A coarse graining method for the identification of transition rates between molecular conformations
A coarse graining method for the identification of transition rates between molecular conformations
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DOI：
10.1063/1.2404953
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发表时间：
2007-01-14
影响因子：
4.4
通讯作者：
Weber, Marcus
中科院分区：
文献类型：
--
作者：
Kube, Susanna;Weber, Marcus
本文所提倡的粗粒化方法包括两个主要步骤。首先，在有限状态空间中用转移概率矩阵将分子态系综的传播描述为马尔可夫链。其次，我们通过稳健Perron聚类分析（PCCA+）的变量聚合得到亚稳态构象。到目前为止，它一直是一个悬而未决的问题，如何在空间中的粗粒化可以转化为一个粗粒化的马尔可夫链，同时保留必要的动态信息。在这篇文章中，我们构造了一个粗糙矩阵，它是构象空间中的正确传播子。这种粗粒化过程延续到速率矩阵，并允许提取分子构象之间的转换速率。这种方法是基于这样的事实，即PCCA+计算分子构象的过渡矩阵的主导特征向量的线性组合。(c)2007年，美国物理学会。
The coarse graining method to be advocated in this paper consists of two main steps. First, the propagation of an ensemble of molecular states is described as a Markov chain by a transition probability matrix in a finite state space. Second, we obtain metastable conformations by an aggregation of variables via Robust Perron Cluster Analysis (PCCA+). Up to now, it has been an open question as to how this coarse graining in space can be transformed to a coarse graining of the Markov chain while preserving the essential dynamic information. In this article, we construct a coarse matrix that is the correct propagator in the space of conformations. This coarse graining procedure carries over to rate matrices and allows to extract transition rates between molecular conformations. This approach is based on the fact that PCCA+ computes molecular conformations as linear combinations of the dominant eigenvectors of the transition matrix. (c) 2007 American Institute of Physics.