Protein E-Peptide Driven Vaccine for Novel Coronavirus: Immunoinformatics

Document Type : Original Article

Authors

1 Student Research Committee, Ahvaz J undishapur University of Medical Sciences, Ahvaz, Iran & Department of Immunology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

2 Department of Immunology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

3 Department of Medical Genetics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran & Cellular and Molecular Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Abstract

Background: The COVID-19 pandemic is a red alarm for global health, so researchers around the world are working on it to design an effective vaccine against it. Protein is one of the candidates for vaccine development which plays an important role in virus pathogenesis. Accordingly, this study was done to evaluate the critical characteristic of this protein as a vaccine candidate using in-silico analysis.
Methods: The sequence of SARS-CoV-2-associated E protein was recruited from NCBI and subjected to the IEDB software to evaluate the most potent epitopes. The capacity of the interactions of HLA-I and HLA-II molecules with selective peptides was studied using IEBD tool kit. The E protein sequence was subjected to B cell and T cell tests to realize the most promising peptides that could act as COVID-19 vaccine.
Results: Among the tested peptides for the T cell-test, this study found two interesting epitopes: VSEETGTLI and LTALRLCAY that exhibit high binding affinity as a strong indicator to HLA-I and HLA-II alleles together. The results of the analysis demonstrated that some epitopes in the E protein have a relatively higher immunogenicity score based on interaction with HLA-II, such as SEETGTLIVNSVLLF, TLIVNSVLLFLAFVV, LAFVVFLLVTLAILT, LAILTALRLCAYCCN, and SVLLFLAFVVFLLVT. Furthermore, two sequences (FVSEET and PSFYVYSRVKNLNSSRVP) were reported as the selective linear epitopes for B cell, on the surface of SARS-CoV-2 E protein and being Immunogenic.
Conclusion: Since E protein can stimulate favorable immune responses, T and B- cell responses, its evaluation in patients with COVID-19 is of a great importance.

Keywords


  1. Pavel STI, Yetiskin H, Uygut MA, Aslan AF, Aydın G, İnan Ö, et al. Development of an inactivated vaccine against SARS CoV-2. Vaccines (Basel). 2021; 9(11):1266. doi: 10.3390/vaccines9111266.
  2. Rosales Mendoza S, Márquez Escobar VA, González Ortega O, Nieto Gómez R, Arévalo Villalobos JI. What does plant-based vaccine technology offer to the fight against COVID-19? Vaccines (Basel). 2020; 8(2):183. doi: 10.3390/vaccines8020183.
  3. Kyriakidis NC, López Cortés A, González EV, Grimaldos AB, Prado EO. SARS-CoV-2 vaccines strategies: A comprehensive review of phase 3 candidates. NPJ Vaccines. 2021; 6(1):28. doi: 10.1038/s41541-021-00292-w.
  4. Li YD, Chi WY, Su JH, Ferrall L, Hung CF, Wu TC. Coronavirus vaccine development: From SARS and MERS to COVID-19. J Biomed Sci. 2020; 27(1):104. doi: 10.1186/s12929-020-00695-2.
  5. Curtis KM, Yount B, Baric RS. Heterologous gene expression from transmissible gastroenteritis virus replicon particles. J Virol. 2002; 76(3):1422-34. doi: 10.1128/jvi.76.3.1422-1434.2002.
  6. Nieto Torres JL, DeDiego ML, Álvarez E, Jiménez Guardeño JM, Regla Nava JA, Llorente M, et al. Subcellular location and topology of severe acute respiratory syndrome coronavirus envelope protein. Virology. 2011; 415(2):69-82. doi: 10.1016/j.virol.2011.03.029.
  7. Ortego J, Escors D, Laude H, Enjuanes L. Generation of a replication-competent, propagation-deficient virus vector based on the transmissible gastroenteritis coronavirus genome. J Virol. 2002; 76(22):11518-29. doi: 10.1128/jvi.76.22.11518-11529.2002.
  8. Ruch TR, Machamer CE. A single polar residue and distinct membrane topologies impact the function of the infectious bronchitis coronavirus E protein. PLoS Pathog. 2012; 8(5):e1002674. doi: 10.1371/journal.ppat.1002674.
  9. Pruitt KD, Katz KS, Sicotte H, Maglott DR. Introducing refseq and locuslink: Curated human genome resources at the NCBI. Trends Genet. 2000; 16(1):44-7. doi: 10.1016/s0168-9525(99)01882-x.
  10. Nielsen M, Lund O. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinformatics. 2009; 10:296. doi: 10.1186/1471-2105-10-296.
  11. Nielsen M, Lundegaard C, Worning P, Lauemøller SL, Lamberth K, Buus S, et al. Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations. Protein Sci. 2003; 12(5):1007-17. doi: 10.1110/ps.0239403.
  12. Calis JJ, Maybeno M, Greenbaum JA, Weiskopf D, De Silva AD, Sette A, et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput Biol. 2013; 9(10):e1003266. doi: 10.1371/journal.pcbi.1003266.
  13. Paul S, Lindestam Arlehamn CS, Scriba TJ, Dillon MB, Oseroff C, Hinz D, et al. Development and validation of a broad scheme for prediction of HLA class II restricted T cell epitopes. J Immunol Methods. 2015; 422:28-34. doi: 10.1016/j.jim.2015.03.022.
  14. Larsen MV, Lundegaard C, Lamberth K, Buus S, Brunak S, Lund O, et al. An integrative approach to CTL epitope prediction: A combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions. Eur J Immunol. 2005; 35(8):2295-303. doi: 10.1002/eji.200425811.
  15. Keşmir C, Nussbaum AK, Schild H, Detours V, Brunak S. Prediction of proteasome cleavage motifs by neural networks. Protein Eng. 2002; 15(4):287-96. doi: 10.1093/protein/15.4.287.
  16. Nielsen M, Lundegaard C, Lund O, Keşmir 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. doi: 10.1007/s00251-005-0781-7.
  17. Parker JM, 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. doi: 10.1021/bi00367a013.
  18. Emini EA, Hughes JV, Perlow D, Boger J. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol. 1985; 55(3):836-9. doi: 10.1128/JVI.55.3.836-839.1985.
  19. Kolaskar AS, Tongaonkar PC. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett. 1990; 276(1-2):172-4. doi: 10.1016/0014-5793(90)80535-q.
  20. Källberg M, Wang H, Wang S, Peng J, Wang Z, Lu H, et al. Template-based protein structure modeling using the RaptorX web server. Nature protocols. 2012; 7(8):1511-22. doi: 10.1038/nprot.2012.085.
  21. Mortola E, Roy P. Efficient assembly and release of SARS coronavirus-like particles by a heterologous expression system. FEBS Lett. 2004; 576(1-2):174-8. doi: 10.1016/j.febslet.2004.09.009.
  22. Schoeman D, Fielding BC. Coronavirus envelope protein: Current knowledge. Virol J. 2019; 16(1):69. doi: 10.1186/s12985-019-1182-0.
  23. Sharmin R, Islam AB. Conserved antigenic sites between MERS-CoV and bat-coronavirus are revealed through sequence analysis. Source Code Biol Med. 2016; 11(1):1-6. doi: 10.1186/s13029-016-0049-7.
  24. Hu J, Wang J, Xu J, Li W, Han Y, Li Y, et al. Evolution and variation of the SARS-CoV genome. Genomics Proteomics Bioinformatics. 2003; 1(3):216-25. doi: 10.1016/s1672-0229(03)01027-1.
  25. Abdelmageed MI, Abdelmoneim AH, Mustafa MI, Elfadol NM, Murshed NS, Shantier SW, et al. Design of multi epitope-based peptide vaccine against E protein of human 2019-nCoV: An immunoinformatics approach. Biomed Res Int. 2020; 2020:2683286. doi: 10.1155/2020/2683286.