SpatialPPI 2.0: Enhancing Protein-Protein Interaction Prediction through Inter-Residue Analysis in Graph Attention Networks

Abstract

Protein-protein interactions (PPIs) are fundamental to cellular functions, and accurate prediction of these interactions is crucial to understanding biological mechanisms and facilitating drug discovery. SpatialPPI 2.0 is an advanced graph neural network-based model that predicts PPIs by utilizing interresidue contact maps derived from both structural and sequence data. By leveraging the comprehensive PINDER dataset, which includes interaction data from the RCSB PDB and the AlphaFold database, SpatialPPI 2.0 improves the specificity and robustness of the prediction of PPI. Unlike the original SpatialPPI, the updated version employs interaction interface prediction as an intermediate step, allowing for a more effective assessment of interactions between isolated proteins. The model utilizes Graph Attention Networks (GAT) and Graph Convolutional Networks (GCN) to capture both local and global structural features. SpatialPPI 2.0 outperforms several state-of-the-art PPI and interface predictors, demonstrating superior accuracy and reliability. Furthermore, the model shows robustness when using structures predicted by AlphaFold, indicating its potential to predict interactions for proteins without experimentally determined structures. SpatialPPI 2.0 offers a promising solution for the accurate prediction of PPIs, providing insight into protein function and supporting advances in drug discovery and synthetic biology. SpatialPPI 2.0 is available at https://github.com/ohuelab/SpatialPPI2.0