DeepWEST: Deep Learning of Kinetic Models with the Weighted Ensemble Simulation Toolkit for Enhanced Sampling.
DeepWEST: Deep Learning of Kinetic Models with the Weighted Ensemble Simulation Toolkit for Enhanced Sampling.
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DOI：
10.1021/acs.jctc.2c00282
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发表时间：
2023-01
影响因子：
5.5
通讯作者：
Anupam Anand Ojha;Saumya Thakur;Surl-Hee Ahn;Rommie E. Amaro
中科院分区：
文献类型：
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作者：
Anupam Anand Ojha;Saumya Thakur;Surl-Hee Ahn;Rommie E. Amaro
计算能力和算法的最新进展使分子动力学（MD）模拟达到更大的时间尺度。然而，对于观察与生物分子过程相关的构象转变，MD模拟仍然存在局限性。几种增强的采样技术试图解决这一挑战，包括加权系综（WE）方法，该方法使用许多加权轨迹对亚稳态之间的转换进行采样，以估计动力学速率常数。然而，势能表面的初始采样对WE的性能具有显著影响，即，收敛和效率。因此，我们引入了深度学习的动力学建模方法，从短MD轨迹中提取统计相关信息，为WE模拟提供良好采样的初始状态分布。这种混合方法克服了系统的任何统计偏差，因为它运行短的无偏MD轨迹，并确定系统的有意义的亚稳态。它被证明提供了一个更精细的自由能景观更接近稳定状态，可以有效地采样动力学性质，如速率常数。
Recent advances in computational power and algorithms have enabled molecular dynamics (MD) simulations to reach greater time scales. However, for observing conformational transitions associated with biomolecular processes, MD simulations still have limitations. Several enhanced sampling techniques seek to address this challenge, including the weighted ensemble (WE) method, which samples transitions between metastable states using many weighted trajectories to estimate kinetic rate constants. However, initial sampling of the potential energy surface has a significant impact on the performance of WE, i.e., convergence and efficiency. We therefore introduce deep-learned kinetic modeling approaches that extract statistically relevant information from short MD trajectories to provide a well-sampled initial state distribution for WE simulations. This hybrid approach overcomes any statistical bias to the system, as it runs short unbiased MD trajectories and identifies meaningful metastable states of the system. It is shown to provide a more refined free energy landscape closer to the steady state that could efficiently sample kinetic properties such as rate constants.