Molecular docking unveils the potential of andrographolide derivatives against COVID-19: an in silico approach

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Abstract

Background: The recent severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection cause high mortality and there is an emergency need to develop a specific drug to treat the novel coronavirus disease, COVID-19. However, some natural and synthetic products with action against SARS-CoV-2 have been reported in recent research, there is no specific drug available for treating COVID-19. In the present study, molecular interaction analysis was performed for 16 semisynthetic andrographolides (AGP) against 5 SARS-CoV-2 enzymes main protease (Mpro, PDB: 6LU7), papain-like protease (PLpro, PDB: 6WUU), spike glycoprotein (S, PDB: 6VXX), NSP15 endoribonuclease (NSP15, PDB: 6VWW), and RNA-dependent RNA polymerase (RdRp, PDB: 6M71). Moreover, the compounds pharmacokinetic and toxic profiles were also analyzed using computational tools. Results: The protein−ligand docking score (kcal/mol) revealed that all the tested AGP derivatives showed a better binding affinity towards all the tested enzymes than hydroxychloroquine (HCQ). Meanwhile, all the tested AGP derivatives showed a better binding score with RdRp and S than remdesivir (REM). Interestingly, compounds 12, 14, and 15 showed a better binding affinity towards the all the tested enzyme than AGP, REM, and HCQ. AGP-16 had shown − 8.7 kcal/mol binding/docking score for Mpro, AGP-15 showed − 8.6 kcal/mol for NSP15, and AGP-10, 13, and 15 exhibited − 8.7, − 8.9, and − 8.7 kcal/mol, respectively, for S. Conclusion: Overall results of the present study concluded that AGP derivatives 14 and 15 could be the best ‘lead’ candidate for the treatment against SARS-CoV-2 infection. However, molecular dynamic studies and pharmacological screenings are essential to developing AGP derivatives 14 and 15 as a drug against COVID-19.

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Veerasamy, R., & Karunakaran, R. (2022). Molecular docking unveils the potential of andrographolide derivatives against COVID-19: an in silico approach. Journal of Genetic Engineering and Biotechnology, 20(1). https://doi.org/10.1186/s43141-022-00339-y

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