- •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
244 |
Smith |
Figure 3 Calculated X-ray diffuse scattering patterns from (a) a full molecular dynamics trajectory of orthorhombic hen egg white lysozyme and (b) a trajectory obtained by fitting to the full trajectory rigid-body side chains and segments of the backbone. A full description is given in Ref. 13.
bodies obtained by fitting to the full simulation. The full simulation scattering is reproduced by the approximate representation to an agreement factor (R-factor) of 6%.
IV. NEUTRON SCATTERING
In contrast to X-rays, the mass of the neutron is such that the energy exchanged in exciting or deexciting picosecond time scale thermal motions is a large fraction of the incident
˚
energy and can be measured relatively precisely. A thermal neutron of 1.8 A wavelength has an energy of 25 meV corresponding to kbT at 300 K. To further examine the neutron scattering case, we perform space Fourier transformation of the van Hove correlation functions [Eqs. (3) and (4)]:
Scoh(Q,
Icoh(Q,
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(16)
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(18)
(19)
Neutrons are scattered by the nuclei of the sample. Because of the random distribution of nuclear spins in the sample, the scattered intensity will contain a coherent part
X-Ray and Neutron Scattering as Dynamics Probes |
245 |
arising from the average neutron–nucleus potential and an incoherent part arising from fluctuations from the average. The coherent scattering arises from selfand cross-correla- tions of atomic motions, and the incoherent scattering, from single-atom motions. Each isotope has a coherent scattering length bi,coh and an incoherent scattering length bi,inc that define the strength of the interaction between the nucleus of the atom and the neutron. For more details on the origin of incoherent and coherent scattering see Ref. 1. We see from Eqs. (16) and (18) that the coherent and incoherent dynamic structure factors are time Fourier transforms of the coherent and incoherent intermediate scattering functions,
ω
Icoh(Q, t) and Iinc(Q, t); these are time-correlation functions [14]. Sinc(Q, ) and Scoh(Q, ω) may contain elastic (ω 0) and inelastic (ω ≠ 0) parts. Elastic scattering probes correlations of atomic positions at long times, whereas the inelastic scattering process probes position correlations as a function of time.
A. Coherent Inelastic Neutron Scattering
The use of coherent neutron scattering with simultaneous energy and momentum resolution provides a probe of time-dependent pair correlations in atomic motions. Coherent inelastic neutron scattering is therefore particularly useful for examining lattice dynamics in molecular crystals and holds promise for the characterization of correlated motions in biological macro-molecules. A property of lattice modes is that for particular wave vectors there are well-defined frequencies; the relations between these two quantities are the phonon dispersion relations [1]. Neutron scattering is presently the most effective technique for determining phonon dispersion curves. The scattering geometry used is illustrated in Figure 4. The following momentum conservation law is obeyed:
ki kf Q q |
(20) |
Figure 4 Schematic vector diagrams illustrating the use of coherent inelastic neutron scattering to determine phonon dispersion relationships. (a) Scattering in real space; (b) a scattering triangle illustrating the momentum transfer, Q, of the neutrons in relation to the reciprocal lattice vector of the sample and the phonon wave vector, q. Heavy dots represent Bragg reflections.
246 |
Smith |
The vibrational excitations have a wave vector q that is measured from a Brillouin zone center (Bragg peak) located at , a reciprocal lattice vector.
If the displacements of the atoms are given in terms of the harmonic normal modes of vibration for the crystal, the coherent one-phonon inelastic neutron scattering cross section can be analytically expressed in terms of the eigenvectors and eigenvalues of the harmonic analysis, as described in Ref. 1.
Example: Lattice Vibrations in L-Alanine
Zwitterionic L-alanine ( H3NEC(CH3)ECO2E) is a dipolar molecule that forms large well-ordered crystals in which the molecules form hydrogen-bonded columns. The strong interactions lead to the presence of well-defined intraand intermolecular vibrations that can usefully be described using harmonic theory.
