In-silico Studies Reveal Potential Epitope based Vaccine against M.leprae Phosphoglycerate Mutase Protein

Authors

  • Renata Triwijaya Bioinformatics Department, School of Life Science, Indonesia International Institute for Life Sciences, Jakarta 13210, Indonesia
  • Winda Hasuki Bioinformatics Department, School of Life Science, Indonesia International Institute for Life Sciences, Jakarta 13210, Indonesia
  • Gabriele Mustika Kresnia Bioinformatics Department, School of Life Science, Indonesia International Institute for Life Sciences, Jakarta 13210, Indonesia
  • Jacqulin Natasya Bioinformatics Department, School of Life Science, Indonesia International Institute for Life Sciences, Jakarta 13210, Indonesia
  • Putri Gabriella Angel Natalia Satya Bioinformatics Department, School of Life Science, Indonesia International Institute for Life Sciences, Jakarta 13210, Indonesia
  • Arli Aditya Parikesit Department of BioinformaticsSchool of Life SciencesIndonesia International Institute for Life SciencesJl. Pulomas Barat Kav.88Jakarta 13210

DOI:

https://doi.org/10.11113/mjfas.v18n1.2286

Keywords:

computational, leprosy, phosphoglycerate mutase, vaccine, epitopes, CTL, MHC

Abstract

Leprosy is an infectious disease caused by Mycobacterium leprae that mainly affects the skin, peripheral nerve, mucosa of the upper respiratory tract, and eyes. There is no vaccine designed specifically to prevent leprosy. The most common vaccine strategy is Bacille Calmette-Guérin (BCG), however its efficacy is highly variable between studies. Current study utilized a computational method to predict antigenic epitopes from Mycobacterium leprae for peptide vaccine development. Molecular docking of top predicted peptides from 6 antigenic B-cell and 3 CTL epitopes were analyzed. These predicted antigenic epitopes might potentially be target peptides for future leprosy vaccines.

References

Wibawa, T., & Satoto, T. B. (2016). Magnitude of Neglected Tropical Diseases in Indonesia at Postmillennium Development Goals Era. Journal of tropical medicine, 2016, 5716785.

A. Tiwari, S. Dandel, R. Djupuri, L. Mieras, J.H. Richardus, Population-wide administration of single dose rifampicin for leprosy prevention in isolated communities: A three year follow-up feasibility study in Indonesia, BMC Infect. Dis. 18 (2018) 1–8.

Coppola M, van den Eeden SJF, Robbins N, et al. Vaccines for Leprosy and Tuberculosis: Opportunities for Shared Research, Development, and Application. Front Immunol. 2018;9:308. Published 2018 Feb 26.

SAGE Working Group. Report on BCG vaccine use for protection against mycobacterial infections including tuberculosis, leprosy, and other nontuberculous mycobacteria (NTM) infections. WHO. Published 2017 Sep 22.

Duthie, M. S., Gillis, T. P., & Reed, S. G. (2011). Advances and hurdles on the way toward a leprosy vaccine. Human vaccines, 7(11), 1172–1183.

Jaiswal AK, Tiwari S, Jamal SB, et al. Reverse vaccinology and subtractive genomics approaches for identifying common therapeutics against Mycobacterium leprae and Mycobacterium lepromatosis. J Venom Anim Toxins Incl Trop Dis. 2021;27:e20200027. Published 2021 Apr 9.

van der Oost J, Martijn A. Huynen, Corné H. Verhees, Molecular characterization of phosphoglycerate mutase in archaea, FEMS Microbiology Letters, Volume 212, Issue 1, 2002, Pages 111–120.

Emini EA, Hughes JV, Perlow DS, Boger J. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol. 1985;55(3):836-839.

Karplus PA, Schulz GE. Prediction of Chain Flexibility in Proteins - A tool for the Selection of Peptide Antigens. Naturwissenschaften 1985; 72:212-3.

Ponomarenko JV, Bui H, Li W, Fusseder N, Bourne PE, Sette A, Peters B. 2008. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics 9:514.

Lamiable A, Thévenet P, Rey J, Vavrusa M, Derreumaux P, Tufféry P. PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Res. 2016 Jul 8;44(W1):W449-54.

Shen Y, Maupetit J, Derreumaux P, Tufféry P. Improved PEP-FOLD approach for peptide and miniprotein structure prediction J. Chem. Theor. Comput. 2014; 10:4745-4758

Thévenet P, Shen Y, Maupetit J, Guyon F, Derreumaux P, Tufféry P. PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides. Nucleic Acids Res. 2012. 40, W288-293.

Larsen MV, Lundegaard C, Lamberth K, Buus S, Lund O, Nielsen M. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinformatics. 2007; 8:424.

Duhovny D, Nussinov R, Wolfson HJ. Efficient Unbound Docking of Rigid Molecules. In Gusfield et al., Ed. Proceedings of the 2'nd Workshop on Algorithms in Bioinformatics(WABI) Rome, Italy, Lecture Notes in Computer Science 2452, pp. 185-200, Springer Verlag, 2002

Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucl. Acids. Res. 33: W363-367, 2005.

N. Andrusier, R. Nussinov and H. J. Wolfson. FireDock: Fast Interaction Refinement in Molecular Docking. Proteins (2007), 69(1):139-159.

E. Mashiach, D. Schneidman-Duhovny, N. Andrusier, R. Nussinov, H. J. Wolfson. FireDock: a web server for fast interaction refinement in molecular docking. Nucleic Acids Res. (2008), 36(Web Server issue):W229-32.

The PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLC.

Bioinformatics 30, 2981-2982. Molecular graphics created with YASARA (www.yasara.org).

Pettersen, E.F.; Goddard, T.D.; Huang, C.C.; Couch, G.S.; Greenblatt, D.M.; Meng, E.C.; Ferrin, T.E., UCSF Chimera—a visualization system for exploratory research and analysis. Journal of computational chemistry, 2004, 25, (13), 1605-1612.

Tahir RA, Wu H, Rizwan MA, Jaffar TH, Saleem S, & Sehgal SA. Immunoinformatics and molecular docking studies reveal potential Epitope based Peptide Vaccine against DENV-NS3 Protein. Journal of Theoretical Biology. 2018.

Fishman JM, Wiles K, Wood KJ. The Acquired Immune System Response to Biomaterials, Including Both Naturally Occurring and Synthetic Biomaterials. Host Response to Biomaterials. Academic Press 2015; 151-187.

Janeway CA Jr, Travers P, Walport M, et al. Immunobiology: The Immune System in Health and Disease. 5th edition. New York: Garland Science; 2001. The major histocompatibility complex and its functions.

Larsen, M.V.; Lundegaard, C.; Lamberth, K.; Buus, S.; Lund, O.; Nielsen, M., Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC bioinformatics, 2007, 8, 424

Ponomarenko J, Bui HH, Li W, et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics. 2008;9:514. Published 2008 Dec 2.

Mirza MU, Rafique S, Ali A, Munir M, Ikram N, Manan A, Salo-Ahen OM, Idrees M. Towards peptide vaccines against Zika virus: Immunoinformatics combined with molecular dynamics simulations to predict antigenic epitopes of Zika viral proteins. Scientific reports, 2016, 6, 37313.

Gupta, E., Gupta, S.R.R. & Niraj, R.R.K. Identification of Drug and Vaccine Target in Mycobacterium leprae: A Reverse Vaccinology Approach. Int J Pept Res Ther 26, 1313–1326 (2020).

Downloads

Published

28-02-2022