TRGH-PPI: Effective and Generalized Prediction of Protein-Protein Interactions Through Transformer and Graph

Protein-protein interaction (PPI) is a fundamental means of function and signaling in biological systems. The significant increase in demand and cost associated with experimental PPI research requires computational tools to automatically predict and understand PPI. However, existing methods either heavily rely on protein sequences for PPI prediction, or focus on protein structure based on the belief that structure is the key determinant of interactions. But in fact, both have a significant impact on the function of proteins. Therefore, we propose an integrated framework TRGH-PPI based on Transformer and GNN, including a protein feature extraction module and a PPI prediction module, which can simultaneously model both types of protein information and predict PPI. In the protein feature extraction module, we repeatedly use Transformer and GCN to iteratively update the sequence representation and structural features of proteins. The combination of Transformer and GCN enables them to leverage their respective advantages, promote model innovation, and improve the efficiency of graph data processing. In the PPI prediction module, we propose dpdGAT, which uses dot product operations more suitable for PPI prediction and has a dynamic attention mechanism. Numerous experiments have shown that TRGH-PPI is superior to current advanced methods and has shown high accuracy and generalization ability in predicting PPI. By integrating the synergistic modeling of sequence and structural features, the model has achieved an average improvement of 2% -5% in key indicators compared to the SOTA on three datasets.