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 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 multiple physiological processes. By leveraging missense variants that alter phase separation propensity, we developed a machine learning approach named PSMutPred to predict the impact of missense mutations on phase separation. PSMutPred demonstrated robust performance in predicting missense variants that affect natural phase separation. In vitro experimental findings 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 unique insights into the understanding of a vast number of VUSs in IDRs, thereby expediting clinical interpretation and diagnosis of disease variants.