In silico analysis and expression of a new chimeric antigen as a vaccine candidate against cutaneous leishmaniasis

Document Type: Original Article


Parasitology Department, Medical Sciences Faculty, Tarbiat Modares University, Tehran, Iran



Objective(s): Since leishmaniasis is one of the health problems in many countries, the development of preventive vaccines against it is a top priority. Peptide vaccines may be a new way to fight the Leishmania infection. In this study, a silicon method was used to predict and analyze B and T cells to produce a vaccine against cutaneous leishmaniasis.
Materials and Methods: Immunodominant epitope of Leishmania were selected from four TSA, LPG3, GP63, and Lmsti1 antigens and linked together using a flexible linker (SAPGTP). The antigenic and allergenic features, 2D and 3D structures, and physicochemical features of a chimeric protein were predicted. Finally, through bioinformatics methods, the mRNA structure was predicted and was produced chemically and cloned into the pLEXY-neo2 vector.
Results: Results indicated, polytope had no allergenic properties, but its antigenicity was estimated to be 0.92%. The amino acids numbers, molecular weight as well as negative and positive charge residuals were estimated 390, ~41KDa, 41, and 30, respectively. The results showed that the designed polytope has 50 post-translationally modified sites. Also, the secondary structure of the protein is composed of 25.38% alpha-helix, 12.31% extended strand, and 62.31% random coil. The results of SDS-PAGE and Western blotting revealed the recombinant protein with ̴ 41 kDa. The results of Ramachandran plot showed that 96%, 2.7%, and 1.3% of amino acid residues were located in the preferred, permitted, and outlier areas, respectively.
Conclusion: It is expected that the TLGL polytope will produce a cellular immune response. Therefore, the polytope could be a good candidate for an anti-leishmanial vaccine.


1. Dumonteil E. DNA vaccines against protozoan parasites: Advances and challenges. J Biomed Biotechnol 2007; 90520: 1-11.
2. Méndez S, Belkaid Y, Seder RA, Sacks D. Optimization  of  DNA vaccination against cutaneous leishmaniasis. Vaccine 2002; 20:3702-3708.
3. Campos-Neto A, Webb JR, Greeson K, Coler RN, Skeiky YAW, Reed SG. Vaccination with plasmid DNA encoding TSA/LmSTI1 leishmanial fusion proteins confers protection against Leishmania major infection in susceptible BALB/c mice. Infect Immun 2002; 70:2828-2836.
4. Alvar J, Vélez ID, Bern C, Herrero M, Desjeux P, Cano J, et al. Leishmaniasis worldwide and global estimates of its incidence. PLoS ONE. 2012; 7:e35671.
5. Singh B, Sundar S. Leishmaniasis: Vaccine candidates and perspectives. Vaccine. 2012; 30:3834-3842.
6. Ahmed SBH, Bahloul C, Robbana C, Askri S, Dellagi K. A comparative evaluation of different DNA vaccine candidates against experimental murine leishmaniasis due to L. major. Vaccine 2004; 22:1631-1639.
7. Maspi N, Abdoli A, Ghaffarifar F. Pro- and anti-inflammatory cytokines in cutaneous leishmaniasis: a review. Pathog and Glob Health. 2016; 110:247-260.
8. Kedzierski L, Sakthianandeswaren A, Curtis J, Andrews P, Junk P, Kedzierska K. Leishmaniasis: Current treatment and prospects for new drugs and vaccines. Curr Med Chem 2009; 16:599-614.
9. Ashford RB. Leishmaniasis in the old world. In: Cox F, Kreier JW, Editors. Microbiology and Microbial Infections. 9th Ed. New York: Arnold pub; 1998; 215-240.
10. Descoteaux A Turco SJ. The lipophosphoglycan of  Leishmania and macrophage protein kinase C. Parasitol Today 1993; 9:468-471.
11. Coler RN, Skeiky YAW, Bernards K, Greeson K, Carter D, Cornellison CD, et al. Immunization with a polyprotein vaccine consisting of the T-cell antigens thiol-specific antioxidant, Leishmania major stress-inducible protein 1, and Leishmania elongation initiation factor protects against leishmaniasis. Infect Immun 2002; 70:4215-4225.
12. Kazi A, Chuah C, Majeed ABA, Leow CH, Lim BH, Leow CY. Current progress of immunoinformatics approach harnessed for cellular- and antibody-dependent vaccine design. Pathog Glob Health. 2018; 112:123-131.
13. El-Manzalawy Y, Dobbs D, Honavar V. Predicting linear B-cell epitopes using string kernels. J Mol Recognit 2008; 21:243-255.
14. Nezafat N, Ghasemi Y, Javadi G, Khoshnoud MJ, Omidinia E. A novel multi-epitope peptide vaccine against cancer: An in silico approach. J Theor Biol 2014; 349:121–134.
15. 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.
16. Larsen JEP, Lund O, Nielsen M. Improved method for predicting linear B-cell epitopes. Immunome Res 2006; 2:2.
17. 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; 25:5425-5432.
18. Chou PY, Fasman GD. Prediction of the secondary structure of proteins from their amino acid sequence. In: Meister A. (Editor). Advances in enzymology and related areas of molecular biology.Wiley online Library, 2006;47:45-148.
19. Emini EA, Hughes J V, Perlow DS, Boger J. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol 1985; 55:836-839.
20. Karplus PA, Schulz GE. Prediction of chain flexibility in proteins-A tool for the selection of peptide antigens. Naturwissenschaften 1985; 72:212-213.
21. Kolaskar AS, Tongaonkar PC. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett 1990; 276:172-174.
22. Saha, Raghava GPS. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins Struct Funct Genet 2006; 65:40-48.
23. Bhasin M, Raghava GPS. Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine 2004; 22:3195–3204.
24. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, et al. Protein identification and analysis tools on the ExPASy Server. In: The proteomics protocols handbook. 2005; 571-607.
25. Zhou J, Wang L, Zhou A, Lu G, Li Q, Wang Z, et al. Bioinformatics analysis and expression of a novel protein ROP48 in Toxoplasma gondii. Acta Parasitol 2016; 61:319-328.
26. Sen TZ, Jernigan RL, Garnier J, Kloczkowski A. GOR V server for protein secondary structure prediction. Bioinformatics 2005; 21:2787-2788.
27. 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; 5:41.
28. Ferrè F, Clote P. DiANNA: A web server for disulfide connectivity prediction. Nucleic Acids Res 2005; 33:230-232.
29. Guex N, Peitsch MC, Schwede T. Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: A historical perspective. Electrophoresis 2009; 30:162-173.
30. 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; 101:2525-2534.
31. Lovell SC, Davis IW, Iii WBA, de Bakker PIW, Word JM, Prisant MG, et al. Structure validation by Calpha geometry: Phipsi and Cbeta deviation. Proteins 2003; 50:437-450.
32. Magnan CN, Zeller M, Kayala MA, Vigil A, Randall A, Felgner PL, et al. High-throughput prediction of protein antigenicity using protein microarray data. Bioinformatics 2010; 26:2936-2943.
33. Doytchinova IA, Flower DR. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 2007; 8:4.
34. Saha S, Raghava GPS. AlgPred: Prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res 2006; 34:202–209.
35. Magnan CN, Randall A, Baldi P. SOLpro: Accurate sequence-based prediction of protein solubility. Bioinformatics 2009; 25:2200–2207.
36. Papadopoulou B, Roy G, Ouellette M. A novel antifolate resistance gene on the amplified H circle of Leishmania. EMBO J 1992; 11:3601–3608.
37. Nasiri V, Dalimi A, Ghaffarifar F, Bolhassani A. Immunogenicity and efficacy of live Lishmania tarentolae expressing KMP11-NTGP96-GFP fusion as a vaccine candidate against experimental visceral leishmaniasis caused by Lishmania infantum. Iran  J Parasitol 2016; 11:144–158.
38. Siavashi V, Sariri R, Nassiri SM, Esmaeiliv,  M, Asadian S, Cheraghi H, et al. Angiogenic activity of endothelial progenitor cells through angiopoietin-1 and angiopoietin-2. Animal Cells Syst  2016; 20:118–129.
39. Coligan JE. Short protocols in protein science: a compendium of methods from current protocols in protein science. John Wiley & Sons Inc; 2003.
40. Shahbazi M, Haghkhah M, Rahbar MR, Nezafat N, Ghasemi Y. In silico sub-unit hexavalent peptide vaccine against an Staphylococcus aureus biofilm-related infection. Int J Pept Res Ther 2016; 22:101–117.
41. Mahmoodi S, Nezafat N, Barzegar A, Negahdaripour M, R. Nikanfar A, Zarghami N, et al. Harnessing bioinformatics for designing a novel multiepitope peptide vaccine against breast cancer. Curr Pharm Biotechnol 2016; 17:1100–1114.
42. Farhadi T, Nezafat N, Ghasemi Y, Karimi Z, Hemmati S, Erfani N. Designing of complex multi-epitope peptide vaccine based on omps of Klebsiella pneumoniae: An in silico approach. Int J Pept Res Ther 2015; 21:325-341.
43. Negahdaripour M, Golkar N, Hajighahramani N, Kianpour S, Nezafat N, Ghasemi Y. Harnessing self-assembled peptide nanoparticles in epitope vaccine design. Biotechnol Adv 2017; 35:575–596.
44. Negahdaripour M, Nezafat N, Hajighahramani N, Soheil Rahmatabadi S, Hossein Morowvat M, Ghasemi Y. In silico study of different signal peptides for secretory production of interleukin-11 in Escherichia coli. Curr Proteomics 2017; 14:112-121.
45. Negahdaripour M, Nezafat N, Ghasemi Y. A panoramic review and in silico analysis of IL-11 structure and function. Cytokine Growth Factor Rev 2016; 32:41-61.
46. Negahdaripour M, Nezafat N, Hajighahramani N, Rahmatabadi SS, Ghasemi Y. Investigating CRISPR-Cas systems in Clostridium botulinum via bioinformatics tools. Infect Genet Evol 2017; 54:355-373.
