Excavating phytochemicals from plants possessing antiviral activities for identifying SARS-CoV hemagglutinin-esterase inhibitors by diligent computational workflow

Abstract

Coronaviruses (CoVs) belong to a group of RNA viruses that cause diseases in vertebrates including. Newer and deadlier than SARS CoV-2 are sought to appear in future for which the scientific community must be prepared with the strategies for their control. Spike protein (S-protein) of all the CoVs require angiotensin-converting enzyme2 (ACE2), while CoVs also require hemagglutinin-acetylesterase (HE) glycoprotein receptor to simultaneously interact with O-acetylated sialic acids on host cells, both these interactions enable viral particle to enter host cell leading to its infection. Target inhibition of viral S-protein and HE glycoprotein receptor can lead to a development of therapy against the SARS CoV-2. The proposition is to recognize molecules from the bundle of phytochemicals of medicinal plants known to possess antiviral potentials as a lead that could interact and mask the active site of, HE glycoprotein which would ideally bind to O-acetylated sialic acids on human host cells. Such molecules can be addressed as ‘HE glycoprotein blockers’. A library of 110 phytochemicals from Withania somnifera, Asparagus racemosus, Zinziber officinalis, Allium sativum, Curcuma longa and Adhatoda vasica was constructed and was used under present study. In silico analysis was employed with plant-derived phytochemicals. The molecular docking, molecular dynamics simulations over the scale of 1000 ns (1 μs) and ADMET prediction revealed that the Withania somnifera (ashwagandha) and Asparagus racemosus (shatavari) plants possessed various steroidal saponins and alkaloids which could potentially inhibit the COVID-19 virus and even other CoVs targeted HE glycoprotein receptor.

Communicated by Ramaswamy H. Sarma

Graphical Abstract

Keywords:

• Coronaviruses (CoVs)
• hemagglutinin-acetylesterase (HE) glycoprotein
• molecular docking
• molecular dynamics simulations

Acknowledgments

The authors gratefully acknowledge the Department of Botany, Bioinformatics and Climate Change Impacts Management, Gujarat University and Department of Microbiology and Biotechnology, School of Sciences, Gujarat University, DST-FIST Sponsored Department for providing an opportunity to access the bioinformatics research facilities. he authors also acknowledge Dr Sivakumar Prasanth Kumar for their valuable suggestion and discussions during analysis of data. The authors sincerely acknowledge Dr Frank J. Gonzalez, Dr Shogo Takahashi and Dr Daxesh Patel for English language assessment.

Disclosure statement

No potential conflict of interest was reported by the authors.

Authors' contributions

C.N.P., D.G.J, D.G and H.A.P. conceptualized and designed the project. C.N.P., R.M.R. and H.A.P. developed methodology. C.N.P. and D.G acquired data. C.N.P., S.P.J. and R.M.P. analyzed and interpreted data. C.N.P., D.G, R.M.P., S.P.J., and H.A.P. wrote manuscript. R.M.P. and R.M.R. provided technical support. R.M.R. and H.A.P. supervised study. The whole manuscript was approved by all authors.

Additional information

Funding

This work is supported by the Financial Assistance Programme – Department of Science and Technology [Grant number GSBTM/MD/JDR/1409/2017-18] and Gujarat Council on Science and Technology [GUJCOST/Supercomputer/2019-20/1359] had provided grants for the purchase of computer hardware only and has been used up. Authors are also thankful to UGC (Start-up Research Grant) for providing fund under the F.30-521/2020(BSR), Under Secretary FD-III Section, University Grant Commission, New Delhi 110002.