Evidence map›Paper›PMID 42538434›Full record

ArticleNature structural & molecular biology2026

Variant characterization in the intrinsically disordered human proteome.

Dalmira Hubrich, Jesus Alvarado Valverde, Chop Yan Lee, Milena Djokic, Mareen Welzel, Kristina Hintz, Joelle Morgan Strom, Katja Luck

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Article in Nature structural & molecular biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Dalmira Hubrich *Institute of Molecular Biology (IMB) gGmbH, Mainz, Germany.
Jesus Alvarado Valverde *Institute of Molecular Biology (IMB) gGmbH, Mainz, Germany.
Chop Yan LeeInstitute of Molecular Biology (IMB) gGmbH, Mainz, Germany.
Milena DjokicInstitute of Molecular Biology (IMB) gGmbH, Mainz, Germany.
Mareen WelzelInstitute of Molecular Biology (IMB) gGmbH, Mainz, Germany.
Kristina HintzInstitute of Molecular Biology (IMB) gGmbH, Mainz, Germany.ORCID http://orcid.org/0000-0002-2996-0852
Joelle Morgan StromInstitute of Molecular Biology (IMB) gGmbH, Mainz, Germany.
Katja LuckInstitute of Molecular Biology (IMB) gGmbH, Mainz, Germany. k.luck@imb-mainz.de.ORCID http://orcid.org/0000-0003-2336-9225

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Variant effect prediction remains a key challenge in precision medicine. Computational models are increasingly successful in the characterization of missense variants in folded protein regions. However, 37% of all annotated missense variants reside in the 25% of the proteome that is intrinsically disordered, lacking positional sequence conservation and stable structures. To advance the characterization of variants in intrinsically disordered protein regions (IDRs), we combined sequence pattern searches with AlphaFold to structurally annotate 1,300 protein-protein interactions with interfaces mediated by short disordered motifs binding to folded domains in partner proteins. These interfaces were selected based on their overlap with uncertain missense variants enabling structural model-based prediction of deleterious effects of 1,187 of these variants in IDRs. Extensive experimental efforts validated the predicted interfaces and deleterious variant effects that were predicted as benign by AlphaMissense, demonstrating that the combination of sequence analysis and structural modeling can readily generate numerous testable hypotheses of variant effects on protein function in IDRs.

Indexed as

Intrinsically Disordered ProteinsProteomeHumansModels, MolecularMutation, MissenseProtein FoldingIntrinsically Disordered ProteinsProteome

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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.