Covid-19 Research

Research Article

Gossypetin Derivatives are also Putative Inhibitors of SARS-COV 2: Results of a Computational Study

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Open Access
Article Type Research Article
Subject Biology Group
OCLC 8717994225
Anna-Gaelle Giguet Valard*, Kevin Raguette, Stephanie Morin, Remi Bellance and Juliette Smith Ravin
Issue: Volume1-Issue6
Pages: 201-212
Received: 2020-09-16
Accepted: 2020-10-08
Published: 2020-10-09

Abstract

SARS-CoV-2 is the third most highly virulent human coronavirus of the 21st century. It is linked with fatal respiratory illness. Currently, there are still no effective treatments of Covid-19. Among many drugs evaluated, few have proven conclusive clinical efficacy. Furthermore, the spread of the disease mandates that ideal medications against Covid-19 be cheap and available worldwide. Therefore, there is a rationale to evaluate whether treatments of natural origin from aromatic and medicinal plants have the ability to prevent and/or treat COVID-19. We evaluated in this study the inhibition of COVID-19 protease by natural plants compounds such asGossypetin-3’-O-glucoside (G3’G). G3’Ghas been isolated from the petals of TaliparitielatumSw. Found almost exclusively in Martinique. It has no crystallography or modelisation studies. Antifungal and antioxidant properties are already published. We study its binding affinity so potential inhibition capability against SARS-CoV2 3CLpro mean protease as compared to other previously tested natural or pharmacological molecules by molecular docking. We propose Gossypetin derivates as good tropical natural compounds candidate that should be further investigated to prevent or treat COVID19.

FullText HTML FullText PDF DOI: 10.37871/jbres1144 Crossmark

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Copyright

© 2020 Giguet Valard AG, et al. Distributed under Creative Commons CC-BY 4.0 Creative CommonsAttribution

How to cite this article

Giguet Valard AG, Raguette K, Morin S, Bellance R, Ravin JS. Gossypetin Derivatives are also Putative Inhibitors of SARS-COV 2: Results of a Computational Study. J Biomed Res Environ Sci. 2020 Oct 09; 1(6): 201-212. doi: 10.37871/jbres1144, Article ID: jbres1144

Subject area(s)

