Research Article

Development of an epitope-based chimeric protein as a vaccine against Lujo virus by utilizing immunoinformatic tools

Enayetul Islam

Enayetul Islam

*Author for correspondence:

E-mail Address: enayetjidan303@gmail.com

https://orcid.org/0000-0001-9261-4461

Department of Genetic Engineering & Biotechnology, University of Chittagong, Chittagong, Bangladesh

Search for more papers by this author

Published Online:12 May 2022https://doi.org/10.2217/fvl-2021-0105

View article

Abstract

Aim: Lujo is a modern zoonotic virus that is potentially fatal and spreads by bodily fluids. In this research, immunoinformatic tools are used to build a vaccine. Methodology: The epitopes of cytotoxic T-lymphocytes, helper T-lymphocytes and linear B-lymphocytes were predicted from the most antigenic protein. The designed vaccine's physiochemical properties and 3D structure have been forecasted. Low free energy and strong binding affinity estimated in molecular docking against Toll-like receptor 4 (TLR4) and dynamic simulation. Furthermore, in silico cloning in the Escherichia coli K12 host system was performed for high level of expression. Conclusion: Finally, immune simulation was used to determine immune responses to the vaccine that was formulated confirming the developed vaccine as a good candidate against Lujo virus.

Keywords:

References

1. Briese T, Paweska JT, McMullan LK et al. Genetic detection and characterization of Lujo virus, a new hemorrhagic fever–associated arenavirus from Southern Africa. PLOS Pathog. 5(5), 1–8 (2009).
Google Scholar
2. Bergeron É, Chakrabarti AK, Bird BH et al. Reverse genetics recovery of Lujo virus and role of virus RNA secondary structures in efficient virus growth. J. Virol. 86(19), 10759–10765 (2012).
Medline CASGoogle Scholar
3. Bull JJ, Sanjuán R, Wilke CO. Theory of lethal mutagenesis for viruses. J. Virol. 81(6), 2930–2939 (2007).
Medline CASGoogle Scholar
4. Rosenberg R. Detecting the emergence of novel, zoonotic viruses pathogenic to humans. Cell. Mol. Life Sci. 72(6), 1115–1125 (2015).
Medline CASGoogle Scholar
5. Sewlall NH, Richards G, Duse A et al. Clinical features and patient management of Lujo hemorrhagic fever. PLOS Negl. Trop. Dis. 8(11), 1–11 (2014).
Google Scholar
6. Charrel RN, de Lamballerie X, Emonet S. Phylogeny of the genus Arenavirus. Curr. Opin. Microbiol. 11(4), 362–368 (2008).
MedlineGoogle Scholar
7. Amman BR, Pavlin BI, Albariño CG et al. Pet rodents and fatal lymphocytic choriomeningitis in transplant patients. Emerg. Infect. Dis. 13(5), 719–725 (2007).
MedlineGoogle Scholar
8. Kunz S, de la Torre JC. Breaking the barrier: host cell invasion by Lujo virus. Cell Host Microbe 22(5), 583–585 (2017).
Medline CASGoogle Scholar
9. Jae LT, Raaben M, Herbert AS et al. Lassa virus entry requires a trigger-induced receptor switch. Science 344(6191), 1506–1510 (2014).
Medline CASGoogle Scholar
10. Zhang L. Multi-epitope vaccines: a promising strategy against tumors and viral infections. Cell. Mol. Immunol. 15(2), 182–184 (2018).
Medline CASGoogle Scholar
11. Kar T, Narsaria U, Basak S et al. A candidate multi-epitope vaccine against SARS-CoV-2. Scientific Reports 10(1), 10895 (2020).
Medline CASGoogle Scholar
12. Palatnik-de-Sousa CB, Soares I da S, Rosa DS. Editorial: epitope discovery and synthetic vaccine design. Front. Immunol. 9, 1–3 (2018).
MedlineGoogle Scholar
13. Lu C, Meng S, Jin Y et al. A novel multi-epitope vaccine from MMSA-1 and DKK1 for multiple myeloma immunotherapy. Brit. J. Haema. 178(3), 413–426 (2017).
Medline CASGoogle Scholar
14. Khan A, Junaid M, Kaushik AC et al. Computational identification, characterization and validation of potential antigenic peptide vaccines from hrHPVs E6 proteins using immunoinformatics and computational systems biology approaches. PLOS ONE 13(5), e0196484 (2018).
MedlineGoogle Scholar
15. Buonaguro L. HEPAVAC Consortium. Developments in cancer vaccines for hepatocellular carcinoma. Can. Immunol. Immunother. 65(1), 93–99 (2016).
