Using Markov state models to study self-assembly.
Using Markov state models to study self-assembly.
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DOI：
10.1063/1.4878494
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发表时间：
2014-02
期刊：
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通讯作者：
Matthew R. Perkett;M. Hagan
中科院分区：
文献类型：
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作者：
Matthew R. Perkett;M. Hagan
马尔可夫状态模型 (MSM) 已被证明是一种用于计算研究分子内过程（例如蛋白质折叠和大分子构象变化）的强大方法。在本文中，我们提出了一种构建 MSM 的新方法，该方法适用于模拟广泛的多分子组装反应。组装过程中形成的独特结构以其无向图为特征，无向图由强亚基相互作用定义。使用最近开发的基于高斯的签名来解释自由子基的空间不均匀性。还研究了这种状态识别的简化。这种方法的可行性在两种不同的病毒自组装粗粒度模型上得到了证明。我们发现 MSM 预测的动态与长时间、无偏差的模拟之间具有良好的一致性，并且 MSM 可以将总体模拟时间缩短几个数量级。
Markov state models (MSMs) have been demonstrated to be a powerful method for computationally studying intramolecular processes such as protein folding and macromolecular conformational changes. In this article, we present a new approach to construct MSMs that is applicable to modeling a broad class of multi-molecular assembly reactions. Distinct structures formed during assembly are distinguished by their undirected graphs, which are defined by strong subunit interactions. Spatial inhomogeneities of free subunits are accounted for using a recently developed Gaussian-based signature. Simplifications to this state identification are also investigated. The feasibility of this approach is demonstrated on two different coarse-grained models for virus self-assembly. We find good agreement between the dynamics predicted by the MSMs and long, unbiased simulations, and that the MSMs can reduce overall simulation time by orders of magnitude.