Abstract
Protein-protein interactions (PPIs) play a critical role in cellular functions, which are essential for maintaining the proper physiological state of organisms. Therefore, identifying PPI sites with high accuracy is crucial. Recently, graph neural networks (GNNs) have achieved significant progress in predicting PPI sites, but there is still potential for further enhancement. In this study, we introduce GTE-PPIS, an innovative PPI site predictor that utilizes two components: a graph transformer and an equivariant GNN, to collaboratively extract features. These extracted features are subsequently processed through a multilayer perceptron to generate the final predictions. Our experimental results show that GTE-PPIS consistently outperforms existing methods on multiple evaluation metrics across benchmark datasets, strongly supporting the effectiveness of our approach.