Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) and Molecular Mechanics Generalized Born Surface Area (MM-GBSA) methods are widely used for drug design/discovery purposes. However, it is not clear if the correlation between predicted and experimental binding affinities can be improved by explicitly considering selected water molecules in the calculation of binding energies, since different and sometimes diverging opinions are found in the literature. In this work, we evaluated how variably populated hydration shells explicitly considered around the ligands may affect the correlation between MM-PB/GBSA computed binding energy and biological activities (IC₅₀ and ΔG_bind, depending on the available experimental data). Four different systems—namely, the DNA-topoisomerase complex, α-thrombin, penicillopepsin, and avidin—were considered and ligand hydration shells populated by 10–70 water molecules were systematically evaluated. We found that the consideration of a hydration shell populated by a number of water residues (N_wat) between 30 and 70 provided, in all of the considered examples, a positive effect on correlation between MM-PB/GBSA calculated binding affinities and experimental activities, with a negligible increment of computational cost.

Nwat-MMGBSA is a variant of MM-PB/GBSA based on the inclusion of a number of explicit water molecules that are the closest to the ligand in each frame of a molecular dynamics trajectory. This method demonstrated improved correlations between calculated and experimental binding energies in both protein-protein interactions and ligand-receptor complexes, in comparison to the standard MM-GBSA. A protocol optimization, aimed to maximize efficacy and efficiency, is discussed here considering penicillopepsin, HIV1-protease, and BCL-XL as test cases. Calculations were performed in triplicates on both classic HPC environments and on standard workstations equipped by a GPU card, evidencing no statistical differences in the results. No relevant differences in correlation to experiments were also observed when performing Nwat-MMGBSA calculations on 4 or 1 ns long trajectories. A fully automatic workflow for structure-based virtual screening, performing from library set-up to docking and Nwat-MMGBSA rescoring, has then been developed. The protocol has been tested against no rescoring or standard MM-GBSA rescoring within a retrospective virtual screening of inhibitors of AmpC β-lactamase and of the Rac1-Tiam1 protein-protein interaction. In both cases, Nwat-MMGBSA rescoring provided a statistically significant increase in the ROC AUCs of between 20 and 30%, compared to docking scoring or to standard MM-GBSA rescoring.

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