METHODS: The initial geometry optimization of every component was conducted through density functional theory (DFT) using TmoleX software. A single-point density functional theory (DFT) computation using Becke Perdew 86 (BP86) and the Triple-Zeta Valence Potential (TZVPD) was performed to produce.cosmo files. COSMO-RS calculations were performed by applying the parameterization file BP_TZVPD_FINE_19.ctd using COSMOthermX software. The practical extraction of oil from plant seeds was performed using a sonicator bath to verify the accuracy of the COSMO-RS predictions.
METHODS: The involved approaches build molecules from fragments that are either isosteric to GSH sub-moieties (ligand-based) or successfully docked to GSH binding sub-pockets (structure-based). Compared to reference GST inhibitor of S-hexyl GSH, ligands with improved rigidity, synthetic accessibility, and affinity to receptor were successfully designed. The method involves joining fragments to create ligands. The ligands were then explored using molecular docking, Cartesian coordinate's optimization, and simplified free energy determination as well as MD simulation and MMPBSA calculations. Several tools were used which include OPENEYE toolkit, Open Babel, Autodock Vina, Gromacs, and SwissParam server, and molecular mechanics force field of MMFF94 for optimization and CHARMM27 for MD simulation. In addition, in-house scripts written in Matlab were used to control fragments connection and automation of the tools.
METHOD: This research commenced with retrieving protein structures from the RCSB database, generating the formation of ligands using the ChemDraw Professional software, conducting molecular docking with the Autodock Vina software, and performing molecular dynamics simulations using the Amber software, along with the evaluation of RMSD values and intermolecular hydrogen bonds. Free energy, electrostatic interactions, and Van der Waals interaction were calculated using MM/GBSA. Acarbose, used as a positive control, and maltose are simulated together with test compound that has the best free energy. The forcefields used for molecular dynamics simulations are ff19SB, gaff2, and tip3p.
METHODS: As a preliminary step, a molecular docking study was performed to investigate the potency of seventy-one compounds from four classes of inhibitors. The molecular characteristics of the best-performing five compounds were predicted by estimating the drug-likeness and drug score. Molecular dynamics simulations (MD) over 100 ns were performed to inspect the relative stability of the best compound within the Omicron receptor-binding site.
RESULTS: The current findings point out the crucial roles of Q493R, G496S, Q498R, N501Y, and Y505H in the RBD region of SARS-CoV-2 Omicron. Raltegravir, hesperidin, pyronaridine, and difloxacin achieved the highest drug scores compared with the other compounds in the four classes, with values of 81%, 57%, 18%, and 71%, respectively. The calculated results showed that raltegravir and hesperidin had high binding affinities and stabilities to Omicron with ΔGbinding of - 75.7304 ± 0.98324 and - 42.693536 ± 0.979056 kJ/mol, respectively. Further clinical studies should be performed for the two best compounds from this study.