Abstract
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.
Highlights
Structure based virtual screening (SBVS) methods are widely applied in drug discovery (Enyedy and Egan, 2008; Sousa et al, 2013)
To optimize the Nwat-MMGBSA protocol for low- or mediumthroughput virtual screening procedures, such as those applied in the hit-to-lead optimization phase of a drug discovery process, we worked on a significant reduction of the overall simulation time in comparison to our previous implementations (Maffucci and Contini, 2013, 2016)
Several articles report that Generalized Born (GB) can provide outcomes comparable to the PB method, at a fraction of the computational cost, especially when relatively short molecular dynamics (MD) trajectories are used for MM-PB/GBSA calculations (Hou et al, 2011a,b; Maffucci and Contini, 2013, 2015, 2016)
Summary
Structure based virtual screening (SBVS) methods are widely applied in drug discovery (Enyedy and Egan, 2008; Sousa et al, 2013). In most of the cases, SBVSs are done in the hit-to-lead development phase of the drug discovery process, with multiple successful outcomes (Enyedy et al, 2001a,b; Vangrevelinghe et al, 2003). In SBVS-related studies, scoring functions are mostly applied for potential hit selection. The scoring functions are based on either empirical, knowledgebased, or molecular mechanics force field derived potentials (Wang et al, 2003; Raha et al, 2007). To make the virtual screening process computational inexpensive, the scoring functions are most likely simplified. Some important contributions known to influence the
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