Coherent inelastic neutron scattering experiments have been combined with normal mode analyses with a molecular mechanics potential function to examine the collective vibrations in deuterated L-alanine [15]. In Figure 5 are shown experimental phonon frequencies νi (q)(ν ω/2π) for several modes propagating along the crystallographic direction b*. The solid lines represent the most probable paths for the dispersion curves νi(q). The theoretical dispersion curves are also given. The comparison between theory and experiment can be used to assess the accuracy with which the theory reproduces longrange interactions in the crystal.
B. Incoherent Neutron Scattering
Neutron scattering from nondeuterated organic molecules is dominated by incoherent scattering from the hydrogen atoms. This is largely because the incoherent scattering cross section (4πb2inc) of hydrogen is approximately 15 times greater than the total scattering cross section of carbon, nitrogen, or oxygen. The measured incoherent scattering thus essentially gives information on self-correlations of hydrogen atom motions. A program for calculating neutron scattering properties from molecular dynamics simulations has been published [16].
In practice, the measured incoherent scattering energy spectrum is divided into elastic, quasielastic, and inelastic scattering. Inelastic scattering arises from vibrations. Quasielastic scattering is typically Lorentzian or a sum of Lorentzians centered on ω 0 and arises from diffusive motions in the sample. Elastic scattering gives information on the self-probability distributions of the hydrogen atoms in the sample.
A procedure commonly used to extract dynamic data directly from experimental incoherent neutron scattering profiles is described in Ref. 17. It is assumed that the atomic position vectors can be decomposed into two contributions, one due to diffusive motion,
ri,d(t), and the other from vibrations, ui,v(t), i.e., |
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Ri(t) ri,d(t) ui,v(t) |
Combining Eq. (21) with Eq. (19) lated, one obtains
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where Id(Q, t) and Iv(Q, t) are obtained
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and assuming that ri,d(t) and ui,v
by substituting Ri(t) in Eq. (19)
(t) are uncorre-
(22)
with ri,d(t) and
Figure 5 (a) Dispersion curves for crystalline zwitterionic L-alanine at room temperature along the b* crystallographic direction determined by coherent inelastic neutron scattering. The and ■ symbols are associated with phonon modes observed in predominantly transverse and purely longitudinal configurations, respectively, i.e., for vectors Q and q perpendicular and parallel to one another, respectively. They correspond to measurements performed around the strong Bragg reflections (200), (040), and (002). The symbols are neutron data points obtained around the (330), (103), and (202) reciprocal lattice points in a mixed configuration. Solid lines indicate the most probable connectivity of the dispersion curves, and dashed lines correspond to the measurements performed at low temperature T 100 K. (b) Theoretical dispersion curves for L-alanine determined from normal mode analysis. (From Ref. 15.)
248 |
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The Fourier transform of Eq. (22) gives |
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where Sd(Q, ω) and Sv(Q, ω) are obtained by Fourier transformation of Id(Q, t) and Iv(Q,
t) and the symbol denotes the convolution product. Appropriate descriptions of ri,d(t)
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Id(Q, t) can be separated into time-dependent and time-independent parts as follows: |
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where Gd(r, t) is the contribution to the van Hove self-correlation function due to diffusive
motion. A0(Q) is thus determined by the diffusive contribution to the space probability distribution of the hydrogen nuclei.
Direct experiment–simulation quasielastic neutron scattering comparisons have been performed for a variety of small molecule and polymeric systems, as described in detail in Refs. 4 and 18–21. The combination of simulation and neutron scattering in the analysis of internal motions in globular proteins was reviewed in 1991 [3] and 1997 [4].
A dynamic transition in the internal motions of proteins is seen with increasing temperature [22]. The basic elements of this transition are reproduced by MD simulation [23]. As the temperature is increased, a transition from harmonic to anharmonic motion is seen, evidenced by a rapid increase in the atomic mean-square displacements. Comparison of simulation with quasielastic neutron scattering experiment has led to an interpretation of the dynamics involved in terms of rigid-body motions of the side chain atoms, in a way analogous to that shown above for the X-ray diffuse scattering [24].
