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.

The scripts and notebooks for IFPAggVis are available at https://doi.org/10.5281/zenodo.10424417 or below.

The Molecular Dynamics simulations used for data analysis are available at https://doi.org/10.5281/zenodo.5017745, https://doi.org/10.5281/zenodo.5017839, and https://doi.org/10.5281/zenodo.5017851.

The results of the analysis with IFPAggVis on the data set are available at https://doi.org/10.5281/zenodo.10423389.

Open Access funding enabled and organized by Projekt DEAL.