Bayesian comparison of Markov models of molecular dynamics with detailed balance constraint

被引:48
作者
Bacallado, Sergio [1 ]
Chodera, John D. [2 ]
Pande, Vijay [1 ,3 ]
机构
[1] Stanford Univ, Dept Biol Struct, Stanford, CA 94305 USA
[2] Univ Calif Berkeley, Calif Inst Quantitat Biosci QB3, Berkeley, CA 94720 USA
[3] Stanford Univ, Dept Chem, Stanford, CA 94305 USA
关键词
Bayes methods; eigenvalues and eigenfunctions; Markov processes; molecular biophysics; molecular dynamics method; Monte Carlo methods; CONFORMATIONAL DYNAMICS; FOLDING DYNAMICS; MASTER EQUATION; SIMULATIONS; KINETICS; CHAINS; SYSTEMS; MOTION;
D O I
10.1063/1.3192309
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Discrete-space Markov models are a convenient way of describing the kinetics of biomolecules. The most common strategies used to validate these models employ statistics from simulation data, such as the eigenvalue spectrum of the inferred rate matrix, which are often associated with large uncertainties. Here, we propose a Bayesian approach, which makes it possible to differentiate between models at a fixed lag time making use of short trajectories. The hierarchical definition of the models allows one to compare instances with any number of states. We apply a conjugate prior for reversible Markov chains, which was recently introduced in the statistics literature. The method is tested in two different systems, a Monte Carlo dynamics simulation of a two-dimensional model system and molecular dynamics simulations of the terminally blocked alanine dipeptide.
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页数:10
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