Vaccine design of coronavirus spike (S) glycoprotein in chicken: immunoinformatics and computational approaches

Translational Medicine Communications - 2020
Eman Ali Awadelkareem1, Sumaia A. Ali2
1Faculty of Veterinary Medicine, University of Khartoum, Khartoum, Sudan
2Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, Sudan University of Science and Technology, Khartoum, Sudan

Tóm tắt

Abstract Background Infectious bronchitis (IB) is a highly contagious respiratory disease in chickens and produces economic loss within the poultry industry. This disease is caused by a single stranded RNA virus belonging to Cronaviridae family. This study aimed to design a potential multi-epitopes vaccine against infectious bronchitis virus spike protein (S). Protein characterization was also performed for IBV spike protein. Methods The present study used various tools in Immune Epitope Database (IEDB) to predict conserved B and T cell epitopes against IBV spike (S) protein that may perform a significant role in provoking the resistance response to IBV infection. Results In B cell prediction methods, three epitopes (1139KKSSYY1144,1140KSSYYT1145,1141SSYYT1145) were selected as surface, linear and antigenic epitopes. Many MHCI and MHCII epitopes were predicted for IBV S protein. Among them 982YYITARDMY990 and 983YITARDMYM991 epitopes displayed high antigenicity, no allergenicity and no toxicity as well as great linkage with MHCI and MHCII alleles. Moreover, docking analysis of MHCI epitopes produced strong binding affinity with BF2 alleles. Conclusion Five conserved epitopes were expected from spike glycoprotein of IBV as the best B and T cell epitopes due to high antigenicity, no allergenicity and no toxicity. In addition, MHC epitopes showed great linkage with MHC alleles as well as strong interaction with BF2 alleles. These epitopes should be designed and incorporated and then tested as multi-epitope vaccine against IBV.

Từ khóa


Tài liệu tham khảo

Seo SH, Collisson EW. Specific cytotoxic T lymphocytes are involved in in vivo clearance of infectious bronchitis virus. J Virol. 1997;71(7):5173–7.

Cavanagh D. Coronavirus avian infectious bronchitis virus. Vet Res. 2007;38(2):281–97.

Ignjatovic J, Sapats S. Avian infectious bronchitis virus. Revue Sci Tech Office Int des Epizooties. 2000;19(2):493–501.

Raj GD, Jones R. Infectious bronchitis virus: immunopathogenesis of infection in the chicken. Avian Pathology. 1997;26(4):677–706.

Yang T, Wang H-N, Wang X, Tang J-N, Lu D, Zhang Y-F, et al. The protective immune response against infectious bronchitis virus induced by multi-epitope based peptide vaccines. Biosci Biotechnol Biochem. 2009;0864:1–5.

Abro SH, Ullman K, Belák S, Baule C. Bioinformatics and evolutionary insight on the spike glycoprotein gene of QX-like and Massachusetts strains of infectious bronchitis virus. Virol J. 2012;9(1):211.

Abdel-Moneim A, Madbouly H, Gelb J, Ladman B. Isolation and identification of Egypt/Beni-Seuf/01 a novel genotype of infectious bronchitis virus. Vet Med J IZA. 2002;50(4):1065–78.

Mo M-L, Hong S-M, Kwon H-J, Kim I-H, Song C-S, Kim J-H. Genetic diversity of spike, 3a, 3b and e genes of infectious bronchitis viruses and emergence of new recombinants in Korea. Viruses. 2013;5(2):550–67.

Miller MM, Taylor RL Jr. Brief review of the chicken major histocompatibility complex: the genes, their distribution on chromosome 16, and their contributions to disease resistance. Poultry Sci. 2016;95(2):375–92.

Shina T, Hosomichi K, Hanzawa K. Comparative genomics of the poultry major histocompatibility complex. Anim Sci J. 2006;77(2):151–62.

Bande F, Arshad SS, Hair Bejo M, Kadkhodaei S, Omar AR. Prediction and in silico identification of novel B-cells and T-cells epitopes in the S1-spike glycoprotein of M41 and CR88 (793/B) infectious bronchitis virus serotypes for application in peptide vaccines. Adv Bioinforma. 2016;2016:5484972.

Dash R, Das R, Junaid M, Akash MF, Islam A, Hosen SZ. In silico-based vaccine design against Ebola virus glycoprotein. Adv Appl Bioinform Chem. 2017;10:11.

Gaafar B, Ali SA, Abd-elrahman KA, Almofti YA. Immunoinformatics approach for multiepitope vaccine prediction from H, M, F, and N proteins of Peste des Petits ruminants virus. J Immunol Res. 2019;2019:1–18.

Zheng J, Lin X, Wang X, Zheng L, Lan S, Jin S, Ou Z, Wu J. In silico analysis of epitope-based vaccine candidates against hepatitis B virus polymerase protein. Viruses. 2017;9(5):112.

Ali SA, Almofti YA, Abd-elrahman KA. Immunoinformatics approach for multiepitopes vaccine prediction against glycoprotein B of avian infectious laryngotracheitis virus. Adv Bioinforma. 2019;2019:1–23.

Gasteiger EGA, Hoogland C, Ivanyi I, Appel RD, Bairoch A. ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 2003;31:3784–8.

Buchan DWMF, Nugent TC, Bryson K, Jones DT. Scalable web services for the PSIPRED protein analysis workbench. Nucleic Acids Res. 2013;41:W349–57.

Ferre F, Clote P. Disulfide connectivity prediction using secondary structure information and diresidue frequencies. Bioinformatics. 2005;21(10):2336–46.

Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–402.

Marchler-Bauer A, Anderson JB, Derbyshire MK, DeWeese-Scott C, Gonzales NR, Gwadz M, Hao L, He S, Hurwitz DI, Jackson JD, Ke Z. CDD: a conserved domain database for interactive domain family analysis. Nucleic Acids Res. 2007;35:D237–40.

Eddy SR. Profile hidden Markov models. Bioinformatics (Oxford, England). 1998;14(9):755–63.

El-Gebali S, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2019;47(D1):D427–32.

Waterhouse AM, Procter JB, Martin DM, Clamp M, Barton GJ. Jalview version 2—a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009;25(9):1189–91.

Bui H-H, Sidney J, Li W, Fusseder N, Sette A. Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC Bioinformatics. 2007;8(1):361.

Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol. 2016;33(7):1870–4.

Vita R, Overton JA, Greenbaum JA, Ponomarenko J, Clark JD, Cantrell JR, et al. The immune epitope database (IEDB) 3.0. Nucleic Acids Res. 2014;43(D1):D405–D12.

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

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.

Kolaskar A, Tongaonkar PC. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett. 1990;276(1–2):172–4.

Sun P, Ju H, Liu Z, Ning Q, Zhang J, Zhao X, et al. Bioinformatics resources and tools for conformational B-cell epitope prediction. Comput Math Methods Med. 2013;2013:943636.

Chan WM, Rogers SE, Nash SM, Buning PG, Meakin R. User’s manual for Chimera grid tools, version 1.8. NASA Ames Research Center, URL: http://people.nas.nasa.gov/~rogers/cgt/doc/man.html. 2003.

Patronov A, Doytchinova I. T-cell epitope vaccine design by immunoinformatics. Open Biol. 2013;3(1):120139.

Abdelbagi M, Hassan T, Shihabeldin M, Bashir S, Ahmed E. Immunoinformatics prediction of peptide-based vaccine against African horse sickness virus. Immunome Res. 2017;13(135):2.

Nielsen M, Lund O, et al. BMC Bioinformatics. 2009;10(1):296.

Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics. 2007;8(1):1–7.

Dimitrov I, Bangov I, Flower DR, Doytchinova I. AllerTOP v. 2—a server for in silico prediction of allergens. J Mol Model. 2014;20(6):2278 1–6.

Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Raghava GP, et al. In silico approach for predicting toxicity of peptides and proteins. PLoS One. 2013;8(9):1–10.

Källberg M, Wang H, Wang S, Peng J, Wang Z, Lu H, et al. Template-based protein structure modeling using the RaptorX web server. Nat Protoc. 2012;7(8):1511–22.

Peng J, Xu J. RaptorX: exploiting structure information for protein alignment by statistical inference. Proteins. 2011;79(S10):161–71.

Peng J, Xu J. A multiple-template approach to protein threading. Proteins. 2011;79(6):1930–9.

Maupetit J, Derreumaux P, Tufféry P. A fast method for large-scale De novo peptide and miniprotein structure prediction. J Comput Chem. 2010;31(4):726–38.

Beaufays J, Lins L, Thomas A, Brasseur R. In silico predictions of 3D structures of linear and cyclic peptides with natural and non-proteinogenic residues. J Pept Sci. 2012;18(1):17–24.

Shen Y, Maupetit J, Derreumaux P, Tufféry P. Improved PEP-FOLD approach for peptide and miniprotein structure prediction. J Chem Theory Comput. 2014;10(10):4745–58.

Andrusier N, Nussinov R, Wolfson HJ. FireDock: fast interaction refinement in molecular docking. Proteins. 2007;69(1):139–59.

Shang J, Zheng Y, Yang Y, Liu C, Geng Q, Luo C, et al. Cryo-EM structure of infectious bronchitis coronavirus spike protein reveals structural and functional evolution of coronavirus spike proteins. PLoS Pathog. 2018;14(4):1–9.

Verma SK, Yadav S, Kumar A. In silico prediction of B-and T-cell epitope on Lassa virus proteins for peptide based subunit vaccine design. Adv Biomed Res. 2015;4:201.

Reche PA, Fernandez-Caldas E, Flower DR, Fridkis-Hareli M, Hoshino Y. Peptide-based immunotherapeutics and vaccines. J Immunol Res. 2014;2014:1–2.

Sylvester SA, Dhama K, Kataria J, Rahul S, Mahendran M. Avian infectious bronchitis: a review. Indian J Comp Microbiol Immunol Infect Dis. 2005;26:1–14.

Idris S, Salih S, Basheir M, Elhadi A, Kamel S, Abd-elrahman K, et al. In silico prediction of peptide based vaccine against Fowlpox virus (FPV). Immunome Res. 2018;14(2):1–11.

Badawi MM, Fadl Alla A, Alam SS, Mohamed WA, Osman D, Alrazig Ali S, et al. Immunoinformatics predication and in silico modeling of epitope-based peptide vaccine against virulent Newcastle disease viruses. Am J Infect Dis Microbiol. 2016;4(3):61–71.

Bashir S, Abd-elrahman KA, Hassan MA, Almofti YA. Multi epitope based peptide vaccine against Marek’s disease virus serotype 1 glycoprotein H and B. Am J Microbiol Res. 2018;6(4):124–39.

Gen J. Cloning and sequencing of the gene encoding the spike protein of the coronavirus IBV. J Gen Virol. 1985;66:719–26.

Yao B, Zheng D, Liang S, Zhang C. Conformational B-cell epitope prediction on antigen protein structures: a review of current algorithms and comparison with common binding site prediction methods. PLoS One. 2013;8(4):1–4.

Sanchez-Trincado JL, Gomez-Perosanz M, Reche PA. Fundamentals and methods for T-and B-cell epitope prediction. J Immunol Res. 2017;2017:1–14.

Demolombe V, de Brevern AG, Felicori L, NGuyen C, de Avila RAM. Valera L, et al. PEPOP 2.0: new approaches to mimic non-continuous epitopes. BMC Bioinformatics. 2019;387:1–14.

Pei J, Briles WE, Collisson EW. Memory T cells protect chicks from acute infectious bronchitis virus infection. Virology. 2003;306(2):376–84.

Thông tin
Thông tin xuất bản

Translational Medicine Communications

Thông tin tác giả