A Novel Chimeric Antigen as a Vaccine Candidate against Leishmania major: In silico Analysis
,
Abstract
Background: Leishmania is a mandatory intracellular pathogen and causing neglected disease. Hence, protection against leishmaniasis by a development vaccine is an important subject. This study aimed to design a poly-epitope vaccine for cutaneous leishmaniasis.
Methods: The present study was conducted in the Parasitology Department of Tarbiat Modares University, Tehran, Iran during 2017-2019. Several bioinformatics methods at online servers were used for prediction of different aspects of poly-epitope, including, physico-chemical attributes, allergenicity, antigenicity, secondary and tertiary structures, B-cell, T-cell and MHC (I, II) potential epitopes of LACK, LEIF, GP63 and SMT antigens of L. major.
Results: After designing the construct (GLSL), the outputs of PTM sites demonstrated that the poly-epitope had 57 potential sites for phosphorylation. Furthermore, the secondary of GLSL structure includes 59.42%, 20.94% and 19.63% for random coil, extended strand and alpha-helix, respectively. The GLSL is an immunogenic protein with an acceptable antigenicity (0.8410) and non-allergen. Afterward, 20 potential epitopes of LACK, LEIF, GP63 and SMT antigens were linked by a flexible linker (SAPGTP), then was synthesized, and sub-cloned in pLEXY– neo2. The results were confirmed the expression of 38.7 kDa poly-epitope in secretory and cytosolic sites, separately.
Conclusion: A good expression in the L. tarentulae and confirmation of the GLSL poly-epitope could be a basis for developing a vaccine candidate against leishmaniasis that should be confirmed via experimental tests in BALB/c mice.
1. Rabhi I, Rabhi S, Ben-Othman R, et al. Comparative analysis of resistant and susceptible macrophage gene expression response to Leishmania major parasite. BMC Genomics. 2013;14:723.
2. Jeibouei S, Bandehpour M, Kazemi B, et al. Designing a DNA vaccine-based Leishmania major polytope (Preliminary report). Iran J Parasitol. 2017;12(3):441–5.
3. Flohé SB, Bauer C, Flohé S. Antigen-pulsed epidermal Langerhans cells protect susceptible mice from infection with the intracellular parasite Leishmania major. Eur J Immunol. 1998;28(11):3800–11.
4. 4Sundar S, Singh B. Identifying vaccine targets for anti-leishmanial vaccine development. Expert Rev Vaccines. 2014;13(4):489–505.
5. 5Seyed N, Zahedifard F, Safaiyan S, et al. In silico analysis of six known Leishmania major antigens and in vitro evaluation of specific epitopes eliciting HLA-A2 restricted CD8 T cell response. PLoS Negl Trop Dis. 2011;5(9):e1295
6. Kazi A, Chuah C, Majeed ABA, et al. Current progress of immunoinformatics approach harnessed for cellular- and antibody-dependent vaccine design. Pathog Glob Health. 2018;112(3):123–131.
7. Parker JMR, Guo D, Hodges RS. New Hydrophilicity Scale Derived from High-Performance Liquid Chromatography Peptide Retention Data: Correlation of Predicted Surface Residues with Antigenicity and X-ray-Derived Accessible Sites. Biochemistry. 1986;25(19):5425–32.
8. Emini EA, Hughes J V, Perlow DS, et al. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol. 1985;55(3):836–9.
9. Karplus PA, Schulz GE. Prediction of chain flexibility in proteins - A tool for the selection of peptide antigens. Naturwissenschaften. 1985;72:212–3.
10. Saha S, Raghava GPS. BcePred: Prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties. Lect Notes Comput Sci. 2004;3239:197–204.
11. Saha S, Raghava GPS. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins. 2006;65(1):40–8.
12. Larsen JEP, Lund O, Nielsen M. Improved method for predicting linear B-cell epitopes. Immunome Res. 2006;2:2.
13. Chou PY, Fasman GD. Prediction of the Secondary Structure of Proteins From Their Amino Acid Sequence. Adv Enzymol Relat Areas Mol Biol. 1978;47:45–148.
14. Kolaskar AS, Tongaonkar PC. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett. 1990;276(1–2):172–4.
15. Fahimi H, Sadeghizadeh M, Mohammadipour M. In silico analysis of an envelope domain III-based multivalent fusion protein as a potential dengue vaccine candidate . Clin Exp Vaccine Res. 2016;5(1):41-9.
16. Garnier J, Gibrat JF, Robson B. GOR method for predicting protein secondary structure from amino acid sequence. Methods Enzymol. 1996;266:540–53.
17. Ferrè F, Clote P. DiANNA: A web server for disulfide connectivity prediction. Nucleic Acids Res. 2005;33(Web Server issue):W230–2.
18. Guex N, Peitsch MC, Schwede T. Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: A historical perspective. Electrophoresis. 2009;30 Suppl 1:S162–73.
19. Zhou J, Wang L, Zhou A, et al. Bioinformatics analysis and expression of a novel protein ROP48 in Toxoplasma gondii. Acta Parasitol. 2016;61(2):319–28.
20. Xu D, Zhang Y. Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J. 2011;101(10):2525–34.
21. Foroutan M, Ghaffarifar F, Sharifi Z, et al. Bioinformatics analysis of ROP8 protein to improve vaccine design against Toxoplasma gondii. Infect Genet Evol. 2018;62:193–204.
22. Narula A, Pandey RK, Khatoon N, et al. Excavating chikungunya genome to design B and T cell multi-epitope subunit vaccine using comprehensive immunoinformatics approach to control chikungunya infection. Infect Genet Evol. 2018;61:4–15.
23. Gasteiger E, Hoogland C, Gattiker A, et al. Protein Identification and Analysis Tools on the ExPASy Server. In: The Proteomics Protocols Handbook. 2005: 571–607.
24. Magnan CN, Randall A, Baldi P. SOLpro: Accurate sequence-based prediction of protein solubility. Bioinformatics. 2009;25(17):2200–7.
25. Magnan CN, Zeller M, Kayala MA, et al. High-throughput prediction of protein antigenicity using protein microarray data. Bioinformatics. 2010;26(23):2936–43.
26. Doytchinova IA, Flower DR. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics. 2007;8:4.
27. Saha S, Raghava GPS. AlgPred: Prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res. 2006;34(Web Server issue):W202-9.
28. Dodangeh S, Fasihi-Ramandi M, Daryani A, et al. In silico analysis and expression of a novel chimeric antigen as a vaccine candidate against Toxoplasma gondii. Microb Pathog. 2019;132:275–81.
29. Siavashi V, Sariri R, Nassiri SM, et al. Angiogenic activity of endothelial progenitor cells through angiopoietin-1 and angiopoietin-2. Animal Cells Syst (Seoul). 2016;20(3):118–29.
30. Romano P, Giugno R, Pulvirenti A. Tools and collaborative environments for bioinformatics research. Brief Bioinform. 2011;12(6):549–61.
31. Jiang P, Cai Y, Chen J, et al. Evaluation of tandem Chlamydia trachomatis MOMP multi-epitopes vaccine in BALB/c mice model. Vaccine. 2017;35(23):3096–103.
32. Barhoumi M, Tanner NK, Banroques J, et al. Leishmania infantum LeIF protein is an ATP-dependent RNA helicase and an eIF4A-like factor that inhibits translation in yeast. FEBS J. 2006;273(22):5086–100.
33. Méndez S, Gurunathan S, Kamhawi S, et al. The Potency and Durability of DNA- and Protein-Based Vaccines Against Leishmania major Evaluated Using Low-Dose, Intradermal Challenge . J Immunol. 2001;166(8):5122–8.
34. Skeiky YAW, Coler RN, Brannon M, et al. Protective efficacy of a tandemly linked, multi-subunit recombinant leishmanial vaccine (Leish-111f) formulated in MPL® adjuvant. Vaccine. 2002;20(27–28):3292–303.
35. Goto Y, Bogatzki LY, Bertholet S, et al. Protective immunization against visceral leishmaniasis using Leishmania sterol 24-c-methyltransferase formulated in adjuvant. Vaccine. 2007;25(42):7450–8.
36. Topuzogullari M, Cakir Koc R, Dincer Isoglu S, et al. Conjugation, characterization and toxicity of lipophosphoglycan-polyacrylic acid conjugate for vaccination against leishmaniasis. J Biomed Sci. 2013;20(1)35.
37. Mazumder S, Maji M, Das A, et al. Potency, efficacy and durability of DNA/DNA, DNA/ protein and protein/protein based vaccination using gp63 against Leishmania donovani in BALB/c mice. PLoS One. 2011;6(2):e14644.
