In-silico epitope based vaccine an excellent solution against Marburg virus

Authors

  • Anum Munir Department of Bioinformatics Government Post Graduate College Mandian, Abbottabad, Pakistan
  • Shumaila Azam Department of Bioinformatics Government Post Graduate College Mandian, Abbottabad, Pakistan. Department of Bioinformatics and bioscience, Capital University of Sciences and Technology, Islamabad, Pakistan
  • Maria Manzoor Department of Bioinformatics Government Post Graduate College Mandian, Abbottabad, Pakistan
  • Azhar Mehmood Department of Bioinformatics Government Post Graduate College Mandian, Abbottabad, Pakistan
  • G. Mujtaba Shah Department of Botany, Hazara University, Mansehra, Pakistan
  • Sahar Fazal Department of Bioinformatics and bioscience, Capital University of Sciences and Technology, Islamabad, Pakistan

DOI:

https://doi.org/10.56511/JIPBS.2016.v3.i04.pp61-66

Abstract

Marburg virus is the most deadly virus identified in the year 1967. MARV comprises a 19-KB noninfectious single-stranded RNA genome that encodes seven basic structural proteins. Right now; there is no accessible treatment or vaccine to cure the MARV disease since couples of immunizations are under clinical trial studies. Here the computational approach is used to predict multi-epitope vaccine candidates against this virus. Both T cell and B cell were checked for the peptides to confirm that they can bring both humoral and cell-mediated immunity. A 9mer T cell epitope ALSLTCAGI interacted with most of the MHC-I alleles and 10mer B cell epitope SLFVQAALYV was identified on antigenicity prediction. The regions; 7 – 12, 139 – 144 and 186 – 191 amino acid residues were more accessible as B cell epitopes. The predicted epitope docked with the human HLA:*A0201. On the basis of the computational analysis, the predicted epitope can be the best accessible solution to cure infections of Marburg virus

Keywords:

Epitope, HLA, MHC, docking, allergenicity, antigenic

Downloads

Download data is not yet available.

References

Friedrich, B. M., Trefry, J. C., Biggins, J. E., Hensley, L. E., Honko, A. N., Smith, D. R., & Olinger, G. G.: Potential Vaccines and Post-Exposure Treatments for Filovirus Infections. Viruses 2012; 4(12): 1619 – 1650. http://doi.org/10.3390/v4091619

Slenczka, W., & Klenk, H. D.: Forty Years of Marburg Virus. The Journal of Infectious Diseases 2007; 196(s2): S131 – S135. http://doi.org/10.1086/520551

Brauburger, K., Hume, A. J., Mühlberger, E., & Olejnik, J.: Forty-Five Years of Marburg Virus Research. Viruses 2012; 4(12): 1878 – 1927. http://doi.org/10.3390/v4101878

Mehedi, M., Groseth, A., Feldmann, H., & Ebihara, H.: Clinical aspects of Marburg hemorrhagic fever. Future Virology 2011; 6(9): 1091 – 1106. http://doi.org/10.2217/fvl.11.79

Thi, E. P., Mire, C. E., Ursic-Bedoya, R., Geisbert, J. B., Lee, A. C., Agans, K. N., et al.: Marburg virus infection in nonhuman primates: Therapeutic treatment by lipid-encapsulated siRNA. Science Translational Medicine 2014; 6(250):250ra116.

Poland GA, Ovsyannikova IG, Jacobson RM.: Application of pharmacogenomics to vaccines. Pharmacogenomics 2009; 10(5):837 – 852.

Flower DR: Bioinformatics for Vaccinology. Chichester: John Wiley & Sons, Ltd. 2008.

Bourdette DN, Edmonds E, Smith C, et al.: A highly immunogenic trivalent T cell receptor peptide vaccine for multiple sclerosis. Mult Scler. 2005; 11(5):552 – 561.

López JA, Weilenman C, Audran R, et al.: A synthetic malaria vaccine elicits a potent CD8(+) and CD4(+) T lymphocyte immune response in humans. Implications for vaccination strategies. Eur J Immunol. 2001; 31(7):1989 – 1998.

Knutson KL, Schiffman K, Disis ML.: Immunization with a HER-2/neu helper peptide vaccine generates HER-2/neu CD8 T-cell immunity in cancer patients. J Clin Invest. 2001; 107(4):477 – 484.

Petrovsky N, Brusic V.: Computational immunology: The coming of age. Immunol Cell Biol. 2002; 80(3):248 – 254.

