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 ΔGbind, 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 (Nwat) 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.

A MMGBSA variant (here referred to as Nwat-MMGBSA), based on the inclusion of a certain number of explicit water molecules (Nwat) during the calculations, has been tested on a set of 20 protein-protein complexes, using the correlation between predicted and experimental binding energy as the evaluation metric. Besides the Nwat parameter, the effect of the force field, the molecular dynamics simulation length, and the implicit solvent model used in the MMGBSA analysis have been also evaluated. We found that considering 30 interfacial water molecules improved the correlation between predicted and experimental binding energies by up to 30%, compared to the standard approach. Moreover, the correlation resulted in being rather sensitive to the force field and, to a minor extent, to the implicit solvent model and to the length of the MD simulation.

The pandemic caused by SARS-CoV-2 is currently representing a major health and economic threat to humanity. So far, no specific treatment to this viral infection has been developed and the emergency still requires an efficient intervention. In this work, we used virtual screening to facilitate drug repurposing against SARS-CoV-2, targeting viral main proteinase and spike protein with 3000 existing drugs. We used a protocol based on a docking step followed by a short molecular dynamic simulation and rescoring by the Nwat-MMGBSA approach. Our results provide suggestions for prioritizing in vitro and/or in vivo tests of already available compounds.

Replica exchange molecular dynamics simulations were performed to test the ability of six AMBER force fields and three implicit solvent models of predicting the native conformation of two helical peptides, three β-hairpins, and three intrinsically disordered peptides. Although a combination of the force field and implicit solvation models able to accurately predict the native structure of all the considered peptides was not identified, we found that the GB-Neck2 model seems to well compensate for some of the conformational biases showed by ff96 and ff99SB/ildn/ildn-φ. Indeed, the force fields of the ff99SB series coupled with GB-Neck2 reasonably discriminated helices from disordered peptides, while a good prediction of β-hairpin conformations was only achieved by performing two independent simulations: one with the ff96/GB-Neck2 combination and the other with GB-Neck2 coupled with any of the ff99SB/ildn/ildn-φ force fields.