38. Ghaffarifar F, Jorjani O, Sharifi Z, et al. Enhancement of immune response induced by DNA vaccine cocktail expressing complete LACK and TSA genes against Leishmania major. APMIS. 2013;121(4):290–8.
39. Goto Y, Bhatia A, Raman VS, et al. Leishmania infantum sterol 24-c-methyltransferase formulated with MPL-SE induces cross-protection against L. major infection. Vaccine. 2009;27(21):2884–90.
40. Nezafat N, Ghasemi Y, Javadi G, et al. A novel multi-epitope peptide vaccine against cancer: An in silico approach. J Theor Biol. 2014;349:121–34.
2. Jeibouei S, Bandehpour M, Kazemi B, et al. Designing a DNA vaccine-based Leishmania major polytope (Preliminary report). Iran J Parasitol. 2017;12(3):441–5.
3. Flohé SB, Bauer C, Flohé S. Antigen-pulsed epidermal Langerhans cells protect susceptible mice from infection with the intracellular parasite Leishmania major. Eur J Immunol. 1998;28(11):3800–11.
4. 4Sundar S, Singh B. Identifying vaccine targets for anti-leishmanial vaccine development. Expert Rev Vaccines. 2014;13(4):489–505.
5. 5Seyed N, Zahedifard F, Safaiyan S, et al. In silico analysis of six known Leishmania major antigens and in vitro evaluation of specific epitopes eliciting HLA-A2 restricted CD8 T cell response. PLoS Negl Trop Dis. 2011;5(9):e1295
6. Kazi A, Chuah C, Majeed ABA, et al. Current progress of immunoinformatics approach harnessed for cellular- and antibody-dependent vaccine design. Pathog Glob Health. 2018;112(3):123–131.
7. Parker JMR, Guo D, Hodges RS. New Hydrophilicity Scale Derived from High-Performance Liquid Chromatography Peptide Retention Data: Correlation of Predicted Surface Residues with Antigenicity and X-ray-Derived Accessible Sites. Biochemistry. 1986;25(19):5425–32.
8. Emini EA, Hughes J V, Perlow DS, et al. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol. 1985;55(3):836–9.
9. Karplus PA, Schulz GE. Prediction of chain flexibility in proteins - A tool for the selection of peptide antigens. Naturwissenschaften. 1985;72:212–3.
10. Saha S, Raghava GPS. BcePred: Prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties. Lect Notes Comput Sci. 2004;3239:197–204.
11. Saha S, Raghava GPS. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins. 2006;65(1):40–8.
12. Larsen JEP, Lund O, Nielsen M. Improved method for predicting linear B-cell epitopes. Immunome Res. 2006;2:2.
13. Chou PY, Fasman GD. Prediction of the Secondary Structure of Proteins From Their Amino Acid Sequence. Adv Enzymol Relat Areas Mol Biol. 1978;47:45–148.
14. Kolaskar AS, Tongaonkar PC. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett. 1990;276(1–2):172–4.
15. Fahimi H, Sadeghizadeh M, Mohammadipour M. In silico analysis of an envelope domain III-based multivalent fusion protein as a potential dengue vaccine candidate . Clin Exp Vaccine Res. 2016;5(1):41-9.
16. Garnier J, Gibrat JF, Robson B. GOR method for predicting protein secondary structure from amino acid sequence. Methods Enzymol. 1996;266:540–53.
17. Ferrè F, Clote P. DiANNA: A web server for disulfide connectivity prediction. Nucleic Acids Res. 2005;33(Web Server issue):W230–2.
18. Guex N, Peitsch MC, Schwede T. Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: A historical perspective. Electrophoresis. 2009;30 Suppl 1:S162–73.
19. Zhou J, Wang L, Zhou A, et al. Bioinformatics analysis and expression of a novel protein ROP48 in Toxoplasma gondii. Acta Parasitol. 2016;61(2):319–28.
20. Xu D, Zhang Y. Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J. 2011;101(10):2525–34.
21. Foroutan M, Ghaffarifar F, Sharifi Z, et al. Bioinformatics analysis of ROP8 protein to improve vaccine design against Toxoplasma gondii. Infect Genet Evol. 2018;62:193–204.
22. Narula A, Pandey RK, Khatoon N, et al. Excavating chikungunya genome to design B and T cell multi-epitope subunit vaccine using comprehensive immunoinformatics approach to control chikungunya infection. Infect Genet Evol. 2018;61:4–15.
23. Gasteiger E, Hoogland C, Gattiker A, et al. Protein Identification and Analysis Tools on the ExPASy Server. In: The Proteomics Protocols Handbook. 2005: 571–607.
24. Magnan CN, Randall A, Baldi P. SOLpro: Accurate sequence-based prediction of protein solubility. Bioinformatics. 2009;25(17):2200–7.
25. Magnan CN, Zeller M, Kayala MA, et al. High-throughput prediction of protein antigenicity using protein microarray data. Bioinformatics. 2010;26(23):2936–43.
26. Doytchinova IA, Flower DR. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics. 2007;8:4.
27. Saha S, Raghava GPS. AlgPred: Prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res. 2006;34(Web Server issue):W202-9.
28. Dodangeh S, Fasihi-Ramandi M, Daryani A, et al. In silico analysis and expression of a novel chimeric antigen as a vaccine candidate against Toxoplasma gondii. Microb Pathog. 2019;132:275–81.
29. Siavashi V, Sariri R, Nassiri SM, et al. Angiogenic activity of endothelial progenitor cells through angiopoietin-1 and angiopoietin-2. Animal Cells Syst (Seoul). 2016;20(3):118–29.
30. Romano P, Giugno R, Pulvirenti A. Tools and collaborative environments for bioinformatics research. Brief Bioinform. 2011;12(6):549–61.
31. Jiang P, Cai Y, Chen J, et al. Evaluation of tandem Chlamydia trachomatis MOMP multi-epitopes vaccine in BALB/c mice model. Vaccine. 2017;35(23):3096–103.
32. Barhoumi M, Tanner NK, Banroques J, et al. Leishmania infantum LeIF protein is an ATP-dependent RNA helicase and an eIF4A-like factor that inhibits translation in yeast. FEBS J. 2006;273(22):5086–100.
33. Méndez S, Gurunathan S, Kamhawi S, et al. The Potency and Durability of DNA- and Protein-Based Vaccines Against Leishmania major Evaluated Using Low-Dose, Intradermal Challenge . J Immunol. 2001;166(8):5122–8.
34. Skeiky YAW, Coler RN, Brannon M, et al. Protective efficacy of a tandemly linked, multi-subunit recombinant leishmanial vaccine (Leish-111f) formulated in MPL® adjuvant. Vaccine. 2002;20(27–28):3292–303.
35. Goto Y, Bogatzki LY, Bertholet S, et al. Protective immunization against visceral leishmaniasis using Leishmania sterol 24-c-methyltransferase formulated in adjuvant. Vaccine. 2007;25(42):7450–8.
36. Topuzogullari M, Cakir Koc R, Dincer Isoglu S, et al. Conjugation, characterization and toxicity of lipophosphoglycan-polyacrylic acid conjugate for vaccination against leishmaniasis. J Biomed Sci. 2013;20(1)35.
37. Mazumder S, Maji M, Das A, et al. Potency, efficacy and durability of DNA/DNA, DNA/ protein and protein/protein based vaccination using gp63 against Leishmania donovani in BALB/c mice. PLoS One. 2011;6(2):e14644.
38. Ghaffarifar F, Jorjani O, Sharifi Z, et al. Enhancement of immune response induced by DNA vaccine cocktail expressing complete LACK and TSA genes against Leishmania major. APMIS. 2013;121(4):290–8.
39. Goto Y, Bhatia A, Raman VS, et al. Leishmania infantum sterol 24-c-methyltransferase formulated with MPL-SE induces cross-protection against L. major infection. Vaccine. 2009;27(21):2884–90.
40. Nezafat N, Ghasemi Y, Javadi G, et al. A novel multi-epitope peptide vaccine against cancer: An in silico approach. J Theor Biol. 2014;349:121–34.
| Files | ||
| Issue | Vol 16 No 2 (2021) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijpa.v16i2.6267 | |
| Keywords | ||
| Leishmania major Chimeric antigen Vaccine In silico analysis | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Bavarsad Ahmadpour N, Dalimi A, Pirestani M, Sadraei J. A Novel Chimeric Antigen as a Vaccine Candidate against Leishmania major: In silico Analysis. Iran J Parasitol. 2021;16(2):186-198.
