Making High-Dimensional Molecular Distribution Functions Tractable through Belief Propagation on Factor Graphs
Making High-Dimensional Molecular Distribution Functions Tractable through Belief Propagation on Factor Graphs
复制标题
通过因子图上的置信传播使高维分子分布函数易于处理
DOI：
10.1021/acs.jpcb.1c05717
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Tiwary, Pratyush
中科院分区：
文献类型：
--
作者：
Smith, Zachary;Tiwary, Pratyush
分子动力学（MD）模拟提供了丰富的全原子和飞秒分辨率的高维数据，但从这些数据中破译机制信息是物理化学和生物物理学中的一个持续挑战。从理论上讲，平衡分布的联合概率包含了所有的热力学信息，但随着维数的增加，它们越来越难以计算和解释。在这里，受概率图形建模工具的启发，我们开发了一个通过置信传播训练的因子图，它有助于将联合概率分解为一种近似的易处理形式，可以很容易地可视化和使用。我们验证了研究通过分析的构象动力学的两个小肽与五个和九个残基。我们的验证包括测试的条件依赖性预测，通过干预计划的启发朱迪亚珍珠。其次，我们直接使用基于置信度传播的近似概率分布作为增强采样的高维静态偏置，其中我们实现了亚稳态之间的自发来回运动，其速度比无偏MD快350倍。我们相信这项工作为思考和处理高维分子模拟开辟了有用的途径。
Molecular dynamics (MD) simulations provide a wealth of high-dimensional data at all-atom and femtosecond resolution but deciphering mechanistic information from this data is an ongoing challenge in physical chemistry and biophysics. Theoretically speaking, joint probabilities of the equilibrium distribution contain all thermodynamic information, but they prove increasingly difficult to compute and interpret as the dimensionality increases. Here, inspired by tools in probabilistic graphical modeling, we develop a factor graph trained through belief propagation that helps factorize the joint probability into an approximate tractable form that can be easily visualized and used. We validate the study through the analysis of the conformational dynamics of two small peptides with five and nine residues. Our validations include testing the conditional dependency predictions through an intervention scheme inspired by Judea Pearl. Second, we directly use the belief propagation-based approximate probability distribution as a high-dimensional static bias for enhanced sampling, where we achieve spontaneous back-and-forth motion between metastable states that is up to 350 times faster than unbiased MD. We believe this work opens up useful ways to thinking about and dealing with high-dimensional molecular simulations.