Improvements in Markov State Model Construction Reveal Many Non-Native Interactions in the Folding of NTL9.
Improvements in Markov State Model Construction Reveal Many Non-Native Interactions in the Folding of NTL9.
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DOI：
10.1021/ct300878a
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发表时间：
2013-04-09
影响因子：
5.5
通讯作者：
Pande, Vijay S.
中科院分区：
文献类型：
--
作者：
Schwantes, Christian R.;Pande, Vijay S.
马尔可夫状态模型 (MSM) 提供了一个自动化框架来研究高维分子模拟的动力学特性。这些模型可以提供人类可理解的基本过程的图像，并已成功用于研究蛋白质折叠、蛋白质聚集、蛋白质配体结合和其他生物物理系统。 MSM 需要构造一个离散状态空间，使得两个点如果能够快速相互转换，则处于相同状态。在下文中，我们提出了一种改进的方法，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）来构造状态空间。我们将此方法应用于 NTL9 的模拟（由 Lindorff-Larsen 等人提供，Science 2011），并表明 MSM 是对之前使用传统距离度量构建的模型的改进。此外，生成的模型通过揭示与紧凑的非本地状态相关的许多缓慢的时间尺度，提供了对非本地接触的作用的深入了解。
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. Science 2011), 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.
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DOI：
10.1073/pnas.2335541100
发表时间：
2003-11-25
影响因子：
11.1
作者：
García, AE;Onuchic, JN
通讯作者：
Onuchic, JN
影响因子：
4.4
作者：
Altis, Alexandros;Nguyen, Phuong H.;Stock, Gerhard
通讯作者：
Stock, Gerhard
影响因子：
15
作者：
Lane TJ;Bowman GR;Beauchamp K;Voelz VA;Pande VS
通讯作者：
Pande VS
影响因子：
5.6
作者：
Clementi, C;Nymeyer, H;Onuchic, JN
通讯作者：
Onuchic, JN
影响因子：
1.6
作者：
Djurdjevac, Natasa;Sarich, Marco;Schuette, Christof
通讯作者：
Schuette, Christof