Error analysis and efficient sampling in Markovian state models for molecular dynamics
Error analysis and efficient sampling in Markovian state models for molecular dynamics
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DOI：
10.1063/1.2116947
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发表时间：
2005-11-22
影响因子：
4.4
通讯作者：
Pande, VS
中科院分区：
文献类型：
--
作者：
Singhal, N;Pande, VS
在以前的工作中，我们描述了一个马尔可夫状态模型（MSM）分析分子动力学轨迹，其中包括分组构象成状态和估计状态之间的转移概率。本文分析了有限抽样引起的模型误差。我们给出了不同的方法与各种近似，以确定报告的平均首次通过时间的精度。这些近似值在一个87状态的玩具马尔可夫系统上得到了验证。此外，我们提出了一个有效的和实用的采样算法，使用这些误差计算，以建立一个MSM，具有相同的精度，平均首次通过时间值，但需要一个数量级较少的样本。我们还展示了如何使用稀疏矩阵方法将这些方法扩展到大型系统。
In previous work, we described a Markovian state model (MSM) for analyzing molecular-dynamics trajectories, which involved grouping conformations into states and estimating the transition probabilities between states. In this paper, we analyze the errors in this model caused by finite sampling. We give different methods with various approximations to determine the precision of the reported mean first passage times. These approximations are validated on an 87 state toy Markovian system. In addition, we propose an efficient and practical sampling algorithm that uses these error calculations to build a MSM that has the same precision in mean first passage time values but requires an order of magnitude fewer samples. We also show how these methods can be scaled to large systems using sparse matrix methods.