Enhancing missense variant pathogenicity prediction with protein language models using VariPred.

ABSTRACT: Computational approaches for predicting the pathogenicity of genetic variants have advanced in recent years. These methods enable researchers to determine the possible clinical impact of rare and novel variants. Historically these prediction methods used hand-crafted features based on structural, evolutionary, or physiochemical properties of the variant. In this study we propose a novel framework that leverages the power of pre-trained protein language models to predict variant pathogenicity. We show that our approach VariPred (Variant impact Predictor) outperforms current state-of-the-art methods by using an end-to-end model that only requires the protein sequence as input. Using one of the best-performing protein language models (ESM-1b), we establish a robust classifier that requires no

SUBMITTER: Lin W 

PROVIDER: S-EPMC10999449 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature