Motta Stefano, Callea Lara, Mulla Shaziya Ismail, Davoudkhani Hamid, Bonati Laura, Pandini Alessandro
Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, 20126, Italy.
Department of Computer Science, Brunel University of London, Uxbridge UB8 3PH, United Kingdom.
Bioinformatics. 2025 Jun 2;41(6). doi: 10.1093/bioinformatics/btaf308.
Molecular dynamics (MD) simulations provide critical insights into biomolecular processes but they generate complex high-dimensional data that are often difficult to interpret directly. Dimensionality reduction methods like principal component analysis, time-lagged independent component analysis, and self-organizing maps (SOMs) have helped in extracting essential information on functional dynamics. However, there is a growing need for a user-friendly and flexible framework for SOM-based analyses of MD simulations. Such a framework should offer adaptable workflows, customizable options, and direct integration with a widely adopted analysis software.
We designed and developed SOMMD, an R package to streamline MD analysis workflows. SOMMD facilitates the interpretation of atomistic trajectories through SOMs, providing tools for each stage of the workflow, from importing a wide range of MD trajectories data types to generating enhanced visualizations. The package also includes three example projects that demonstrate how SOM can be applied in real-world scenarios, including cluster analysis, pathways mapping and transition networks reconstruction.
SOMMD is available on CRAN (https://CRAN.R-project.org/package=SOMMD) and on GitHub (https://github.com/alepandini/SOMMD).
分子动力学（MD）模拟为生物分子过程提供了关键见解，但它们会生成复杂的高维数据，这些数据通常难以直接解释。诸如主成分分析、时间滞后独立成分分析和自组织映射（SOM）等降维方法有助于提取有关功能动力学的基本信息。然而，对于基于SOM的MD模拟分析，越来越需要一个用户友好且灵活的框架。这样的框架应提供可适应的工作流程、可定制的选项，并能与广泛采用的分析软件直接集成。
我们设计并开发了SOMMD，这是一个用于简化MD分析工作流程的R包。SOMMD通过SOM促进对原子轨迹的解释，为工作流程的每个阶段提供工具，从导入各种MD轨迹数据类型到生成增强的可视化。该包还包括三个示例项目，展示了SOM如何应用于实际场景，包括聚类分析、路径映射和过渡网络重建。
SOMMD可在CRAN（https://CRAN.R-project.org/package=SOMMD）和GitHub（https://github.com/alepandini/SOMMD）上获取。
