DCPPS: Prediction of Kinase-Specific Phosphorylation Sites Using Dynamic Embedding and Cross-Representation Interaction.

Substrate-specific kinases catalyze addition of phosphate groups to specific amino acids, resulting in kinase-specific phosphorylation. It participates in various signaling pathways and regulation processes. The relevant computational methods can accelerate study of protein function research, disease exploration, and drug development. Existing approaches typically rely on global and local sequences to extract predictive features but often neglect position information and critical feature interaction, which is essential for effective feature representation. In this work, we propose a novel kinase-specific phosphorylation site prediction model, DCPPS, by leveraging dynamic embedding encoding and interaction between global and local representations. Specifically, to enrich sequence position information and strengthen features, we construct a dynamic embedding encoding (DEE) to capture amino acid semantics and positional information of upstream and downstream amino acids, dynamically optimizing feature embeddings. Considering the lack of in-depth feature interaction between local and global information, we design a cross-representation interaction unit (CRIU) to facilitate in-depth mining and complementary improvement of potential connections between multi-source features. Results of kinase-specific phosphorylation and multiple extended experiments show that DCPPS has better predictive performance and scalability. Further ablation studies demonstrate that incorporating global protein information, DEE, and CRIU markedly enhances phosphorylation site prediction accuracy, particularly in mitigating class imbalance.