1. bookVolume 67 (2021): Issue 2 (June 2021)
Journal Details
License
Format
Journal
First Published
04 Apr 2014
Publication timeframe
4 times per year
Languages
English
access type Open Access

In silico studies of selected xanthophylls as potential candidates against SARS-CoV-2 targeting main protease (Mpro) and papain-like protease (PLpro)

Published Online: 17 Jul 2021
Page range: 1 - 8
Received: 06 May 2021
Accepted: 30 May 2021
Journal Details
License
Format
Journal
First Published
04 Apr 2014
Publication timeframe
4 times per year
Languages
English
Summary

Introduction: The main protease (Mpro) and the papain-like protease (PLpro) are essential for the replication of SARS-CoV-2. Both proteases can be targets for drugs acting against SARS-CoV-2.

Objective: This paper aims to investigate the in silico activity of nine xanthophylls as inhibitors of Mpro and PLpro.

Methods: The structures of Mpro (PDB-ID: 6LU7) and PLpro (PDB-ID: 6W9C) were obtained from RCSB Protein Data Bank and developed with BIOVIA Discovery Studio. Active sites of proteins were performed using CASTp. For docking the PyRx was used. Pharmacokinetic parameters of ADMET were evaluated using SwissADME and pkCSM.

Results: β-cryptoxanthin exhibited the highest binding energy: –7.4 kcal/mol in the active site of Mpro. In PLpro active site, the highest binding energy had canthaxanthin of –9.4 kcal/mol, astaxanthin –9.3 kcal/mol, flavoxanthin –9.2 kcal/mol and violaxanthin –9.2 kcal/mol. ADMET studies presented lower toxicity of xanthophylls in comparison to ritonavir and ivermectin.

Conclusion: Our findings suggest that xanthophylls can be used as potential inhibitors against SARS-CoV-2 main protease and papain-like protease.

Keywords

1. Zeidler A, Karpinski TM. SARS-CoV, MERS-CoV, SARS-CoV-2 comparison of three emerging corona-viruses. Jundishapur J Microbiol 2020; 13:e103744. doi: https://dx.doi.org/10.5812/jjm.103744 Search in Google Scholar

2. Zeidler A, Karpiński TM. What do we know about SARS-CoV-2 virus and COVID-19 disease? J Pre Clin Clin Res 2020; 14:33-38. doi: https://dx.doi.org/10.26444/jpccr/123794. Search in Google Scholar

3. Finkel Y, Mizrahi O, Nachshon A, Weingarten-Gabbay S, Morgenstern D, Yahalom-Ronen Y, et al. The coding capacity of SARS-CoV-2. Nature 2021; 589:125-130. doi: https://dx.doi.org/10.1038/s41586-020-2739-1 Search in Google Scholar

4. Cherian SS, Agrawal M, Basu A, Abraham P, Gangakhedkar RR, Bhargava B. Perspectives for repurposing drugs for the coronavirus disease 2019. Indian J Med Res 2020; 151:160-171. doi: https://dx.doi.org/10.4103/ijmr.IJMR_585_20 Search in Google Scholar

5. Iacob S, Iacob DG. SARS-CoV-2 treatment approaches: numerous options, no certainty for a versatile virus. Front Pharmacol 2020; 11:1224. doi: https://dx.doi.org/10.3389/fphar.2020.01224 Search in Google Scholar

6. Mody V, Ho J, Wills S, Mawri A, Lawson L, Ebert MCCJC, et al. Identification of 3-chymotrypsin like protease (3CLPro) inhibitors as potential anti-SARS-CoV-2 agents. Commun Biol 2021; 4:1-10. doi: https://dx.doi.org/10.1038/s42003-020-01577-x Search in Google Scholar

7. WHO Coronavirus (COVID-19) Dashboard n.d. https://covid19.who.int (accessed May 5, 2021). Search in Google Scholar

8. Karpiński TM, Ożarowski M, Seremak-Mrozikiewicz A, Wolski H, Włodkowic D. The 2020 race towards SARS-CoV-2 specific vaccines. Theranostics 2021;11:1690–702. https://dx.doi.org/10.7150/thno.53691. Search in Google Scholar

