LHPre: Phage Host Prediction with VAE-based Class Imbalance Correction and Lyase Sequence Embedding.

Abstract

The escalation of antibiotic resistance underscores the need for innovative approaches to combat bacterial infections. Phage therapy has emerged as a promising solution, wherein host determination plays an important role. Phage lysins, characterized by their specificity in targeting and cleaving corresponding host bacteria, serve as key players in this paradigm. In this study, we present a novel approach by leveraging genes of phage-encoded lytic enzymes for host prediction, culminating in the development of LHPre. Initially, gene fragments of phage-encoded lytic enzymes and their respective hosts were collected from the database. Secondly, DNA sequences were encoded using the Frequency Chaos Game Representation (FCGR) method, and pseudo samples were generated employing the Variational Autoencoder (VAE) model to address class imbalance. Finally, a prediction model was constructed using the Vision Transformer(Vit) model. Five-fold cross-validation results demonstrated that LHPre surpassed other state-of-the-art phage host prediction methods, achieving accuracies of 85.04%, 90.01%, and 93.39% at the species, genus, and family levels, respectively.