Fri, Jun 28, 2024
Volume 16, Issue 6 (Special issue (Nov-Dec) 2022)
mljgoums 2022, 16(6): 26-34 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Prajapat R, Jain S. Effective Binding Affinity of Inhibitor N-(3-(Carbamoylamino) Phenyl) Acetamide against the SARS-CoV-2 NSP13 Helicase. mljgoums 2022; 16 (6) :26-34
URL: http://mlj.goums.ac.ir/article-1-1475-en.html
URL: http://mlj.goums.ac.ir/article-1-1475-en.html
1- Department of Biochemistry, Pacific Institute of Medical Sciences, Sai Tirupati University, Udaipur, Rajasthan, India , rajneesh030041@gmail.com
2- Department of Biochemistry, Pacific Institute of Medical Sciences, Sai Tirupati University, Udaipur, Rajasthan, India
2- Department of Biochemistry, Pacific Institute of Medical Sciences, Sai Tirupati University, Udaipur, Rajasthan, India
Abstract: (1475 Views)
Background and objectives: The outbreak of coronavirus disease 2019 (COVID-19) has become a global health emergency. The severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) NSP13 helicase plays an important role in SARS-CoV-2 replication and could serve as a target for the development of antivirals. The objective of the study was to perform homology modeling and docking analysis of SARS-CoV-2 NSP13 helicase as a drug target.
Methods: The structure and function of SARS-CoV-2 NSP13 helicase were predicted by in-silico modeling studies. The SWISS-MODEL structure assessment tool was used for homology modeling and visual analysis of the crystal structure of the protein. The validation for structure models was performed using PROCHECK. Model quality was estimated based on the QMEAN and ProSA. The MCULE-1-Click docking and InterEvDock-2.0 server were used for protein-ligand docking.
Results: The SARS-CoV-2 NSP13 helicase model corresponded to probability confirmation with 90.9% residue of the core section, which highlights the accuracy of the predicted model. ProSA Z-score of -9.17 indicated the good quality of the model. Inhibitor N-(3-(carbamoylamino) phenyl) acetamide exhibited effective binding affinity against the NSP13 helicase. The docking results revealed that Lys-146, Leu-147, Ile-151, Tyr-185, Lys-195, Tyr-224, Val-226, Leu-227, Ser-229 residues exhibit good binding interactions with inhibitor ligand N-(3-(carbamoyl amino) phenyl) acetamide.
Conclusion: Hence, the proposed inhibitor could potently inhibit SARS-CoV-2 NSP13 helicase, which is thought to play key roles during viral replication. The results of this study indicate that N-(3-(carbamoylamino) phenyl) acetamide could be a valuable lead molecule with great potential for SARS-CoV-2 NSP13 helicase inhibition.
Methods: The structure and function of SARS-CoV-2 NSP13 helicase were predicted by in-silico modeling studies. The SWISS-MODEL structure assessment tool was used for homology modeling and visual analysis of the crystal structure of the protein. The validation for structure models was performed using PROCHECK. Model quality was estimated based on the QMEAN and ProSA. The MCULE-1-Click docking and InterEvDock-2.0 server were used for protein-ligand docking.
Results: The SARS-CoV-2 NSP13 helicase model corresponded to probability confirmation with 90.9% residue of the core section, which highlights the accuracy of the predicted model. ProSA Z-score of -9.17 indicated the good quality of the model. Inhibitor N-(3-(carbamoylamino) phenyl) acetamide exhibited effective binding affinity against the NSP13 helicase. The docking results revealed that Lys-146, Leu-147, Ile-151, Tyr-185, Lys-195, Tyr-224, Val-226, Leu-227, Ser-229 residues exhibit good binding interactions with inhibitor ligand N-(3-(carbamoyl amino) phenyl) acetamide.
Conclusion: Hence, the proposed inhibitor could potently inhibit SARS-CoV-2 NSP13 helicase, which is thought to play key roles during viral replication. The results of this study indicate that N-(3-(carbamoylamino) phenyl) acetamide could be a valuable lead molecule with great potential for SARS-CoV-2 NSP13 helicase inhibition.
Research Article: Research Article |
Subject:
Biochemistry
Received: 2022/01/17 | Accepted: 2022/12/19 | Published: 2022/11/25 | ePublished: 2022/11/25
Received: 2022/01/17 | Accepted: 2022/12/19 | Published: 2022/11/25 | ePublished: 2022/11/25
References
1. Ullrich S, Nitsche C. The SARS-CoV-2 main protease as drug target. Bioorg Med Chem Lett. 2020; 30(17): 127377 [View at Publisher] [DOI:10.1016/j.bmcl.2020.127377] [PubMed] [Google Scholar]
2. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020; 20(5): 533-534. [View at Publisher] [DOI:10.1016/S1473-3099(20)30120-1] [PubMed] [Google Scholar]
3. Gupta B, Kalhan S, Shukla S, Bahadur S, Singh G, Pathak R. Evaluating Association between ABO Blood Groups and COVID 19. mljgoums. 2021; 15 (6) :1-7. [View at Publisher] [Google Scholar]
4. Wu F, Zhao S, B. Yu, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020; 579 (7798): 265-269. [View at Publisher] [DOI:10.1038/s41586-020-2008-3] [PubMed] [Google Scholar]
5. Priya R, Andurkar SP, Dixit JV. Determinants of outcome in covid-19 cases: a cross-sectional analytical study. Al Ameen Journal of Medical Sci. 2021; 14 (1): 39-42. [View at Publisher] [Google Scholar]
6. Chan JFW, Yuan S, Kok KH, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet. 2020; 395 (10223): 514-523. [View at Publisher] [DOI:10.1016/S0140-6736(20)30154-9] [PubMed] [Google Scholar]
7. Yang Y, Peng F, Wang R, et al. The deadly coronaviruses: the 2003 SARS pandemic and the 2020 novel coronavirus epidemic in China. J Autoimmun. 2020; 109: 102434. [View at Publisher] [DOI:10.1016/j.jaut.2020.102434] [PubMed] [Google Scholar]
8. Adake P, Acharya A, Halemani S, Petimani M. Clinical Features of COVID-19 Patients with Preexisting Hypothyroidism: A Retrospective Study. mljgoums. 2022; 16 (1) :9-12. [View at Publisher] [Google Scholar]
9. Song Z, Xu Y, Bao L, Zhang L, Yu P, Qu Y, Zhu H, Zhao W, Han Y, Qin C. From SARS to MERS, Thrusting Coronaviruses into the Spotlight. Viruses. 2019 Jan 14;11(1):59. [View at Publisher] [DOI:10.3390/v11010059] [PubMed] [Google Scholar]
10. Song Z, Xu Y, Bao L. From SARS to MERS: thrusting coronaviruses into the spotlight. Viruses. 2019; 11(1):59. doi: 10.3390/v11010059. [DOI:10.3390/v11010059]
11. Anand K, Ziebuhr J, Wadhwani P, Mesters JR, Hilgenfeld R. Coronavirus main proteinase (3CLpro) structure: basis for design of anti-SARS drugs. Science. 2003; 300(5626):1763-1767. [View at Publisher] [DOI:10.1126/science.1085658] [PubMed] [Google Scholar]
12. Arun MR, Sheeba MR, Rishma FSF. Historical Analysis and Scientific Overview of Coronaviruses. Al Ameen Journal of Medical Sci. 2020; 13 (03): 141-148. [View at Publisher] [Google Scholar]
13. Chen Y, Liu Q, Guo D. Emerging coronaviruses: genome structure, replication, and pathogenesis. J Med Virol. 2020; 92(4):418-423. [View at Publisher] [DOI:10.1002/jmv.25681] [PubMed] [Google Scholar]
14. Gordon DE, Jang GM, Bouhaddou M, Xu J, Obernier K, White KM, et al. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature. 2020; 583(7816): 459-468. [View at Publisher] [DOI:10.1038/s41586-020-2286-9] [PubMed] [Google Scholar]
15. Wu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020;579(7798):265-269. [View at Publisher] [DOI:10.1038/s41586-020-2008-3] [PubMed] [Google Scholar]
16. Zhang C, Huang S, Zheng F, Dai Y. Controversial treatments: an updated understanding of the coronavirus disease 2019. J Med Virol. 2020. [View at Publisher] [DOI:10.1002/jmv.25788] [PubMed] [Google Scholar]
