Nonlinear discovery of slow molecular modes using state-free reversible VAMPnets
Nonlinear discovery of slow molecular modes using state-free reversible VAMPnets
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DOI：
10.1063/1.5092521
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发表时间：
2019-06-07
影响因子：
4.4
通讯作者：
Ferguson, Andrew L.
中科院分区：
文献类型：
--
作者：
Chen, Wei;Sidky, Hythem;Ferguson, Andrew L.
加速沿着集体变量（CV）的增强采样分子模拟的成功是基于与控制系统长时间构象动力学的缓慢集体运动相一致的变量的可用性。对于除了最简单的分子系统之外的所有系统，直观地了解这些缓慢的CV是具有挑战性的，并且直接从分子模拟轨迹中发现它们的数据驱动的发现一直是分子模拟社区的中心焦点，以揭示重要的物理机制并驱动增强的采样。在这项工作中，我们引入了无状态可逆VAMPnets（SRV）作为一种深度学习架构，它学习非线性CV近似值到传输算子的谱分解的主要慢特征函数，该传输算子随时间演化平衡尺度概率分布。学习的CV的可伸缩性自然地施加在网络训练中，而无需添加正则化。CV本身是输入坐标的显式可微函数，因此非常适合用于增强的采样计算。我们证明了实用的SRV捕获简约的非线性表示复杂的系统动力学的应用程序中的1D和2D玩具系统，其中真正的本征函数是完全可计算的丙氨酸二肽和WW域蛋白质的分子动力学模拟。
The success of enhanced sampling molecular simulations that accelerate along collective variables (CVs) is predicated on the availability of variables coincident with the slow collective motions governing the long-time conformational dynamics of a system. It is challenging to intuit these slow CVs for all but the simplest molecular systems, and their data-driven discovery directly from molecular simulation trajectories has been a central focus of the molecular simulation community to both unveil the important physical mechanisms and drive enhanced sampling. In this work, we introduce state-free reversible VAMPnets (SRV) as a deep learning architecture that learns nonlinear CV approximants to the leading slow eigenfunctions of the spectral decomposition of the transfer operator that evolves equilibrium-scaled probability distributions through time. Orthogonality of the learned CVs is naturally imposed within network training without added regularization. The CVs are inherently explicit and differentiable functions of the input coordinates making them well-suited to use in enhanced sampling calculations. We demonstrate the utility of SRVs in capturing parsimonious nonlinear representations of complex system dynamics in applications to 1D and 2D toy systems where the true eigenfunctions are exactly calculable and to molecular dynamics simulations of alanine dipeptide and the WW domain protein.