- •Foreword
- •Preface
- •Contents
- •Introduction
- •Oren M. Becker
- •Alexander D. MacKerell, Jr.
- •Masakatsu Watanabe*
- •III. SCOPE OF THE BOOK
- •IV. TOWARD A NEW ERA
- •REFERENCES
- •Atomistic Models and Force Fields
- •Alexander D. MacKerell, Jr.
- •II. POTENTIAL ENERGY FUNCTIONS
- •D. Alternatives to the Potential Energy Function
- •III. EMPIRICAL FORCE FIELDS
- •A. From Potential Energy Functions to Force Fields
- •B. Overview of Available Force Fields
- •C. Free Energy Force Fields
- •D. Applicability of Force Fields
- •IV. DEVELOPMENT OF EMPIRICAL FORCE FIELDS
- •B. Optimization Procedures Used in Empirical Force Fields
- •D. Use of Quantum Mechanical Results as Target Data
- •VI. CONCLUSION
- •REFERENCES
- •Dynamics Methods
- •Oren M. Becker
- •Masakatsu Watanabe*
- •II. TYPES OF MOTIONS
- •IV. NEWTONIAN MOLECULAR DYNAMICS
- •A. Newton’s Equation of Motion
- •C. Molecular Dynamics: Computational Algorithms
- •A. Assigning Initial Values
- •B. Selecting the Integration Time Step
- •C. Stability of Integration
- •VI. ANALYSIS OF DYNAMIC TRAJECTORIES
- •B. Averages and Fluctuations
- •C. Correlation Functions
- •D. Potential of Mean Force
- •VII. OTHER MD SIMULATION APPROACHES
- •A. Stochastic Dynamics
- •B. Brownian Dynamics
- •VIII. ADVANCED SIMULATION TECHNIQUES
- •A. Constrained Dynamics
- •C. Other Approaches and Future Direction
- •REFERENCES
- •Conformational Analysis
- •Oren M. Becker
- •II. CONFORMATION SAMPLING
- •A. High Temperature Molecular Dynamics
- •B. Monte Carlo Simulations
- •C. Genetic Algorithms
- •D. Other Search Methods
- •III. CONFORMATION OPTIMIZATION
- •A. Minimization
- •B. Simulated Annealing
- •IV. CONFORMATIONAL ANALYSIS
- •A. Similarity Measures
- •B. Cluster Analysis
- •C. Principal Component Analysis
- •REFERENCES
- •Thomas A. Darden
- •II. CONTINUUM BOUNDARY CONDITIONS
- •III. FINITE BOUNDARY CONDITIONS
- •IV. PERIODIC BOUNDARY CONDITIONS
- •REFERENCES
- •Internal Coordinate Simulation Method
- •Alexey K. Mazur
- •II. INTERNAL AND CARTESIAN COORDINATES
- •III. PRINCIPLES OF MODELING WITH INTERNAL COORDINATES
- •B. Energy Gradients
- •IV. INTERNAL COORDINATE MOLECULAR DYNAMICS
- •A. Main Problems and Historical Perspective
- •B. Dynamics of Molecular Trees
- •C. Simulation of Flexible Rings
- •A. Time Step Limitations
- •B. Standard Geometry Versus Unconstrained Simulations
- •VI. CONCLUDING REMARKS
- •REFERENCES
- •Implicit Solvent Models
- •II. BASIC FORMULATION OF IMPLICIT SOLVENT
- •A. The Potential of Mean Force
- •III. DECOMPOSITION OF THE FREE ENERGY
- •A. Nonpolar Free Energy Contribution
- •B. Electrostatic Free Energy Contribution
- •IV. CLASSICAL CONTINUUM ELECTROSTATICS
- •A. The Poisson Equation for Macroscopic Media
- •B. Electrostatic Forces and Analytic Gradients
- •C. Treatment of Ionic Strength
- •A. Statistical Mechanical Integral Equations
- •VI. SUMMARY
- •REFERENCES
- •Steven Hayward
- •II. NORMAL MODE ANALYSIS IN CARTESIAN COORDINATE SPACE
- •B. Normal Mode Analysis in Dihedral Angle Space
- •C. Approximate Methods
- •IV. NORMAL MODE REFINEMENT
- •C. Validity of the Concept of a Normal Mode Important Subspace
- •A. The Solvent Effect
- •B. Anharmonicity and Normal Mode Analysis
- •VI. CONCLUSIONS
- •ACKNOWLEDGMENT
- •REFERENCES
- •Free Energy Calculations
- •Thomas Simonson
- •II. GENERAL BACKGROUND
- •A. Thermodynamic Cycles for Solvation and Binding
- •B. Thermodynamic Perturbation Theory
- •D. Other Thermodynamic Functions
- •E. Free Energy Component Analysis
- •III. STANDARD BINDING FREE ENERGIES
- •IV. CONFORMATIONAL FREE ENERGIES
- •A. Conformational Restraints or Umbrella Sampling
- •B. Weighted Histogram Analysis Method
- •C. Conformational Constraints
- •A. Dielectric Reaction Field Approaches
- •B. Lattice Summation Methods
- •VI. IMPROVING SAMPLING
- •A. Multisubstate Approaches
- •B. Umbrella Sampling
- •C. Moving Along
- •VII. PERSPECTIVES
- •REFERENCES
- •John E. Straub
- •B. Phenomenological Rate Equations
- •II. TRANSITION STATE THEORY
- •A. Building the TST Rate Constant
- •B. Some Details
- •C. Computing the TST Rate Constant
- •III. CORRECTIONS TO TRANSITION STATE THEORY
- •A. Computing Using the Reactive Flux Method
- •B. How Dynamic Recrossings Lower the Rate Constant
- •IV. FINDING GOOD REACTION COORDINATES
- •A. Variational Methods for Computing Reaction Paths
- •B. Choice of a Differential Cost Function
- •C. Diffusional Paths
- •VI. HOW TO CONSTRUCT A REACTION PATH
- •A. The Use of Constraints and Restraints
- •B. Variationally Optimizing the Cost Function
- •VII. FOCAL METHODS FOR REFINING TRANSITION STATES
- •VIII. HEURISTIC METHODS
- •IX. SUMMARY
- •ACKNOWLEDGMENT
- •REFERENCES
- •Paul D. Lyne
- •Owen A. Walsh
- •II. BACKGROUND
- •III. APPLICATIONS
- •A. Triosephosphate Isomerase
- •B. Bovine Protein Tyrosine Phosphate
- •C. Citrate Synthase
- •IV. CONCLUSIONS
- •ACKNOWLEDGMENT
- •REFERENCES
- •Jeremy C. Smith
- •III. SCATTERING BY CRYSTALS
- •IV. NEUTRON SCATTERING
- •A. Coherent Inelastic Neutron Scattering
- •B. Incoherent Neutron Scattering
- •REFERENCES
- •Michael Nilges
- •II. EXPERIMENTAL DATA
- •A. Deriving Conformational Restraints from NMR Data
- •B. Distance Restraints
- •C. The Hybrid Energy Approach
- •III. MINIMIZATION PROCEDURES
- •A. Metric Matrix Distance Geometry
- •B. Molecular Dynamics Simulated Annealing
- •C. Folding Random Structures by Simulated Annealing
- •IV. AUTOMATED INTERPRETATION OF NOE SPECTRA
- •B. Automated Assignment of Ambiguities in the NOE Data
- •C. Iterative Explicit NOE Assignment
- •D. Symmetrical Oligomers
- •VI. INFLUENCE OF INTERNAL DYNAMICS ON THE
- •EXPERIMENTAL DATA
- •VII. STRUCTURE QUALITY AND ENERGY PARAMETERS
- •VIII. RECENT APPLICATIONS
- •REFERENCES
- •II. STEPS IN COMPARATIVE MODELING
- •C. Model Building
- •D. Loop Modeling
- •E. Side Chain Modeling
- •III. AB INITIO PROTEIN STRUCTURE MODELING METHODS
- •IV. ERRORS IN COMPARATIVE MODELS
- •VI. APPLICATIONS OF COMPARATIVE MODELING
- •VII. COMPARATIVE MODELING IN STRUCTURAL GENOMICS
- •VIII. CONCLUSION
- •ACKNOWLEDGMENTS
- •REFERENCES
- •Roland L. Dunbrack, Jr.
- •II. BAYESIAN STATISTICS
- •A. Bayesian Probability Theory
- •B. Bayesian Parameter Estimation
- •C. Frequentist Probability Theory
- •D. Bayesian Methods Are Superior to Frequentist Methods
- •F. Simulation via Markov Chain Monte Carlo Methods
- •III. APPLICATIONS IN MOLECULAR BIOLOGY
- •B. Bayesian Sequence Alignment
- •IV. APPLICATIONS IN STRUCTURAL BIOLOGY
- •A. Secondary Structure and Surface Accessibility
- •ACKNOWLEDGMENTS
- •REFERENCES
- •Computer Aided Drug Design
- •Alexander Tropsha and Weifan Zheng
- •IV. SUMMARY AND CONCLUSIONS
- •REFERENCES
- •Oren M. Becker
- •II. SIMPLE MODELS
- •III. LATTICE MODELS
- •B. Mapping Atomistic Energy Landscapes
- •C. Mapping Atomistic Free Energy Landscapes
- •VI. SUMMARY
- •REFERENCES
- •Toshiko Ichiye
- •II. ELECTRON TRANSFER PROPERTIES
- •B. Potential Energy Parameters
- •IV. REDOX POTENTIALS
- •A. Calculation of the Energy Change of the Redox Site
- •B. Calculation of the Energy Changes of the Protein
- •B. Calculation of Differences in the Energy Change of the Protein
- •VI. ELECTRON TRANSFER RATES
- •A. Theory
- •B. Application
- •REFERENCES
- •Fumio Hirata and Hirofumi Sato
- •Shigeki Kato
- •A. Continuum Model
- •B. Simulations
- •C. Reference Interaction Site Model
- •A. Molecular Polarization in Neat Water*
- •B. Autoionization of Water*
- •C. Solvatochromism*
- •F. Tautomerization in Formamide*
- •IV. SUMMARY AND PROSPECTS
- •ACKNOWLEDGMENTS
- •REFERENCES
- •Nucleic Acid Simulations
- •Alexander D. MacKerell, Jr.
- •Lennart Nilsson
- •D. DNA Phase Transitions
- •III. METHODOLOGICAL CONSIDERATIONS
- •A. Atomistic Models
- •B. Alternative Models
- •IV. PRACTICAL CONSIDERATIONS
- •A. Starting Structures
- •C. Production MD Simulation
- •D. Convergence of MD Simulations
- •WEB SITES OF INTEREST
- •REFERENCES
- •Membrane Simulations
- •Douglas J. Tobias
- •II. MOLECULAR DYNAMICS SIMULATIONS OF MEMBRANES
- •B. Force Fields
- •C. Ensembles
- •D. Time Scales
- •III. LIPID BILAYER STRUCTURE
- •A. Overall Bilayer Structure
- •C. Solvation of the Lipid Polar Groups
- •IV. MOLECULAR DYNAMICS IN MEMBRANES
- •A. Overview of Dynamic Processes in Membranes
- •B. Qualitative Picture on the 100 ps Time Scale
- •C. Incoherent Neutron Scattering Measurements of Lipid Dynamics
- •F. Hydrocarbon Chain Dynamics
- •ACKNOWLEDGMENTS
- •REFERENCES
- •Appendix: Useful Internet Resources
- •B. Molecular Modeling and Simulation Packages
- •Index
Index
Ab initio method, 222, 224 |
B-factors, 155, 161 |
Ab initio molecular dynamics, 417 |
Barnase, 382–383 |
Acetanilide, 241 |
Basicity, 427 |
Acid–base equilibrium, 428 |
Bayesian |
Acidity, 427 |
explanatory variables, 329–330 |
Activation free energy, 417 |
models, 322–326, 327–329 |
Active analog approach (AAA), 351, 353 |
parameter estimation, 316–317 |
Adopted basis Newton–Raphson minimiza- |
probability theory, 314–316 |
tion (ABNR), 81–82 |
statistics, 313–349 |
L-Alanine, 246 |
β-Barrel model, 380–381 |
Alanine dipeptide, 383 |
β-Hairpin, 285 |
Alanine hexapeptide, 88–89, 387 |
β-Lactamase, 382 |
Alchemy, 169 |
Binding site, 2, 69, 71, 295–296 |
α-Lactalbumin, 382 |
Biological membranes, 465 |
α-Trichosanthin, 291 |
BIOTECH, 278 |
Alzheimer’s disease, 372 |
BLAST, 278–279 |
AMBER, 12, 17, 138, 289, 443, 450, |
PSI-BLAST, 279, 300 |
497 |
BLOCKs database, 332 |
9-Aminoacridine, 446 |
BLOSUM matrix, 279, 336 |
Amphoteric character, 423 |
BMS force field, 451 |
Analytical continuum electrostatics approach, |
Boltzmann |
142 |
distribution function, 41 |
Angular momentum, 49 |
factor, 72, 76, 373 |
Anharmonic effects, 156 |
principle, 147 |
ANOLEA, 278, 295 |
relation, 55 |
Antisense, 448 |
Born, 99 |
Apomyoglobin, 382 |
free energy, 188 |
AQUA, 278, 294 |
generalized, 98 |
Arrhenius equation, 382 |
radii, 99 |
Articulated body, 121 |
solvation free energy, 94, 398 |
Atom truncation (see Potential energy) |
Born–Oppenheimer energy, 138 |
Atomic force microscopy, 441 |
Boundary conditions |
Autoionization, 423 |
finite, 112 |
503
504
Boundary element method, 99, 141–142 Bovine pancreatic trypsin inhibitor (BPTI),
2, 382, 383
Bovine protein tyrosine phosphate, 230 Brain lipid-binding protein (BLBP), 296–298 Branch-and-bound algorithm, 257, 267 Brownian
dynamics, 57, 452 rotational motion, 491
Canonical partition function, 41 CAP-DNA complex, 3 Cartesian coordinate, 115 CASP, 294
CATH, 277–278
CFF (consistent force field), 14 CHARMM, 12, 14, 17, 21, 141, 283, 289,
294, 399, 443, 450, 497 Chemical informatics, 363 Chemical library design, 364 Chemical reaction, 417 Chemical reactivity, 222 Chemical shifts, 254
Chemoinformatics (or cheminformatics), 363 Chromosome, 449
Citrate synthase, 231 Cluster
analysis, 85–86 sampling methods, 364
Coarse-grain potentials, 65 Combinatorial chemical synthesis, 363 Combining rules, 11
Comparative field analysis (CoMFA), 351, 353, 359
Comparative modeling (homology modeling), 3, 275–312
alignment, 279–280 errors, 290–294 evaluation, 294–295
identifying related proteins, 277–279 loop modeling, 285–286
model building, 280–289 side chain modeling, 286–289
COMPASS, 14 COMPOSER, 278, 280–281
Computer aided drug design (CADD), 351 Condensed phase spectroscopy, 222 Configuration interaction, 395, 421 Conformational analysis, 69–90
cluster analysis, 85–86
principal component analysis (PCA), 86– 89, 384
Index
[Conformational analysis]
principal coordinate analysis (PCoorA), 86–89, 384
side chain, 314, 321, 339–344 similarity measures, 84
Conformational constraints, 187 energy surface, 153 equilibrium, 427 flexibility, 222 restraints, 184 transitions, 2–3
Conformation sampling, 70–77, 286 distance geometry, 75–76 enumeration, 70, 75, 286
genetic algorithms, 73–74, 286
high temperature molecular dynamics, 70– 71, 286
J-walking, 74, 76
minimum perturbation method, 286 Monte Carlo simulations (MC), 71–73,
286
multiple copy simultaneous search (MCSS), 286
parallel tempering, 74, 76–77 Conformation
minimization, 77–82 optimization, 77–83 simulated annealing, 82–83
space, 70–71, 76, 86, 87–89, 289, 373–374 CONGEN, 278
Conjugated gradients minimization (CG), 79–81, 82, 284 (see also Minimization)
Conservation
angular momentum, 43, 51 energy, 43, 51
linear momentum, 43, 49, 51 Constant-pressure simulation, 53, 60 Constant-temperature (isothermal) simula-
tion, 53, 58 Constraint, 53, 62
dynamics, 122 Lagrange multipliers, 63 RATTLE, 50
SHAKE, 50, 63 Continuum
boundary conditions, 98 electrostatic approximation, 140 model, 94, 417, 459 Poisson–Laplace equation, 418 reaction field, 170
treatment of solvation, 94
Index
Convergence, 457 Coordinate frame global, 119
local, 119 Correlation function, 54
auto-correration function, 54 cross-correlation function, 54
pair correlation function, 421, 423 Coulomb’s law (see Electrostatics) Coupled cluster, 421
Covariance matrix, 87, 156 Crambin, 384 Creutzfeldt–Jakob disease, 372 CRYSTAL, 34
CURVES, 458 Cystic fibrosis, 372 Cytochrome c, 384
Cytosine-5-methyltransferase, 3
DALI, 277–278 Databases
mining, 363, 364
sequence and structure, 277–279 Daunomycin, 446
Debye model, 491
Debye–Huckel approximation, 143 Debye–Waller factor, 161, 249, 480, 489 Density function method, 222, 395
Kohn–Sham orbital, 223, 417 Detailed balance, 70
DIALS & WINDOWS, 458 Dielectric
boundaries, 22, 141 constant, 22, 417 continuum, 94, 170, 188 distance-dependent, 442, 450 effective, 98
permittivity of a vacuum, 92 R-dependent, 22
reaction field, 188 Diffusion
constant, 485 equation method, 290
Dihydrofolate reductase, 24 Dipalmitoylphosphatidylcholine (DPPC), 465 Direct sampling methods, 364
Dirichlet
distribution, 324, 327–329 mixture priors, 330–332
DISCO, 364
Dispersion (see van der Waals)
505
Distance geometry, 75–76, 258, 281, 359 bound smoothing, 258
embedding, 260 metric matrix, 259 refinement, 260
self-correction method, 264 Distance matrix, 84, 85, 87 Diversity analysis, 363 DNA, 1–3, 222, 441
2′-5′-phosphodiester, 448 B, 127, 442
base sequence, 444 base stacking, 447
environmental influence, 444 helicoidal parameters, 458 hydration, 458
iDNA, 449
peptide nucleic acids, 448 phase transition, 448 phosphoramidate, 448 polymerase III, 298 quadraplex, 449 ribbonlike model, 451 segmented rod model, 452 triplex, 443
water activity, 444, 458 Z, 442, 449
DOCK, 365 Docking, 296, 353
flexible docking, 69, 74, 76 ligand-receptor, 361
Donor-acceptor energetic interaction, 393 Dopaminergic ligand-receptor system, 352 Downhill simplex minimization, 79 DRAGON, 278
DREIDING, 14 Drew dodecamer, 443 Drug
computational screening, 69 computer-aided drug discovery, 69 discovery and design, 4, 85, 363 structure-based drug discovery, 69 therapy, 448
Dummy atoms, 177
Dynamic average, 41, 42, 270 Dynamicin, 446
Dynamic structure factor, 478
ECEPP (empirical conformational energy program for peptides), 15, 289
EcoRI, 445
Einstein relation, 485
506
Elastic incoherent structure factor, 479 Electron transfer
properties, 393, 404 protein, 393
rate, 394, 408 Electrostatics, 91
Coulomb’s law, 93, 95
Coulombic interactions, 96, 97, 105 electric field, 92
Ewald (see Ewald) potential, 92
Empirical force fields (see Potential energy function)
Empirical valence bond method, 222 Energy barriers, 70, 71, 76, 83, 86, 383–388 Energy basins, 86
Energy landscape, 71, 82–83, 373–374, 383–388
Ensemble average, 41, 270
canonical, 41, 58, 70 isobaric–isoenthalpic, 61 isothermal–isobaric, 58, 62, 470 microcanonical, 58, 127, 470 structure, 270
Entropy distance, 329 Enzyme, 221, 222
-catalyzed reactions, 222 Eosinophil neurotoxin, 291 Equipartition theorem, 49, 155 Erabutoxin, 293
Ergodic hypothesis, 42, 70, 76 ERRAT, 278
ESP (electrostatic potential), 21 Esperamicin, 446
Estrogen receptor, 445 Evolutionary algorithms, 360
Ewald, 105, 188, 191, 443, 447, 455 conducting boundary conditions, 106 particle mesh Ewald (PME), 27, 111, 188,
443
particle mesh (PM3), 110 summation, 105, 170, 399, 469
tinfoil boundary conditions, 192 (see also Ewald conducting boundary conditions)
Exchange repulsion (see van der Waals) Extended electrostatics, 454
FastA, 278–279
Fast multipole (FMA), 99, 103, 110, 454 Field integrated electrostatic approach, 142 Finite-difference relaxation algorithm, 141
Index
Finite difference, 99 Flavodoxin, 276, 282
Flexibility (see Molecular flexibility) Fluorescence spectroscopy, 238 Fock operator, 417
Folding simulations, 382–383 Folding temperature, 373
Folds, structural, 275, 279–280, 298, 371 Force field, 4, 71, 283
Formamide, 431
Frank–Condon
ionization potential, 400 transition, 408
Free energy, 137 absolute binding, 172 alchemical, 170 cavity formation, 139
charging free energy, 140 component analysis, 181 conformational, 184 coupling coordinates, 176 difference, 169, 417
dual topology, 180 endpoint corrections, 177 λ-Dynamics, 194
Gibbs, 58
Helmholtz, 58, 172, 421 hybrid energy function, 176 landscape, 388
perturbation formula/theory, 172, 174, 403, 449
simulations, 362, 403, 407 single topology, 180
slow growth method, 403 solvation, 141, 459 standard binding, 181
thermodynamic coupling, 137 thermodynamic cycle, 170, 362 thermodynamic integration, 137, 177, 185,
403
weighted histogram analysis method (WHAM), 186
FREEHELIX, 458
Frequentist probability theory, 317–322 FSSP database, 332
Funnel, energy, 373
Gaussian distribution, 54
GAUSSIAN, 21
Gauss’s law, 93, 97
GenBank, 277, 313
GeneCensus, 278
Index
Generalized born, 98, 142
Genetic algorithms (GA), 73–74, 257, 286, 290, 360
crossover operation, 73–74 migration operator, 73 mutation operator, 73–74
Gibbs sampling, 327–329 Glass temperature, 373 Global minimum, 82, 289 Global optimization, 82–83 Glucocorticoid receptor, 445
3-Glycerophosphate synthase, 291 Go model, 378, 380
G-protein coupled receptors, 353 Gradient RMS (GRMS), 82 Graph theoretic indicies, 359 Grid
-based sampling, 364 GRID, 365
Search, 78 GROMOS, 12, 450, 497
Hamiltonian, 41, 43 Hammett
equation, 358
σ parameter, 359 Hansch
Corwin, 351
π parameter, 358 Harmonic
analysis, 153 approximation, 118 potential, 71
HARMONY, 295 Hartree–Fock, 232, 395, 421
Heisenberg uncertainty principle, 95 Helmholtz (see Free energy) Hemoglobin, 2
Hessian matrix, 81
High throughput screening, 363 HIV protease, 4
HMG-D chromosomal, 445
Holonomic distance constraints, 122 Homeodomain, 445
Homology modeling, (see Comparative modeling)
Hoogsteen, 451 HP model, 378
HSSP database, 332
Hybrid QM–MM method (see Quantum mechanical/molecular mechanical)
507
Hydrocarbon chain dynamics, 488 Hydrophobic, 139
container, 100 Hydrogen bond
CEH O, 447
Hypernetted chain approximation, 420
ICM, 278
Ileal lipid-binding protein, 293 Image approximation, 100 Immunoglobulin fold, 285
Implicit solvent method (see Solvent) Infrared spectroscopy, 491
InsightII, 278 Insulin, 2
Integral equation, 170 Integrators
integration time step, 49 leapfrog, 45, 46, 123 Runge–Kutta, 123 stability, 50
Sto¨rmer–Verlet-leapfrog, 123 time reversibility, 43, 51 velocity Verlet, 45, 47 Verlet, 123
Interleukin 1β, 44, 293 Internal coordinate
molecular dynamics (ICMD), 115, 269 simulation, 115, 452
Ion channel, 467 Ionic
product, 424 strength, 142
J-walking, 74, 76 JUMNA, 452
Jumping among minima model (JAM), 165, 384
Karmers theory, 438 Kinematics equation, 120 Kinetic rate constant, 200
Knowledge-based potentials, 135, 147 (see also Coarse-grain potentials)
Kohn, W., 7
Kullback–Leibler divergence (see Entropy distance)
Kurtosis, 54
Lac repressor, 445 Lagrange–Hamilton formalism, 122
508
Lambda (λ) dynamics, 194 (see also Free energy)
λ repressor, 288 Lanczos algorithm, 157 Lattice models, 376–379 Langevin
dipole method, 399 equation, 56 generalized, 438 mode analysis, 163
Lattice summation methods (see Ewald) Leapfrog (see Integrators)
Legendre polynomial, 491 Lennard–Jones (see van der Waals) Levinthal paradox, 371
Lid method, 386
Linear free energy relationship (LFER), 358 Linear response approximation, 176, 239 Liouville formulation, 63
Liouville operator, 63 Lipid
bilayer structure, 465, 471
neutron scattering measurements, 477 polar group, 473
-soluble protein, 465
London’s dispersion interactions (see van der Waals)
LOOK, 278
Lysozyme, 2, 243, 372, 384
Marcus Theory, 394, 408
Markov chain Monte Carlo (MCMC), 322, 326–327
Master equation, 375 Maxwell’s equations, 93
Mean force field approximation, 145 Membranes, 3
molecular dynamics, 476 Methotrexate, 24 Methylamine, 428
Metric tensor, 118
Metropolis algorithm, 72, 76, 326 Metropolis–Hastings method, 326–327 Minimally frustrated random energy model,
374–376
Minimum perturbation method, 286 Minimization, 71, 77–82
adopted basis Newton–Raphson (ABNR), 81–82
conjugated gradients (CG), 79–81, 82, 116 downhill simplex method, 79
energy gradients, 121
Index
[Minimization]
gradient RMS (GRMS), 82 grid search, 78 minimization protocol, 82 Newton–Raphson (NR), 81
steepest descent (SD), 79–80, 82
MMFF (Merck molecular force field), 14, 21 ModBase, 278, 282–285, 299
MODELLER, 278–279, 292
Modeling, 3, 69, 275–312
ab initio modeling, 289–290
homology modeling (see Comparative protein structure modeling)
Models lattice, 289
simplified, 289 Molar refractivity, 359 Molecular
descriptors, 364 orbital theory, 417 polarization, 422 shape analysis, 359 similarity, 364
surface, 141 (see also Solvent accessible surface area)
trees, 123
Molecular dynamics (MD), 2, 39, 69, 74, 76, 83, 247, 261, 284, 286, 290, 361, 380, 384, 407, 418
high temperature MD, 70–71, 76, 77 simulation practice, 48
simulation protocol, 51 Molecular flexibility, 2–4, 69 MOLSCRIPT, 287
Monte Carlo simulations (MC), 39, 71, 74, 76, 77, 83, 115, 117, 257, 286, 290, 418 (see also Conformation sampling)
Markov chain Monte Carlo, 322, 326–327 metropolis algorithm, 72
trial move, 72–73 Mossbauser spectroscopy, 238 MP2 method, 232
Mulliken population analysis, 397
Multiple copy simultaneous search (MCSS), 286
Multiple linear regression (MLR), 358 Multiple time step method (MTS), 53, 63 Multipole expansion, 100
Myoglobin, 2, 382, 383
Nanotechnology, 441, 449
Nernst equation, 394
Index
Neural network artificial, 360
Neutron scattering, 239, 244
coherent inelastic neutron scattering, 245 elastic incoherent structure factor, 248 incoherent neutron scattering, 246 inelastic incoherent scattering intensity,
248
Newton, Sir Isaac, 169 Newton
equation of motion, 42 -Euler analysis, 124
-Raphson minimization (NR), 81 second law of motion, 42
Newtonian dynamics, 123 Nitrous acid, 446
