Advertisement

Applied Biochemistry and Biotechnology

, Volume 187, Issue 1, pp 90–100 | Cite as

Predicting Promiscuous T Cell Epitopes for Designing a Vaccine Against Streptococcus pyogenes

  • Samira Ebrahimi
  • Hassan Mohabatkar
  • Mandana Behbahani
Article
  • 91 Downloads

Abstract

One of the most dangerous human pathogens with high prevalence worldwide is Streptococcus pyogenes, which has major impacts on global morbidity and mortality. A major challenge for S. pyogenes vaccine development is the detection of epitopes that confer protection from infection by multiple S. pyogenes types. Our aim was to identify the most conserved and immunogenic antigens of S. pyogenes, which can be a potential candidate for vaccine design in the future. Eight important surface proteins were analyzed. Using different prediction servers, strongest epitopes were selected. They had the ability to stimulate the humoral and cell-mediated immune system. Molecular docking was performed for measuring free-binding energy of selected epitopes. Seven epitopes from three surface proteins were selected as potential candidates for vaccine development. Conservation of selected epitopes among different Streptococcus types was checked. Further in vitro and in vivo tests are required to validate the suitability of the epitopes for vaccine design.

Keywords

Streptococcus pyogenes T cell epitope prediction Molecular docking Surface protein Vaccine design 

Notes

Funding information

This study was supported by the University of Isfahan.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Steer, A. C., Law, I., Matatolu, L., Beall, B. W., & Carapetis, J. R. (2009). Global emm type distribution of group A streptococci: systematic review and implications for vaccine development. The Lancet Infectious Diseases, 9(10), 611–616.CrossRefGoogle Scholar
  2. 2.
    Lancefield, R. C. (1933). A serological differentiation of human and other groups of hemolytic streptococci. The Journal of Experimental Medicine, 57(4), 571–595.CrossRefGoogle Scholar
  3. 3.
    Cunningham, M. W. (2000). Pathogenesis of group A streptococcal infections. Clinical Microbiology Reviews, 13(3), 470–511.CrossRefGoogle Scholar
  4. 4.
    Carapetis, J. R., McDonald, M., & Wilson, N. J. (2005). Acute rheumatic fever. Lancet, 366(9480), 155–168.CrossRefGoogle Scholar
  5. 5.
    Carapetis, J. R., Steer, A. C., Mulholland, E. K., & Weber, M. (2005). The global burden of group A streptococcal diseases. The Lancet Infectious Diseases, 5(11), 685–694.CrossRefGoogle Scholar
  6. 6.
    Cunningham, M. W. (2004). T cell mimicry in inflammatory heart disease. Molecular Immunology, 40(14-15), 1121–1127.CrossRefGoogle Scholar
  7. 7.
    Bauer, M. J., Georgousakis, M. M., Vu, T., Henningham, A., Hofmann, A., Rettel, M., Hafner, L. M., Sriprakash, K. S., & Mcmillan, D. J. (2012). Evaluation of novel Streptococcus pyogenes vaccine candidates incorporating multiple conserved sequences from the C-repeat region of the M-protein. Vaccine, 30(12), 2197–2205.  https://doi.org/10.1016/j.vaccine.2011.12.115.CrossRefGoogle Scholar
  8. 8.
    Hendrickx, A. P. A., Willems, R. J. L., Bonten, M. J. M., & van Schaik, W. (2009). LPxTG surface proteins of enterococci. Trends in Microbiology, 17(9), 423–430.CrossRefGoogle Scholar
  9. 9.
    Rodríguez-Ortega, M. J., Norais, N., Bensi, G., Liberatori, S., Capo, S., Mora, M., Scarselli, M., Doro, F., Ferrari, G., & Garaguso, I. (2006). Characterization and identification of vaccine candidate proteins through analysis of the group A Streptococcus surface proteome. Nature Biotechnology, 24(2), 191–197.CrossRefGoogle Scholar
  10. 10.
    Wei, Z., Fu, Q., Liu, X., Xiao, P., Lu, Z., & Chen, Y. (2012). Identification of Streptococcus equi ssp. zooepidemicus surface associated proteins by enzymatic shaving. Veterinary Microbiology, 159(3-4), 519–525.CrossRefGoogle Scholar
  11. 11.
    John, L., John, G. J., & Kholia, T. (2012). A reverse vaccinology approach for the identification of potential vaccine candidates from Leishmania spp. Applied Biochemistry and Biotechnology, 167(5), 1340–1350.CrossRefGoogle Scholar
  12. 12.
    Talukdar, S., & Zutshi, S. (2014). Identification of potential vaccine candidates against Streptococcus pneumoniae by reverse vaccinology approach. Applied Biochemistry and Biotechnology, 172(6), 3026–3041.CrossRefGoogle Scholar
  13. 13.
    Iurescia, S., Fioretti, D., Fazio, V. M., & Rinaldi, M. (2012). Epitope-driven DNA vaccine design employing immunoinformatics against B-cell lymphoma: a biotech’s challenge. Biotechnology Advances, 30(1), 372–383.CrossRefGoogle Scholar
  14. 14.
    Michielin, O., & Karplus, M. (2002). Binding free energy differences in a TCR–peptide-MHC complex induced by a peptide mutation: a simulation analysis. Journal of Molecular Biology, 324(3), 547–569.CrossRefGoogle Scholar
  15. 15.
    Soria-guerra, R. E., Nieto-gomez, R., Govea-alonso, D. O., & Rosales-mendoza, S. (2015). An overview of bioinformatics tools for epitope prediction: implications on vaccine development. Journal of Biomedical Informatics, 53, 405–414.  https://doi.org/10.1016/j.jbi.2014.11.003.CrossRefGoogle Scholar
  16. 16.
    Doytchinova, I. A., & Flower, D. R. (2007). VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics, 8, 4.CrossRefGoogle Scholar
  17. 17.
    A. Krogh (n.d.) Prediction of transmembrane helices in proteins, 2006-05-21) http://www.Cbs.Dtu.dk/services/TMHMM-2.0.
  18. 18.
    Saha, S., & Raghava, G. P. S. (2006). Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins: Structure, Function, and Bioinformatics, 65(1), 40–48.CrossRefGoogle Scholar
  19. 19.
    Singh, H., & Raghava, G. P. S. (2003). ProPred1: prediction of promiscuous MHC class-I binding sites. Bioinformatics, 19(8), 1009–1014.CrossRefGoogle Scholar
  20. 20.
    Singh, H., & Raghava, G. P. S. (2001). ProPred: prediction of HLA-DR binding sites. Bioinformatics, 17(12), 1236–1237.CrossRefGoogle Scholar
  21. 21.
    Bhattacharya, T., Daniels, M., Heckerman, D., Foley, B., Frahm, N., Kadie, C., Carlson, J., Yusim, K., McMahon, B., & Gaschen, B. (2007). Founder effects in the assessment of HIV polymorphisms and HLA allele associations. Science, 21(5818), 1583–1586.CrossRefGoogle Scholar
  22. 22.
    Guan, P., Doytchinova, I. A., Zygouri, C., & Flower, D. R. (2003). MHCPred: a server for quantitative prediction of peptide–MHC binding. Nucleic Acids Research, 31(13), 3621–3624.CrossRefGoogle Scholar
  23. 23.
    Oprea, M., & Antohe, F. (2013). Biologicals reverse-vaccinology strategy for designing T-cell epitope candidates for Staphylococcus aureus endocarditis vaccine. Biologicals, 41(3), 148–153.  https://doi.org/10.1016/j.biologicals.2013.03.001. CrossRefGoogle Scholar
  24. 24.
    Singh, S., Singh, H., Tuknait, A., Chaudhary, K., Singh, B., Kumaran, S., & Raghava, G. P. S. (2015). PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues. Biology Direct, 10(1), 73.CrossRefGoogle Scholar
