Schwantes Christian R, Pande Vijay S
Department of Chemistry, Stanford University, Stanford, CA, USA.
J Chem Theory Comput. 2013 Apr 9;9(4):2000-2009. doi: 10.1021/ct300878a.
Markov State Models (MSMs) provide an automated framework to investigate the dynamical properties of high-dimensional molecular simulations. These models can provide a human-comprehensible picture of the underlying process, and have been successfully used to study protein folding, protein aggregation, protein ligand binding, and other biophysical systems. The MSM requires the construction of a discrete state-space such that two points are in the same state if they can interconvert rapidly. In the following, we suggest an improved method, which utilizes second order Independent Components Analysis (also known as time-structure based Independent Components Analysis, or tICA), to construct the state-space. We apply this method to simulations of NTL9 (provided by Lindorff-Larsen et al. ), and show that the MSM is an improvement over previously built models using conventional distance metrics. Additionally, the resulting model provides insight into the role of non-native contacts by revealing many slow timescales associated with compact, non-native states.
马尔可夫状态模型（MSMs）提供了一个自动化框架，用于研究高维分子模拟的动力学特性。这些模型可以提供一个易于理解的潜在过程图景，并已成功用于研究蛋白质折叠、蛋白质聚集、蛋白质配体结合以及其他生物物理系统。MSM 需要构建一个离散状态空间，使得如果两点能够快速相互转换，则它们处于相同状态。在下文中，我们提出一种改进方法，该方法利用二阶独立成分分析（也称为基于时间结构的独立成分分析，或tICA）来构建状态空间。我们将此方法应用于NTL9的模拟（由Lindorff-Larsen等人提供），并表明该MSM比使用传统距离度量构建的先前模型有所改进。此外，所得模型通过揭示许多与紧凑的非天然状态相关的慢时间尺度，深入了解了非天然接触的作用。
