ArticleNature communications2024
Decoding Missense Variants by Incorporating Phase Separation via Machine Learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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Who cites it
11 citing papers in PubMed.
- Molecular dynamics simulations of intrinsically disordered protein regions enable biophysical interpretation of variant-effect predictors.HGG advances · 2026Article
- Rethinking bioinformatics in liquid-liquid phase separation: data resources, predictive models, and an event-centric perspective.Briefings in bioinformatics · 2026Review
- Pathogenic variations illuminate functional constraints in intrinsically disordered proteins.iScience · 2026Article
- Enhancing missense variant classification in predicted intrinsically disordered regions.PloS one · 2026Article
- Proteome-wide computational analyses reveal links between protein condensate formation and RNA biology.Science advances · 2025Article
- Missense mutations in intrinsically disordered protein regions link pathogenicity and phase separation.The Journal of biological chemistry · 2025Article
- DRP-PSM: Multi-Level Feature Integration Reveals Hierarchical Contributions to Pathogenic Synonymous Mutation Prediction.ACS omega · 2025Article
- Pan-Cancer Analysis of Mutations Affecting Protein Liquid-Liquid Phase Separation Revealing Clinical Implications.Biology · 2025Article
- Assessing variant effect predictors and disease mechanisms in intrinsically disordered proteins.PLoS computational biology · 2025Article
- Functions of Intrinsically Disordered Regions.Biology · 2025Review
- Genomic landscape of childhood acute lymphoblastic leukemia in Malaysia: insights from array-CGH.Molecular cytogenetics · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational models have made significant progress in predicting the effect of protein variants. However, deciphering numerous variants of uncertain significance (VUS) located within intrinsically disordered regions (IDRs) remains challenging. To address this issue, we introduce phase separation, which is tightly linked to IDRs, into the investigation of missense variants. Phase separation is vital for multiple physiological processes. By leveraging missense variants that alter phase separation propensity, we develop a machine learning approach named PSMutPred to predict the impact of missense mutations on phase separation. PSMutPred demonstrates robust performance in predicting missense variants that affect natural phase separation. In vitro experiments further underscore its validity. By applying PSMutPred on over 522,000 ClinVar missense variants, it significantly contributes to decoding the pathogenesis of disease variants, especially those in IDRs. Our work provides insights into the understanding of a vast number of VUSs in IDRs, expediting clinical interpretation and diagnosis.
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Registered trials
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.