Medline CASGoogle Scholar
16. Kuo T, Wang C, Badakhshan T, Chilukuri S, BenMohamed L. The challenges and opportunities for the development of a T-cell epitope-based herpes simplex vaccine. Vaccine 32(50), 6733–6745 (2014).
Medline CASGoogle Scholar
17. Saadi M, Karkhah A, Nouri HR. Development of a multi-epitope peptide vaccine inducing robust T cell responses against brucellosis using immunoinformatics based approaches. Infect. Genet. Evol. 51, 227–234 (2017).
Medline CASGoogle Scholar
18. Soria-Guerra RE, Nieto-Gomez R, Govea-Alonso DO, Rosales-Mendoza S. An overview of bioinformatics tools for epitope prediction: implications on vaccine development. Jour. Biomed. Inform. 53, 405–414 (2015).
MedlineGoogle Scholar
19. Bryson CJ, Jones TD, Baker MP. Prediction of immunogenicity of therapeutic proteins. BioDrugs 24(1), 1–8 (2010).
Medline CASGoogle Scholar
20. Gatnik MF, Worth AP. Review of Software Tools for Toxicity Prediction JRC Sci. Tech. Rep. (1018-5593), 11–13 10.2788/6010 (2010).
Google Scholar
21. Stadler MB, Stadler BM. Allergenicity prediction by protein sequence. FASEB J. 17(9), 1141–1143 (2003).
Medline CASGoogle Scholar
22. Backert L, Kohlbacher O. Immunoinformatics and epitope prediction in the age of genomic medicine. Genome. Med. 7(1), 119 (2015).
MedlineGoogle Scholar
23. Tosta SF de O, Passos MS, Kato R et al. Multi-epitope-based vaccine against yellow fever virus applying immunoinformatics approaches. J. Biomol. Struct. Dyn. 39, 1–17 (2020).
Google Scholar
24. Nezafat N, Eslami M, Negahdaripour M, Rahbar MR, Ghasemi Y. Designing an efficient multi-epitope oral vaccine against Helicobacter pylori using immunoinformatics and structural vaccinology approaches. Mol. Biosyst. 13(4), 699–713 (2017).
Medline CASGoogle Scholar
25. Nezafat N, Karimi Z, Eslami M, Mohkam M, Zandian S, Ghasemi Y. Designing an efficient multi-epitope peptide vaccine against Vibrio cholerae via combined immunoinformatics and protein interaction based approaches. Comput. Biol. Chem. 62, 82–95 (2016).
Medline CASGoogle Scholar
26. 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. 349, 121–134 (2014).
Medline CASGoogle Scholar
27. Lennerz V, Gross S, Gallerani E et al. Immunologic response to the survivin-derived multi-epitope vaccine EMD640744 in patients with advanced solid tumors. Can. Immunol Immunoth. 63(4), 381–394 (2014).
Medline CASGoogle Scholar
28. Chen C, Huang H, Mazumder R et al. Computational clustering for viral reference proteomes. Bioinformatics 32(13), 2041–2043 (2016).
Medline CASGoogle Scholar
29. Ilinskaya AN, Dobrovolskaia MA. Understanding the immunogenicity and antigenicity of nanomaterials: past, present and future. Toxicol. Appl. Pharmacol. 299, 70–77 (2016).
Medline CASGoogle Scholar
30. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinfo. 8(1), 4 (2007).
MedlineGoogle Scholar
31. Larsen MV, Lundegaard C, Lamberth K, Buus S, Lund O, Nielsen M. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinfo. 8, 424 (2007).
MedlineGoogle Scholar
32. Moutaftsi M, Peters B, Pasquetto V et al. A consensus epitope prediction approach identifies the breadth of murine T(CD8⁺)-cell responses to vaccinia virus. Nat. Biotechnol. 24(7), 817–819 (2006).
Medline CASGoogle Scholar
33. Calis JJA, Maybeno M, Greenbaum JA et al. Properties of MHC class i presented peptides that enhance immunogenicity. PLOS Com. Bio. 9(10), e1003266 (2013).
MedlineGoogle Scholar
34. Dimitrov I, Bangov I, Flower DR, Doytchinova I. AllerTOP v.2 – a server for in silico prediction of allergens. J Mol. Model. 20(6), 2278 (2014).
MedlineGoogle Scholar
35. Gupta S, Kapoor P, Chaudhary K et al. In silico approach for predicting toxicity of peptides and proteins. PLOS ONE 8(9), e73957 (2013).
Medline CASGoogle Scholar
36. Melssen M, Slingluff CL. Vaccines targeting helper T cells for cancer immunotherapy. Cur. Opi. Immun. 47, 85–92 (2017).
Medline CASGoogle Scholar
37. Wang P, Sidney J, Kim Y et al. Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinfo. 11, 568 (2010).
MedlineGoogle Scholar
38. Dhanda SK, Gupta S, Vir P, Raghava GPS. “Prediction of IL4 Inducing Peptides”. (2013). https://www.hindawi.com/journals/jir/2013/263952/
Google Scholar
39. Nagpal G, Usmani SS, Dhanda SK et al. Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential. Sci. Rep. 7(1), 1–10 (2017).
Medline CASGoogle Scholar
40. Dhanda SK, Vir P, Raghava GP. Designing of interferon-gamma inducing MHC class-II binders. Biology Direct 8(1), 30 (2013).
MedlineGoogle Scholar
41. Zabel F, Kündig TM, Bachmann MF. Virus-induced humoral immunity: on how B cell responses are initiated. Cur. Op. Viro. 3(3), 357–362 (2013).
Medline CASGoogle Scholar
42. Saha S, Raghava GPS. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65(1), 40–48 (2006).
Medline CASGoogle Scholar
43. Pasquale AD, Preiss S, Silva FTD, Garçon N. Vaccine Adjuvants: from 1920 to 2015 and Beyond. Vaccines 3(2), 320–343 (2015).
Google Scholar
44. Alderson MR, McGowan P, Baldridge JR, Probst P. TLR4 agonists as immunomodulatory agents. J. Endotox. Res. 12(5), 313–319 (2006).
Medline CASGoogle Scholar
45. Arai R, Ueda H, Kitayama A, Kamiya N, Nagamune T. Design of the linkers which effectively separate domains of a bifunctional fusion protein. Pro. Eng. Des. Sel. 14(8), 529–532 (2001).
CASGoogle Scholar
46. Hebditch M, Carballo-Amador MA, Charonis S, Curtis R, Warwicker J. Protein–Sol: a web tool for predicting protein solubility from sequence. Bioinformatics 33(19), 3098–3100 (2017).
Medline CASGoogle Scholar
47. Ko J, Park H, Heo L, Seok C. GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Res. 40(Web Server issue), W294–297 (2012).
Medline CASGoogle Scholar
48. Heo L, Park H, Seok C. GalaxyRefine: protein structure refinement driven by side-chain repacking. Nucleic Acids Res. 41(W1), W384–W388 (2013).
MedlineGoogle Scholar
49. Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 35(Web Server issue), W407–W410 (2007).
MedlineGoogle Scholar
50. Craig DB, Dombkowski AA. Disulfide by Design 2.0: a web-based tool for disulfide engineering in proteins. BMC Bioinfo. 14(1), 346 (2013).
MedlineGoogle Scholar
51. Grote A, Hiller K, Scheer M et al. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res. 33(Suppl. 2), W526–W531 (2005).
Medline CASGoogle Scholar
52. Meng XY, Zhang HX, Mezei M, Cui M. Molecular Docking: a powerful approach for structure-based drug discovery. Curr. Comput. Aid. Dru. Des. 7(2), 146–157 (2011).
Medline CASGoogle Scholar
53. Tovchigrechko A, Vakser IA. GRAMM-X public web server for protein–protein docking. Nucleic Acids Res. 34(Web Server issue), W310–W314 (2006).
Medline CASGoogle Scholar
54. Weng G, Wang E, Wang Z et al. HawkDock: a web server to predict and analyze the protein–protein complex based on computational docking and MM/GBSA. Nucleic Acids Res. 47(W1), W322–W330 (2019).
Medline CASGoogle Scholar
55. Salmaso V, Moro S.Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: an overview. Front. Pharmacol. 9, 1–16 (2018).
MedlineGoogle Scholar
56. López-Blanco JR, Aliaga JI, Quintana-Ortí ES, Chacón P. iMODS: internal coordinates normal mode analysis server. Nucleic Acids Res. 42(Web Server issue), W271–276 (2014).
Medline CASGoogle Scholar
57. Rapin N, Lund O, Bernaschi M, Castiglione F. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PLOS ONE 5(4), e9862 (2010).
MedlineGoogle Scholar
58. Dawood RM, Moustafa RI, Abdelhafez TH et al. A multiepitope peptide vaccine against HCV stimulates neutralizing humoral and persistent cellular responses in mice. BMC Infec. Dis. 19(1), 932 (2019).
MedlineGoogle Scholar
59. Kadam A, Sasidharan S, Saudagar P. Computational design of a potential multi-epitope subunit vaccine using immunoinformatics to fight Ebola virus. Infec. Gen. Evo. 85, 104464 (2020).
CASGoogle Scholar
60. Majee P, Jain N, Kumar A. Designing of a multi-epitope vaccine candidate against Nipah virus by in silico approach: a putative prophylactic solution for the deadly virus. J. Biomol. Struc. Dyn. 0(0), 1–20 (2020).
Google Scholar
61. Sabetian S, Nezafat N, Dorosti H, Zarei M, Ghasemi Y. Exploring dengue proteome to design an effective epitope-based vaccine against dengue virus. J. Biomol. Struct. Dyn. 37(10), 2546–2563 (2019).
Medline CASGoogle Scholar
62. Sayed SB, Nain Z, Khan MdSA, Abdulla F, Tasmin R, Adhikari UK. Exploring Lassa virus proteome to design a multi-epitope vaccine through immunoinformatics and immune simulation analyses. Int. J. Pept. Res. Ther. 2, 1–19 (2020).
Google Scholar
63. Shahid F, Ashfaq UA, Javaid A, Khalid H. Immunoinformatics guided rational design of a next generation multi epitope-based peptide (MEBP) vaccine by exploring Zika virus proteome. Infec. Gene. Evo. 80, 104199 (2020).
CASGoogle Scholar
64. Oli AN, Obialor WO, Ifeanyichukwu MO et al. Immunoinformatics and vaccine development: an overview. Immunoter. Ther. 9, 13–30 (2020).
Medline CASGoogle Scholar
65. Islam E. Development of chemokine CXCL12-dependent immunotoxin against small cell lung cancer using in silico approaches. Infor. Med. Unlo. 25, 100676 (2021).
Google Scholar
66. 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. 349, 121–134 (2014).
Medline CASGoogle Scholar
67. Verma S, Sugadev R, Kumar A, Chandna S, Ganju L, Bansal A. Multi-epitope DnaK peptide vaccine against S. Typhi: an in silico approach. Vaccine 36(28), 4014–4022 (2018).
Medline CASGoogle Scholar
68. Jiang P, Cai Y, Chen J et al. Evaluation of tandem Chlamydia trachomatis MOMP multi-epitopes vaccine in BALB/c mice model. Vaccine 35(23), 3096–3103 (2017).
Medline CASGoogle Scholar
69. Lu IN, Farinelle S, Sausy A, Muller CP. Identification of a CD4 T-cell epitope in the hemagglutinin stalk domain of pandemic H1N1 influenza virus and its antigen-driven TCR usage signature in BALB/c mice. Cell. Mol. Immu. 14(6), 511–520 (2017).
Medline CASGoogle Scholar
70. Rojas JM, Avia M, Martín V, Sevilla N.IL-10: a multifunctional cytokine in viral infections. J. Immunol. Res. 2017, 1–14 (2017).
Google Scholar
71. Malmgaard L. Induction and regulation of IFNs during viral infections. J. Interf. Cyto. Res. 24(8), 439–454 (2004).
Medline CASGoogle Scholar
72. Shamriz S, Ofoghi H, Moazami N. Effect of linker length and residues on the structure and stability of a fusion protein with malaria vaccine application. Com. Bio. Med. 76, 24–29 (2016).
Medline CASGoogle Scholar
73. Meza B, Ascencio F, Sierra-Beltrán AP, Torres J, Angulo C. A novel design of a multi-antigenic, multistage and multi-epitope vaccine against Helicobacter pylori: an in silico approach. Infect. Genet. Evol. 49, 309–317 (2017).
Medline CASGoogle Scholar
74. Dar HA, Zaheer T, Shehroz M et al. Immunoinformatics-aided design and evaluation of a potential multi-epitope vaccine against Klebsiella pneumoniae. Vaccines (Basel) 7(3), 1–17 (2019).
Google Scholar
75. Liu T, Wang Y, Luo X et al. Enhancing protein stability with extended disulfide bonds. Proc. Natl Acad. Sci. USA 113(21), 5910–5915 (2016).
Medline CASGoogle Scholar
76. Yahaya MAF, Bakar ARA, Stanslas J, Nordin N, Zainol M, Mehat MZ. Insights from molecular docking and molecular dynamics on the potential of vitexin as an antagonist candidate against lipopolysaccharide (LPS) for microglial activation in neuroinflammation. BMC Biotech. 21(1), 38 (2021).
Medline CASGoogle Scholar
77. Brandstadter JD, Yang Y. Natural killer cell responses to viral infection. J. Innate. Immun. 3(3), 274–279 (2011).
Medline CASGoogle Scholar

Vol. 17, No. 8

Supplemental Materials

Metrics

Downloaded 25 times

History

Received 4 May 2021

Accepted 25 April 2022

Published online 12 May 2022

Published in print August 2022

Information

Keywords

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.futuremedicine.com/doi/suppl/10.2217/fvl-2021-0105

Financial & competing interests disclosure

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

No writing assistance was utilized in the production of this manuscript.

PDF download