Abstract
The main protease of SARS-CoV-2 is one of the important targets to design and develop antiviral drugs. In this study, we have selected 40 antiviral phytochemicals to find out the best candidates which can act as potent inhibitors against the main protease. Molecular docking is performed using AutoDock Vina and GOLD suite to determine the binding affinities and interactions between the phytochemicals and the main protease. The selected candidates strongly interact with the key Cys145 and His41 residues. To validate the docking interactions, 100 ns molecular dynamics (MD) simulations on the five top-ranked inhibitors including hypericin, cyanidin 3-glucoside, baicalin, glabridin, and α-ketoamide-11r are performed. Principal component analysis (PCA) on the MD simulation discloses that baicalin, cyanidin 3-glucoside, and α-ketoamide-11r have structural similarity with the apo-form of the main protease. These findings are also strongly supported by root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA) investigations. PCA is also used to find out the quantitative structure-activity relationship (QSAR) for pattern recognition of the best ligands. Multiple linear regression (MLR) of QSAR reveals the R 2 value of 0.842 for the training set and 0.753 for the test set. Our proposed MLR model can predict the favorable binding energy compared with the binding energy detected from molecular docking. ADMET analysis demonstrates that these candidates appear to be safer inhibitors. Our comprehensive computational and statistical analysis show that these selected phytochemicals can be used as potential inhibitors against the SARS-CoV-2.Communicated by Ramaswamy H. Sarma.
Keywords: Antiviral phytochemicals; COVID-19; SARS-CoV-2; molecular docking; molecular dynamics.
【저자키워드】 COVID-19, SARS-CoV-2, molecular docking, molecular dynamics, Antiviral phytochemicals, 【초록키워드】 Antiviral, antiviral drugs, molecular docking, docking, molecular dynamics, protease, inhibitors, principal component analysis, MD simulation, binding energy, Pattern recognition, gold, Linear regression, target, molecular, inhibitor, Quantitative, predict, interactions, Hypericin, AutoDock, RMSD, RMSF, Interaction, Analysis, PCA, statistical analysis, similarity, Multiple linear regression, Principal component, training set, binding affinities, surface area, Root-mean-square deviation, AutoDock vina, glucoside, ligands, candidate, root, residues, Cys145, His41, solvent, gyration, radius of gyration, Multiple, radius, deviation, baicalin, MLR, FIVE, fluctuation, glabridin, selected, performed, develop, supported, can be used, determine, reveal, the binding affinity, R 2, 【제목키워드】 Antiviral, molecular, approach, effective, identify,