The complex network of water moleculeswithin the bindingpocketof a target protein undergoes alterations upon ligand binding, presentinga significant challenge for conventional molecular modeling methodsto accurately characterize and compute the associated energy changes.We have previously developed an empirical method, HydraMap (J. Chem. Inf. Model. 2020, 60, 4359-4375), which employs statistical potentials to predicthydration sites and compute desolvation energy, achieving a reasonablebalance between accuracy and speed. In this work, we present its improvedversion, namely, HydraMap v.2. We updated the statistical potentialsfor protein-water interactions through an analysis of 17 042crystal protein structures. We also introduced a new feature to evaluateligand-water interactions by incorporating statistical potentialsderived from the solvated structures of 9878 small organic moleculesproduced by molecular dynamics simulations. By combining these potentials,HydraMap v.2 can predict and compare the hydration sites in a bindingpocket before and after ligand binding, identifying key water moleculesinvolved in the binding process, such as those forming bridging hydrogenbonds and unstable ones that can be replaced. We demonstrated theapplication of HydraMap v.2 in explaining the structure-activityrelationship of a panel of MCL-1 inhibitors. The desolvation energiescalculated by summing the energy change of each hydration site beforeand after ligand binding showed good correlation with known ligandbinding affinities on six target proteins. In conclusion, HydraMapv.2 offers a cost-effective solution for estimating the desolvationenergy during protein-ligand binding and also is practicalin guiding lead optimization in structure-based drug discovery.