AntiretrovirologyAntivirologyBiologyInfectious Diseases

University/Institute

University of Antilles

References

  1. Du L, He Y, Zhou Y, Liu S, Zheng BJ, Jiang S. The spike protein of SARS-CoV--a target for vaccine and therapeutic development. Nat Rev Microbiol. 2009 Mar;7(3):226-36. doi: 10.1038/nrmicro2090. Epub 2009 Feb 9. PMID: 19198616; PMCID: PMC2750777.
  2. Khan SA, Zia K, Ashraf S, Uddin R, Ul-Haq Z. Identification of chymotrypsin-like protease inhibitors of SARS-CoV-2 via integrated computational approach. J Biomol Struct Dyn. 2020 Apr 13:1-10. doi: 10.1080/07391102.2020.1751298. Epub ahead of print. PMID: 32238094.
  3. Lin Q, Zhao S, Gao D, Lou Y, Yang S, Musa SS, Wang MH, Cai Y, Wang W, Yang L, He D. A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action. Int J Infect Dis. 2020 Apr;93:211-216. doi: 10.1016/j.ijid.2020.02.058. Epub 2020 Mar 4. PMID: 32145465; PMCID: PMC7102659.
  4. Chen Z, Nakamura T. Statistical evidence for the usefulness of Chinese medicine in the treatment of SARS. Phytother Res. 2004 Jul;18(7):592-4. doi: 10.1002/ptr.1485. PMID: 15305324.
  5. Tahir Ul Qamar M, Alqahtani SM, Alamri MA, Chen LL. Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants. J Pharm Anal. 2020 Aug;10(4):313-319. doi: 10.1016/j.jpha.2020.03.009. Epub 2020 Mar 26. PMID: 32296570; PMCID: PMC7156227.
  6. Anand K, Yang H, Bartlam M, Rao Z, Hilgenfeld R. Coronavirus main proteinase: target for antiviral drug therapy. Coronaviruses with Special Emphasis on First Insights Concerning SARS. 2005:173–99. doi: 10.1007/3-7643-7339-3_9. PMCID: PMC7123552.
  7. Yang Y, Zhang L, Geng H, Deng Y, Huang B, Guo Y, Zhao Z, Tan W. The structural and accessory proteins M, ORF 4a, ORF 4b, and ORF 5 of Middle East respiratory syndrome coronavirus (MERS-CoV) are potent interferon antagonists. Protein Cell. 2013 Dec;4(12):951-61. doi: 10.1007/s13238-013-3096-8. Epub 2013 Dec 8. PMID: 24318862; PMCID: PMC4875403.
  8. Naksuk N, Lazar S, Peeraphatdit TB. Cardiac safety of off-label COVID-19 drug therapy: a review and proposed monitoring protocol. Eur Heart J Acute Cardiovasc Care. 2020 Apr;9(3):215-221. doi: 10.1177/2048872620922784. Epub 2020 May 6. PMID: 32372695; PMCID: PMC7235441.
  9. Ruamviboonsuk P, Lai TYY, Chang A, Lai CC, Mieler WF, Lam DSC; for Asia-Pacific Vitreo-Retina Society. Chloroquine and Hydroxychloroquine Retinal Toxicity Consideration in the Treatment of COVID-19. Asia Pac J Ophthalmol (Phila). 2020 Mar-Apr;9(2):85-87. doi: 10.1097/APO.0000000000000289. PMID: 32349115; PMCID: PMC7227199.
  10. S Khaerunnisa, H Kurniawan, R Awaluddin, S Suhartati, S Soetjipto. Potential Inhibitor of COVID-19 Main Protease (Mpro) From Several Medicinal Plant Compounds by Molecular Docking Study. Medicine & Pharmacology, preprint, mars. 2020. doi: 10.20944/preprints202003.0226.v1.
  11. AK Srivastava, A Kumar, N Misra. On the Inhibition of COVID-19 Protease by Indian Herbal Plants: An In Silico Investigation. arXiv. 2020.
  12. https://tinyurl.com/y5u8umsd
  13. Dong L, Hu S, Gao J. Discovering drugs to treat coronavirus disease 2019 (COVID-19). Drug Discov Ther. 2020;14(1):58-60. doi: 10.5582/ddt.2020.01012. PMID: 32147628.
  14. Wang G, Zhu W. Molecular docking for drug discovery and development: a widely used approach but far from perfect. Future Med Chem. 2016 Sep;8(14):1707-10. doi: 10.4155/fmc-2016-0143. Epub 2016 Aug 31. PMID: 27578269.
  15. Aanouz I, Belhassan A, El-Khatabi K, Lakhlifi T, El-Ldrissi M, Bouachrine M. Moroccan Medicinal plants as inhibitors against SARS-CoV-2 main protease: Computational investigations. J Biomol Struct Dyn. 2020 May 6:1-9. doi: 10.1080/07391102.2020.1758790. Epub ahead of print. PMID: 32306860; PMCID: PMC7212546.
  16. Joshi T, Joshi T, Sharma P, Mathpal S, Pundir H, Bhatt V, Chandra S. In silico screening of natural compounds against COVID-19 by targeting Mpro and ACE2 using molecular docking. Eur Rev Med Pharmacol Sci. 2020 Apr;24(8):4529-4536. doi: 10.26355/eurrev_202004_21036. PMID: 32373991.
