Decoding Missense Variants by Incorporating Phase Separation via Machine Learning.

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. B

SUBMITTER: Feng M 

PROVIDER: S-EPMC11436885 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature