S2site: accurate protein binding site prediction with geometric deep learning and protein language model

Le résumé fourni par la source

Predicting a protein’s binding sites helps to understand the functional mechanisms of protein interactions, which provides insights into drug discovery. Although experimentally determining the protein complex’s structure can accurately identify the binding residues, the process is labor-intensive and expensive. With the recent advances in the protein language model (PLM) and geometric deep learning, we introduce S2Site (Sequence and Structure based Binding Site Prediction), an end-to-end framework that incorporates the geometric deep learning model with the PLM to identify the protein binding sites. S2Site consistently outperforms various state-of-the-art methods in the protein binding site prediction of three different interactions, including protein-protein, antigen-antibody, and protein-peptide binding sites. Compared to methods based on multiple sequence alignments, PLM allows S2Site to predict protein binding sites on a large scale efficiently. Our experiments also show that both sequence and structural features contribute to the performance of binding site prediction. Overall, S2Site is a robust and practical model for efficiently identifying binding residues of various protein-ligand interactions. The source code and model can be accessed at https://github.com/LW-21/S2Site .

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