Evidence map›Paper›PMID 42159250›Full record

ReviewProtein science : a publication of the Protein Society2026

Sketching microprotein portraits.

Gabriel Diaz, Philippe Valenti, Marc Gueroult, Simon Marques-Prieto, Kenza Benachenhou, Jennifer Zanet, Matthieu Chavent

Abstract readReview
In one paragraph

Review in Protein science : a publication of the Protein Society, 2026. 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. Sketching microprotein portraits.Protein science : a publication of the Protein Society · 2026
    Review
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

7 authors.

Gabriel DiazLMGM, Centre de Biologie Intégrative (CBI), CNRS, Université de Toulouse UT, Toulouse, France.
Philippe ValentiMCD, Centre de Biologie Intégrative (CBI), CNRS, Université de Toulouse UT, Toulouse, France.
Marc GueroultLMGM, Centre de Biologie Intégrative (CBI), CNRS, Université de Toulouse UT, Toulouse, France.ORCID 0000-0003-1036-7490
Simon Marques-PrietoMCD, Centre de Biologie Intégrative (CBI), CNRS, Université de Toulouse UT, Toulouse, France.ORCID 0000-0001-9908-0319
Kenza BenachenhouLMGM, Centre de Biologie Intégrative (CBI), CNRS, Université de Toulouse UT, Toulouse, France.
Jennifer ZanetMCD, Centre de Biologie Intégrative (CBI), CNRS, Université de Toulouse UT, Toulouse, France.ORCID 0000-0003-4649-4643
Matthieu ChaventLMGM, Centre de Biologie Intégrative (CBI), CNRS, Université de Toulouse UT, Toulouse, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The illustrations of intricate molecular machineries inside cells created by David Goodsell continue to inspire the scientific community. Here, we aim to extend his artworks to include microproteins, a newly recognized class of small proteins with less than 100 amino acids, encoded by small open reading frames. Given the rapidly expanding number of identified microproteins, potentially exceeding the number of canonical proteins, we highlight, in this perspective article, diverse computational approaches to classify these proteins. By predicting localization, assessing structural homology, and modeling environments and dynamics of microproteins, these methods could provide clues about the subcellular localization of these microproteins and their structural domain homology, guiding further investigation into their biological functions in living systems.

Indexed as

ProteinsAnimalsHumansMicropeptidesModels, MolecularMicropeptidesProteinsmembranemicroproteinsmodelingorganellesstructure predictionsubcellular localization

Identifiers

PMID42159250
PMCPMC13240139

What OpenQuestion holds

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LicenceCC BY-NC
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.