ProtPhage: a deep learning framework for phage viral protein identification and functional annotation

Phages, viruses that infect bacteria, offer a promising strategy against antibiotic-resistant pathogens. Phage viral proteins (PVPs) are essential for phage-host interactions, yet their identification and functional annotation remain challenging due to high sequence diversity, limited experimental data, and class imbalance. To address these issues, we propose ProtPhage, a novel framework that leverages the ProtT5 protein language model for richer sequence representation compared to traditional methods. Additionally, ProtPhage incorporates an asymmetric loss function to mitigate class imbalance, significantly improving the prediction of the minority class "minor capsid," with an F1 score 33.07$\%$ higher than the best existing model. Extensive experiments demonstrate that ProtPhage outperforms current methods across multiple metrics, including accuracy, precision, recall, and F1 score. A case study on the Mycobacterium phage PDRPxv genome further validates its practical utility, while expanded experiments highlight its potential in phage-host prediction. By integrating advanced deep learning techniques, ProtPhage establishes a new standard for PVP identification and annotation, contributing to the broader field of computational phage biology.