Graphogly: a Protein Post-Translational Modification Classification Model Based on Contextual Protein Language Embedding and Graph Convolutional Neural Networks

• Published in: 2025 IEEE 2nd International Conference on Deep Learning and Computer Vision (DLCV) pp. 1 - 6
• Main Authors: Zheng, Luzhou, Chen, Yuehui, Cao, Yi, Wang, Dong, Zhao, Yaou
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.06.2025
• Subjects: Biological system modeling; Computational modeling; Computer architecture; Context modeling; Convolutional neural networks; Graph neural network; Graph neural networks; Multilayer perceptrons; O-linked glycosylation; Predictive models; Protein language model; Proteins; Representation learning
• DOI: 10.1109/DLCV65218.2025.11088561

O-linked glycosylation is a complex form of posttranslational modification in human proteins that plays a crucial regulatory role in a wide range of cell types. It is intimately involved in cellular metabolic activities and signalling networks. In particular, abnormal patterns of O-linked glycosylation have been implicated in the onset and progression of many diseases, including cancer and diabetes. Therefore, the accurate identification of O-linked threonine glycosylation sites (OTGs) remains a challenging task that usually requires extensive laboratory experimentation. In this study we use the GraphOGly model to identify O-linked threonine glycosylation sites. In the data representation phase, we use ProtT5, a pre-trained protein language model, to generate contextual embeddings for individual amino acid residues. This is followed by the application of a graph neural network architecture consisting of three GCNConv layers for feature extraction, together with a self-attention layer and a multilayer perceptron module for further representation learning. The experimental results demonstrate that GraphOGly is superior to other existing tools in predicting O-linked glycosylation sites, and the final experimental results after 5-fold cross-validation are ACC 88.73%,Sn 88.96%,Sp 88.49%,MCC 0.7748,AUC 0.9408.