Characterization of SARS-CoV-2 Isolate (MZ558159) Reported from India for in Silico Drug Designing

Prajapat, Rajneesh; Jain, Suman

doi:10.52547/rabms.8.4.175

Volume 8, Issue 4 (11-2022) Journal of Research in Applied and Basic Medical Sciences 2022, 8(4): 175-188 | Back to browse issues page

‎ 10.52547/rabms.8.4.175

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Prajapat R, Jain S. Characterization of SARS-CoV-2 Isolate (MZ558159) Reported from India for in Silico Drug Designing. Journal of Research in Applied and Basic Medical Sciences 2022; 8 (4) :175-188
URL: http://ijrabms.umsu.ac.ir/article-1-235-en.html

Characterization of SARS-CoV-2 Isolate (MZ558159) Reported from India for in Silico Drug Designing

Rajneesh Prajapat ^*

, Suman Jain

Ph.D Pacific Institute of Medical Sciences, Sai Tirupati University, Udaipur, Rajasthan, India , rajneesh030041@gmail.com

Abstract: (1262 Views)

Background & Aims: There is inadequate information available about the genomics and proteomics characterization of SARS-CoV-2 isolates reported from India and other part of the globe. This characterization is important for the in-silico drug designing, as there are no approved medications available to treat SARS-CoV-2 infection. The present study based on the characterization of SARS-CoV-2 (MZ558159) isolate reported from India using homology modeling, validation, and in silico drug designing methods.
Materials & Methods: Genome sequence of SARS-CoV-2 (MZ558159) was retrieved from NCBI, and four protein sequences e.g., QXN18496, QXN18498, QXN18504, and QXN18497 were selected for the homology modeling, validation, and in silico drug designing. SWISS-MODEL and UCLA-DOE server were used for homology modeling. Validation for structure model performed using PROCHECK and molecular docking using MCULE-1-Click server.
Results: The surface glycoprotein (QXN18496) model corresponding to probability conformation with 93.6%, envelope protein (QXN18498) with 88.9%, nucleocapsid phosphoprotein (QXN18504) with 93.6%, and ORF3a protein (QXN18497) with 91.8% residues in core section of φ-ψ plot that specifies accuracy of prediction models. The corresponding ProSA Z-score score -12.67, -0.01, -4.4, and -2.87 indicates the good quality of the models. Molecular dynamic simulation and docking studies revealed that inhibitor binds effectively at the SARS-CoV-2 (MZ558159) proteins. Predicted inhibitor 2-acetamido-2-deoxy-beta-D-glucopyranose exhibited effective binding affinity against surface glycoprotein (QXN18496).
Conclusion: The results of this study established inhibitor 2-Acetamido-2-deoxy-beta-D-glucopyranose as valuable lead molecule with great potential for surface glycoprotein (QXN18496).

Keywords: SARS-CoV-2, COVID-19, φ-ψ plot, ProSA

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Type of Study: orginal article | Subject: General

References

1. Peiris JS. Coronavirus as a possible cause of severe acute respiratory syndrome. Lancet 2003; 361:1319-25. [DOI:10.1016/S0140-6736(03)13077-2] [PMID]

2. Jaiswal G, Kumar V. In-silico design of a potential inhibitor of SARS-CoV-2 S protein. PLoS ONE 2020; 15(10): e0240004. [DOI:10.1371/journal.pone.0240004] [PMID] [PMCID]

3. Drosten C. Identification of a novel coronavirus in patients with severe acute respiratory syndrome. N Engl J Med 2003; 348:1967-76. [DOI:10.1056/NEJMoa030747] [PMID]

4. Novosad P, Jain R, Campion A, Asher S. COVID-19 mortality effects of underlying health conditions in India: a modeling study. BMJ Open 2020;10 (12): e043165. [DOI:10.1136/bmjopen-2020-043165] [PMID] [PMCID]

5. Li F. Structure, Function, and Evolution of Coronavirus Spike Proteins. Annu Rev Virol 2016 29; 3(1):237-61 [DOI:10.1146/annurev-virology-110615-042301] [PMID] [PMCID]

6. Lim J, Jeon S, Shin HY, Kim MJ, Seong YM, Lee WJ, Choe KW, Kang YM, Lee B, Park SJ. Case of the Index Patient Who Caused Tertiary Transmission of COVID-19 Infection in Korea: The Application of Lopinavir/Ritonavir for the Treatment of COVID-19 Infected Pneumonia Monitored by Quantitative RT-PCR. J Korean Med Sci 2020; 35(6): e79. [DOI:10.3346/jkms.2020.35.e79] [PMID] [PMCID]

7. Gao J, Tian Z, Yang X. Breakthrough: Chloroquine phosphate has shown apparent efficacy in treatment of COVID-19 associated pneumonia in clinical studies. Biosci Trends 2020;14(1):72-3. doi: 10.5582/bst.2020.01047. [DOI:10.5582/bst.2020.01047] [PMID]

8. Naulaerts S, Meysman P, Bittremieux W, Vu TN, WV Berghe, Goethals B, Laukens K. A primer to frequent itemset mining for bioinformatics. Briefings Bioinform 2015;16:216-31. [DOI:10.1093/bib/bbt074] [PMID] [PMCID]

9. Rasouli H, Fazeli-Nasab B. Structural validation and homology modeling of lea 2 protein in bread wheat. Am Eurasian J Agric Environ Sci 2014; 14:1044-8. [Google Scholar]

10. Prajapat R, Jain S, Vaishnav MK, Sogani S. Structural Modeling and Validation of Growth/Differentiation Factor 15 [NP_004855] Associated with Pregnancy Complication- Hyperemesis Gravidarum. J Krishna Instit Med Sci 2020; 9(3):40-7. [URL]

11. Prajapat R, Marwal A, Shaikh Z, Gaur RK. Geminivirus Database (GVDB): First database of family geminiviridae and its genera Begomovirus. Pak J Biol Sci 2012;15:702-6. [DOI:10.3923/pjbs.2012.702.706] [PMID]

12. Luthy R, Bowie JU, Eisenberg D. Assessment of protein models with three-dimensional profiles. Nature 1992;356:83-5. [DOI:10.1038/356083a0] [PMID]

13. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: A program to check the stereochemical quality of protein structures. J Applied Cryst 1993;26:283-91. [DOI:10.1107/S0021889892009944]

14. Vriend G. WHAT IF: A molecular modeling and drug design program. J Mol Graphics 1990;8:52-6. [DOI:10.1016/0263-7855(90)80070-V] [PMID]

15. Sehgal SA, Tahir RA, Shafique S, Hassan M, Rashid S. Molecular modeling and docking analysis of CYP1A1 associated with head and neck cancer to explore its binding regions. J Theoret Comput Sci 2014;1(3):1-6. doi: 10.4172/2376-130X.1000112 [DOI:10.4172/2376-130X.1000112]

16. Agrawal P, Thakur Z, Kulharia M. Homology modeling and structural validation of tissue factor pathway inhibitor. Bioinformation 2013; 9: 808-12. [DOI:10.6026/97320630009808] [PMID] [PMCID]

17. Benkert P, Tosatto SCE, Schomburg D. QMEAN: A comprehensive scoring function for model quality assessment. Proteins: Struct Funct Bioinform 2008; 71:261-77. [DOI:10.1002/prot.21715] [PMID]

18. Benkert P, Kunzli M, Schwede T. QMEAN server for protein model quality estimation. Nucleic Acids Res 2009; 37:W510-W4. [DOI:10.1093/nar/gkp322] [PMID] [PMCID]

19. Novotny WF, Girard TJ, Miletich JP, Broze GJ. Platelets secrete a coagulation inhibitor functionally and antigenically similar to the lipoprotein associated coagulation inhibitor. Blood 1988;72: 2020-5. https://doi.org/10.1182/blood.V72.6.2020.2020 [DOI:10.1182/blood.V72.6.2020.bloodjournal7262020] [PMID]

20. Wiederstein M, Sippl MJ. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 2007;35:W407-W10. [DOI:10.1093/nar/gkm290] [PMID] [PMCID]