47. Irajie C, Mohkam M, Nezafat N, Hosseinzadeh S, Aminlari M, Ghasemi Y. In silico analysis of glutaminase from different species of Escherichia and Bacillus. Iran J Basic Med Sci 2016; 41:406-414.
48. Rahmatabadi SS, Sadeghian I, Nezafat N, Negahdaripour M, Hajighahramani N, Hemmati S, et al. In silico investigation of pullulanase enzymes from various Bacillus species. Curr Proteomics 2017; 14:175-185.
49. Mousavi P, Mostafavi-Pour Z, Morowvat MH, Nezafat N, Zamani M, Berenjian A, et al. In silico analysis of several signal peptides for the excretory production of reteplase in Escherichia coli. Curr Proteomics 2017; 14:326-335.
50. Adu-Bobie J, Capecchi B, Serruto D, Rappuoli R, and Pizza M. Two years into reverse vaccinology. In: Vaccine 2003; 605-610.
51. Delany I, Rappuoli R, Seib KL. Vaccines, reverse vaccinology, and bacterial pathogenesis. Cold Spring Harb Perspect Med 2013; 3: a012476.
52. Jeibouei S, Bandehpour M, Kazemi B, Haghighi A. Designing a DNA vaccine-based Leishmania major polytope (Preliminary report). Iran J Parasitol 2017; 12:441-445.
53. Kashyap M, Jaiswal V, Farooq U. Prediction and analysis of promiscuous T cell-epitopes derived from the vaccine candidate antigens of Leishmania donovani binding to MHC class-II alleles using in silico approach. Infect Genet Evol 2017; 53:107-115.
54. Vakili B, Eslami M, Hatam GR, Zare B, Erfani N, Nezafat N, et al. Immunoinformatics-aided design of a potential multi-epitope peptide vaccine against Leishmania infantum. Int J Biol Macromol 2018; 120:1127-1139.
55. Berzofsky, Jay A, Berkower, Ira J. Immunogenicity and Antigen structure. In: Fundamental Immunology. 2012; 539-582.
56. Ikai A. Thermostability and aliphatic index of globular proteins. J Biochem 1980; 88:1895–1898.
57. Hajighahramani N, Nezafat N, Eslami M, Negahdaripour M, Rahmatabadi SS, Ghasemi Y. Immunoinformatics analysis and in silico designing of a novel multi-epitope peptide vaccine against Staphylococcus aureus. Infect Genet Evol 2017; 48:83-94.
58. Lee TY, Hsu JBK, Chang WC, Wang TY, Hsu PC, Huang H Da. A comprehensive resource for integrating and displaying protein post-translational modifications. BMC Res Notes 2009; 2:111.
59. Wang Y, Wang G, Cai J, Yin H. Review on the identification and role of Toxoplasma gondii antigenic epitopes. Parasitol Res. 2016;115: 459-468.
60. Shaddel M, Ebrahimi M, Tabandeh MR. Bioinformatics analysis of single and multi-hybrid epitopes of GRA-1, GRA-4, GRA-6 and GRA-7 proteins to improve DNA vaccine design against Toxoplasma gondii. J Parasit Dis 2018; 42:269-276.
61. Goodswen SJ, Kennedy PJ, Ellis JT. Enhancing in silico protein-based vaccine discovery for eukaryotic pathogens using predicted peptide-MHC binding and peptide conservation scores. PLoS One 2014; 9:e115745.
62. Goswami AM. Structural modeling and in silico analysis of non-synonymous single nucleotide polymorphisms of human 3β-hydroxysteroid dehydrogenase type 2. Meta Gene 2015; 5:162-172.
63. Cai H, Li Y, Zhang H, Feng F. Effects of gene design on recombinant protein expression: A review. Shengwu Gongcheng Xuebao/Chinese J  Biotechnol  2013; 29: 1201-1213.
64. Dehury B, Panda D, Sahu J, Sahu M, Sarma K, Barooah M, et al. In silico identification and characterization of conserved miRNAs and their target genes in sweet potato (Ipomoea batatas L.) expressed sequence tags (ESTs). Plant Signal Behav 2013; 8:e26543.
65. Basile G, Peticca M. Recombinant protein expression in Leishmania tarentolae. Mol  Biotechnol  2009; 43: 273-278.
66. Fritsche C, Sitz M, Weiland N, Breitling R, Pohl HD. Characterization of the growth behavior of Leishmania tarentolae -A new expression system for recombinant proteins. J Basic Microbiol 2007; 47:384-393.
67. Kushnir S, Gase K, Breitling R, Alexandrov K. Development of an inducible protein expression system based on the protozoan host Leishmania tarentolae. Protein Expr Purif  2005; 42:37-46.
68. Phan HP, Sugino M, Niimi T. The production of recombinant human laminin-332 in a Leishmania tarentolae expression system. Protein Expr Purif 2009; 68:79-84.
69. Foroutan M, Ghaffarifar F, Sharifi Z, Dalimi A, Pirestani M. Bioinformatics analysis of ROP8 protein to improve vaccine design against Toxoplasma gondii. Infect Genet Evol 2018; 62:193-204.