Department of Chemistry, University of Warwick, Coventry CV4 7AL, United Kingdom.
J Chem Inf Model. 2023 Apr 10;63(7):2181-2195. doi: 10.1021/acs.jcim.3c00023. Epub 2023 Mar 30.
Recent advances in machine learning methods have had a significant impact on protein structure prediction, but accurate generation and characterization of protein-folding pathways remains intractable. Here, we demonstrate how protein folding trajectories can be generated using a directed walk strategy operating in the space defined by the residue-level contact-map. This double-ended strategy views protein folding as a series of discrete transitions between connected minima on the potential energy surface. Subsequent reaction-path analysis for each transition enables thermodynamic and kinetic characterization of each protein-folding path. We validate the protein-folding paths generated by our discretized-walk strategy against direct molecular dynamics simulations for a series of model coarse-grained proteins constructed from hydrophobic and polar residues. This comparison demonstrates that ranking discretized paths based on the intermediate energy barriers provides a convenient route to identifying physically sensible folding ensembles. Importantly, by using directed walks in the protein contact-map space, we circumvent several of the traditional challenges associated with protein-folding studies, namely, long time scales required and the choice of a specific order parameter to drive the folding process. As such, our approach offers a useful new route for studying the protein-folding problem.
近年来，机器学习方法的进展对蛋白质结构预测产生了重大影响，但准确生成和描述蛋白质折叠途径仍然具有挑战性。在这里，我们展示了如何使用在残基水平接触图定义的空间中运行的定向游走策略来生成蛋白质折叠轨迹。这种双端策略将蛋白质折叠视为在势能表面上连接的最小点之间的一系列离散跃迁。对每个跃迁进行后续反应路径分析，可以对每个蛋白质折叠路径进行热力学和动力学特性描述。我们将我们的离散游走策略生成的蛋白质折叠路径与一系列由疏水和极性残基构建的模型粗粒蛋白质的直接分子动力学模拟进行了比较。这种比较表明，根据中间能量障碍对离散路径进行排序是识别物理合理折叠集合的一种便捷途径。重要的是，通过在蛋白质接触图空间中使用定向游走，我们回避了与蛋白质折叠研究相关的几个传统挑战，即需要长时间尺度和选择特定的序参数来驱动折叠过程。因此，我们的方法为研究蛋白质折叠问题提供了一种有用的新途径。