21. Jaiswal G, Kumar V. In-silico design of a potential inhibitor of SARS-CoV-2 S protein. PLoS ONE 2020;15(10):e0240004. [DOI:10.1371/journal.pone.0240004] [PMID] [PMCID]

22. Prajapat R, Marwal A, Gaur RK. Recognition of errors in the refinement and validation of three-dimensional structures of AC1 proteins of begomovirus strains by using ProSA-web. J Viruses 2014; 6. doi.10.1155/2014/752656. [DOI:10.1155/2014/752656]

23. Bowie JU, Luthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science 1991;253:164-70. [DOI:10.1126/science.1853201] [PMID]

24. Mustufa MMA, Chandra S, Wajid S. Homology modeling and molecular docking analysis of human RAC-alpha serine/threonine protein kinase. Int J Pharma Bio Sci 2014; 5:1033-42. [URL]

25. Rekik I, Chaabene Z, Grubb CD, Drira N, Cheour F, Elleuch A. In silico characterization and molecular modeling of double-strand break repair protein MRE11 from Phoenix dactylifera v deglet nour. Theor. Biol Med Model 2015; 12:23. doi: 10.1186/s12976-015-0013-2. [DOI:10.1186/s12976-015-0013-2] [PMID] [PMCID]

26. Prajapat R, Jain S, Vaishnav MK, Sogani S. In Silico Characterization of Surface Glycoprotein [QHD43416] of SARS-Coronavirus. Chinese J Med Res 2020; 3(2): 32-6. doi: 10.37515/cjmr.091X.3201 [DOI:10.37515/cjmr.091X.3201]

27. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The Protein Data Bank, Nucleic Acids Res 2000; 28: 235-42. [DOI:10.1093/nar/28.1.235] [PMID] [PMCID]

28. Benkert P, Biasini M, Schwede T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 2011; 27(3):343-50. [DOI:10.1093/bioinformatics/btq662] [PMID] [PMCID]

29. Studer G, Rempfer C, Waterhouse AM, Gumienny G, Haas J, Schwede T. QMEANDisCo - distance constraints applied on model quality estimation. Bioinformatics 2020;36:1765-71. https://doi.org/10.1093/bioinformatics/btaa058 [DOI:10.1093/bioinformatics/btz828] [PMCID]

30. Aires-de-Sousa M. Hemmer JG. Prediction of 1H NMR Chemical Shifts Using Neural Networks. Anal Chem 2002;74(1):80-90. [DOI:10.1021/ac010737m] [PMID]

31. Wiederstein M, Sippl MJ. Protein sequence randomization: Efficient estimation of protein stability using knowledge-based potentials. J Mol Biol 2005;345:1199-212. [DOI:10.1016/j.jmb.2004.11.012] [PMID]

32. Gao J, Lu G, Qi J, Li Y, Wu Y, Deng Y, Geng H, Li H, Wang Q, Xiao H, Tan W, Yan J, Gao GF. Structure of the fusion core and inhibition of fusion by a heptad repeat peptide derived from the S protein of Middle East respiratory syndrome coronavirus. J Virol 2013; 87(24):13134-40. doi: 10.1128/JVI.02433-13. [DOI:10.1128/JVI.02433-13] [PMID] [PMCID]

33. Ramachandra SC, Prashant A, Vishwanath P. COVID-19 Induced Cytokine Storm and the Impact of Obesity and Vitamin D Deficiency. J Krishna Instit Med Sci 2021;10(1):1-14. [Google Scholar]

34. National Center for Biotechnology Information (2022). PubChem Compound Summary for CID 24139. https://pubchem.ncbi.nlm.nih.gov/compound/2-acetamido-2-deoxy-beta-D-glucopyranose. [URL]

35. Lokhande KB, Doiphode S, Vyas R, Swamy KV. Molecular docking and simulation studies on SARS-CoV-2 Mpro reveals Mitoxantrone, Leucovorin, Birinapant, and Dynasore as potent drugs against COVID-19. J Biomol Struct Dyn 2020; 1-12. doi:10.1080/07391102.2020.1805019. [DOI:10.1080/07391102.2020.1805019] [PMID] [PMCID]