Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics
Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics
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DOI：
10.1063/1.2714538
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发表时间：
2007-04-21
影响因子：
4.4
通讯作者：
Swope, William C.
中科院分区：
文献类型：
--
作者：
Chodera, John D.;Singhal, Nina;Swope, William C.
为了满足在长时间尺度上模拟生物大分子构象动力学的挑战，最近的努力一直致力于构建随机动力学模型，通常以离散状态马尔可夫模型的形式，从短分子动力学模拟。为了构建有用的模型，忠实地表示动态感兴趣的时间尺度，它是必要的配置空间分解成一组动力学亚稳态。以前的尝试来定义这些国家依赖于要么先验知识的自由度慢或应用的构象聚类技术，假设构象不同的集群也是动力学不同的。在这里，我们提出了一个自动算法的第一个版本的动力学亚稳态，通常适用于溶剂化大分子的发现。给定从定义良好的起始分布开始的分子动力学轨迹，该算法通过将构象空间划分和聚集成动力学相关区域的连续迭代来发现长寿命的动力学亚稳态。作者将这种方法应用于明确的溶剂末端封闭的丙氨酸中的三种肽，21个残基的螺旋F-s肽和工程化的12个残基的β-发夹trpzip 2-，以评估其产生物理上有意义的状态和忠实的动力学模型的能力。(c)2007年，美国物理学会。
To meet the challenge of modeling the conformational dynamics of biological macromolecules over long time scales, much recent effort has been devoted to constructing stochastic kinetic models, often in the form of discrete-state Markov models, from short molecular dynamics simulations. To construct useful models that faithfully represent dynamics at the time scales of interest, it is necessary to decompose configuration space into a set of kinetically metastable states. Previous attempts to define these states have relied upon either prior knowledge of the slow degrees of freedom or on the application of conformational clustering techniques which assume that conformationally distinct clusters are also kinetically distinct. Here, we present a first version of an automatic algorithm for the discovery of kinetically metastable states that is generally applicable to solvated macromolecules. Given molecular dynamics trajectories initiated from a well-defined starting distribution, the algorithm discovers long lived, kinetically metastable states through successive iterations of partitioning and aggregating conformation space into kinetically related regions. The authors apply this method to three peptides in explicit solvent-terminally blocked alanine, the 21-residue helical F-s peptide, and the engineered 12-residue beta-hairpin trpzip2-to assess its ability to generate physically meaningful states and faithful kinetic models. (c) 2007 American Institute of Physics.