Research Data Summary for: "Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data"

Here, we summarise available data and source code regarding the publication "Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data". Abstract Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets. All the scripts (https://doi.org/10.5281/zenodo.10424417) and data (https://doi.org/10.5281/zenodo.10423389) used in this paper are available open source at Zenodo.

本节对有关《系统分析、汇总与可视化分子动力学模拟数据交互指纹》一文的可用数据和源代码进行概述。摘要：计算方法，如分子对接或分子动力学（MD）模拟，已被开发出来以模拟和探究生物分子之间的相互作用。然而，使用这些方法获得的交互作用难以分析和评估。交互指纹（IFPs）被提出，用以从静态3D坐标中提取交互作用，并将其转换为1D比特向量。最近，该概念已被应用于从MD模拟中推导出IFPs，通过引入系统的时空运动和动力学，增加了额外的复杂性。因此，从单个MD模拟中可以获得大量IFPs，相较于从静态3D结构中得到的IFPs，这些IFPs的分析难度更大。科学贡献：我们引入了一种新的方法，以系统地汇总从MD模拟数据中推导出的IFPs。此外，我们提出了可视化方法，以有效分析和比较MD模拟数据中推导出的IFPs，从而考虑交互作用的时序演变，并比较不同MD模拟中的IFPs。该成果已实现为免费Python库，因此其他研究人员可以轻松采用，并应用于不同的MD模拟数据集。本文中使用的所有脚本（https://doi.org/10.5281/zenodo.10424417）和数据（https://doi.org/10.5281/zenodo.10423389）均以开源形式在Zenodo上提供。