Department of Computer Science, University of Texas at Austin, Austin, TX, United States of America.
Latvian Institute of Organic Synthesis, Riga, Latvia.
PLoS One. 2024 Aug 27;19(8):e0307226. doi: 10.1371/journal.pone.0307226. eCollection 2024.
Sampling rare events in proteins is crucial for comprehending complex phenomena like cryptic pocket opening, where transient structural changes expose new binding sites. Understanding these rare events also sheds light on protein-ligand binding and allosteric communications, where distant site interactions influence protein function. Traditional unbiased molecular dynamics simulations often fail to sample such rare events, as the free energy barrier between metastable states is large relative to the thermal energy. This renders these events inaccessible on the timescales typically simulated by unbiased molecular dynamics, limiting our understanding of these critical processes. In this paper, we proposed a novel unsupervised learning approach termed as slow feature analysis (SFA) which aims to extract slowly varying features from high-dimensional temporal data. SFA trained on small unbiased molecular dynamics simulations launched from AlphaFold generated conformational ensembles manages to capture rare events governing cryptic pocket opening, protein-ligand binding, and allosteric communications in a kinase. Metadynamics simulations using SFA as collective variables manage to sample 'deep' cryptic pocket opening within a few hundreds of nanoseconds which was beyond the reach of microsecond long unbiased molecular dynamics simulations. SFA augmented metadynamics also managed to capture conformational plasticity of protein upon ligand binding/unbinding and provided novel insights into allosteric communication in receptor-interacting protein kinase 2 (RIPK2) which dictates protein-protein interaction. Taken together, our results show how SFA acts as a dimensionality reduction tool which bridges the gap between AlphaFold, molecular dynamics simulation and metadynamics in context of capturing rare events in biomolecules, extending the scope of structure-based drug discovery in the era of AlphaFold.
采样蛋白质中的稀有事件对于理解复杂现象至关重要，如隐藏口袋的打开，其中瞬态结构变化暴露了新的结合位点。了解这些稀有事件也揭示了蛋白质-配体结合和变构通讯，其中远程站点相互作用影响蛋白质功能。传统的无偏分子动力学模拟通常无法采样这些稀有事件，因为亚稳态之间的自由能势垒相对于热能较大。这使得这些事件在无偏分子动力学通常模拟的时间尺度上无法访问，限制了我们对这些关键过程的理解。在本文中，我们提出了一种新的无监督学习方法，称为慢特征分析（SFA），旨在从高维时间数据中提取缓慢变化的特征。SFA 基于从 AlphaFold 生成的构象集合训练，成功捕获了控制隐藏口袋打开、蛋白质-配体结合和激酶变构通讯的稀有事件。使用 SFA 作为集体变量的元动力学模拟成功地在几百纳秒内采样了“深”隐藏口袋打开，这超出了微秒长无偏分子动力学模拟的范围。SFA 增强的元动力学还成功地捕获了配体结合/解吸时蛋白质的构象可塑性，并为受体相互作用蛋白激酶 2（RIPK2）中的变构通讯提供了新的见解，这决定了蛋白质-蛋白质相互作用。总之，我们的结果表明，SFA 如何作为一种降维工具，在捕捉生物分子中稀有事件的背景下，连接 AlphaFold、分子动力学模拟和元动力学之间的差距，在 AlphaFold 时代扩展了基于结构的药物发现的范围。