  17. GA Gyebi, OB Ogunro, AP Adegunloye, OM Ogunyemi, SO Afolabi. Potential inhibitors of coronavirus 3-chymotrypsin-like protease (3CL pro ): An in silico screening of alkaloids and terpenoids from African medicinal plants. J Biomol Struct Dyn. 2020;1‑13. doi: 10.1080/07391102.2020.1764868.
  18. Frantz François-Haugrin, Max Monan, Emmanuel Nossin, Juliette Smith-Ravin, Odile Marcelin. Antioxidant activity of an isomer of gossypitrin (gossypetin-3’-O-glucoside) isolated in the petals of Talipariti elatum Sw., and determination of total phenolic content of the total flower. J Pharmacogn Phytochem 2016;5(5):200-208.
  19. https://tinyurl.com/yybspabz
  20. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The Protein Data Bank. Nucleic Acids Res. 2000 Jan 1;28(1):235-42. doi: 10.1093/nar/28.1.235. PMID: 10592235; PMCID: PMC102472.
  21. Xu Z, Peng C, Shi Y, Zhu Z, Mu K, Wang X, Zhu W. Nelfinavir was predicted to be a potential inhibitor of 2019-nCov main protease by an integrative approach combining homology modelling, molecular docking and binding free energy calculation. bioRxiv; 2020. doi: 10.1101/2020.01.27.921627.
  22. Jin Z, Du X, Xu Y, Deng Y, Liu M, Zhao Y, Zhang B, Li X, Zhang L, Peng C, Duan Y, Yu J, Wang L, Yang K, Liu F, Jiang R, Yang X, You T, Liu X, Yang X, Bai F, Liu H, Liu X, Guddat LW, Xu W, Xiao G, Qin C, Shi Z, Jiang H, Rao Z, Yang H. The crytal structure of 2019-nCoV main protease in complex with an inhibitor N3. Nature. 2020;582:289-293.
  23. https://tinyurl.com/yyw4p86s
  24. Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, Wang J, Yu B, Zhang J, Bryant SH. PubChem Substance and Compound databases. Nucleic Acids Res. 2016 Jan 4;44(D1):D1202-13. doi: 10.1093/nar/gkv951. Epub 2015 Sep 22. PMID: 26400175; PMCID: PMC4702940.
  25. Lowe DM, Corbett PT, Murray-Rust P, Glen RC. Chemical name to structure: OPSIN, an open source solution. Journal of Chemical Information and Modeling. 2011 Mar;51(3):739-753. DOI: 10.1021/ci100384d.
  26. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform. 2011 Oct 7;3:33. doi: 10.1186/1758-2946-3-33. PMID: 21982300; PMCID: PMC3198950.
  27. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001 Mar 1;46(1-3):3-26. doi: 10.1016/s0169-409x(00)00129-0. PMID: 11259830.
  28. Huang B. MetaPocket: a meta approach to improve protein ligand binding site prediction. OMICS. 2009 Aug;13(4):325-30. doi: 10.1089/omi.2009.0045. PMID: 19645590.
  29. Dolinsky TJ, Nielsen JE, McCammon JA, Baker NA. PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations. Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W665-7. doi: 10.1093/nar/gkh381. PMID: 15215472; PMCID: PMC441519.
  30. MHM Olsson, CR Sondergaard, M Rostkowski, JH Jensen. PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical p Ka Predictions. J Chem Theory Comput. 2011;525‑537. doi: 10.1021/ct100578z.
  31. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009 Dec;30(16):2785-91. doi: 10.1002/jcc.21256. PMID: 19399780; PMCID: PMC2760638.
  32. Xu W, Lucke AJ, Fairlie DP. Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets. J Mol Graph Model. 2015 Apr;57:76-88. doi: 10.1016/j.jmgm.2015.01.009. Epub 2015 Feb 1. PMID: 25682361.
  33. Quiroga R, Villarreal MA. Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening. PLoS One. 2016 May 12;11(5):e0155183. doi: 10.1371/journal.pone.0155183. PMID: 27171006; PMCID: PMC4865195.
  34. Koes DR, Baumgartner MP, Camacho CJ. Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. J Chem Inf Model. 2013 Aug 26;53(8):1893-904. doi: 10.1021/ci300604z. Epub 2013 Feb 12. PMID: 23379370; PMCID: PMC3726561.
  35. Wang R, Wang S. How does consensus scoring work for virtual library screening? An idealized computer experiment. J Chem Inf Comput Sci. 2001 Sep-Oct;41(5):1422-6. doi: 10.1021/ci010025x. PMID: 11604043.
  36. TF Vieira, SF Sousa. Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening. Appl Sci. 2019;4538. doi: 10.3390/app9214538.
  37. Y Yang, MS Islam, J Wang, Y Li, X Chen. Traditional Chinese Medicine in the Treatment of Patients Infected with 2019-New Coronavirus (SARS-CoV-2): A Review and Perspective. Int J Biol Sci. 2020;1708‑1717. doi: 10.7150/ijbs.45538.
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