计算机科学
分子动力学
水准点(测量)
采样(信号处理)
补语(音乐)
生成语法
空格(标点符号)
人工智能
机器学习
统计物理学
物理
计算化学
化学
大地测量学
操作系统
表型
滤波器(信号处理)
基因
生物化学
互补
地理
计算机视觉
作者
Jiarui Lu,Bozitao Zhong,J. Tang
出处
期刊:Cornell University - arXiv
日期:2023-06-05
标识
DOI:10.48550/arxiv.2306.03117
摘要
The dynamic nature of proteins is crucial for determining their biological functions and properties, and molecular dynamics (MD) simulations stand as a predominant tool to study such phenomena. By utilizing empirically derived force fields, MD simulations explore the conformational space through numerically evolving the system along MD trajectories. However, the high-energy barrier of the force fields can hamper the exploration of MD, resulting in inadequately sampled ensemble. In this paper, we propose leveraging score-based generative models (SGMs) trained on general protein structures to perform protein conformational sampling to complement traditional MD simulations. We argue that SGMs can provide a novel framework as an alternative to traditional enhanced sampling methods by learning multi-level score functions, which directly sample a diversity-controllable ensemble of conformations. We demonstrate the effectiveness of our approach on several benchmark systems by comparing the results with long MD trajectories and state-of-the-art generative structure prediction models. Our framework provides new insights that SGMs have the potential to serve as an efficient and simulation-free methods to study protein dynamics.
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