Potent multi-target natural inhibitors against SARS-CoV-2 from medicinal plants of the Himalaya: a discovery from hybrid machine learning, chemoinformatics, and simulation assisted screening

Abstract

The emergence and immune evasion ability of SARS-CoV-2 Omicron strains, mainly BA.5.2 and BF.7 and other variants of concern have raised global apprehensions. With this context, the discovery of multitarget inhibitors may be proven more comprehensive paradigm than its one-drug-to-one target counterpart. In the current study, a library of 271 phytochemicals from 25 medicinal plants from the Indian Himalayan Region has been virtually screened against SARS-CoV-2 by targeting nine virus proteins, viz., papain-like protease, main protease, nsp12, helicase, nsp14, nsp15, nsp16, envelope, and nucleocapsid for screening of a multi-target inhibitor against the viral replication. Initially, 94 phytochemicals were screened by a hybrid machine learning model constructed by combining 6 confirmatory bioassays against SARS-CoV-2 replication using an instance-based learner lazy k-nearest neighbour classifier. Further, 25 screened compounds with excellent drug-like properties were subjected to molecular docking. The phytochemical Cepharadione A from the plant Piper longum showed binding potential against four proteins with the highest binding energy of −10.90 kcal/mol. The compound has acceptable absorption, distribution, metabolism, excretion, and toxicity properties and exhibits stable binding behaviour in terms of root mean square deviation (0.068 ± 0.05 nm), root-mean-square fluctuation, hydrogen bonds, solvent accessible surface area (83.88–161.89 nm²), and molecular mechanics Poisson-Boltzmann surface area during molecular dynamics simulation of 200 ns with selected target proteins. Concerning the utility of natural compounds in the therapeutics formulation, Cepharadione A could be further investigated as a remarkable lead candidate for the development of therapeutic drugs against SARS-CoV-2.

Communicated by Ramaswamy H. Sarma

Keywords:

• COVID-19
• SARS-CoV-2
• virtual screening
• multi-target inhibitor
• plants
• Himalaya

Acknowledgments

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for supporting through Large Groups Project under grant number (RGP.2/119/44). We are thankful to the Director, G.B. Pant National Institute of Himalayan Environment, Kosi-Katarmal, Almora, Uttarakhand, 263643, India and the Vice Chancellor, S.S.J. University, Almora, Uttarakhand, 263601 India for providing the necessary facilities and encouraging inter-institutional R&D work.

Author contributions

P.M. and M.N. conducted the machine learning and molecular docking experiments and planned the outline of this research, generated the idea, carried out the QSAR analysis, and wrote the manuscript, S.M., S.W., T.J., and P.S. carried out the simulation analysis. J.C.K, M.A.R., S.C., and critically reviewed, read the manuscript, and guided during the idea generation. All authors have reviewed and revised the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data availability Initial X-ray structures are available at Protein Data Bank (https://www.rcsb.org/). Datasets used from different bioassays are available at PubChem (https://pubchem.ncbi.nlm.nih.gov/). All other data are available in the main text.

Additional information

Funding

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for supporting through Large Groups Project under grant number (RGP.2/119/44). We are thankful to the Director, G.B. Pant National Institute of Himalayan Environment, Kosi-Katarmal, Almora, Uttarakhand, 263643, India and the Vice Chancellor, S.S.J. University, Almora, Uttarakhand, 263601 India for providing the necessary facilities and encouraging inter-institutional R&D work.