Brusic V, Bajic VB, Petrovsky N.: Computational methods for prediction of T-cell epitopes – a framework for modelling, testing, and applications. Methods 2004; 34(4):436 – 443.

Peters B, Bulik S, Tampe R, Van Endert PM, Holzhütter HG.: Identifying MHC class I epitopes by predicting the TAP transport efficiency of epitope precursors. J Immunol. 2003; 171(4):1741 – 1749.

Bhasin M, Raghava GP.: Analysis and prediction of affinity of TAP binding peptides using cascade SVM. Protein Sci. 2004; 13(3):596 – 607.

Nielsen M, Lundegaard C, Lund O, Kesmir C.: The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage. Immunogenetics 2005; 57(1 – 2):33 – 41.

Oany, A., Pervin, T., & Emran, A.: Design of an epitope-based peptide vaccine against spike protein of human coronavirus: an in silico approach. Drug Design, Development and Therapy 2015; 1139. http://doi.org/10.2147/DDDT.S67861

Apweiler R, Bairoch A, Wu CH, et al.: UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2004; 32(Suppl 1):D115 – D119.

Doytchinova IA, Flower DR. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 2007; 8:4.

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.

Buus S, Lauemøller SL, Worning P, et al.: Sensitive quantitative predictions of peptideMHC binding by a ‘Query by Committee’ artificial neural network approach. Tissue Antigens 2003; 62(5):378 – 384.

Peters B, Sette A.: Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics 2005; 6:132.

Tenzer S, Peters B, Bulik S, et al.: Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding. Cell Mol Life Sci. 2005; 62:1025 – 1037.

Muh HC, Tong JC, Tammi MT.: AllerHunter: a SVM-pairwise system for assessment of allergenicity and allergic cross-reactivity in proteins. PLoS One 2009; 4(6):e5861.

Liao L, Noble WS.: Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships. J Comput Biol. 2003; 10(6):857 – 868.

Chen YPP, Wong L, editors.: A Better Gap Penalty for Pairwise-SVM: Proceedings of the 3rd Asia-Pacific Bioinformatics, Singapore, January 2005; 17 – 21.

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(Web Server issue):W288 – W293.

Berman HM, Westbrook J, Feng Z, et al.: The protein data bank. Nucleic Acids Res. 2000; 28(1):235 – 242.

Kolaskar AS, Tongaonkar PC.: A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett. 1990; 276(1 – 2):172 – 174.

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. Naturwissenschaften. 1985; 72(4):212 – 213.

Larsen JE, Lund O, Nielsen M.: Improved method for predicting linear Bcell epitopes. Immunome Res. 2006; 2(1):2.

Chou PY, Fasman GD.: Empirical predictions of protein conformation. Annu Rev Biochem. 1978; 47:251 – 276.

Marshall SJ.: Developing countries face double burden of disease. Bull World Health Organ 2004; 82(7):556.

De Groot AS: Rappuoli R.Genome-derived vaccines. Expert Rev Vaccines 2004; 3(1):59 – 76.

Korber B, LaBute M, Yusim K: Immunoinformatics comes of age. PLoS Comput Biol. 2006; 2(6):e71.

Fauci AS.: Emerging and re-emerging infectious diseases: influenza as a prototype of the host-pathogen balancing act. Cell 2006; 124(4):665 – 670.

Purcell AW, McCluskey J, Rossjohn J.: More than one reason to rethink the use of peptides in vaccine design. Nat Rev Drug Discov. 2007; 6(5): 404 – 414.

Shrestha B, Diamond MS.: Role of CD8+ T cells in control of West Nile virus infection. J Virol. 2005; 78(15):8312 – 8321.

McKeever TM, Lewis SA, Smith C, Hubbard R.: Vaccination and allergic disease: a birth cohort study. Am J Public Health 2004; 94(6):985 – 989.

Khan MK, Zaman S, Chakraborty S, et al.: In silico predicted mycobacterial epitope elicits in vitro T-cell responses. Mol Immunol. 2014; 61(1):16 – 22.

Published

05-10-2016
Statistics
225 Views | 152 Downloads

How to Cite

Anum Munir, Shumaila Azam, Maria Manzoor, Azhar Mehmood, G. Mujtaba Shah, and Sahar Fazal. “In-Silico Epitope Based Vaccine an Excellent Solution Against Marburg Virus”. Journal of Innovations in Pharmaceutical and Biological Sciences, vol. 3, no. 4, Oct. 2016, pp. 61-66, doi:10.56511/JIPBS.2016.v3.i04.pp61-66.

Issue

Section

Research Article