Voxel-Based Deep Learning Method for Local Detection of Protein-Ligand Binding Sites

Abstract

Accurate detection of ligand binding sites on proteins plays a crucial role in drug design, functional annotation, and biological classification. Deep learning (DL) models applied to computational binding site prediction have improved the characterization of binding site properties that are often difficult to obtain experimentally. However, most binding site detection DL algorithms search exhaustively around the whole protein, which is computationally expensive. Existing DL models are also sensitive to the choice of hyperparameters and architecture, which can lead to inaccurate predictions. To address these limitations, we present VoxelProt-ligand, a local voxel-based deep learning method for binding site detection in protein-ligand interactions. VoxelProt-ligand is built upon an architecture that encodes protein surfaces as sparse, octree-based 3D voxel grids. These sparse grids are used to train a 3D-CNN for classifying surface regions as binding or non-binding. Accurate position and volume of binding sites is achieved with a local search that leverages protein surface geometry and an energy score. VoxelProt-ligand is trained on the MaSIF-ligand dataset and evaluated on HOLO4K and COACH420. Our results shows close agreement with the ground truth in all evaluation datasets for the joint criteria of whether a protein contains binding sites and average number of detected binding sites per protein. Importantly, VoxelProt-ligand improves detection of binding site volume, achieving significantly higher binding pocket shape overlap than existing methods, while maintaining competitive binding site localization success rates. These results indicate that VoxelProt-ligand offers a viable path toward applications in computational drug discovery that depend on accurate determination of binding pocket geometry.