17. Lurie N, Saville M, Hatchett R, Halton J. Developing COVID-19 vaccines at pandemic speed. N Engl J Med. 2020; 382(21):1969-1973. [View at Publisher] [DOI:10.1056/NEJMp2005630] [PubMed] [Google Scholar]
18. Ullrich S, Nitsche C. The SARS-CoV-2 main protease as drug target. Bioorg Med Chem Lett. 2020; 30(17):127377. [View at Publisher] [DOI:10.1016/j.bmcl.2020.127377] [PubMed] [Google Scholar]
19. Wu C, Liu Y, Yang Y. Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. Acta Pharm Sin. 2020; 10(5):766-788. [View at Publisher] [DOI:10.1016/j.apsb.2020.02.008] [PubMed] [Google Scholar]
20. Shu T, Huang M, Wu D. et al. SARS-Coronavirus-2 Nsp13 Possesses NTPase and RNA Helicase Activities That Can Be Inhibited by Bismuth Salts. Virol Sin. 2020; 35: 321-329. [View at Publisher] [DOI:10.1007/s12250-020-00242-1] [PubMed] [Google Scholar]
21. Habtemariam S, Nabavi SF, Banach M, Berindan-Neagoe I, Sarkar K, Sil PC, et al. Should We Try SARS-CoV-2 Helicase Inhibitors for COVID-19 Therapy?. Arch Med Res. 2020; 51(7):733-735. [View at Publisher] [DOI:10.1016/j.arcmed.2020.05.024] [PubMed] [Google Scholar]
22. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, et al. The sequence of the human genome. Science. 2001; 291: 1304-1351. [View at Publisher] [DOI:10.1126/science.1058040] [PubMed] [Google Scholar]
23. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990; 215: 403-410. [View at Publisher] [DOI:10.1016/S0022-2836(05)80360-2]
24. Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, Heer FT, de Beer TAP, Rempfer C, Bordoli L, Lepore R, Schwede T. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018; 46: W296-W303. [View at Publisher] [DOI:10.1093/nar/gky427] [PubMed] [Google Scholar]
25. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK - a program to check the stereochemical quality of protein structures. J. App. Cryst. 1993; 26: 283-291. [View at Publisher] [DOI:10.1107/S0021889892009944]
26. Vriend G. WHAT IF: A molecular modeling and drug design program. J Mol Graphics. 1990; 8: 52-56. [View at Publisher] [DOI:10.1016/0263-7855(90)80070-V] [PubMed] [Google Scholar]
27. 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] [Google Scholar]
28. Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci. 1993; 2: 1511-1519. [View at Publisher] [DOI:10.1002/pro.5560020916] [PubMed] [Google Scholar]
29. Benkert P, Kunzli M, Schwede T. QMEAN server for protein model quality estimation. Nucleic Acids Res 2009; 37: W510-W514. [View at Publisher] [DOI:10.1093/nar/gkp322] [Google Scholar]
30. 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) 2: W407-W410. [View at Publisher] [DOI:10.1093/nar/gkm290] [PubMed] [Google Scholar]
31. 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. [View at Publisher] [DOI:10.1155/2014/752656] [Google Scholar]
32. Quignot C, Rey J, Yu J, Tufféry P, Guérois R, Andreani J. InterEvDock2: an expanded server for protein docking using evolutionary and biological information from homology models and multimeric inputs. Nucleic Acids Res. 2018; 46 (W1):W408-W416. [View at Publisher] [DOI:10.1093/nar/gky377] [PubMed] [Google Scholar]
33. Varma PBS, Yesubabu A, Subrahmanyam K. Identify virtual ligand hits using consensus scoring approach for drug target S. Aureus Int J of Eng & Tech. 2018; 7 (2.7) 84-87. [View at Publisher] [DOI:10.14419/ijet.v7i2.7.10265]
34. Bowie JU, Luthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science. 1991; 253: 164-170. [View at Publisher] [DOI:10.1126/science.1853201] [PubMed]
35. 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: 1033-1042.
36. Prajapat R, Jain S, Vaishnav MK, Sogani S. In Silico Characterization of Surface Glycoprotein (QHD43416) of Severe Acute Respiratory Syndrome-Coronavirus. Chinese J Med Res. 2020; 3(2): 32-36. [View at Publisher] [DOI:10.37515/cjmr.091X.3201] [Google Scholar]
37. Benkert P, Biasini M, Schwede T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics. 2011, 27: (3) 343-350. [View at Publisher] [DOI:10.1093/bioinformatics/btq662] [PubMed] [Google Scholar]
38. Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci. 1993;2(9):1511-1519. [View at Publisher] [DOI:10.1002/pro.5560020916] [PubMed] [Google Scholar]
39. Newman JA, Douangamath A, Yadzani S, Yosaatmadja Y, Aimon A, Brandão-Neto J, Dunnett L, Gorrie-Stone T, Skyner R, Fearon D, Schapira M, von Delft F, Gileadi O. Structure, mechanism and crystallographic fragment screening of the SARS-CoV-2 NSP13 helicase. Nat Commun. 2021; 12(1):4848. [View at Publisher] [DOI:10.1038/s41467-021-25166-6] [PubMed] [Google Scholar]
40. White MA, Lin W, Cheng X. Discovery of COVID-19 Inhibitors Targeting the SARS-CoV-2 Nsp13 Helicase. J Phys Chem Lett. 2020; 11(21):9144-9151. [View at Publisher] [DOI:10.1021/acs.jpclett.0c02421] [PubMed] [Google Scholar]
41. 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-W410. [View at Publisher] [DOI:10.1093/nar/gkm290] [PubMed] [Google Scholar]
42. 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 degletnour. Theor Biol Med Model. 2015; 12:23. [View at Publisher] [DOI:10.1186/s12976-015-0013-2] [PubMed] [Google Scholar]
43. Wiederstein M, Sippl MJ. Protein sequence randomization: Efficient estimation of protein stability using knowledge-based potentials. J Mol Biol. 2005; 345: 1199-1212. [View at Publisher] [DOI:10.1016/j.jmb.2004.11.012] [PubMed] [Google Scholar]
44. Benkert, P., Biasini, M., Schwede, T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics. 2011; 27, 343-350. [View at Publisher] [DOI:10.1093/bioinformatics/btq662] [PubMed] [Google Scholar]
45. Studer G, Rempfer C, Waterhouse AM, Gumienny G, Haas J, Schwede T. QMEANDisCo - distance constraints applied on model quality estimation. Bioinformatics. 2020; 36: 1765-1771. [View at Publisher] [DOI:10.1093/bioinformatics/btz828] [PubMed] [Google Scholar]
46. Freidel MR, Armen RS. Mapping major SARS-CoV-2 drug targets and assessment of druggability using computational fragment screening: Identification of an allosteric small-molecule binding site on the Nsp13 helicase. PLoS One. 2021; 16(2): e0246181. [View at Publisher] [DOI:10.1371/journal.pone.0246181] [PubMed] [Google Scholar]
47. Vardhan S, Sahoo SK. Exploring the therapeutic nature of limonoids and triterpenoids against SARS-CoV-2 by targeting nsp13, nsp14, and nsp15 through molecular docking and dynamics simulations. J Trad Compl Med. 2022; 12(1): 44-54. [View at Publisher] [DOI:10.1016/j.jtcme.2021.12.002] [PubMed] [Google Scholar]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Enamad
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.