9. Antiviral Therapy. COVID-19 Treatment Guidelines n.d. https://www.covid19treatmentguide-lines.nih.gov/antiviral-therapy/ (accessed May 5, 2021). Search in Google Scholar

10. Pereira AG, Otero P, Echave J, Carreira-Casais A, Chamorro F, Collazo N, et al. Xanthophylls from the sea: algae as source of bioactive carotenoids. Mar Drugs 2021; 19:188. doi: https://dx.doi.org/10.3390/md19040188 Search in Google Scholar

11. Karpiński TM, Adamczak A. Fucoxanthin – an antibacterial carotenoid. Antioxidants (Basel) 2019; 8:239. doi: https://dx.doi.org/10.3390/antiox8080239 Search in Google Scholar

12. Sampathkumar SJ, Srivastava P, Ramachandran S, Sivashanmugam K, Gothandam KM. Lutein: A potential antibiofilm and antiquorum sensing molecule from green microalga Chlorella pyrenoidosa. Microb Pathog 2019; 135:103658. doi: https://dx.doi.org/10.1016/j.micpath.2019.103658 Search in Google Scholar

13. Pap R, Pandur E, Jánosa G, Sipos K, Agócs A, Deli J. Lutein exerts antioxidant and anti-inflammatory effects and influences iron utilization of BV-2 microglia. Antioxidants (Basel) 2021; 10. doi: https://dx.doi.org/10.3390/antiox10030363 Search in Google Scholar

14. Uppal S, Dergunov SA, Zhang W, Gentleman S, Redmond TM, Pinkhassik E, et al. Xanthophylls modulate palmitoylation of mammalian β-carotene oxygenase 2. Antioxidants (Basel) 2021; 10. doi: https://dx.doi.org/10.3390/anti-ox10030413 Search in Google Scholar

15. Oh J, Kim JH, Park JG, Yi Y-S, Park KW, Rho HS, et al. Radical scavenging activity-based and AP-1-targeted anti-inflammatory effects of lutein in macrophage-like and skin keratinocytic cells. Mediators Inflamm 2013; 2013:787042. doi: https://dx.doi.org/10.1155/2013/787042 Search in Google Scholar

16. Kim K-N, Heo S-J, Kang S-M, Ahn G, Jeon Y-J. Fucoxanthin induces apoptosis in human leukemia HL-60 cells through a ROS-mediated Bcl-xL pathway. Toxicol In Vitro 2010; 24:1648-1654. doi: https://dx.doi.org/10.1016/j.tiv.2010.05.023 Search in Google Scholar

17. Talukdar J, Bhadra B, Dattaroy T, Nagle V, Dasgupta S. Potential of natural astaxanthin in alleviating the risk of cytokine storm in COVID-19. Biomed Pharmacother 2020; 132:110886. doi: https://dx.doi.org/10.1016/j.biopha.2020.110886 Search in Google Scholar

18. Tamama K. Potential benefits of dietary seaweeds as protection against COVID-19. Nutr Rev 2020; 2020:nuaa126. doi: https://dx.doi.org/10.1093/nutrit/nuaa126. Search in Google Scholar

19. Ahammad F, Alam R, Mahmud R, Akhter S, Talukder EK, Tonmoy AM, et al. Pharmacoinformatics and molecular dynamics simulation-based phytochemical screening of neem plant (Azadirachta indica) against human cancer by targeting MCM7 protein. Brief Bioinform 2021:bbab098. doi: https://dx.doi.org/10.1093/bib/bbab098 Search in Google Scholar

20. Ferreira LLG, Andricopulo AD. ADMET modeling approaches in drug discovery. Drug Discov Today 2019; 24:1157-1165. doi: https://dx.doi.org/10.1016/j.drudis.2019.03.015 Search in Google Scholar

21. Agnihotry S, Pathak RK, Srivastav A, Shukla PK, Gautam B. Molecular Docking and structure-based drug design. In: Singh DB. (ed.). Computer-aided drug design. Singapore 2020:115-131. doi: https://dx.doi.org/10.1007/978-981-15-6815-2_6 Search in Google Scholar

22. PubChem. PubChem n.d. https://pubchem.ncbi.nlm.nih.gov. Accessed May 5, 2021. Search in Google Scholar

23. Dallakyan S, Olson AJ. Small-molecule library screening by docking with PyRx. Methods Mol Biol 2015; 1263:243-250. doi: https://dx.doi.org/10.1007/978-1-4939-2269-7_19 Search in Google Scholar

