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Molecular simulations enable the study of biomolecules and their dynamics onan atomistic scale. A common task is to compare several simulation conditions -like mutations or different ligands - to find significant differences andinterrelations between them. However, the large amount of data produced forever larger and more complex systems often renders it difficult to identify thestructural features that are relevant for a particular phenomenon. We present aflexible software package named PENSA that enables a comprehensive and thoroughinvestigation into biomolecular conformational ensembles. It provides a widevariety of featurizations and feature transformations that allow for a completerepresentation of biomolecules like proteins and nucleic acids, including waterand ion cavities within the biomolecular structure, thus avoiding bias thatwould come with manual selection of features. PENSA implements various methodsto systematically compare the distributions of these features across ensemblesto find the significant differences between them and identify regions ofinterest. It also includes a novel approach to quantify the state-specificinformation between two regions of a biomolecule which allows, e.g., thetracing of information flow to identify signaling pathways. PENSA also comeswith convenient tools for loading data and visualizing results in ways thatmake them quick to process and easy to interpret. PENSA is an open-sourcePython library maintained at https://github.com/drorlab/pensa along with anexample workflow and a tutorial. Here we demonstrate its usefulness inreal-world examples by showing how it helps to determine molecular mechanismsefficiently.

Systematic Analysis of Biomolecular Conformational Ensembles with PENSA