Extracting Kinetic and Stationary Distribution Information from Short MD Trajectories via a Collection of Surrogate Diffusion Models. (vol 5, pg 47, 2009)

作     者：Calderon, Christopher P. Arora, Karunesh 

作者机构：Department of Computational and Applied Mathematics and Department of Statistics Rice University Houston Texas 77005-1892 and Department of Chemistry Biophysics Program University of Michigan 930 North University Avenue Ann Arbor Michigan 48109 

出 版 物：《JOURNAL OF CHEMICAL THEORY AND COMPUTATION》 (化学理论与计算杂志)

年 卷 期：2009年第5卷第8期

页      面：2192-2192页

核心收录：

学科分类：07[理学] 0703[理学-化学] 0702[理学-物理学] 

基　　金：NSF [DNIS 0240058, ACI-0325081] NIHT90 DK070121-04 Rice University, AMD Cray CNS-0421109 

主　　题：Transport properties Diffusion Approximation Computational chemistry Computer simulations 

摘      要：Low-dimensional stochastic models can summarize dynamical information and make long time predictions associated with observables of complex atomistic systems. Maximum likelihood based techniques for estimating low-dimensional surrogate diffusion models from relatively short time series are presented. It is found that a heterogeneous population of slowly evolving conformational degrees of freedom modulates the dynamics. This underlying heterogeneity results in a collection of estimated low-dimensional diffusion models. Numerical techniques for exploiting this finding to approximate skewed histograms associated with the simulation are presented. In addition, statistical tests are used to assess the validity of the models and determine physically relevant sampling information, e.g. the maximum sampling frequency at which one can discretely sample from an atomistic time series and have a surrogate diffusion model pass goodness-of-fit tests. The information extracted from such analyses can possibly be used to assist umbrella sampling computations as well as help in approximating effective diffusion coefficients. The techniques are demonstrated on simulations of adenylate kinase.