Kube Susanna, Weber Marcus
Zuse Institute Berlin ZIB, Takustrasse 7, D-14195 Berlin, Germany.
J Chem Phys. 2007 Jan 14;126(2):024103. doi: 10.1063/1.2404953.
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.
本文所倡导的粗粒化方法包括两个主要步骤。首先，分子态系综的传播在有限状态空间中通过转移概率矩阵被描述为一个马尔可夫链。其次，我们通过稳健的佩龙聚类分析（PCCA+）对变量进行聚合来获得亚稳构象。到目前为止，关于如何在保留基本动态信息的同时将这种空间上的粗粒化转化为马尔可夫链的粗粒化一直是一个悬而未决的问题。在本文中，我们构建了一个粗粒化矩阵，它是构象空间中的正确传播子。这种粗粒化过程可以推广到速率矩阵，并允许提取分子构象之间的转移速率。这种方法基于这样一个事实，即PCCA+将分子构象计算为转移矩阵主导特征向量的线性组合。
