Variational Approach to Molecular Kinetics.

The eigenvalues and eigenvectors of the molecular dynamics propagator (or transfer operator) contain the essential information about the molecular thermodynamics and kinetics. This includes the stationary distribution, the metastable states, and state-to-state transition rates. Here, we present a variational approach for computing these dominant eigenvalues and eigenvectors. This approach is analogous to the variational approach used for computing stationary states in quantum mechanics. A corresponding method of linear variation is formulated. It is shown that the matrices needed for the linear variation method are correlation matrices that can be estimated from simple MD simulations for a given basis set. The method proposed here is thus to first define a basis set able to capture the relevant conformational transitions, then compute the respective correlation matrices, and then to compute their dominant eigenvalues and eigenvectors, thus obtaining the key ingredients of the slow kinetics.

[1]  Albert C. Pan,et al.  Building Markov state models along pathways to determine free energies and rates of transitions. , 2008, The Journal of chemical physics.

[2]  Thomas J Lane,et al.  MSMBuilder2: Modeling Conformational Dynamics at the Picosecond to Millisecond Scale. , 2011, Journal of chemical theory and computation.

[3]  Fiete Haack,et al.  Adaptive Spectral Clustering for Conformation Analysis , 2010 .

[4]  M. Karplus,et al.  Hidden complexity of free energy surfaces for peptide (protein) folding. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[5]  B. L. de Groot,et al.  Essential dynamics of reversible peptide folding: memory-free conformational dynamics governed by internal hydrogen bonds. , 2001, Journal of molecular biology.

[6]  V. Pande,et al.  Error analysis and efficient sampling in Markovian state models for molecular dynamics. , 2005, The Journal of chemical physics.

[7]  Toni Giorgino,et al.  Identification of slow molecular order parameters for Markov model construction. , 2013, The Journal of chemical physics.

[8]  William Swope,et al.  Describing Protein Folding Kinetics by Molecular Dynamics Simulations. 1. Theory , 2004 .

[9]  T. Darden,et al.  Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems , 1993 .

[10]  Wilfred F van Gunsteren,et al.  Comparing geometric and kinetic cluster algorithms for molecular simulation data. , 2010, The Journal of chemical physics.

[11]  Frank Noé,et al.  A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems , 2012, Multiscale Model. Simul..

[12]  Vijay S Pande,et al.  Simple few-state models reveal hidden complexity in protein folding , 2012, Proceedings of the National Academy of Sciences.

[13]  Frank Noé,et al.  Markov state models based on milestoning. , 2011, The Journal of chemical physics.

[14]  R. Glen,et al.  Identifying and correcting non-Markov states in peptide conformational dynamics. , 2010, The Journal of chemical physics.

[15]  F. Noé Probability distributions of molecular observables computed from Markov models. , 2008, The Journal of chemical physics.

[16]  R. Dror,et al.  Improved side-chain torsion potentials for the Amber ff99SB protein force field , 2010, Proteins.

[17]  R. Hegger,et al.  Dihedral angle principal component analysis of molecular dynamics simulations. , 2007, The Journal of chemical physics.

[18]  Vijay S Pande,et al.  Progress and challenges in the automated construction of Markov state models for full protein systems. , 2009, The Journal of chemical physics.

[19]  Charles R. MacCluer,et al.  The Many Proofs and Applications of Perron's Theorem , 2000, SIAM Rev..

[20]  Joshua A. Kritzer,et al.  Relationship between side chain structure and 14-helix stability of beta3-peptides in water. , 2005, Journal of the American Chemical Society.

[21]  Kyle A. Beauchamp,et al.  Markov state model reveals folding and functional dynamics in ultra-long MD trajectories. , 2011, Journal of the American Chemical Society.

[22]  Eric Vanden-Eijnden,et al.  Markovian milestoning with Voronoi tessellations. , 2009, The Journal of chemical physics.

[23]  K. Dill,et al.  Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics. , 2007, The Journal of chemical physics.

[24]  C. Schütte,et al.  Supplementary Information for “ Constructing the Equilibrium Ensemble of Folding Pathways from Short Off-Equilibrium Simulations ” , 2009 .

[25]  R. Elber Simulations of allosteric transitions. , 2011, Current opinion in structural biology.

[26]  F. Rao,et al.  The protein folding network. , 2004, Journal of molecular biology.

[27]  Jeremy C. Smith,et al.  Dynamical fingerprints for probing individual relaxation processes in biomolecular dynamics with simulations and kinetic experiments , 2011, Proceedings of the National Academy of Sciences.

[28]  Gerhard Reinelt,et al.  Computing Best Transition Pathways in High-Dimensional Dynamical Systems: Application to the AlphaL \leftrightharpoons Beta \leftrightharpoons AlphaR Transitions in Octaalanine , 2006, Multiscale Model. Simul..

[29]  G. Hummer,et al.  Coarse master equations for peptide folding dynamics. , 2008, The journal of physical chemistry. B.

[30]  G. de Fabritiis,et al.  Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations , 2011, Proceedings of the National Academy of Sciences.

[31]  Vijay S Pande,et al.  Improvements in Markov State Model Construction Reveal Many Non-Native Interactions in the Folding of NTL9. , 2013, Journal of chemical theory and computation.

