Evidence map›Paper›PMID 41944598›Full record

ArticleProtein science : a publication of the Protein Society2026

A composition-matching algorithm, MatchIDR, identifies prion-like domains that localize to stress granules.

Sean M Cascarina, Kacy R Paul, Larissa L Ford, Eric D Ross

Abstract read
In one paragraph

Article 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

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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

4 authors.

Sean M CascarinaDepartment of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, Colorado, USA.ORCID https://orcid.org/0000-0002-3054-2332
Kacy R PaulDepartment of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, Colorado, USA.
Larissa L FordDepartment of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, Colorado, USA.
Eric D RossDepartment of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, Colorado, USA.

Funding

Functional and Pathological Assembly of Prion-like DomainsR35GM130352 · NIGMS · COLORADO STATE UNIVERSITY · PI Eric D Ross · 2019 to 2026
$3.1M
NIGMS NIH HHS R35 GM130352NIGMS NIH HHS R35GM130352
6 · The paper itself

Abstract

Intrinsically disordered regions (IDRs) play important molecular roles in cells even though they do not adopt a stable structure. Relative to structured regions, IDRs have skewed amino acid compositions favoring polar and charged amino acids. This feature is a major contributor to the biophysical behavior and in vivo activity of IDRs, but the relationship between composition and activity depends strongly on which amino acids are enriched within the IDRs. Here, we present a new search algorithm, MatchIDR, that takes as input one or more IDR sequences and finds the nearest compositional matches within a proteome. Using MatchIDR with both artificially designed and native yeast IDRs as query sequences, we successfully identify IDRs from multiple organisms that localize (or do not localize) to yeast stress granules, as expected from the known activities of the query sequences. Our results demonstrate that composition-based proteome searches can be an effective strategy for identifying new IDRs with similar in vivo activities. MatchIDR is available at https://github.com/RossLabCSU/MatchIDR.

Indexed as

AlgorithmsIntrinsically Disordered ProteinsPrionsSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsStress GranulesProtein DomainsProteomeIntrinsically Disordered ProteinsPrionsProteomeSaccharomyces cerevisiae Proteinscomposition‐driven activityintrinsically disordered regionliquid‐liquid phase separationmembraneless organelleprion‐like domainstress granule

Identifiers

PMID41944598
PMCPMC13055198

What OpenQuestion holds

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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.