Chen Lexin, Leung Jeremy M G, Zsigmond Krisztina, Chong Lillian T, Miranda-Quintana Ramón Alain
Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32603, United States.
Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States.
J Chem Inf Model. 2025 May 26;65(10):4775-4782. doi: 10.1021/acs.jcim.5c00240. Epub 2025 May 6.
State-of-the-art molecular dynamics (MD) simulation methods can generate diverse ensembles of pathways for complex biological processes. Analyzing these pathways using statistical mechanics tools demands identifying key states that contribute to both the dynamic and equilibrium properties of the system. This task becomes especially challenging when analyzing multiple MD simulations simultaneously, a common scenario in enhanced sampling techniques like the weighted ensemble strategy. Here, we present a new module of the MDANCE package designed to streamline the analysis of pathway ensembles. This module integrates n-ary similarity, cheminformatics-inspired tools, and hierarchical clustering to improve analysis efficiency. We present the theoretical foundation behind this approach, termed Sampling Hierarchical Intrinsic N-ary Ensembles (SHINE), and demonstrate its application to simulations of alanine dipeptide and adenylate kinase.
最先进的分子动力学（MD）模拟方法可以为复杂的生物过程生成各种不同的路径集合。使用统计力学工具分析这些路径需要识别对系统的动态和平衡特性都有贡献的关键状态。当同时分析多个MD模拟时，这项任务变得特别具有挑战性，这在诸如加权系综策略等增强采样技术中是常见的情况。在这里，我们展示了MDANCE软件包的一个新模块，旨在简化对路径集合的分析。该模块集成了n元相似性、受化学信息学启发的工具和层次聚类，以提高分析效率。我们展示了这种方法背后的理论基础，称为采样层次内在n元系综（SHINE），并展示了其在丙氨酸二肽和腺苷酸激酶模拟中的应用。
