Graphsite: ligand-binding site classification using deep graph neural network

Abstract

Binding sites are concave surfaces on proteins that bind to small molecules called ligands. Types of molecules that bind to the protein determine its biological function. Meanwhile, the binding process between small molecules and the protein is also crucial to various biological functionalities. Therefore, identifying and classifying such binding sites would enormously contribute to biomedical applications such as drug repurposing. Deep learning is a modern artificial intelligence technology. It utilizes deep neural networks to handle complex tasks such as image classification and language translation. Previous work has proven the capability of deep learning models handle binding sites wherein the binding sites are represented as pixels or voxels. Graph neural networks (GNNs) are deep learning models that operate on graphs. GNNs are promising for handling binding sites related tasks - provided there is an adequate graph representation to model the binding sties. In this communication, we describe a GNN-based computational method, GraphSite, that utilizes a novel graph representation of ligand-binding sites. A state-of-the-art GNN model is trained to capture the intrinsic characteristics of these binding sites and classify them. Our model generalizes well to unseen data and achieves test accuracy of 81.28% on classifying 14 binding site classes.