Elucidating the Multiple Roles of Hydration in Protein-Ligand Binding via Layerwise Relevance Propagation and Big Data Analytics

Hydration is a key player in protein-ligand association. No computational method for modeling hydration has so far consistently improved the scoring performance of docking approaches. Using molecular dynamics on thousands of proteins in conjunction with modern deep learning approaches allowed the successful modeling of hydration during scoring of protein-ligand binding poses. This on-the-fly inclusion of hydration information resulted in unprecedented accuracy in binding pose prediction. Big-data analytics based on relevance deduced from the trained neural network revealed that the correct prediction of binding poses depends on three essential pillars of hydration, i.e. water-mediated interactions, desolvation, and enthalpically stable water layers around the bound ligand. The latter form of hydration may open new avenues for optimizing ligands for diverse protein targets.