[32]  Joseph A. Bank,et al.  Supporting Online Material Materials and Methods Figs. S1 to S10 Table S1 References Movies S1 to S3 Atomic-level Characterization of the Structural Dynamics of Proteins , 2022 .

[33]  Vijay S Pande,et al.  Simulating oligomerization at experimental concentrations and long timescales: A Markov state model approach. , 2008, The Journal of chemical physics.

[34]  M. Parrinello,et al.  Canonical sampling through velocity rescaling. , 2007, The Journal of chemical physics.

[35]  Gerrit Groenhof,et al.  GROMACS: Fast, flexible, and free , 2005, J. Comput. Chem..

[36]  F. Noé,et al.  Transition networks for modeling the kinetics of conformational change in macromolecules. , 2008, Current opinion in structural biology.

[37]  Jeremy C. Smith,et al.  Hierarchical analysis of conformational dynamics in biomolecules: transition networks of metastable states. , 2007, The Journal of chemical physics.

[38]  Berk Hess,et al.  LINCS: A linear constraint solver for molecular simulations , 1997 .

[39]  A. Caflisch,et al.  Kinetic analysis of molecular dynamics simulations reveals changes in the denatured state and switch of folding pathways upon single‐point mutation of a β‐sheet miniprotein , 2008, Proteins.

[40]  S. Röblitz Statistical Error Estimation and Grid-free Hierarchical Refinement in Conformation Dynamics , 2009 .

[41]  M. N. Jacobi,et al.  Identification of metastable states in peptide's dynamics. , 2010, The Journal of chemical physics.

[42]  K. Verhey,et al.  Kinesin assembly and movement in cells. , 2011, Annual review of biophysics.

[43]  W. E,et al.  Towards a Theory of Transition Paths , 2006 .

[44]  Frank Noé,et al.  On the Approximation Quality of Markov State Models , 2010, Multiscale Model. Simul..

[45]  Frank Noé,et al.  EMMA: A Software Package for Markov Model Building and Analysis. , 2012, Journal of chemical theory and computation.

[46]  Paul Tavan,et al.  Extracting Markov Models of Peptide Conformational Dynamics from Simulation Data. , 2005, Journal of chemical theory and computation.

[47]  P. Deuflhard,et al.  A Direct Approach to Conformational Dynamics Based on Hybrid Monte Carlo , 1999 .

[48]  Frank Noé,et al.  Markov models of molecular kinetics: generation and validation. , 2011, The Journal of chemical physics.

[49]  C. Brooks,et al.  Statistical clustering techniques for the analysis of long molecular dynamics trajectories: analysis of 2.2-ns trajectories of YPGDV. , 1993, Biochemistry.

[50]  Eric J. Deeds,et al.  Understanding ensemble protein folding at atomic detail , 2006, Proceedings of the National Academy of Sciences.

[51]  R. Dror,et al.  How Fast-Folding Proteins Fold , 2011, Science.

[52]  J. Chodera,et al.  Probability distributions of molecular observables computed from Markov models. II. Uncertainties in observables and their time-evolution. , 2010, The Journal of chemical physics.

[53]  Marcus Weber,et al.  A coarse graining method for the identification of transition rates between molecular conformations. , 2007, The Journal of chemical physics.

[54]  Frank Noé,et al.  Markov models and dynamical fingerprints: Unraveling the complexity of molecular kinetics , 2012 .

[55]  Vijay S Pande,et al.  Protein folded states are kinetic hubs , 2010, Proceedings of the National Academy of Sciences.

[56]  Dmitry Nerukh,et al.  Sensitivity of peptide conformational dynamics on clustering of a classical molecular dynamics trajectory. , 2008, The Journal of chemical physics.

[57]  Jeremy C. Smith,et al.  Transition Networks for the Comprehensive Characterization of Complex Conformational Change in Proteins. , 2006, Journal of chemical theory and computation.

[58]  Bettina Keller,et al.  An Analysis of the Validity of Markov State Models for Emulating the Dynamics of Classical Molecular Systems and Ensembles. , 2011, Journal of chemical theory and computation.

[59]  J. Cate,et al.  Ribosome structure and dynamics during translocation and termination. , 2010, Annual review of biophysics.

[60]  Frank Noé,et al.  Kinetic characterization of the critical step in HIV-1 protease maturation , 2012, Proceedings of the National Academy of Sciences.

[61]  Hans C Andersen,et al.  A Bayesian method for construction of Markov models to describe dynamics on various time-scales. , 2010, The Journal of chemical physics.

[62]  P. Deuflhard,et al.  Identification of almost invariant aggregates in reversible nearly uncoupled Markov chains , 2000 .

[63]  Frank Noé,et al.  Dynamic neutron scattering from conformational dynamics. II. Application using molecular dynamics simulation and Markov modeling. , 2013, The Journal of chemical physics.

[64]  Frank Noé,et al.  Dynamic neutron scattering from conformational dynamics. I. Theory and Markov models. , 2013, The Journal of chemical physics.

[65]  Vijay S. Pande,et al.  Everything you wanted to know about Markov State Models but were afraid to ask. , 2010, Methods.

[66]  P. Deuflhard,et al.  Robust Perron cluster analysis in conformation dynamics , 2005 .