  25. 25.
    Comeau, S. R., Gatchell, D. W., Vajda, S., & Camacho, C. J. (2004). ClusPro: an automated docking and discrimination method for the prediction of protein complexes. Bioinformatics, 20(1), 45–50.CrossRefGoogle Scholar
  26. 26.
    Garboczi, D. N., Ghosh, P., Utz, U., & Fan, Q. R. (1996). Structure of the complex between human T-cell receptor, viral peptide and HLA-A2. Nature, 384(6605), 134–141.CrossRefGoogle Scholar
  27. 27.
    Altschul, S. F., Madden, T. L., Schäffer, A. A., Zhang, J., Zhang, Z., Miller, W., & Lipman, D. J. (1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research, 25(17), 3389–3402.CrossRefGoogle Scholar
  28. 28.
    Kreikemeyer, B., Gámez, G., Margarit, I., Giard, J., Hammerschmidt, S., Hartke, A., & Podbielski, A. (2017). International journal of medical microbiology genomic organization, structure, regulation and pathogenic role of pilus constituents in major pathogenic Streptococci and Enterococci. International Journal of Medical Microbiology, 301(3), 240–251.  https://doi.org/10.1016/j.ijmm.2010.09.003. CrossRefGoogle Scholar
  29. 29.
    Oyarzun, P., Ellis, J. J., Gonzalez-galarza, F. F., Jones, A. R., Middleton, D., Boden, M., & Kobe, B. (2015). A bioinformatics tool for epitope-based vaccine design that accounts for human ethnic diversity: application to emerging infectious diseases. Vaccine, 33(10), 1267–1273.  https://doi.org/10.1016/j.vaccine.2015.01.040.CrossRefGoogle Scholar
  30. 30.
    Oliveira, F. M., Coelho, I. E. V., Lopes, M. D., Taranto, A. G., Junior, M. C., Santos, L. L. D., Villar, J. A. P. F., Fonseca, C. T., & Lopes, D. D. O. (2016). The use of reverse vaccinology and molecular modeling associated with cell proliferation stimulation approach to select promiscuous epitopes from Schistosoma mansoni. Applied Biochemistry and Biotechnology, 179(6), 1023–1040.CrossRefGoogle Scholar
  31. 31.
    Simonelli, L., Beltramello, M., Yudina, Z., Macagno, A., Calzolai, L., & Varani, L. (2010). Rapid structural characterization of human antibody—antigen complexes through experimentally validated computational docking. Journal of Molecular Biology, 396(5), 1491–1507.  https://doi.org/10.1016/j.jmb.2009.12.053.CrossRefGoogle Scholar
  32. 32.
    Dale, J. B., Fischetti, V. A., Carapetis, J. R., Steer, A. C., Sow, S., Kumar, R., Mayosi, B. M., Rubin, F. A., Mulholland, K., Maria, J., Schödel, F., & Henao-restrepo, A. M. (2013). Group A streptococcal vaccines: paving a path for accelerated development. Vaccine, 31, 216–222.  https://doi.org/10.1016/j.vaccine.2012.09.045. CrossRefGoogle Scholar
  33. 33.
    Oehmcke, S., Shannon, O., Mörgelin, M., & Herwald, H. (2010). Clinica Chimica Acta streptococcal M proteins and their role as virulence determinants. Clinica Chimica Acta, 411(17-18), 1172–1180.  https://doi.org/10.1016/j.cca.2010.04.032. CrossRefGoogle Scholar
  34. 34.
    Purcell, A. W., McCluskey, J., & Rossjohn, J. (2007). More than one reason to rethink the use of peptides in vaccine design. Nature Reviews. Drug Discovery, 6(5), 404–414.CrossRefGoogle Scholar
  35. 35.
    Shaila, M. S., Nayak, R., Prakash, S. S., Georgousakis, M., Brandt, E., McMillan, D. J., Batzloff, M. R., Pruksakorn, S., Good, M. F., & Sriprakash, K. S. (2007). Comparative in silico analysis of two vaccine candidates for group A streptococcus predicts that they both may have similar safety profiles. Vaccine, 25(18), 3567–3573.CrossRefGoogle Scholar
  36. 36.
    Sun, D. X., Seyer, J. M., Kovari, I., Sumrada, R. A., & Taylor, R. K. (1991). Localization of protective epitopes within the pilin subunit of the Vibrio cholerae toxin-coregulated pilus. Infection and Immunity, 59(1), 114–118.Google Scholar
  37. 37.
    Pizza, M., Scarlato, V., Masignani, V., Giuliani, M. M., Arico, B., Comanducci, M., Jennings, G. T., Baldi, L., Bartolini, E., & Capecchi, B. (2000). Identification of vaccine candidates against serogroup B meningococcus by whole-genome sequencing. Science, 287(5459), 1816–1820.CrossRefGoogle Scholar
  38. 38.
    Mohabatkar, H., & Mohammadzadegan, R. (2007). Computational comparison of T-cell epitopes of gp120 of Iranian HIV-1 with different subtypes of the virus. Pakistan Journal of Biological Sciences, 10(23), 4295–4298.CrossRefGoogle Scholar
  39. 39.
    Moosavi, F., Mohabatkar, H., & Mohsenzadeh, S. (2010). Computer-aided analysis of structural properties and epitopes of Iranian HPV-16 E7 oncoprotein. Interdisciplinary Sciences, Computational Life Science, 2(4), 367–372.CrossRefGoogle Scholar
  40. 40.
    Zhang, X. W. (2013). Brief communication A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes. Computational Biology and Chemistry, 45, 30–35.  https://doi.org/10.1016/j.compbiolchem.2013.03.003.CrossRefGoogle Scholar
  41. 41.
    Mahdavi, M., Keyhanfar, M., Jafarian, A., Mohabatkar, H., & Rabbani, M. (2014). Immunization with a novel chimeric peptide representing B and T cell epitopes from HER2 extracellular domain (HER2 ECD) for breast cancer. Tumor Biology, 35(12), 12049–12057.CrossRefGoogle Scholar
  42. 42.
    Ebrahimi, S., & Mohabatkar, H. (2018). Prediction of T-cell epitopes for designing a reverse vaccine against streptococcal bacteria. Molecular Biology Research Communications, 7, 35–41.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Samira Ebrahimi
    • 1
  • Hassan Mohabatkar
    • 1
  • Mandana Behbahani
    • 1
  1. 1.Department of Biotechnology, Faculty of Advanced Sciences and TechnologiesUniversity of IsfahanIsfahanIran

Personalised recommendations