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

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 our method is to generate multiple sequence alignment (MSA) that can represent evolutionary information of VF related protein sequences. Then, we utilize the MSA Transformer to extract features from the MSA data that capture coevolutionary information and homologous protein information. Using these coevolutionary features along with the residue level information, we propose MSA-composition, which consists of latent vectors for amino acids in matrix form. Our approach achieved a prediction accuracy of 0.869, outperforming existing state-of-the-arts (SOTA) models. We conducted experiments to interpret the relationship between MVP's performance and coevolutionary information, and presented the interpretation results. To further investigate the MSA transformer model, we performed experiments of pruning attention blocks, which shows attention blocks that play a crucial role in VF prediction are also significant to VF proteins with high coevolutionary information. In summary, MVP ( http://bhi4.snu.ac.kr:7978 ) successfully incorporates coevolutionary information for predicting VF proteins using MSA transformer.