- •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
Long-Range Forces and Potential |
113 |
clear yet whether the artifact can be fully fixed in nonspherical systems, although it seems likely.
There are other inherent limitations to the finite boundary method. Due to the finite size of the system, water molecules cannot undergo natural unrestricted diffusion, and examination of water dynamics must be restricted to interior positions. Other transport properties may be affected as well by the size constraint. Existing boundary potentials cannot fully prevent artifactual structure and dynamics of the water near the vacuum/ solvent interface, and thus the layer of solvent must be large enough to allow sufficient bulklike solvent about the solute molecule, reducing the cost advantage over periodic boundary conditions. However, these potentials will probably continue to improve, leading to more accurate simulations with minimal solvation.
Periodic boundary conditions have long been a popular choice for simulations using explicit solvent. Using such a simulation, the density and pressure of the system can be easily specified. Furthermore, since the energy of the system is periodic, there are no preferred positions within the simulation cell. Regardless of position within the central cell, an atom is surrounded on all sides by atoms or their images. Unlike finite boundary conditions, there is no vacuum interface to be ‘‘fixed’’ via boundary potentials. Thus it is believed that periodic boundary conditions best model the environment of bulk matter, at least when long-range electrostatics are not involved.
Wood and Parker [50] discuss the possible artifacts due to periodic boundary conditions, in the context of short-range interactions. They suggest the still sensible approach of varying the unit cell size and shape to empirically test for the size of artifacts in finite systems. The implicit assumption is that these artifacts should vanish in the limit of large unit cell sizes. These early simulations established that artifacts due to periodicity in typical simulations (e.g., no phase transitions occurring) are negligible for systems that have only short-range interactions. When electrostatic interactions are involved, there are finite size periodicity artifacts, and the assumption that these artifacts vanish in the limit of large unit cell sizes is not immediately evident, due to the subtleties of conditional convergence discussed above. However, it seems clear from recent work [9,51] that the correct limiting behavior is reached for large unit cell sizes, and thus the question becomes, At what rate do the artifacts vanish?
Smith and Pettitt [52] studied the free energy of rotation of a dipole in an ideal dielectric at various temperatures. They conclude that finite size artifacts are not large in high dielectric solvents such as water at room temperature. Problems may arise in low dielectric solvents. Similar conclusions about the effect of solvent dielectric were reached in studies of charging free energies by Hummer et al. [51]. In the case of simple point ions they were able to correct these artifacts. The consensus at this point is that finite size artifacts due to periodicity are manageable in simulations of molecules in water at room temperature. The artifacts may be much larger in simulations of charged or polar molecules in low dielectric solvents [49]. In any case the suggestion of Wood and Parker [50] that the results be examined as a function of unit cell size is still pertinent.
REFERENCES
1.JD Jackson. Classical Electrodynamics. New York: Wiley, 1975.
2.P Auffinger, DL Beveridge. Chem Phys Lett 234:413, 1995.
3.JS Bader, D Chandler. J Phys Chem 96:6423, 1992.
4.G Hummer, LR Pratt, AE Garcia, BJ Berne, SW Rick. J Phys Chem B 101:3017, 1997.
114 |
Darden |
5.G Hummer, LR Pratt, AE Garcia, S Garde, B Berne, SW Rick. J Phys Chem B 102:3841, 1998.
6.H Ashbaugh, R Wood. J Phys Chem B 102:3844, 1998.
7.J Aqvist, T Hansson. J Phys Chem B 102:3837, 1998.
8.GH Hardy. A Course of Pure Mathematics. Cambridge, UK: Cambridge Univ Press, 1975.
9.T Darden, D Pearlman, L Pedersen. J Chem Phys 109:10921, 1998.
10.JA McCammon, PG Wolynes, M Karplus. Biochemistry 18:927, 1979.
11.M Levitt. Cold Spring Harbor Symp Quant Biol 47:251, 1982.
12.R Lavery, H Sklenar, K Zakrzewski, B Pullman. J Biomol Struct Dynam 3:989, 1986.
13.BE Hingerty, RH Ritchie, TL Ferrel, JE Turner. Biopolymers 24:427, 1985.
