Perkett Matthew R, Hagan Michael F
Martin Fisher School of Physics, Brandeis University, Waltham, Massachusetts 02474, USA.
J Chem Phys. 2014 Jun 7;140(21):214101. doi: 10.1063/1.4878494.
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.
马尔可夫状态模型（MSMs）已被证明是一种用于计算研究分子内过程（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MSMs的新方法，该方法适用于对广泛的多分子组装反应进行建模。组装过程中形成的不同结构通过其无向图来区分，这些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特征来考虑自由亚基的空间不均匀性。还研究了对这种状态识别的简化。该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MSMs预测的动力学与长时间的无偏模拟之间有很好的一致性，并且MSMs可以将整体模拟时间减少几个数量级。