Non-bond interactions (see Potential energy) Nonuniform charge distribution, 138 Normal mode analysis, 115, 153, 237
anharmonicity, 163 anharmonicity factor, 164
dihedral angle space normal mode analysis, 156, 158
refinement, 160
single parameter model, 159 Nuclear hormone receptors, 445
Nuclear magnetic resonance (NMR), 238, 253
spectroscopy, 3, 69, 76, 84, 294, 295, 314 structure determination, 254
Nuclear Overhauser effect (NOE), 253 ambiguity, 255, 265
assignment method (ARIA), 262, 265 automatic assignment, 265
intensity, 255 spin diffusion, 267
structural restraints, 255
Off-lattice models, 379–381 Oligonucleotide, 441
123D, 278
Onsager–Kirkwood parameter, 432 Onsager’s reaction field, 417
OPLS (optimized potential for liquid simulations), 17, 21
Optical tweezers, 441
Overhauser spectroscopy (NOSEY), 161 Oxidoreductase, 287
PAM matrix, 335, 336
Parallel tempering, 74, 76–77
Partial least squares (PLS), 359
509
Particle mesh Ewald (PME) (see Ewald) Pauli exclusion principle, 10, 138, 224
PDB (protein data bank), 277–278, 313, 336 Peptide bond
cis–trans transition, 71 Percus–Yevick approximations, 420
Periodic boundary conditions, 53, 96, 104, 401, 454
Pharmacophore, 351, 353
modeling by distance geometry, 76 prediction, 364
Phase space, 41 PhD, 278
Phosphoglycerate Kinase, 248 PIR database, 277
Poisson’s
-Boltzmann equation, 100, 139, 143, 189, 398, 403
equation, 93, 98, 140 Pople, J.A., 7
Pores, 467 Potential energy
electrostatic (see Electrostatics) force field, 16, 41, 468 function, 8, 95
hybrid (see Free energy) non-bond pairs, 96 optimization, 27 parameters, 8
shifting, 105
spherical truncation, 447 switching, 105, 447 transferability, 16
truncation methods, 97, 105, 454 twin-range approach, 100
Potential of mean force (PMF), 55, 134, 135, 136, 184, 289, 427, 447 (see also Umbrella sampling; Free energy)
PRESAGE, 278
Principal component analysis (PCA), 86–89 Principal coordinate analysis (PCoorA), 86–
89
Prion protein, 384–385 PrISM, 278 PROCHECK, 278, 294 PROCHECKNMR, 294 ProCyon, 278 PROFIT, 278
PROSAII, 278, 295, 298, 299, 300 Protein: characteristic motion, 40 Protein folding, 69, 289–290, 371–391
atomistic models, 382–388
510
[Protein folding] energy basins, 384
energy landscape, 373–376, 383–388 folding simulations, 382–383 folding temperature, 373
free energy landscape, 388 funnel, 373–374, 386–388 go model, 378, 380
HP model, 378
lattice models, 376–379
minimally frustrated model, 374–376 off-lattice models, 379–381
reaction coordinate, 373–374 simple models, 374–376 topological mapping, 385–388 unfolding simulations, 382–383
PROVE, 278 PSI-BLAST, 279, 300
Quantitative structure-activity relationship (QSAR), 351, 358
nonlinear, 360 Quantum dispersion, 138
Quantum mechanical/molecular mechanical (QM/MM), 3, 196, 222, 419, 446
boundary, 226
frozen orbital approach, 226 generalized hybrid orbital, 226 link atom approach, 226
local self-consistent field, 226 Quantum mechanics, 221 Quasi-harmonic analysis, 86, 154, 164 Quaternion, 119
Radial distribution function, 421, 474 Ramachandran map, 2, 321, 341 Random number generator, 73
Rare events, 199 RASMOL, 299 Rate equations, 200
Rational library design, 363 Reaction coordinate, 184, 199
Reaction field methods, 105, 140, 145, 454 Reaction path
conjugate peak refinement, 217 construction, 214
cost function, 211 diffusional path, 213
dominant reaction pathway, 209 Elber and Larplus paths, 211 MaxFlux reaction path, 212 Onsager–Machlup path, 213 variational method, 211
Index
Receptor essential volume, 357 Receptor excluded volume, 357 Redox potentials, 399
Reference interaction site model (RISM), 144, 163, 419
Reorganization energy inner shell, 395 outer shell, 395
