Coevolutionary signals in multiple sequence alignments improve virulence factor prediction with an MSA Transformer.

ABSTRACT: Identification of virulence factors (VFs) is critical for expanding our knowledge on bacterial pathogenesis and also for developing targeted strategies for the prevention and treatment of related infectious diseases. Understanding virulence factors requires to consider coevolutionary information, as it reveals the evolutionary interdependencies between amino acid residues, which can provide some biological insights into their functional and structural roles in bacterial pathogenicity. Previous studies have conducted VF predictions without considering coevolutionary information of proteins. In this paper, we introduce MSA-VF Predictor (MVP), a novel deep learning-based method that effectively captures coevolutionary features inherent in protein sequences for VF prediction. The first step of

SUBMITTER: Kim T 

PROVIDER: S-EPMC12630895 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature