Evidence map›Paper›PMID 41254899›Full record

ArticleJournal of chemical information and modeling2025

eRMSF: A Python Package for Ensemble-Based RMSF Analysis of Biomolecular Systems.

Pablo Ricardo Arantes, Rodrigo Ligabue-Braun, Conrado Pedebos

Abstract read
In one paragraph

Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Pablo Ricardo ArantesGraduate 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.ORCID 0000-0003-1946-2750
Rodrigo Ligabue-BraunGraduate 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.ORCID 0000-0002-2555-9754
Conrado PedebosGraduate 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Molecular Dynamics SimulationSoftware

Identifiers

PMID41254899
PMCPMC12690589

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.