Article

Decoding Missense Variants by Incorporating Phase Separation via Machine Learning

2024-04-01

Abstract excerpt

Computational models have made significant progress in predicting the effect of protein variants. However, deciphering numerous variants of unknown significance (VUS) located within intrinsically disordered regions (IDRs) remains challenging. To address this issue, we introduced phase separation (PS), which is tightly linked to IDRs, into the investigation of missense variants. Phase separation is vital for multip...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 8ec09ba2-9480-54fc-a62d-8bf1abfb92df
DOI: 10.1101/2024.04.01.587546

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Decoding Missense Variants by Incorporating Phase Separation via Machine LearningDOI 10.1101/2024.04.01.587546Read

Missense mutations in intrinsically disordered protein regions link pathogenicity and phase separationSemantic proximity Read

Interpretable Prediction of Phase Separation and Disease Variant Effects in Intrinsically Disordered RegionsSemantic proximity Read

Phase Separation as a Missing Mechanism for Interpretation of Disease Mutations.Semantic proximity Read

Pathogenic variations illuminate functional constraints in intrinsically disordered proteinsSemantic proximity Read

Variant characterization in the intrinsically disordered human proteomeSemantic proximity Read

Assessing variant effect predictors and disease mechanisms in intrinsically disordered proteinsSemantic proximity Read

Molecular dynamics simulations of intrinsically disordered protein regions enable biophysical interpretation of variant effect predictorsSemantic proximity Read

Disease mutations in disordered regions--exception to the rule?Semantic proximity Read

A machine learning framework for predicting and modulating condition-dependent protein phase separationSemantic proximity Read

Accurate Identification and Mechanistic Evaluation of Pathogenic Missense Variants with <i>Rhapsody-2</i>Semantic proximity Read

Protein Language Model Identifies Disordered, Conserved Motifs Implicated in Phase SeparationSemantic proximity Read

EpiPred: A gene-specific machine learning model for classifying missense variants in the epilepsy-related gene <i>STXBP1</i>Semantic proximity Read