School of Mechanical and Materials Engineering, Washington State University, Pullman, Washington 99164, United States.
J Chem Theory Comput. 2022 Oct 11;18(10):6297-6309. doi: 10.1021/acs.jctc.2c00254. Epub 2022 Sep 13.
It is very challenging to sample a molecular process with large activation energies using molecular dynamics simulations. Current enhanced sampling methodologies, such as umbrella sampling and metadynamics, rely on the identification of appropriate reaction coordinates for a system. In this paper, we developed a method for log-probability estimation via invertible neural networks for enhanced sampling (LINES). This iterative scheme utilizes a normalizing flow machine learning model to learn the underlying free energy surface (FES) of a system as a function of molecular coordinates and then applies a gradient-based optimization method to the learned normalizing flow to identify reaction coordinates. A biasing potential is then evaluated over a tabulated grid of the reaction coordinate values, which can be applied to the next round of simulations for enhanced sampling, resulting in more efficient sampling. We tested the accuracy and efficiency of the LINES method in sampling the FES using the alanine dipeptide system. We also demonstrated the effectiveness of identification of reaction coordinates through simulation of cyclobutanol unbinding from β-cyclodextrin and the folding/unfolding of CLN025─a variant of the peptide Chignolin. The LINES method can be extended to the study of large-scale protein systems with complex nonlinear reaction pathways.
使用分子动力学模拟对具有较大活化能的分子过程进行采样是非常具有挑战性的。目前的增强采样方法，如伞状采样和元动力学，依赖于为系统识别适当的反应坐标。在本文中，我们开发了一种通过可逆变分神经网络进行对数概率估计的增强采样方法（LINES）。该迭代方案利用归一化流机器学习模型将系统的潜在自由能表面（FES）学习为分子坐标的函数，然后将基于梯度的优化方法应用于学习的归一化流以识别反应坐标。然后在反应坐标值的表格网格上评估偏置势，可将其应用于下一轮增强采样的模拟中，从而实现更有效的采样。我们使用丙氨酸二肽系统测试了 LINES 方法在 FES 采样中的准确性和效率。我们还通过模拟环丁醇从β-环糊精的解吸以及 CLN025 的折叠/去折叠（肽 Chignolin 的一种变体）证明了识别反应坐标的有效性。LINES 方法可以扩展到研究具有复杂非线性反应途径的大规模蛋白质系统。
