从增强采样到蛋白质动力学的人可读表示
From Enhanced Sampling to Human-Readable Representations of Protein Dynamics
AI总结：
本文提出一种自动化框架，将增强采样轨迹转化为蛋白质动力学的人可读表示，通过结合频率选择性非谐振模分析和加权动态交叉相关矩阵分析，识别相关和反相关运动的残基对和域，生成物理可解释的CVs，应用于五种蛋白质并展示其在生物相关域和运动识别中的有效性。
摘要：
理解蛋白质构象动力学对于阐明生物功能至关重要，但因时间尺度范围广和集体运动的复杂性而具有挑战性。增强采样方法克服了传统分子动力学的时间尺度限制，但其有效性依赖于集体变量（CVs）的选择，这些变量往往难以定义且可能缺乏物理可解释性。特别是，从机器学习或集体振动模式衍生的集体变量可以高效捕捉慢动力学，但难以映射到直观的结构描述符。本文提出了一种完全自动化的框架，将增强采样轨迹转化为蛋白质动力学的人可读表示。我们的方法结合了沿频率选择性非谐振模分析衍生的CVs进行增强采样，并利用加权动态交叉相关矩阵对加权轨迹进行后分析。从这些中，我们识别出表现出相关和反相关运动的残基对和域，从而获得简单的域-域距离，作为物理可解释的CVs。我们将此方法应用于五种蛋白质，包括KRAS和HIV-1蛋白酶，并展示了其在识别生物相关域和运动方面的有效性，无需先验系统特定知识。将轨迹投影到这些距离上会产生自由能面，能够重现已知的构象状态，同时具有低统计不确定性和最大化独立动态信息。该工作流程使复杂CVs能够系统地转换为简单的几何描述符，而不会损失本质动力学。其通用性和自动化使其广泛适用于解释增强采样模拟并生成可解释的构象集合，以整合新兴的机器学习方法。
英文摘要：
Understanding protein conformational dynamics is essential for elucidating biological function but remains challenging due to the wide range of timescales and the complexity of collective motions. Enhanced sampling methods overcome timescale limitations of conventional molecular dynamics, yet their effectiveness depends on the choice of collective variables (CVs), which are often difficult to define and may lack physical interpretability. In particular, collective variables derived from machine learning or collective vibrational modes can efficiently capture slow dynamics but are not easily mapped onto intuitive structural descriptors. Here, we present a fully automated framework that transforms enhanced sampling trajectories into human-readable representations of protein dynamics. Our approach combines enhanced sampling along CVs derived from frequency-selective anharmonic mode analysis with a post hoc analysis of biased trajectories using weighted dynamic cross-correlation matrices. From these, we identify residue pairs and domains exhibiting correlated and anti-correlated motions, yielding simple domain-domain distances that serve as physically interpretable CVs. We apply this method to five proteins, including KRAS and HIV-1 protease, and show that it consistently identifies biologically relevant domains and motions without prior system-specific knowledge. Projection onto these distances produces free energy surfaces that reproduce known conformational states with low statistical uncertainty while maximizing independent dynamical information. This workflow enables systematic recasting of complex CVs into simple geometric descriptors without loss of essential dynamics. Its generality and automation make it broadly applicable for interpreting enhanced sampling simulations and generating interpretable conformational ensembles for integration with emerging machine learning approaches.
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