Jaeger-Honz Sabrina, Klein Karsten, Schreiber Falk
Department of Computer and Information Science, University of Konstanz, Universitätsstrasse 10, 78464, Constance, Germany.
Faculty of Information Technology, Monash University, Clayton, VIC, 3800, Australia.
J Cheminform. 2024 Mar 12;16(1):28. doi: 10.1186/s13321-024-00822-3.
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.
诸如分子对接或分子动力学（MD）模拟等计算方法已被开发出来，用于模拟和探索生物分子之间的相互作用。然而，使用这些方法获得的相互作用难以分析和评估。人们提出了相互作用指纹（IFP），以便从静态三维坐标中得出相互作用，并将其转化为一维位向量。最近，这一概念已被应用于从MD模拟中得出IFP，通过加入系统的时间运动和动力学增加了一层复杂性。结果，从一次MD模拟中可获得许多IFP，与从静态三维结构得出的IFP相比，产生了大量难以分析的单个IFP。科学贡献：我们引入了一种新方法，用于系统地汇总从MD模拟数据中得出的IFP。此外，我们提出了可视化方法，以有效地分析和比较从MD模拟数据中得出的IFP，从而考虑相互作用的时间演变，并比较不同MD模拟中的IFP。这已被实现为一个免费的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。
