EMMVEP: An Ensemble Method for Protein Missense Variant Effect Prediction Based on Multi-Source Feature Fusion

Missense mutations are common in the coding genome and can alter protein functions. Distinguishing pathogenic from benign variants remains challenging despite computational advances. In the present work, we introduce EMMVEP, an ensemble-based approach designed for predicting the effects of protein missense mutations. EMMVEP leverages categorical boosting to integrate different types of features: one-hot encoding from protein sequence, physicochemical and environment properties extracted from AlphaFold database, and allele frequency information from gnomAD. When evaluated on a benchmark dataset with 112,832 clinical significance labels, our method achieved AUC and AUPR of 0.907 and 0.879, outperforming 20 general VEP methods. To aid in the identification of pathogenic mutations among the vast number of rare variants discovered through large-scale sequencing studies, we provide the pathogenicity probabilities of 216 million potential amino acid substitutions in 19,233 human protein-encoding genes. Our work demonstrates that EMMVEP can offer valuable independent insights for missense mutation interpretation in proteins, with significant applicability in both research and clinical contexts.