AFFIPred: AlphaFold2 structure-based Functional Impact Prediction of missense variations.

ABSTRACT: Protein structure holds immense potential for pathogenicity prediction, albeit structure-based predictors are limited compared to the sequence-based counterparts due to the "structure knowledge gap" between large number of available protein sequences and relatively limited number of structures. Leveraging the highly accurate protein structures predicted by AlphaFold2 (AF2), we introduce AFFIPred, an ensemble machine learning classifier that combines sequence and AF2-based structural characteristics to predict missense variant pathogenicity. Based on the assessments on unseen datasets, AFFIPred reached a comparable level of performance with the state-of-the-art predictors such as AlphaMissense. We also showed that the recruitment of AF2 structures that are full-length and represent the unbo

SUBMITTER: Pir MS 

PROVIDER: S-EPMC11751861 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature