Girsanov Reweighting Enhanced Sampling Technique (GREST): On-the-Fly Data-Driven Discovery of and Enhanced Sampling in Slow Collective Variables
Girsanov Reweighting Enhanced Sampling Technique (GREST): On-the-Fly Data-Driven Discovery of and Enhanced Sampling in Slow Collective Variables
复制标题
Girsanov 重新加权增强采样技术 (GREST)：慢速集体变量的动态数据驱动发现和增强采样
DOI：
10.1021/acs.jpca.3c00505
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Ferguson, Andrew L.
中科院分区：
文献类型：
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作者：
Shmilovich, Kirill;Ferguson, Andrew L.
微观现象的分子动力学模拟受到短积分时间步长的限制，这是数值稳定性所需的，但限制了实际可实现的模拟时间尺度。集体变量（CV）增强采样技术将偏差应用于预定义的集体坐标，以促进障碍跨越、相空间探索和稀有事件的采样。这些技术的有效性取决于选择良好的CV相关的分子运动的长期动力学演化的系统。在这项工作中，我们介绍了Girsanov重新加权增强采样技术（GREST）作为一种自适应采样方案，交错轮的数据驱动的慢CV发现和增强采样沿着这些坐标。由于慢CV是固有的动态量，在我们的方法中的一个关键成分是使用热力学和动力学Girsanov重新加权校正严格估计慢CV从有偏的模拟数据。我们展示了我们的方法上的玩具1D 4阱电位，一个简单的生物分子系统丙氨酸二肽，和Trp-Leu-Ala-Leu-Leu（WLALL）五肽。在每种情况下，GREST学习适当的慢CV，并从系统的零先验知识开始驱动对所有热可达亚稳态的采样。我们通过一个公开的开源Python包使社区可以访问GREST。
Molecular dynamics simulations of microscopic phenomena are limited by the short integration time steps which are required for numerical stability but which limit the practically achievable simulation time scales. Collective variable (CV) enhanced sampling techniques apply biases to predefined collective coordinates to promote barrier crossing, phase space exploration, and sampling of rare events. The efficacy of these techniques is contingent on the selection of good CVs correlated with the molecular motions governing the long-time dynamical evolution of the system. In this work, we introduce Girsanov Reweighting Enhanced Sampling Technique (GREST) as an adaptive sampling scheme that interleaves rounds of data-driven slow CV discovery and enhanced sampling along these coordinates. Since slow CVs are inherently dynamical quantities, a key ingredient in our approach is the use of both thermodynamic and dynamical Girsanov reweighting corrections for rigorous estimation of slow CVs from biased simulation data. We demonstrate our approach on a toy 1D 4-well potential, a simple biomolecular system alanine dipeptide, and the Trp-Leu-Ala-Leu-Leu (WLALL) pentapeptide. In each case GREST learns appropriate slow CVs and drives sampling of all thermally accessible metastable states starting from zero prior knowledge of the system. We make GREST accessible to the community via a publicly available open source Python package.