Evidence map›Paper›PMID 41083482›Full record

ArticleNature communications2025

ProFlex as a linguistic bridge for decoding protein dynamics in normal mode analysis.

Damian J Magill, Timofey A Skvortsov

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Damian J MagillIFF Health and Biosciences, Dangé-Saint-Romain, France. damian.magill@iff.com.
Timofey A SkvortsovSchool of Pharmacy, Queen's University Belfast, Belfast, Northern Ireland, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence is revolutionizing structural bioinformatics, with AlphaFold arguably being the most impactful development to date. The structural atlases generated by these methods present significant opportunities for unraveling biological mysteries but also pose challenges in leveraging such massive datasets effectively. In this work, we explore the dynamic landscape of hundreds of thousands of AlphaFold-predicted structures using normal mode analysis. The resulting data serve to empirically define an alphabet summarizing relative protein flexibility, termed ProFlex. Leveraging ProFlex, we describe the flexibility information space occupied by this massive dataset. We believe leveraging the data compression offered by ProFlex-like approaches opens opportunities for understanding protein function, refining structural predictions, and rendering analyses computationally tractable.

Indexed as

Computational BiologyProteinsArtificial IntelligenceDatabases, ProteinLinguisticsProtein ConformationProtein FoldingProteins

Identifiers

PMID41083482
PMCPMC12518519

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.