Antiviral phytochemicals as potent inhibitors against NS3 protease of dengue virus. (July 2021) - Rahman, Md. Mahbubur; Biswas, Sourav; Islam, Kazi Jahidul; Paul, Archi Sundar; Mahato, Shiplob Kumar; Ali, Md. Ackas; Halim, Mohammad A.
Abstract: Dengue, a mosquito-borne disease, has appeared as a major infectious disease globally. The virus requires its proteins to replicate and reproduce in the host cell. The NS3 protease converts the polyprotein to functional proteins with the help of the NS2B cofactor. Thus, NS3 protease is a promising target to develop antiviral inhibitors against the dengue virus. A systematic screening including ADMET properties, molecular docking, molecular dynamics (MD) simulation, binding free energy calculation, and QSAR studies is carried out to predict potent inhibitors against the NS3 protease. From the screening of 40 antiviral phytochemicals, ADMET properties analysis was used to screen out ligands that violate ADME rules and have probable toxicity. Cyanidin 3-Glucoside, Dithymoquinone, and Glabridin were predicted to be potent inhibitors against the NS3 protease according to their binding affinity. These ligands showed several noncovalent interactions, including hydrogen bond, hydrophobic interaction, electrostatic interaction, pi-sulfur interactions. The ligand-protein complexes were further scrutinized using 250 ns molecular dynamics simulation. The MM-PBSA binding free energy calculation was conducted to investigate their binding stability in dynamic conditions. The calculated pIC50(mM) value was predicted using the QSAR model with 89.91% goodness of fit. The predicted biologocal activity value for the ligands indicates they might have good potency. Graphical abstract: Image 1 Highlights: 40 Antiviral phytochemicals screened against DENV-2 NS3-NS2B protease. Initially, ADMET and Molecular docking were performed for screening. 250ns MD and MM/PBSA were implemented for the three selected candidates. Phytochemicals Glabridin altered the protein-cofactor protein interactions interface. QSAR model were studied for in-silico activity prediction.