CG mapping of lipids......Page 323
Other lipid CG models......Page 325
Which lipid FF to choose for a simulation?......Page 327
CHARMM-GUI......Page 328
Nanodisc builder......Page 329
MARTINI builder......Page 330
Simulations with OPLS-AA FF......Page 331
Simulations with MARTINI FF......Page 332
MD simulation software packages......Page 333
Analysis tools......Page 334
G-protein-coupled receptors......Page 335
β2-adrenergic receptor (β2AR)......Page 338
Adenosine A2A receptor (A2AR)......Page 339
Opsin receptor......Page 341
XylE/LacY transporters......Page 342
Inwardly rectifying potassium (Kir2.1) channel......Page 344
Glycoproteins......Page 346
Mitochondrial membrane protein......Page 348
Amyloid β (Aβ) peptide......Page 349
The human islet amyloid polypeptide (hIAPP)......Page 353
α-synuclein (aSyn)......Page 359
Fibroblast growth factor (FGFs)......Page 361
Pleckstrin homology (PH) domain......Page 366
Actin-binding proteins (ABPs)......Page 367
Viral fusion proteins......Page 368
Curvature induction and membrane remodeling by FAM134B reticulon homology domain assist selective ER-phagy......Page 371
Lipid droplet biogenesis is a liquid phase separation spatially regulated by seipin and membrane curvature......Page 373
Lipid-protein interactions are unique fingerprints for membrane proteins......Page 376
Conclusions and future directions......Page 380
References......Page 382
Introduction......Page 408
Experimental structure determination methods......Page 411
Models for assembly......Page 413
Confined models......Page 416
Interactions between capsid proteins......Page 417
Energy landscapes of coarse-grained capsid models......Page 419
``Magic number´´ clusters......Page 421
Non-spherical shells with polyhedral symmetries......Page 422
Open tubes......Page 425
Helical capsids......Page 427
Hierarchical self-assembly of addressable capsids......Page 429
Conclusions and outlook......Page 430
References......Page 431
Aggregation of disease-related peptides......Page 438
Introduction......Page 439
Computer simulation models for amyloid protein aggregation......Page 440
Structures of small aggregates......Page 445
Exploring the early aggregates of amyloid peptides at quasi-atomic level with hydrodynamics......Page 447
Primary and secondary nucleation from simulations......Page 451
Recent advances in structures of Aβ40/42 oligomers from simulations......Page 454
Conclusions......Page 456
References......Page 457
Computational studies of protein aggregation mediated by amyloid: Fibril elongation and secondary nucleation......Page 464
Introduction......Page 465
``Fast-deposition´´ versus ``lock-and-dock´´ mechanisms......Page 467
Insights into the ``dock-and-lock´´ mechanism of fibril elongation......Page 471
Initial docking of peptides driven by water release......Page 472
Structural rearrangement in the locking step......Page 473
Experimental background......Page 481
Insights into surface-induced nucleation of peptides......Page 483
Protein/peptide-surface interactions......Page 484
Insights from simulations of nucleation processes on surfaces......Page 486
Insights from CG simulations of secondary nucleation......Page 488
Peptide-fibril interactions characterized by simulations at high resolution......Page 490
Summary and outlook......Page 496
References......Page 498
Introduction......Page 508
Markov chain Monte Carlo simulations......Page 510
Small update MC simulations......Page 511
Folding and aggregation using all-atom MC simulations......Page 512
Exploring fibril formation with lattice models......Page 517
Conclusion......Page 519
References......Page 520
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