@article{Mi2025, 
author = {Jia Mi and Zhikang Liu and Chang Li and Jing Wan},
title = {GLM-EP: An Equivariant Graph Neural Network and Protein Language Model Integrated Framework for Predicting Essential Proteins in Bacteriophages},
year = {2025},
journal = {Computer Modeling in Engineering & Sciences},
volume = {145},
number = {3},
pages = {4089-4106},
keywords = {Essential proteins, bacteriophages, protein language models, graph neural networks},
url = {https://www.sciopen.com/article/10.32604/cmes.2025.074364},
doi = {10.32604/cmes.2025.074364},
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: https://github.com/MiJia-ID/GLM-EP (accessed on 01 November 2025).}
}