PEGN-PSP: Prediction of General Protein Phosphorylation Sites Using Protein Embeddings and Graph Neural Network

Phosphorylation ranks among the most crucial post-translational modifications (PTMs), significantly influencing the conformation, activity, and functionality of proteins, and is intricately associated with numerous pathophysiological processes. Therefore, proposing a scientifically valid computational method for precise prediction of phosphorylation sites carries substantial importance. In this study, we propose and evaluate a novel method, PEGN-PSP, based on graph techniques and language models for the prediction of general phosphorylation sites. This method employs an adaptive feature fusion strategy, combining sequence embeddings and pretrained model embeddings to enhance the model's capability in representing features. The use of graph neural network attention mechanisms not only captures local patterns of protein sequences, but also effectively captures long-range dependency information between residues in protein sequences. Independent test results indicate that, in comparison to the current state-of-the-art methods for general phosphorylation site prediction, PEGN-PSP improves the Matthews correlation coefficient for S/T sites by 4.6% and for Y sites by 6.5%. Additionally, PEGN-PSP has good robustness in predicting lysine crotonylation sites, indicating that our method PEGN-PSP has strong potential in predicting other protein post-translational modification sites.