Pablo Arantes (@pabloarantes.bsky.social)

🎉 New publication in JCIM at @pubs.acs.org! We present eRMSF, a Python package for ensemble-based RMSF analysis of biomolecular systems, supporting MD, PDB, and ML ensembles. Proud to collaborate with Rodrigo Ligabue-Braun and @conradopedebos.bsky.social pubs.acs.org/doi/10.1021/...

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eRMSF: A Python Package for Ensemble-Based RMSF Analysis of Biomolecular Systems Understanding molecular flexibility and dynamics across different structural ensembles is essential for interpreting the behavior of complex biological systems. Here, we introduce eRMSF, a fast and user-friendly Python package built with MDAKit from MDAnalysis, designed to perform ensemble-based root mean square fluctuation (RMSF) analyses. Unlike traditional approaches limited to molecular dynamics trajectories, eRMSF extends flexibility analysis to ensembles generated by different methods, such as MD simulations, BioEmu (a deep learning tool for equilibrium ensemble prediction), subsampled AlphaFold2 (AlphaFold ensemble generation), and other computational or experimental sources. By enabling RMSF calculations across heterogeneous ensembles, eRMSF provides a unified framework to evaluate residue or atomic fluctuations in both simulated and predicted structures. Users can easily customize atom, residue, or region selections, tailoring analyses to specific research questions. This approach delivers high-resolution insights into localized motions, complements global stability assessments, and reveals dynamic regions often overlooked by single-method analyses. The repository for eRMSF is available at https://github.com/pablo-arantes/ermsfkit. https://pubs.acs.org/doi/10.1021/acs.jcim.5c02413