Example: Change in Dynamics on Denaturing
Phosphoglycerate Kinase
In this example we examine the change in the experimental dynamic neutron scattering signal on strong denaturation of a globular protein, phosphoglycerate kinase (PGK) [25]. Evidence for this comes from the EISF plotted in Figure 6. The main difference in the EISF is in the asymptote as Q → ∞, which is significantly lower in the case of the denatured protein. The asymptotic value can be shown to correspond to a nondiffusing fraction of the hydrogen atoms in the protein. Whereas the nondiffusing fraction is 40% in the native protein, it is reduced to 18% in the denatured protein.
Inelastic Incoherent Scattering Intensity. For a system executing harmonic dynamics, the transform in Eq. (4) can be performed analytically and the result expanded in a power series over the normal modes in the sample. The following expression is obtained [26]:
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nλωλ
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X-Ray and Neutron Scattering as Dynamics Probes |
249 |
Figure 6 Apparent elastic incoherent structure factor A′0(Q) for ( ) denatured and ( ) native phosphoglycerate kinase. The solid line represents the fit of a theoretical model in which a fraction of the hydrogens of the protein execute only vibrational motion (this fraction is given by the dotted line) and the rest undergo diffusion in a sphere. For more details see Ref. 25.
In Eq. (26), M is the hydrogen mass, λ labels the mode, eλ,i is the atomic eigenvector for hydrogen i in mode λ, and ωλ is the mode angular frequency. nλ is the number of quanta of energy ωλ exchanged between the neutron and mode λ. Inλ is a modified Bessel
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mean-square displacement for atom i in the direction of Q. |
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Equation (26) is an exact quantum mechanical expression for the scattered intensity. A detailed interpretation of this equation is given in Ref. 27. Inserting the calculated eigenvectors and eigenvalues into the equation allows the calculation of the incoherent scattering in the harmonic approximation for processes involving any desired number of quanta exchanged between the neutrons and the sample, e.g., one-phonon scattering involving the exchange of one quantum of energy ωλ, two-phonon scattering, and so on.
The label λ in Eq. (26) runs over all the modes of the sample. In the case of an isolated molecule, λ runs over the 3N 6 normal modes of the molecule, where N is the number of atoms.
Example: Vibrations in Staphylococcal Nuclease
Vibrations in proteins can be conveniently examined using normal mode analysis of isolated molecules. The results of such analyses indicate the presence of a variety of vibra-
250 |
Smith |
Figure 7 Experimental and theoretical inelastic neutron scattering spectrum from staphylococcal nuclease at 25 K. The experimental spectrum was obtained on the TFXA spectrometer at Oxford. The calculated spectrum was obtained from a normal mode analysis of the isolated molecule. (From Ref. 28.)
tions, with frequencies upward of a few inverse centimeters (cm 1). Incoherent inelastic neutron scattering combined with normal mode analysis is well suited to examine low frequency vibrations in proteins. This is primarily due to the fact that large-amplitude displacements scatter neutrons strongly. Experiments on bovine pancreatic trypsin inhibitor (BPTI), combined with normal mode analysis of the isolated protein, demonstrated that low frequency underdamped vibrations do exist in the protein [3]. More recently, the TFXA spectrometer at the Rutherford-Appleton laboratory in Oxford was used to measure a spectrum of the high frequency local vibrations in the globular protein staphylococcal nuclease [28]. Figure 7 presents a comparison of the experimental dynamic structure factor at 25 K, with that calculated from a normal mode analysis of the protein. Comparison between the calculated and experimental profiles allows an assessment of the accuracy of the dynamical model and the assignment of the various vibrational features making up the experimental spectrum.
V.CONCLUSIONS
In this chapter, basic scattering properties have been described that can be measured for biological samples so as to obtain information on their internal motions. These properties were presented in such a way as to highlight their interface with computer simulation. As experimental intensities and resolutions improve and computer simulations become more and more powerful, it can be expected that the combination of simulation with X-ray and neutron scattering experiments will play an increasingly important role in elucidating the dynamic aspects of biological macromolecular folding and function.