An Efficient Implementation of the Nwat-MMGBSA Method to Rescore Docking Results in Medium-Throughput Virtual Screenings

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.

In Silico Drug Repurposing for SARS-CoV-2 Main Proteinase and Spike Proteins

“…In addition, we applied a solid VS procedure we recently developed and which was shown to be successful in discriminating active from inactive compounds within the screening of classical small molecules and protein–protein interaction inhibitors. 20 …”

Virtual Screening of an FDA Approved Drugs Database on Two COVID-19 Coronavirus Proteins

“…Results of Nwat-MMGBSA rescoring, using 30 explicit waters, are the only reported, since Nwat=30 was considered a reasonable value in previous publications. [12,15] As it can be observed, the protease inhibitor Indinavir, [16] currently used to treat HIV infections, has been selected by both targets. Additionally, the other protease inhibitors Lopinavir [17] and Atazanavir [18] are also top ranked for 3CL-PRO.…”

“…The Virtual Screening (VS) was done accordingly to our recently developed protocol. [12] The protocol is applied using a set of scripts (available for download as Supplementary Material within ref. [12]) that do the following steps automatically: 1.…”

“…Rescoring using the Nwat-MMGBSA method. [12,15] All screenings were performed by using the ligand database prepared as stated before (no tautomers or alternative protonation states). The following parameters were set for the screenings: 6LU7: center="-10.2858 12.3088 69.3271", rad=16, speed=1, sfunct=chemplp QHD43415: center="-15.124 15.0521 -24.6152", rad=14, speed=1, sfunct=chemplp Nwat-MMGBSA rescoring was requested for the top 1% compounds (about 30 molecules for each target).…”

In silico Prediction, Characterization, Molecular Docking, and Dynamic Studies on Fungal SDRs as Novel Targets for Searching Potential Fungicides Against Fusarium Wilt in Tomato

“…It has been found that the top docking ranked poses are the lowest ranked poses using MM/GBSA rescoring, that indicates the rescoring of few top poses, if binding could not be determined through docking programs or binding is nonspecific. In many studies it has been well demonstrated that MM/GBSA approach is most accurate and reliable for ranking (“scoring”) the efficacy/affinities of a ligand binding to the receptor proteins in the protein–ligand docked complexes ( Singh and Warshel, 2010 ; Sun et al, 2014 ; Wright et al, 2014 ; Genheden and Ryde, 2015 ; Maffucci et al, 2018 ). MD simulations analysis therefore, could be employed for accurate ranking of ligands following the post docking program in terms of their binding affinities ( Okimoto et al, 2009 ; Chen, 2015 ).…”