机器学习通过精确的动力学和热力学预测破译了分子机制
Junlin Dong1,2, Shiyu Wang1,2,3, Wenqiang Cui1,2
1Research Center for Computer-Aided Drug Discovery, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Journal of chemical theory and computation
|February 23, 2024
概括
我们开发了一种新方法,使用统一的多元近似和投影 (UMAP) 和基于层次密度的空间集群应用程序与噪音 (HDBSCAN),以揭示复杂的生物系统动态. 这种方法准确地模拟了蛋白质关联动力学,有助于药物设计.
科学领域:
- 计算生物学 计算生物学
- 生物物理学的生物物理.
- 分子动力学分子动力学
背景情况:
- 时间滞后的独立组件分析 (tICA) 和马尔科夫状态模型 (MSM) 是分析分子动态的常见方法,但与复杂的生物系统作斗争.
- 在复杂的系统中阐明过渡机制往往需要艰难的选择集体变量.
研究的目的:
- 开发一个集成的,无监督的尺寸缩小框架,以加强对复杂生物系统动态的分析.
- 建立一个多尺度机械学阐明框架,以更好地理解形状变化和动力学.
主要方法:
- 实施了一种集成的无监督维度缩小模型,将统一的多元近似和投影 (UMAP) 与基于层次密度的噪音应用的空间集群 (HDBSCAN) 结合起来.
- 应用这个UMAP-HDBSCAN架构与马尔科夫状态模型 (MSM) 层次化分析分子动力学模拟数据.
- 在各种数据集中使用计算指标和实验动态可观测值验证了模型的稳定性和可解释性.
主要成果:
- UMAP-HDBSCAN-MSM框架有效地产生低维的配置嵌入,比传统方法更好地保留全球和本地数据结构.
- 确定了全球运动连接性和局部构造状态,建立了一个多尺度机械阐明框架.
- 预测的Mcl1-BH3关联动力学 (0.76s−1) 与实验表面等离子体共振数据 (0.12s−1) 密切匹配.
结论:
- 设计的工作流提供了一个强大的和可解释的多尺度框架,用于研究复杂生物系统中的识别模式.
- 这种方法提升了蛋白质功能动力学和合理药物设计的探索,提供了一个强大的机制阐明工具.
- 与传统技术相比,集成的UMAP-HDBSCAN-MSM方法在分析分子动态方面取得了显著的改进.
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