Integrating Structural Features with Protein Language Models to Predict Protein-Ligand Binding Sites

Protein interactions are essential to biological function. Consequently, developing accurate models to predict ligand-binding sites is crucial not only for advancing our understanding of biological processes but also for applications like drug discovery. Advances in computational power have enabled machine learning techniques, including Protein Language Models (PLMs). In this study, we used the ESM-2 [1] PLM for binding site prediction and explored the integration of three-dimensional structural features to enhance its performance. We demonstrate that incorporating simple structural features can enhance the performance of sequence-based models. However, not all structural features are beneficial, indicating that some structural information is already embedded within the models themselves.