14.BM Pettitt, M Karplus. Chem Phys Lett 121:194, 1985.
15.J Novotny, R Bruccoleri, M Karplus. J Mol Biol 177:787, 1984.
16.D Eisenberg, AD, McLachlan. Nature 319:199, 1986.
17.T Ooi, M Oobatake, G Nemethy, HA, Scheraga. Proc Natl Acad Sci USA 84:3086, 1987.
18.WC Still, A Tempczyk, RC Hawley, T Hendrickson. J Am Chem Soc 112:6127, 1990.
19.AA Rashin. J Phys Chem 94:1725, 1990.
20.R Bharadwaj, A Windemuth, S Sridharan, B Honig, A Nicholls. J Comput Chem 16:898, 1995.
21.A Nicholls, B Honig. J Comput Chem 12:435, 1991.
22.AC Belch, ML Berkowitz. Chem Phys Lett 113:278, 1985.
23.ML Berkowitz, JA McCammon. Chem Phys Lett 90:215, 1982.
24.D Beglov, B Roux. J Chem Phys 100:9050, 1994.
25.H Friedman. Mol Phys 29:1533, 1975.
26.HJC Berendsen. In: J Hermans, ed. Molecular Dynamics and Protein Structure. Western Springs, IL: Polycrystal Book Service, 1985.
27.JJ Biesiadecki, RD Skeel. J Comput Phys 109:318, 1993.
28.HQ Ding, N Karasawa, WA Goddard. J Chem Phys 97:4309, 1992.
29.L Greengard, V Rokhlin. J Comput Phys 73:325, 1987.
30.FS Lee, A Warshel. J Chem Phys 97:3100, 1992.
31.CA White, M Head-Gordon. J Chem Phys 101:6593, 1994.
32.T Bishop, R Skeel, K Schulten. J Comput Chem 18:1785, 1997.
33.PJ Steinbach, BR Brooks. J Comput Chem 15:667, 1994.
34.SE Feller, RW Pastor, A Rojnuckarin, S Bogusz, BR Brooks. J Phys Chem 100:17011, 1996.
35.MP Allen, DJ Tildesley. Computer Simulation of Liquids. Oxford, UK: Clarendon Press, 1987.
36.T Darden, L Perera, L Li, L Pedersen. Struct Fold Des 7:R55, 1999.
37.SW DeLeeuw, JW Perram, ER Smith. Proc Roy Soc Lond A373:27, 1980.
38.C Kittel. Introduction to Solid State Physics. New York: Wiley, 1986.
39.JW Perram, HG Petersen, SW DeLeeuw. Mol Phys 65:875, 1988.
40.CG Lambert, TA Darden, JA Board. J Comput Phys 126:274, 1996.
41.F Figuerido, R Levy, R Zhou, B Berne. J Chem Phys 106:9835, 1997.
42.RW Hockney, JW Eastwood. Computer Simulation Using Particles. New York: McGraw-Hill, 1981.
43.TA Darden, DM York, LG Pedersen. J Chem Phys 98:10089, 1993.
44.U Essmann, L Perera, ML Berkowitz, T Darden, H Lee, LG Pedersen. J Chem Phys 103: 8577, 1995.
45.T Darden, A Toukmaji, L Pedersen. J Chim Phys 94:1346, 1997.
46.E Pollock, J Glosli. Comput Phys Commun 95:93, 1996.
47.X Shui, L McFail-Isom, G Hu, LD Williams. Biochemistry 37:8341, 1998.
48.G Hummer, L Pratt, A Garcia. J Phys Chem A 102:7885, 1998.
49.PH Hunenberger, JA McCammon. J Chem Phys 110:1856, 1999.
50.WW Wood, FR Parker. In: G Ciccotti, D Frenkel, IR McDonald, eds. Simulations of Liquids and Solids. Amsterdam: North-Holland, 1987.
51.G Hummer, LR Pratt, AE Garcia. J Phys Chem 100:1206, 1996.
52.PE Smith, BM Pettitt. J Chem Phys 105:4289, 1996.