Rocky 2022

Molecular recognition plays a critical role in biological processes. By binding to another biological molecule such as carbohydrate, DNA/RNA, small molecule, or protein, proteins perform many physiological functions. We developed a computational method to predict ligand binding sites that use the physicochemical properties of triangles of protein atoms instead of relying on the identification of surface cavities or estimations of binding energy as other methods do. Any three atoms on the surface of the target protein form a triangle. Based on the chemical properties and environment of the three surface atoms, the triangles can be classified into different categories in which they either do or do not prefer to bind to particular types of ligands. By simply calculating the binding propensity scores of different atom triangles of target proteins, this method predicts the binding sites with up to 90% accuracy. We recently integrated the triangle preferences with a deep-learning method to create a machine learning-based DNA binding site prediction algorithm, DeepDISE, and are developing analogous protein-carbohydrate, protein-protein and protein-small molecule binding site prediction algorithms using deep learning technology. Our goal is to create innovative and transformative tools that will aid the research community in elucidating how proteins recognize, modify, and/or regulate their binding targets to conduct their physiological functions.