In silico identification and validation of natural antiviral compounds as potential inhibitors of SARS-CoV-2 methyltransferase

Abstract

The novel Coronavirus disease 2019 (COVID-19) is potentially fatal and caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Due to the unavailability of any proven treatment or vaccination, the outbreak of COVID-19 is wreaking havoc worldwide. Hence, there is an urgent need for therapeutics targeting SARS-CoV-2. Since, botanicals are an important resource for several efficacious antiviral agents, natural compounds gaining significant attention for COVID-19 treatment. In the present study, methyltranferase (MTase) of the SARS-CoV-2 is targeted using computational approach. The compounds were identified using molecular docking, virtual screening and molecular dynamics simulation studies. The binding mechanism of each compound was analyzed considering the stability and energetic parameter using in silico methods. We have found four natural antiviral compounds Amentoflavone, Baicalin, Daidzin and Luteoloside as strong inhibitors of methyltranferase of SARS-CoV-2. ADMET prediction and target analysis of the selected compounds showed favorable results. MD simulation was performed for four top-scored molecules to analyze the stability, binding mechanism and energy requirements. MD simulation studies indicated energetically favorable complex formation between MTase and the selected antiviral compounds. Furthermore, the structural effects on these substitutions were analyzed using the principles of each trajectories, which validated the interaction studies. Our analysis suggests that there is a very high probability that these compounds may have a good potential to inhibit Methyltransferase (MTase) of SARS-CoV-2 and to be used in the treatment of COVID-19. Further studies on these natural compounds may offer a quick therapeutic choice to treat COVID-19.

Communicated by Ramaswamy H. Sarma

Keywords:

• Antiviral natural compounds
• docking
• MTase
• SARS-CoV-2
• amentoflavone
• molecular dynamic simulation

Acknowledgements

We are thankful to ProteinInsights (www.proteininsights.com) for providing computational resources.

Disclosure statement

Authors have no conflict of interest.

Additional information

Funding

We are thankful to the Department of Science & Technology, Ministry of Science & Technology, Women Scientist Scheme A (WOS-A) Govt. of India, under Grant number: SR/WOS-A/LS-110/2018(G).