Computational Screening of Plant-Derived Natural Products against SARS-CoV-2 Variants

Abstract

The present study explores the efficacy of plant-derived natural products (PDNPs) against spike glycoproteins (S-glycoprotein) of SARS-CoV-2 variants using molecular docking, ADMET, molecular dynamics (MD) simulation and density-functional theory (DFT) analysis. In all, 100 PDNPs were screened against spike glycoprotein of SARS-CoV-2 variants, namely alpha (B.1.1.17), beta (B.1.351), delta (B.1.617), gamma (P.1) and omicron (B.1.1.529). Results showed that rutin, EGCG, hesperidin, withanolide G, rosmarinic acid, diosmetin, myricetin, epicatechin and quercetin were the top hit compounds against each of the SARS-CoV-2 variants. The most active compounds, rutin, hesperidin, EGCG and rosmarinic acid gave binding scores of −10.2, −8.1, −8.9, −8.3 and −9.2 kcal/mol, against omicron, delta, alpha, beta and gamma variants, respectively. Further, the stability of docked complexes was confirmed by the analysis of molecular descriptors (RMSD, RMSF, SASA, Rg and H-bonds) in molecular dynamic simulation analysis. Moreover, the physiochemical properties and drug-likeness of the tested compounds showed that they have no toxicity or carcinogenicity and may be used as druggable targets. In addition, the DFT study revealed the higher activity of the tested compounds against the target proteins. This led us to conclude that rutin, hesperidin, EGCG and rosmarinic acid are good candidates to target the S-glycoproteins of SARS-CoV-2 variants. Further, in vivo and clinical studies needed to develop them as drug leads against existing or new SARS-CoV-2 variants are currently underway in our laboratory.

Keywords

• SARS-CoV-2 variants
• plant-derived natural products
• molecular docking
• molecular dynamic simulation
• Density-Functional Theory