Protein-protein and protein-nucleic acid binding site prediction via interpretable hierarchical geometric deep learning

GraphRBF is a state-of-the-art protein-protein/nucleic acid interaction site prediction model built by enhanced graph neural networks and prioritized radial basis function neural networks. This project serves users to use our software to directly predict protein binding sites or train our model on a new database.
Identification of protein-protein and protein-nucleic acid binding sites provides insights into biological processes related to protein functions and technical guidance for disease diagnosis and drug design. However, accurate predictions by computational approaches remain highly challenging due to the limited knowledge of residue binding patterns. The binding pattern of a residue should be characterized by the spatial distribution of its neighboring residues combined with their physicochemical information interaction, which yet can not be achieved by previous methods. Here, we design GraphRBF, a hierarchical geometric deep learning model to learn residue binding patterns from big data. To achieve it, GraphRBF describes physicochemical information interactions by designing an enhanced graph neural network and characterizes residue spatial distributions by introducing a prioritized radial basis function neural network. After training and testing, GraphRBF shows great improvements over existing state-of-the-art methods and strong interpretability of its learned representations.