In Silico molecular docking and dynamic analysis of natural compounds against major non-structural proteins of SARS-COV-2

Abstract

COVID-19 has infected millions and significantly affected the global economy and healthcare systems. Despite continuous lockdowns, symptomatic management with currently available medications, and numerous vaccination drives, it is still far more difficult to control. Against COVID-19 infection, the pressure to develop vaccines and drugs has led to using some currently available medications like remdesivir, azithromycin, hydroxychloroquine and ritonavir. Understanding the importance and potential of harmless molecules to tackle SARS-COV-2, we designed the present study to identify potential natural phytocompounds. In the present study, we docked natural compounds and standard drugs against SARS-COV-2 proteins: papain-like protease, main protease and helicase. ADME/T and ProTox-II analyses were used to determine the toxicity of phytocompounds and drugs. The docking analysis revealed that podophyllotoxin gave the highest binding affinity scores of −8.1, −7.1 and −7.4 kcal/mol against PLpro, Mpro and helicase, respectively. Among the control drugs, doxycycline hydrochloride showed the highest binding affinity of −10.5, −8.4 and −8.8 kcal/mol against PLpro, Mpro and helicase. The results of this study revealed that podophyllotoxin and doxycycline hydrochloride could be promising inhibitors against SARS-Cov-2. Molecular dynamic simulations were executed for the best docked (PLpro-podophyllotoxin) complex, and the results displayed stable conformation and convergence. Energy plot results predicted a global minima average energy of −95 kcal/mol and indicated podophyllotoxin’s role in stabilizing protein and making it compact and complex. FarPPI server used MM/GBSA approach to determine free binding affinity, and helicase–gallic acid complex showed the highest affinity, respectively. Therefore, it can be concluded that there is still a need for in vitro and in vivo studies to support further and validate these findings and validate these findings.

Communicated by Ramaswamy H. Sarma

Keywords:

• In silico
• SARS-CoV-2
• ADMET
• docking
• compounds
• molecular simulation

Acknowledgement

The authors extend their appreciation to Researchers Supporting Project number (RSP-2021/357), King Saud University, Riyadh, for funding this work.

Disclosure statement

The authors have read the manuscript and declare no conflict of interest.

Data availability statement

In this study, all the data generated has been published in this article.

Author’s contributions

Conceptualization, M.U.R., A.A., Methodology, A.A., M.U.R; Software, A.A., R.A, T.A.W; Validation, A.A., M.U.R., S.A.G., Formal analysis, A.A., R.A. A.A. T.A.W; Investigation, A.A., M.U.R., S.A.G., Writing–original draft preparation, A.A., M.U.R.; Writing–- review and editing., A.A., M.U.R., S.Z., Z.I. Visualization., A.A., Z.I., S.Z.,; Supervision., M.U.R., S.A.G. All authors have read and agreed to the published manuscript version.

Additional information

Funding

The authors extend their appreciation to Researchers Supporting Project number (RSP-2021/357), King Saud University, Riyadh, for funding this work.