Targeting the 3CLpro and RdRp of SARS-CoV-2 with phytochemicals from medicinal plants of the Andean Region: molecular docking and molecular dynamics simulations

Abstract

Given the highly contagious nature of SARS-CoV-2, it has resulted in an unprecedented number of COVID-19 infected and dead people worldwide. Since there is currently no vaccine available in the market, the identification of potential drugs is urgently needed to control the pandemic. In this study, 92 phytochemicals from medicinal plants growing in the Andean region were screened against SARS-CoV-2 3 C-like protease (3CLpro) and RNA-dependent RNA polymerase (RdRp) in their active sites through molecular docking. The cutoff values were set from the lowest docking scores of the FDA-approved drugs that are being used to treat COVID-19 patients (remdesivir, lopinavir, and ritonavir). Compounds with docking scores that were lower than cutoff values were validated by molecular dynamics simulation with GROMACS, using root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and intermolecular hydrogen bonds (H-bonds). Furthermore, binding free energies were estimated using the MM-PBSA method, and ADMET profiles of potential inhibitors were assessed. Computational analyses revealed that the interaction with hesperidin (theoretical binding energies, ΔG_bind = −15.18 kcal/mol to 3CLpro and ΔG_bind = −9.46 kcal/mol to RdRp) remained stable in both enzymes, unveiling its remarkable potential as a possible multitarget antiviral agent to treat COVID-19. Importantly, lupinifolin with an estimated binding affinity to 3CLpro higher than hesperidin (ΔG_bind = −20.93 kcal/mol) is also a potential inhibitor of the 3CLpro. These two compounds displayed suitable pharmacological and structural properties to be drug candidates, demonstrating to be worthy of further research.

Communicated by Ramaswamy H. Sarma

Keywords:

• SARS-CoV-2
• molecular docking
• molecular dynamics
• MM-PBSA
• ADMET

Acknowledgements

The authors thank Dr. Francisco Javier Flores for revising and improving our manuscript. Portions of the computing for this project was performed at the OSU High-Performance Computing Center at Oklahoma State University.

Disclosure statement

The authors declare no conflict of interest.