year 16, Issue 6 (November - December 2022)                   Iran J Med Microbiol 2022, 16(6): 506-519 | Back to browse issues page


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Pirmoradi S. In-silico Designing of Immunogenic Construct Based on Peptide Epitopes Using Immuno-informatics Tools Against Tuberculosis. Iran J Med Microbiol 2022; 16 (6) :506-519
URL: http://ijmm.ir/article-1-1594-en.html
Department of Biochemistry, Shahid Chamran University of Ahvaz, Ahvaz, Iran , pirmoradi150@gmail.com
Abstract:   (1540 Views)

Background and Aim: Mycobacterium tuberculosis is a health problem in countries. Despite the global prevalence of tuberculosis and the lack of appropriate drugs, further progress is still needed with the help of modern methods of preparing epitope-based vaccines for tuberculosis.
Materials and Methods: In this study, specific T and B cell epitopes required for producing chimeric vaccines with the help of servers such as IEDB were determined. The antigenicity, allergenicity, and toxicity of the selected epitopes by various other servers such as VaxiJenv2.0, AllerTOP, and Toxinpred were determined, and the vaccine of the epitope was then configured with the help of special linkers. Then, analyze the structure vaccine by some other bioinformatics servers such as PRABI, SWISS-MODEL, PROCHECK, and PEPCALC, was investigated and finally detected using docking techniques to evaluate the interaction with the epitope through MVD software.
Results: The results showed that the vaccine, in terms of in silico evaluations of two-dimensional and three-dimensional structures, has a good condition. Also, the percentage of optimal placement of amino acids and bonds by PROCHECK server, % 99 percent of optimal placement of amino acids in the chimer structure was established. Also, it was non-toxic and nonallergenic and had the desired antigenicity.
Conclusion: In general, the vaccine that was able to have a favorable interaction with some components of the immune system (HLA) in the docking process, which indicates the optimal identification of this structure by the humoral and cellular immune system, of course, more reliable proof of it requires clinical phase processes.

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Type of Study: Original Research Article | Subject: Microbial Bioinformatics
Received: 2021/12/24 | Accepted: 2022/07/11 | ePublished: 2022/09/9

References
1. Bennett JE, Dolin R, Blaser MJ. Mandell, douglas, and bennett's principles and practice of infectious diseases: 2-volume set: Elsevier Health Sciences. Philadelphia, Saunders, Elsevier; 2014.
2. DruSzczyńSka M, Kowalewicz-Kulbat M, Fol M, WłOdarczyk M, RuDnIcka W. Latent M. tuberculosis infection--pathogenesis, diagnosis, treatment and prevention strategies. Pol J Microbiol. 2012;61(1):3-10. [DOI:10.33073/pjm-2012-001] [PMID]
3. Maji A, Misra R, Dhakan DB, Gupta V, Mahato NK, Saxena R, et al. Gut microbiome contributes to impairment of immunity in pulmonary tuberculosis patients by alteration of butyrate and propionate producers. Environ Microbiol. 2018;20(1):402-19. [DOI:10.1111/1462-2920.14015] [PMID]
4. Singhvi N, Singh Y, Shukla P. Computational approaches in epitope design using DNA binding proteins as vaccine candidate in Mycobacterium tuberculosis. Infect Genet Evol. 2020;83:104357. [DOI:10.1016/j.meegid.2020.104357] [PMID]
5. Gholami A, Moosavi L. Incidence Rate of ExtraPulmonary Tuberculosis in Urmia city during 2004-2007. Nurs midwifery res j. 2010;8(2):0.
6. Ong E, He Y, Yang Z. Epitope promiscuity and population coverage of Mycobacterium tuberculosis protein antigens in current subunit vaccines under development. Infect Genet Evol. 2020;80:104186. [DOI:10.1016/j.meegid.2020.104186] [PMID]
7. Barker LF, Brennan MJ, Rosenstein PK, Sadoff JC. Tuberculosis vaccine research: the impact of immunology. Curr Opin Immunol. 2009;21(3):331-8. [DOI:10.1016/j.coi.2009.05.017] [PMID]
8. Daniel T, Bates J, Downes K. History of tuberculosis. Tuberculosis: pathogenesis, protection, and control. 1994 (16):13-24. [DOI:10.1128/9781555818357.ch2] [PMID] [PMCID]
9. Dimitrov I, Bangov I, Flower DR, Doytchinova I. AllerTOP v. 2-a server for in silico prediction of allergens. J Mol Model. 2014;20(6):1-6. [DOI:10.1007/s00894-014-2278-5] [PMID]
10. Dimitrov I, Naneva L, Doytchinova I, Bangov I. AllergenFP: allergenicity prediction by descriptor fingerprints. Bioinformatics. 2014;30(6):846-51. [DOI:10.1093/bioinformatics/btt619] [PMID]
11. Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Open Source Drug Discovery C, et al. In silico approach for predicting toxicity of peptides and proteins. PloS One. 2013;8(9):e73957. [DOI:10.1371/journal.pone.0073957] [PMID] [PMCID]
12. Thevenet P, Shen Y, Maupetit J, Guyon F, Derreumaux P, Tuffery P. PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides. Nucleic Acids Res. 2012;40(W1):W288-W93. [DOI:10.1093/nar/gks419] [PMID] [PMCID]
13. Shen Y, Maupetit J, Derreumaux P, Tufféry P. Improved PEP-FOLD approach for peptide and miniprotein structure prediction. J Chem Theory Comput. 2014;10(10):4745-58. [DOI:10.1021/ct500592m] [PMID]
14. Zhang Y, Skolnick J. Scoring function for automated assessment of protein structure template quality. Proteins. 2004;57(4):702-10. [DOI:10.1002/prot.20264] [PMID]
15. Kumar V, Kancharla S, Kolli P, Jena M. Reverse vaccinology approach towards the in-silico multiepitope vaccine development against SARS-CoV-2. F1000Research. 2021;10. [DOI:10.12688/f1000research.36371.1] [PMID] [PMCID]
16. Ikai A. Thermostability and aliphatic index of globular proteins. J Biochem. 1980;88(6):1895-8.
17. Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 2017;45(W1):W24-W9. [DOI:10.1093/nar/gkx346] [PMID] [PMCID]
18. Singh H, Ansari HR, Raghava GPS. Improved method for linear B-cell epitope prediction using antigen's primary sequence. PloS One. 2013;8(5):e62216. [DOI:10.1371/journal.pone.0062216] [PMID] [PMCID]
19. El‐Manzalawy Y, Dobbs D, Honavar V. Predicting linear B‐cell epitopes using string kernels. J Mol Recognit: An Interdisciplinary Journal. 2008;21(4):243-55. [DOI:10.1002/jmr.893] [PMID] [PMCID]
20. Faria AR, Costa MM, Giusta MS, Grimaldi Jr G, Penido MLO, Gazzinelli RT, et al. High-throughput analysis of synthetic peptides for the immunodiagnosis of canine visceral leishmaniasis. PLoS Negl Trop Dis. 2011;5(9):e1310. [DOI:10.1371/journal.pntd.0001310] [PMID] [PMCID]
21. Kaye K, Frieden TR. Tuberculosis control: the relevance of classic principles in an era of acquired immunodeficiency syndrome and multidrug resistance. Epidemiol Rev. 1996;18(1):52-63. [DOI:10.1093/oxfordjournals.epirev.a017916] [PMID]
22. Cosivi O, Grange JM, Daborn CJ, Raviglione MC, Fujikura T, Cousins D, et al. Zoonotic tuberculosis due to Mycobacterium bovis in developing countries. Emerging Infect Dis. 1998;4(1):59. [DOI:10.3201/eid0401.980108] [PMID] [PMCID]
23. Singh H, Raghava GPS. ProPred: prediction of HLA-DR binding sites. Bioinformatics. 2001;17(12):1236-7. [DOI:10.1093/bioinformatics/17.12.1236] [PMID]
24. Yang X, Yu X. An introduction to epitope prediction methods and software. Rev Med Virol. 2009;19(2):77-96. [DOI:10.1002/rmv.602] [PMID]
25. Waaler H. Tuberculosis and poverty. Int J Tuberc Lung Dis. 2002 6(9):745-6.
26. Elhag M, Sati A, Saadaldin M, Hassan M. Immunoinformatics prediction of epitope-based peptide vaccine against Mycobacterium tuberculosis PPE65 family protein. bioRxiv2019 p. 755983. [DOI:10.1101/755983]
27. Colditz GA, Brewer TF, Berkey CS, Wilson ME, Burdick E, Fineberg HV, et al. Efficacy of BCG vaccine in the prevention of tuberculosis: meta-analysis of the published literature. Jama. 1994;271(9):698-702. [DOI:10.1001/jama.1994.03510330076038] [PMID]
28. Iseman MD. Evolution of drug-resistant tuberculosis: a tale of two species. Proc Natl Acad Sci. 1994;91(7):2428-9. [DOI:10.1073/pnas.91.7.2428] [PMID] [PMCID]
29. Schaaf HS, Marais BJ, Whitelaw A, Hesseling AC, Eley B, Hussey GD, et al. Culture-confirmed childhood tuberculosis in Cape Town, South Africa: a review of 596 cases. BMC Infect Dis. 2007;7(1):1-8. [DOI:10.1186/1471-2334-7-140] [PMID] [PMCID]
30. Riccomi A, Piccaro G, Christensen D, Palma C, Andersen P, Vendetti S. Parenteral vaccination with a tuberculosis subunit vaccine in presence of retinoic acid provides early but transient protection to M. tuberculosis infection. Front Immunol. 2019;10:934. [DOI:10.3389/fimmu.2019.00934] [PMID] [PMCID]
31. Reginald K, Chan Y, Plebanski M, Poh CL. Development of peptide vaccines in dengue. Curr Pharm Des. 2018;24(11):1157-73. [DOI:10.2174/1381612823666170913163904] [PMID] [PMCID]
32. Bahrami AA, Payandeh Z, Khalili S, Zakeri A, Bandehpour M. Immunoinformatics: in silico approaches and computational design of a multi-epitope, immunogenic protein. Int Rev Immunol. 2019;38(6):307-22. [DOI:10.1080/08830185.2019.1657426] [PMID]
33. Watt J, Liu J. Preclinical progress of subunit and live attenuated Mycobacterium tuberculosis vaccines: A review following the first in human efficacy trial. Pharmaceutics. 2020;12(9):848. [DOI:10.3390/pharmaceutics12090848] [PMID] [PMCID]
34. Lu LL, Suscovich TJ, Fortune SM, Alter G. Beyond binding: antibody effector functions in infectious diseases. Nat Rev Immunol. 2018;18(1):46-61. [DOI:10.1038/nri.2017.106] [PMID] [PMCID]
35. Krocova Z, Plzakova L, Pavkova I, Kubelkova K, Macela A, Ozanic M, et al. The role of B cells in an early immune response to Mycobacterium bovis. Microb Pathog. 2020;140:103937. [DOI:10.1016/j.micpath.2019.103937] [PMID]
36. Singhvi N, Gupta V, Gaur M, Sharma V, Puri A, Singh Y, et al. Interplay of human gut microbiome in health and wellness. Indian J Microbiol. 2020;60(1):26-36. [DOI:10.1007/s12088-019-00825-x] [PMID] [PMCID]
37. Eickhoff CS, Terry FE, Peng L, Meza KA, Sakala IG, Van Aartsen D, et al. Highly conserved influenza T cell epitopes induce broadly protective immunity. Vaccine. 2019;37(36):5371-81. [DOI:10.1016/j.vaccine.2019.07.033] [PMID] [PMCID]
38. Patankar YR, Sutiwisesak R, Boyce S, Lai R, Lindestam Arlehamn CS, Sette A, et al. Limited recognition of Mycobacterium tuberculosis-infected macrophages by polyclonal CD4 and CD8 T cells from the lungs of infected mice. Mucosal Immunol. 2020;13(1):140-8. [DOI:10.1038/s41385-019-0217-6] [PMID] [PMCID]
39. Russell SL, Lamprecht DA, Mandizvo T, Jones TT, Naidoo V, Addicott KW, et al. Compromised metabolic reprogramming is an early indicator of CD8+ T cell dysfunction during chronic Mycobacterium tuberculosis infection. Cell reports. 2019;29(11):3564-79. [DOI:10.1016/j.celrep.2019.11.034] [PMID] [PMCID]
40. Meza B, Ascencio F, Sierra-Beltrán AP, Torres J, Angulo C. A novel design of a multi-antigenic, multistage and multi-epitope vaccine against Helicobacter pylori: an in silico approach. Infect Genet Evol. 2017;49:309-17. [DOI:10.1016/j.meegid.2017.02.007] [PMID]
41. Chen R. Bacterial expression systems for recombinant protein production: E. coli and beyond. Biotechnol Adv. 2012;30(5):1102-7. [DOI:10.1016/j.biotechadv.2011.09.013] [PMID]
42. Rosano GL, Ceccarelli EA. Recombinant protein expression in Escherichia coli: advances and challenges. Front Immunol. 2014;5:172. [DOI:10.3389/fmicb.2014.00172]

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