GLM-EP: An Equivariant Graph Neural Network and Protein Language Model Integrated Framework for Predicting Essential Proteins in Bacteriophages

Recognizing essential proteins within bacteriophages is fundamental to uncovering their replication and survival mechanisms and contributes to advances in phage-based antibacterial therapies. Despite notable progress, existing computational techniques struggle to represent the interplay between sequence-derived and structure-dependent protein features. To overcome this limitation, we introduce GLM-EP, a unified framework that fuses protein language models with equivariant graph neural networks. By merging semantic embeddings extracted from amino acid sequences with geometry-aware graph representations, GLM-EP enables an in-depth depiction of phage proteins and enhances essential protein identification. Evaluation on diverse benchmark datasets confirms that GLM-EP surpasses conventional sequence-based and independent deep-learning methods, yielding higher F1 and AUROC outcomes. Component-wise analysis demonstrates that GCNII, EGNN, and the gated multi-head attention mechanism function in a complementary manner to encode complex molecular attributes. In summary, GLM-EP serves as a robust and efficient tool for bacteriophage genomic analysis and provides valuable methodological perspectives for the discovery of antibiotic-resistance therapeutic targets. The corresponding code repository is available at: https://github.com/MiJia-ID/GLM-EP (accessed on 01 November 2025).