通过单参数社区检测学习蛋白质-配体解离路径
Learning protein-ligand unbinding pathways via single-parameter community detection
AI总结：
本文提出一种仅需单个自由参数的社区检测算法来聚类分子动力学轨迹，从而推断蛋白质-配体解离路径，并在链霉亲和素-生物素和A2a腺苷受体-ZM241385体系上验证了其识别反应坐标的有效性。
摘要：
理解生物分子复合物的动力学，例如蛋白质-配体的结合与解离，需要理解此类系统在亚稳态之间所采取的路径。在分子动力学模拟数据中，路径本身通常无法直接观测，而需要从模拟轨迹中推断出来。本文提出了一种基于社区检测算法对轨迹进行聚类的新方法，该方法仅需定义一个自由参数。以链霉亲和素-生物素复合物作为基准系统，并以A2a腺苷受体与抑制剂ZM241385的复合物作为一个精细应用实例，我们展示了此类轨迹聚类如何对应于路径，以及该方法如何有助于识别所考虑的结合或解离过程的反应坐标。
英文摘要：
Understanding the dynamics of biomolecular complexes, e.g., of protein-ligand (un)binding, requires the understanding of paths such systems take between metastable states. In MD simulation data, paths are usually not observable per se, but need to be inferred from simulation trajectories. Here we present a novel approach to cluster trajectories based on a community detection algorithm that requires the definition of only a single free parameter. Using the streptavidin-biotin complex as benchmark system and the A\textsubscript{2a} adenosine receptor in complex with the inhibitor ZM241385 as an elaborate application, we demonstrate how such clusters of trajectories correspond to pathways, and how the approach helps in the identification of reaction coordinates for a considered (un)binding process.
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