
- •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
200 |
Straub |
Figure 1 Double well potential for a generic conformational transition showing the regions of reactant and product states separated by the transition state surface.
to mount the barrier and cross from the reactant to the product state. Moreover, once the barrier is crossed and the system loses energy it will spend many oscillations in the product well before recrossing the barrier. When we examine the equilibrium probability distribution along such a reaction coordinate, we will note two macrostate maxima in the probability distribution separated by a probability minimum. The position of this minimum is often a good choice for the transition state dividing surface.
One possible definition of a transition state dividing surface would be to divide a single energy basin down the middle, recognizing that the reactants sit to the left of the minimum and the products to the right. This would be a bad idea. Such a choice of the ‘‘transition state’’ dividing surface would lead to fast oscillations between the reactant and product state populations as the system moves left to right and back again in the energy basin. Therefore, a condition is needed: There must be a separation in time scales between the fast, transient motion within a macrostate and the slow, activated dynamics of motion between macrostates.
B. Phenomenological Rate Equations
When it is possible to recognize distinct macrostates, we can write the phenomenological rate equations
dNR |
kPRNR(t) kRPNP(t) |
(1) |
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Reaction Rates and Transition Pathways |
201 |
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dNP |
kPRNR(t) kRPNP(t) |
(2) |
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where kPR is the rate of transition from reactant to product while kRP is the rate of reaction from product to reactant. It is these macroscopic population changes that are usually measured in chemical kinetics experiments.
These rate equations are easily solved. At long times, the chemical dynamics reaches a stationary ‘‘equilibrium’’ and the populations of reactants and products cease to change. The relative populations of reactants and products are given by the condition of ‘‘detailed balance,’’ where the rate of transition from products to reactants equals the rate of transition from reactants to products, or
kPRNReq kRPNPeq |
(3) |
This relation defines the equilibrium constant between reactants and products, Keq
NPeq/NeqR kPR/kRP.
At short times, there is a relaxation of the reactant and product state populations to their equilibrium values. For example, the deviation from the equilibrium concentration
of products is given by |
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δNP(t) NP(t) NPeq δNP(0) exp [ (kPR kRP)t] |
(4) |
where δNP(0) is a measure of the initial deviation from the equilibrium population of product. What is measured experimentally as the rate of change in the population of the product states is the kinetic rate constant k kPR kRP, which is the sum of the forward and backward rate constants.
The next step in understanding the chemical kinetics of this system is the calculation of the kinetic rate constant from a knowledge of the energetics of the reaction system.
II. TRANSITION STATE THEORY
The original microscopic rate theory is the transition state theory (TST) [10–12]. This theory is based on two fundamental assumptions about the system dynamics. (1) There is a transition state dividing surface that separates the short-time intrastate dynamics from the long-time interstate dynamics. (2) Once the reactant gains sufficient energy in its reaction coordinate and crosses the transition state the system will lose energy and become deactivated product. That is, the reaction dynamics is activated crossing of the barrier, and every activated state will successfully react to form product.
A. Building the TST Rate Constant
Given the foregoing assumptions, it is a simple matter to construct an expression for the transition state theory rate constant as the probability of (1) reaching the transition state dividing surface and (2) having a momentum along the reaction coordinate directed from reactant to product. Stated another way, kPRTST is the equilibrium flux of reactant states across
202 |
Straub |
the transition state surface toward the product states. A typical trajectory that satisfies the assumptions of transition state theory is shown in Figure 2.
The transition state theory rate constant can be constructed as follows. The total flux of trajectories across the transition state dividing surface will be equal to the rate of transition kPRTST times the population of reactants at equilibrium NReq, or
flux(R → P) kPRTSTNReq. |
(5) |
That means that the transition rate is equal to the relative probability of being an activated reactant state times the average forward flux
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kPR |
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(6) |
NReq |
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NReq |
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where the average is taken over the positive momenta only. p is the momentum conjugate to the reaction coordinate, and µ is the reduced mass. Here we take the simple case of a linear reaction coordinate.
The average flux across the transition state dividing surface will be proportional to the relative probability of being found at the transition state,
N‡ |
1 |
∫dQ dq δ (q q‡)e β (Q,q) |
(7) |
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where q‡ is the location of the transition state surface along the reaction coordinate q and δ(q q‡) counts only those phase points at the transition state. Z is a normalization constant proportional to the canonical partition function for the total system. The total
Figure 2 A typical trajectory satisfying the assumptions of transition state theory. The reactive trajectory crosses the transition state surface once and only once on its way from activated reactant to deactivated product.
Reaction Rates and Transition Pathways |
203 |
energy of the system is taken to be a Hamiltonian function of the positions and momenta of both the reactive (q, p) and nonreactive (Q, P) degrees of freedom as (Γ, γ) (P, p) (Q, q). dΓ dγ is an incremental volume of the total phase space of nonreactive dΓ dQ dP and reactive dγ dq dp degrees of freedom. Similarly we find that
NReq |
1 |
∫dQ dqθ(q‡ q)e β (Q,q) |
1 |
∫dqθ(q‡ q)e β (q) |
(8) |
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where θ(q‡ q), the Heaviside step function, counts only those configurations in which the reaction coordinate is to the reactant side of the transition state dividing surface. Note that the nonreactive Q coordinates orthogonal to the reaction coordinate q have been averaged over to define the potential of mean force (q) as
1 |
e β (q) |
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∫dQ e β (Q,q) |
(9) |
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where Zγ is a normalization constant. The probability of finding the system along the reaction coordinate q will be proportional to exp[ β (q)]. The potential (q) can be used to define the probability distribution of the reduced system. It is possible to compute an exact transition state theory rate constant using (q). However, if the dynamics of the reaction coordinate is studied on the effective potential (q), the assumption is that the degrees of freedom Q are averaged over during the time scale for the essential dynamics of the reaction coordinate q.
It is a remarkable fact that the microscopic rate constant of transition state theory depends only on the equilibrium properties of the system. No knowledge of the system dynamics is required to compute the transition state theory estimate of the reaction rate constant kTST.
B. Some Details
We counted the contribution of only those trajectories that have a positive momentum at the transition state. Trajectories with negative momentum at the transition state are moving from product to reactant. If any of those trajectories were deactivated as products, their contribution would need to be subtracted from the total. Why? Because those trajectories are ones that originated from the product state, crossed the transition state twice, and were deactivated in the product state. In the TST approximation, only those trajectories that originate in the reactant well are deactivated as product and contribute to the reactive flux. We return to this point later in discussing dynamic corrections to TST.
A few more comments are in order. The backward rate constant can be computed from the condition of detailed balance
kPRTSTNReq kRPTSTNPeq |
(10) |
Suppose that the reactant well can be approximated as harmonic and the activation energy is much larger than the thermal energy. In that case we can approximate the rate constant as
TST |
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ω0 |
e |
βεPR‡ |
(11) |
kPR |
2π |
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