CrossPPI: A Cross - Fusion Based Model for Protein - Protein Binding Affinity Prediction.

Abstract

Many biological processes depend on protein-protein interactions (PPIs), which are particularly important across biology, medicine, and biotechnology. It is essential to accurately predict the binding affinity between protein pairs to prioritize candidate interactions in large-scale studies and expedite drug discovery. The application of cross-attention mechanisms between ligand and receptor protein sequences is often neglected in current computational models, limiting their capacity to accurately represent inter-protein dependencies. In this study, we introduce CrossPPI, a novel deep learning framework that integrates structural and sequential features of interacting proteins to improve binding affinity prediction. To model intricate interactions between protein pairs, CrossPPI uses a transformer-based cross-fusion module and a dual-view feature-extraction approach that combines Graph Attention Networks (GATs) and Convolutional Neural Networks (CNNs). On the test dataset of 300 protein-protein pairs, CrossPPI achieved a Pearson correlation coefficient (PCC) of 0.7616, a Spearman correlation coefficient (SCC) of 0.7644, a mean absolute error (MAE) of 1.2869, and a root mean square error (RMSE) of 1.6824, indicating its ability to predict the binding affinity of two proteins. The results highlight CrossPPIs capability to predict inter-protein binding affinities by leveraging an attention-based integration of sequence and structural features.