SpatConv Enables the Accurate Prediction of Protein Binding Sites by a Pretrained Protein Language Model and an Interpretable Bio-spatial Convolution

Protein interactions with molecules, such as other proteins, peptides, or small ligands, play a critical role in biological processes, and the identification of protein binding sites is crucial for understanding the mechanisms underlying diseases such as cancer. Traditional protein binding site prediction models usually extract residue features manually and then employ a graph or point-cloud-based architecture borrowed from other fields. Therefore, substantial information loss and limited learning ability cause them to fail to capture residue binding patterns. To solve these challenges, we introduce a general network that predicts the binding residues of proteins, peptides, and metal ions on proteins. SpatConv extracts sequence features from a pretrained large protein language model and structure features from a local coordinate framework. SpatConv learns residue binding patterns through a specially designed, graph-free bio-spatial convolution, which characterizes the complex spatial environments around the residues. After training and testing, SpatConv demonstrates great improvements over the state-of-the-art predictors and reveals novel biological insights into the relationship between binding sites and physicochemical properties. Notably, SpatConv exhibits robust performance across predicted and experimental structures, enhancing its reliability. Additionally, when applying it to the spike protein structure of severe acute respiratory syndrome coronavirus 2, SpatConv successfully identifies antibody binding sites and predicts potential binding regions, providing strong evidence supporting new drug development. A user-friendly online server for SpatConv is freely available at http://liulab.top/SpatConv/server.