24. Bank RPD. RCSB PDB: Homepage n.d. https://www.rcsb.org/. Accessed May 5, 2021. Search in Google Scholar

25. BIOVIA Discovery Studio – BIOVIA – Dassault Systèmes® n.d. https://www.3ds.com/products-services/biovia/products/molecular-modeling-simulation/biovia-discovery-studio/. Accessed May 5, 2021. Search in Google Scholar

26. Tian W, Chen C, Lei X, Zhao J, Liang J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res 2018; 46:W363-W367. doi: https://dx.doi.org/10.1093/nar/gky473 Search in Google Scholar

27. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings1PII of original article: S0169-409X(96)00423-1. Adv Drug Del Rev 2001; 46:3-2 doi: 6. https://dx.doi.org/10.1016/S0169-409X(00)00129-0 Search in Google Scholar

28. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 2017; 7:42717. doi: https://dx.doi.org/10.1038/srep42717 Search in Google Scholar

29. Pires DEV, Blundell TL, Ascher DB. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 2015; 58:4066-4072. doi: https://dx.doi.org/10.1021/acs.jmedchem.5b00104 Search in Google Scholar

30. Ishack S, Lipner SR. Bioinformatics and immunoinformatics to support COVID-19 vaccine development. J Med Virol 2021;10.1002/jmv.27017. doi: https://dx.doi.org/10.1002/jmv.27017 Search in Google Scholar

31. Li X, Yu J, Zhang Z, Ren J, Peluffo AE, Zhang W, et al. Network bioinformatics analysis provides insight into drug repurposing for COVID-19. Med Drug Discov 2021; 10:100090. doi: https://dx.doi.org/10.1016/j.medidd.2021.100090 Search in Google Scholar

32. Amendola G, Ettari R, Previti S, Di Chio C, Messere A, Di Maro S, et al. Lead discovery of SARS-CoV-2 main protease inhibitors through covalent docking-based virtual screening. J Chem Inf Model 2021; 61:2062-2073. doi: https://dx.doi.org/10.1021/acs.jcim.1c00184 Search in Google Scholar

33. Samad A, Ahammad F, Nain Z, Alam R, Imon RR, Hasan M, et al. Designing a multi-epitope vaccine against SARS-CoV-2: an immunoinformatics approach. J Biomol Struct Dyn 2020:10. 1080/07391102.2020.1792347. doi: https://dx.doi.org/10.1080/07391102.2020.1792347 Search in Google Scholar

34. Gupta SS, Kumar A, Shankar R, Sharma U. In silico approach for identifying natural lead molecules against SARS-COV-2. J Mol Graph Model 2021; 106:107916. https://dx.doi.org/10.1016/j.jmgm.2021.107916 Search in Google Scholar

35. Pekel H, Ilter M, Sensoy O. Inhibition of SARSCoV-2 main protease: a repurposing study that targets the dimer interface of the protein. J Biomol Struct Dyn 2021:1-16. doi: https://dx.doi.org/10.1080/07391102.2021.1910571 Search in Google Scholar

36. Wen L, Tang K, Chik KK-H, Chan CC-Y, Tsang JO-L, Liang R, et al. In silico structure-based discovery of a SARS-CoV-2 main protease inhibitor. Int J Biol Sci 2021; 17:1555-1564. doi: https://dx.doi.org/10.7150/ijbs.59191 Search in Google Scholar

37. Ismail MI, Ragab HM, Bekhit AA, Ibrahim TM. Targeting multiple conformations of SARSCoV2 papain-like protease for drug repositioning: An in-silico study. Comput Biol Med 2021; 131:104295. doi: https://dx.doi.org/10.1016/j.compbiomed.2021.104295 Search in Google Scholar

38. Jade D, Ayyamperumal S, Tallapaneni V, Joghee Nanjan CM, Barge S, Mohan S, et al. Virtual high throughput screening: Potential inhibitors for SARS-CoV-2 PLPRO and 3CLPRO proteases. Eur J Pharmacol 2021; 901:174082. doi: https://dx.doi.org/10.1016/j.ejphar.2021.174082 Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo