Biochemistry Department, University of Zurich, Zurich CH-8057, Switzerland.
J Chem Theory Comput. 2020 Oct 13;16(10):6383-6396. doi: 10.1021/acs.jctc.0c00604. Epub 2020 Sep 24.
Molecular dynamics simulations are a popular means to study biomolecules, but it is often difficult to gain insights from the trajectories due to their large size, in both time and number of features. The Sapphire (States And Pathways Projected with HIgh REsolution) plot allows a direct visual inference of the dominant states visited by high-dimensional systems and how they are interconnected in time. Here, we extend this visual inference into a clustering algorithm. Specifically, the automatic procedure derives from the Sapphire plot states that are kinetically homogeneous, structurally annotated, and of tunable granularity. We provide a relative assessment of the kinetic fidelity of the Sapphire-based partitioning in comparison to popular clustering methods. This assessment is carried out on trajectories of -butane, a β-sheet peptide, and the small protein BPTI. We conclude with an application of our approach to a recent 100 μs trajectory of the main protease of SARS-CoV-2.
分子动力学模拟是研究生物分子的一种常用方法，但由于轨迹的时间和特征数量都很大，因此通常很难从中获得深入的了解。 Sapphire（State And Pathways Projected with HIgh REsolution）图允许直接直观地推断高维系统访问的主要状态以及它们在时间上的相互连接方式。在这里，我们将这种直观推断扩展到聚类算法中。具体来说，自动过程从 Sapphire 图中导出在动力学上均匀、结构上注释且具有可调粒度的状态。我们对基于 Sapphire 的分区与流行的聚类方法相比在动力学保真度方面进行了相对评估。这项评估是针对 -丁烷、β-折叠肽和小蛋白 BPTI 的轨迹进行的。最后，我们将我们的方法应用于最近 SARS-CoV-2 主要蛋白酶的 100 μs 轨迹。