RESP (restrained electrostatic potential), 21 Restriction endonuclease (see also EcoRI),
445
Retinoic acid binding protein I, 291 R-factor, 161
Ribonuclease A, 2, 291 Ribozyme, 447
Rigid body dynamics, 122
RMS distance (RMSD), 84, 276, 287–289 RNA, 2, 441
messenger, 446 tetraloop, 446 transfer, 441, 446 TRNA-Asp, 447
Rotamers, side chain, 278, 286–289, 321, 339–344
Rotational dynamics, 491 Rubredoxin, 401, 410 Runge–Kutta (see Integrators)
Salt effect, 398 Scalar couplings, 254
Scaled particle theory (SPT), 139 Scattering
Intensity, 240
Intermediate scattering function, 478 Schro¨dinger equation, 95, 223
SCOP, 277–278 Screening
broad, 363 target, 363
Secondary structure α-helix, 2, 281 β-sheet, 2
prediction by Bayesian methods, 338–339 SEGMOD, 281
Semiempirical method, 222, 225 Sequence alignment
Bayesian (see also Bayesian), 322–336 multiple, 332
Sequence comparison, 279 Sequence profiles, 330–332
Sequence-structure alignment, 336–338 Side chain conformational analysis, 314,
321, 339–344
Index
Similarity metric, 264 Simplectic, 123
Simplex minimization, 79
Simulated annealing, 71, 82–83, 117, 257, 261, 284, 360, 380, 417
Skewness, 54
Slater determinant, 223
Slow growth method (see Free energy) SN2 Reaction, 226, 433 Solution-phase reaction, 222 Solvation, 222, 442
inducible mulipole solvation model, 142 Solvatochromism, 426
Solvent
accessible surface area (SASA), 135, 138, 141, 146
boundary potential, 135, 145 effect, 133, 417
implicit, 133
implicit and explicit mix, 145 SPC/E (see Water models)
Spherical truncation (see Potential energy) SQUID, 278, 294
Staphylococcal nuclease, 249 Staphylococcal protein A, 388 Statistical analysis
standard error, 457
Steepest descent minimization (SD), 79–80, 82 Stochastic
boundary conditions, 100 dynamics, 56
Sto¨rmer–Verlet-leapfrog (see Integrators) Streptococcal protein G, 388
Structural genomics, 298–300 Superposition principle, 93 Surface
accessibility, 338–339 constrained all-atom solvent, 145 tension, 139
SWISS-MODEL, 278, 300
SWISS-PROT, 277, 300 Switching (see Potential energy) SYBYL, 278, 356
Symmetrical oligomers, 266 Symplectic, 123
TATA box, 3, 444 binding protein, 445
Tautomerization, 432 Telomeres, 449 Thermodynamic
average, 41, 42
cycle (see Free energy)
511
[Thermodynamic]
integration (see Free energy) perturbation theory (see Free energy)
THREADER, 278 Threading, 3, 280, 314 Through-bond couplings, 254 TIP3P (see Water models) TOPITS, 278
Torsion angle dynamics, 261 Trajectory analysis
mean-square displacement, 485 root-mean-square average, 54 time series, 53
Transcription factor, 445 Transition state, 199
energy barrier, 199 surfaces, 199
Transition state theory (TST), 201 corrections, 204
dynamic recrossings, 207 interstate dynamics, 201 rate constant, 201, 204 reactive flux method, 205
Transmembrane potential, 143 Transmission coefficient, 208 TrEMBL, 277–278 Triosephosphate isomerase, 3, 228 TRP operator, 446
Umbrella potentials, 194
sampling, 184, 187, 194, 410, 447 Unfolding simulations, 382–383
van der Waals, 10, 96 dispersion, 96 exchange repulsion, 96 Lennard–Jones, 10, 97
Variational principles, 421 VERIFY3D, 278, 295 Verlet (see Intergrators) Verlet neighbor list, 52 Verloop, 358
Vibrational spectra, 30 Villin, 382
Virtual molecular library, 69 Voronoi techniques, 359
Water
SPC, 399
SPC/E, 22, 399
TIP3P, 22, 399, 450
TIP4P, 399
512
Watson and Crick, 441, 451 Weighted histogram analysis method
(WHAM) (see Free energy) WHATCHECK, 278, 294 WHAT IF, 278
Wigner self energy, 109
X-ray Bragg diffraction, 241
Index
X-ray crystallography, 1–2, 3, 69, 84, 275, 281, 294, 295, 314
X-ray diffuse scattering, 242 X-ray scattering, 239, 240
Zero point energy, 119
ZIF268, 446
Zinc finger proteins, 445
