eRMSF: A Python Package for Ensemble-Based RMSF Analysis of Biomolecular Systems
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Abstract
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.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Scalable emulation of protein equilibrium ensembles with generative deep learning
Lewis, S., Hempel, T., Jiménez Luna, J., Gastegger, M., Xie, Y., Foong, A. Y. K., García Satorras, V., Abdin, O., Veeling, B. S., Zaporozhets, I., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.05.626885 · Cited by 349 · see citing works
Scalable emulation of protein equilibrium ensembles with generative deep learning
Lewis, S., Hempel, T., Jiménez-Luna, J., Gastegger, M., Xie, Y., Foong, A. Y. K., Satorras, V. G., Abdin, O., Veeling, B. S., Zaporozhets, I., et al. (2025) · Science · doi:10.1126/science.adv9817 · Cited by 335 · see citing works
High-throughput prediction of protein conformational distributions with subsampled AlphaFold2
Monteiro da Silva, G., Cui, J. Y., Dalgarno, D. C., Lisi, G. P., & Rubenstein, B. M. (2024) · Nature Communications · doi:10.1038/s41467-024-46715-9 · Cited by 196 · see citing works
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- Authors
- Arantes, P. R., Ligabue-Braun, R., & Pedebos, C.
- Affiliations
- Universidade Federal de Ciências da Saúde de Porto Alegre (BR)
- Graduate Program in Biosciences (PPG Bio), Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA); Rua Sarmento Leite, 245 - Centro Histórico, Porto Alegre 90050-170, Brasil
- Corresponding author
- Arantes, Pablo Ricardo
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2025-11-19
- Volume
- 65
- Issue
- 23
- Pages
- 12648-12654
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC12690589
- OpenAlex
- W4416395846
- Cited by
- 5
- References
- 4
- Citations by year
- 2026: 5
- Type
- software paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Funders
- Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico (150566/2024-7)
- Funda??o de Amparo ? Pesquisa do Estado do Rio Grande do Sul
- Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- RMSF, molecular flexibility, structural ensembles, MDAnalysis, biomolecular dynamics, Python
- MeSH terms
- Software, Molecular Dynamics Simulation
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