@Article{cmes.2025.074364,
AUTHOR = {Jia Mi, Zhikang Liu, Chang Li, Jing Wan},
TITLE = {GLM-EP: An Equivariant Graph Neural Network and Protein Language Model Integrated Framework for Predicting Essential Proteins in Bacteriophages},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {145},
YEAR = {2025},
NUMBER = {3},
PAGES = {4089--4106},
URL = {http://www.techscience.com/CMES/v145n3/65009},
ISSN = {1526-1506},
ABSTRACT = {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: <a href="https://github.com/MiJia-ID/GLM-EP" target="_blank">https://github.com/MiJia-ID/GLM-EP</a> (accessed on 01 November 2025).},
DOI = {10.32604/cmes.2025.074364}
}