Enhanced Sampling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling and Machine Learning

采样(信号处理) 自适应采样 计算机科学 机器学习 数据科学 人工智能 化学 纳米技术 材料科学 数学 蒙特卡罗方法 统计 滤波器(信号处理) 计算机视觉
作者
Mingyuan Zhang,Hao Wu,Yong Wang
出处
期刊:Journal of Chemical Theory and Computation [American Chemical Society]
标识
DOI:10.1021/acs.jctc.4c00764
摘要

Biomolecular simulations often suffer from the "time scale problem", hindering the study of rare events occurring over extended time scales. Enhanced sampling techniques aim to alleviate this issue by accelerating conformational transitions, yet they typically necessitate well-defined collective variables (CVs), posing a significant challenge. Machine learning offers promising solutions but typically requires rich training data encompassing the entire free energy surface (FES). In this work, we introduce an automated iterative pipeline designed to mitigate these limitations. Our protocol first utilizes a CV-free count-based adaptive sampling method to generate a data set rich in rare events. From this data set, slow modes are identified using Koopman-reweighted time-lagged independent component analysis (KTICA), which are subsequently leveraged by on-the-fly probability enhanced sampling (OPES) to efficiently explore the FES. The effectiveness of our pipeline is demonstrated and further compared with the common Markov State Model (MSM) approach on two model systems with increasing complexity: alanine dipeptide (Ala2) and deca-alanine (Ala10), underscoring its applicability across diverse biomolecular simulations.
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