Decoding bovine coronavirus immune targets: an epitope informatics approach

Swati Rani1, Mehnaj Khatoon1, Jagadish Hiremath1, Kuralayanapalya Puttahonnappa Suresh1(), Jayashree Anandakumar1, Nagendra Nath Barman2, Sheethal Manjunath3, Yamini Sri S1, Sharanagouda S. Patil1

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Animal Diseases ›› 2024, Vol. 4 ›› Issue (1) : 15. DOI: 10.1186/s44149-024-00118-x
Original Article

Decoding bovine coronavirus immune targets: an epitope informatics approach

  • Swati Rani1, Mehnaj Khatoon1, Jagadish Hiremath1, Kuralayanapalya Puttahonnappa Suresh1(), Jayashree Anandakumar1, Nagendra Nath Barman2, Sheethal Manjunath3, Yamini Sri S1, Sharanagouda S. Patil1
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Abstract

Bovine coronavirus (BCoV) poses a significant threat to the global cattle industry, causing both respiratory and gastrointestinal infections in cattle populations. This necessitates the development of efficacious vaccines. While several inactivated and live BCoV vaccines exist, they are predominantly limited to calves. The immunization of adult cattle is imperative for BCoV infection control, as it curtails viral transmission to calves and ameliorates the impact of enteric and respiratory ailments across all age groups within the herd. This study presents an in silico methodology for devising a multiepitope vaccine targeting BCoV. The spike glycoprotein (S) and nucleocapsid (N) proteins, which are integral elements of the BCoV structure, play pivotal roles in the viral infection cycle and immune response. We constructed a remarkably effective multiepitope vaccine candidate specifically designed to combat the BCoV population. Using immunoinformatics technology, B-cell and T-cell epitopes were predicted and linked together using linkers and adjuvants to efficiently trigger both cellular and humoral immune responses in cattle. The in silico construct was characterized, and assessment of its physicochemical properties revealed the formation of a stable vaccine construct. After 3D modeling of the vaccine construct, molecular docking revealed a stable interaction with the bovine receptor bTLR4. Moreover, the viability of the vaccine’s high expression and simple purification was demonstrated by codon optimization and in silico cloning expression into the pET28a (+) vector. By applying immunoinformatics approaches, researchers aim to better understand the immune response to bovine coronavirus, discover potential targets for intervention, and facilitate the development of diagnostic tools and vaccines to mitigate the impact of this virus on cattle health and the livestock industry. We anticipate that the design will be useful as a preventive treatment for BCoV sickness in cattle, opening the door for further laboratory studies.

Keywords

Immunoinformatics / Bovine coronavirus / Multiepitope vaccine / Molecular docking / In silico cloning

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Swati Rani, Mehnaj Khatoon, Jagadish Hiremath, Kuralayanapalya Puttahonnappa Suresh, Jayashree Anandakumar, Nagendra Nath Barman, Sheethal Manjunath, Yamini Sri S, Sharanagouda S. Patil. Decoding bovine coronavirus immune targets: an epitope informatics approach. Animal Diseases, 2024, 4(1): 15 https://doi.org/10.1186/s44149-024-00118-x

References

[1]
Aasim, Sharma R., Patil, C.R., Kumar, A. and Sharma, K. 2022. Identification of vaccine candidate against Omicron variant of SARS-CoV-2 using immunoinformatic approaches. In Silico Pharmacology 10 (1): 12. https://doi.org/10.1007/s40203-022-00128-y.
[2]
Ahmad, I., Ali, S.S., Zafar, B., Hashmi, H.F., Shah, I., Khan, S., Suleman, M., Khan, M., Ullah, S., Ali, S., Khan, J., Ali, M., Khan, A. and Wei, D.Q. 2022. Development of multiepitope subunit vaccine for protection against the norovirus’ infections based on computational vaccinology. Journal of Biomolecular Structure and Dynamics 40 (7): 3098–3109. https://doi.org/10.1080/07391102.2020.1845799.
[3]
Ali, M., Pandey, R.K., Khatoon, N., Narula, A., Mishra, A. and Prajapati, V.K. 2017. Exploring dengue genome to construct a multiepitope based subunit vaccine by utilizing immunoinformatics approach to battle against dengue infection. Scientific Reports 7 (1): 1–13. https://doi.org/10.1038/s41598-017-09199-w.
[4]
Almofti, Y.A., K.A. Abd-elrahman, and E.E.M. Eltilib. 2021. Vaccinomic approach for novel multi epitopes vaccine against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). BMC Immunology 22 (1): 22. https://doi.org/10.1186/s12865-021-00412-0.
[5]
Amanna, I.J., and M.K. Slifka. 2011. Contributions of humoral and cellular immunity to vaccine-induced protection in humans. Virology 411 (2): 206–215. https://doi.org/10.1016/j.virol.2010.12.016.
[6]
Arpin, C., Dechanet, J, and Van Kooten, C. 1995. Generation of memory B cells and plasma cells in vitro. Science 268 (5211): 720–722. https://doi.org/10.1126/science.7537388.
[7]
Awadelkareem, E.A., and S. Hamdoun. 2022. Insilco Vaccine Design of spike and hemagglutinin esterase proteins of Bovine Coronavirus, 1–20. https://doi.org/10.21203/rs.3.rs-1791707/v1.
[8]
Aziz, S., Waqas, M., Halim, S.A., Ali, A., Iqbal, A., Iqbal, M., Khan, A. and Al-Harrasi, A. 2022. Exploring whole proteome to contrive multiepitope-based vaccine for NeoCoV: An immunoinformtics and in-silico approach. Frontiers in Immunology 13: 1–26. https://doi.org/10.3389/fimmu.2022.956776.
[9]
Bacchetta, R., S. Gregori, and M.-G. Roncarolo. 2005. CD4+ regulatory T cells: Mechanisms of induction and effector function. Autoimmunity Reviews 4 (8): 491–496. https://doi.org/10.1016/j.autrev.2005.04.005.
[10]
Chauhan, V., Rungta, T., Goyal, K. and Singh, M.P. 2019. Designing a multiepitope based vaccine to combat Kaposi Sarcoma utilizing immunoinformatics approach. Scientific Reports 9 (1): 1–15. https://doi.org/10.1038/s41598-019-39299-8.
[11]
Chen, R. 2012. Bacterial expression systems for recombinant protein production: E. coli and beyond. Biotechnology Advances 30 (5): 1102–1107. https://doi.org/10.1016/j.biotechadv.2011.09.013.
[12]
Cho, K.O., Hasoksuz, M., Nielsen, P.R., Chang, K.O., Lathrop, S. and Saif, L.J. 2001. Cross-protection studies between respiratory and calf diarrhea and winter dysentery coronavirus strains in calves and RT-PCR and nested PCR for their detection. Archives of Virology 146: 2401–19. https://doi.org/10.1007/s007050170011.
[13]
Clark, M.A. 1993. Bovine coronavirus. British Veterinary Journal 149 (1): 51–70. https://doi.org/10.1016/S0007-1935(05)80210-6.
[14]
Connelley, T., Nicastri, A., Sheldrake, T., Vrettou, C., Fisch, A., Reynisson, B., Buus, S., Hill, A., Morrison, I., Nielsen, M. and Ternette, N. 2022. Immunopeptidomic analysis of BoLA-I and BoLA-DR presented peptides from Theileria parva infected cells. Vaccines 10 (11): 1907. https://doi.org/10.3390/vaccines10111907.
[15]
Dimitrov, I., Bangov, I., Flower, D.R. and Doytchinova, I. 2014. AllerTOP vol 2 - A server for in silico prediction of allergens. Journal of Molecular Modeling 20 (6): 2278. https://doi.org/10.1007/s00894-014-2278-5.
[16]
Doytchinova, I.A., and D.R. Flower. 2007. VaxiJen: A server for prediction of protective antigens, tumor antigens and subunit vaccines. BMC Bioinformatics 8: 1–7. https://doi.org/10.1186/1471-2105-8-4.
[17]
Du, Z., Su, H., Wang, W., Ye, L., Wei, H., Peng, Z., Anishchenko, I., Baker, D. and Yang, J. 2021. The trRosetta server for fast and accurate protein structure prediction. Nature Protocols 16 (12): 5634–5651. https://doi.org/10.1038/s41596-021-00628-9.
[18]
Fisch, A., Reynisson, B., Benedictus, L., Nicastri, A., Vasoya, D., Morrison, I., Buus, S., Ferreira, B.R., Kinney Ferreira de Miranda Santos, I., Ternette, N. et al. 2021. Integral use of immunopeptidomics and immunoinformatics for the characterization of antigen presentation and rational identification of BoLA-DR–presented peptides and epitopes. The Journal of Immunology 206 (10): 2489–2497. https://doi.org/10.4049/jimmunol.2001409.
[19]
Friend Tambunan, U.S., and A. Aditya. 2012. HPV bioinformatics: in silico detection, drug design and prevention agent development. In Topics on cervical cancer with an advocacy for prevention. InTech. https://doi.org/10.5772/27456.
[20]
Fulton, R.W., Herd, H.R., Sorensen, N.J., Confer, A.W., Ritchey, J.W., Ridpath, J.F. and Burge, L.J. 2015. Enteric disease in postweaned beef calves associated with Bovine coronavirus clade 2. Journal of Veterinary Diagnostic Investigation 27 (1): 97–101. https://doi.org/10.1177/1040638714559026.
[21]
Fulton, R.W., d'Offay, J.M., Dubovi, E.J. and Eberle, R. 2016. Bovine herpesvirus-1: Genetic diversity of field strains from cattle with respiratory disease, genital, fetal disease and systemic neonatal disease and their relationship to vaccine strains. Virus Research 223: 115–121. https://doi.org/10.1016/j.virusres.2016.06.017.
[22]
Gao, T., Gao, Y., Liu, X., Nie, Z., Sun, H., Lin, K., Peng, H. and Wang, S. 2021. Identification and functional analysis of the SARS-CoV-2 nucleocapsid protein. BMC Microbiology 21 (1): 58. https://doi.org/10.1186/s12866-021-02107-3.
[23]
Gazi, M.A., Kibria, M.G., Mahfuz, M., Islam, M.R., Ghosh, P., Afsar, M.N., Khan, M.A. and Ahmed, T. 2016. Functional, structural and epitopic prediction of hypothetical proteins of Mycobacterium tuberculosis H37Rv: An in silico approach for prioritizing the targets. Gene 591 (2): 442–455. https://doi.org/10.1016/j.gene.2016.06.057.
[24]
Geng, H.L., Meng, X.Z., Yan, W.L., Li, X.M., Jiang, J., Ni, H.B. and Liu, W.H. 2023. Prevalence of bovine coronavirus in cattle in China: A systematic review and meta-analysis. Microbial Pathogenesis 176: 106009. https://doi.org/10.1016/j.micpath.2023.106009.
[25]
Ghosh, P., Bhakta, S., Bhattacharya, M., Sharma, A.R., Sharma, G., Lee, S.S., Chakraborty, C. 2021a. A novel multi-epitopic peptide vaccine candidate against Helicobacter pylori: In-silico identification, design, cloning and validation through molecular dynamics. International Journal of Peptide Research and Therapeutics 27 (2): 1149–1166. https://doi.org/10.1007/s10989-020-10157-w.
[26]
Ghosh, R., Chakraborty, A., Biswas, A. and Chowdhuri, S. 2021b. Evaluation of green tea polyphenols as novel corona virus (SARS CoV-2) main protease (Mpro) inhibitors–An in silico docking and molecular dynamics simulation study. Journal of Biomolecular Structure and Dynamics 39 (12): 4362–4374. https://doi.org/10.1080/07391102.2020.1779818.
[27]
Gong, Q.L., Li, D., Diao, N.C., Liu, Y., Li, B.Y., Tian. T., Ge. G.Y., Zhao, B., Song, Y.H., Li, D.L., Leng, X. and Du, R. 2020. Mink Aleutian disease seroprevalence in China during 1981–2017: A systematic review and meta-analysis. Microbial Pathogenesis 139: 103908. https://doi.org/10.1016/j.micpath.2019.103908.
[28]
Gurao, A., S.K. Kashyap, and R. Singh. 2017. β-defensins: An innate defense for bovine mastitis. Veterinary World 10 (8): 990–998. https://doi.org/10.14202/vetworld.2017.990-998.
[29]
Hasoksuz, M., Sreevatsan, S., Cho, K.O., Hoet, A.E. and Saif, L.J. 2002. Molecular analysis of the S1 subunit of the spike glycoprotein of respiratory and enteric bovine coronavirus isolates. Virus Research 84 (1–2): 101–9. https://doi.org/10.1016/s0168-1702(02)00004-7.
[30]
Hayward, S., A. Kitao, and H.J.C. Berendsen. 1997. Model-free methods of analyzing domain motions in proteins from simulation: A comparison of normal mode analysis and molecular dynamics simulation of lysozyme, proteins. Wiley-Liss, Inc. https://doi.org/10.1002/(sici)1097-0134(199703)27:3<425::aid-prot10>3.0.co;2-n.
[31]
He, C.Q., Liu, Y.X., Wang, H.M., Hou, P.L., He, H.B. and Ding, N.Z. 2016. New genetic mechanism, origin and population dynamic of bovine ephemeral fever virus. Veterinary Microbiology 182: 50–56. https://doi.org/10.1016/j.vetmic.2015.10.029.
[32]
Heo, L., Hahnbeom P., and Chaok, S. 2013. GalaxyRefine: Protein structure refinement driven by side-chain repacking. Nucleic acids research 41 (W1): W384–W388. https://doi.org/10.1093/nar/gkt458.
[33]
Hoque, H., Islam, R., Ghosh, S., Rahaman, M. M., Jewel, N. A., and Miah, M. A. 2021. Implementation of in silico methods to predict common epitopes for vaccine development against Chikungunya and Mayaro viruses. Heliyon 7(3): e06396. https://doi.org/10.1016/j.heliyon.2021.e06396.
[34]
Jung, H.E., and H.K. Lee. 2021. Current understanding of the innate control of toll-like receptors in response to SARS-CoV-2 infection. Viruses 13 (11): 2132. https://doi.org/10.3390/v13112132.
[35]
Kalita, P., Padhi, A.K., Zhang, K.Y.J. and Tripathi, T. 2020. Design of a peptide-based subunit vaccine against novel coronavirus SARS-CoV-2. Microbial Pathogenesis 145: 104236. https://doi.org/10.1016/j.micpath.2020.104236.
[36]
Kar, T., Narsaria, U., Basak, S., Deb, D., Castiglione, F., Mueller, D.M. and Srivastava, A.P. 2020. A candidate multiepitope vaccine against SARS-CoV-2. Scientific Reports 10 (1): 10895. https://doi.org/10.1038/s41598-020-67749-1.
[37]
Saeed, K., Javad, R.M., Latif, M.S., Jafar, A., Abolfazl, J. and Hojat, B. 2017. In silico prediction and in vitro verification of a novel multi-epitope antigen for HBV detection. Molecular Genetics, Microbiology and Virology 32 (4): 230–240. https://doi.org/10.3103/S0891416817040097.
[38]
Kin, N., Miszczak, F., Diancourt, L., Caro, V., Moutou, F., Vabret, A. and Ar Gouilh, M. 2016. Comparative molecular epidemiology of two closely related coronaviruses, bovine coronavirus (BCoV) and human coronavirus OC43 (HCoV-OC43), reveals a different evolutionary pattern. Infection, Genetics and Evolution 40: 186–191. https://doi.org/10.1016/j.meegid.2016.03.006.
[39]
Kozakov, D., Hall, R., Xia, B., Porter, K.A., Padhorny, D., Yueh, C., Beglov, D. and Vajda, S. 2017. The ClusPro web server for protein-protein docking. Nature Protocols 12 (2): 255–278. https://doi.org/10.1038/nprot.2016.169.
[40]
Kumar Pandey, R., Ojha, R,. Mishra, A. and Kumar Prajapati, V. 2018. Designing B- and T-cell multi-epitope based subunit vaccine using immunoinformatics approach to control Zika virus infection. Journal of Cellular Biochemistry 119 (9): 7631–7642. https://doi.org/10.1002/jcb.27110.
[41]
Kumar Verma, S., S. Yadav, and A. Kumar. 2015. In silico prediction of B- and T- cell epitope on Lassa virus proteins for peptide based subunit vaccine design.Advanced Biomedical Research 4(1): 201 https://doi.org/10.4103/2277-9175.166137.
[42]
Li, L., Zhao, Z., Yang, X., Su, Z., Li, W., Chen, S., Wang, L., Sun, T., Du, C., Li, Z., et al. 2023. A newly identified spike protein targeted linear B-cell epitope based dissolvable microneedle array successfully eliciting neutralizing activities against SARS-CoV-2 wild-type strain in mice. Advanced Science 10 (20): 1–13. https://doi.org/10.1002/advs.202207474.
[43]
Liu, L., H?gglund, S., Hakhverdyan, M., Alenius, S., Larsen, L.E. and Belák, S. 2006. Molecular epidemiology of bovine coronavirus on the basis of comparative analyses of the S gene. Journal of Clinical Microbiology 44 (3): 957–960. https://doi.org/10.1128/JCM.44.3.957-960.2006.
[44]
López-Blanco, J.R., Aliaga, J.I., Quintana-Ortí, E.S. and Chacón, P. 2014. IMODS: Internal coordinates normal mode analysis server. Nucleic Acids Research 42 (W1): 271–276. https://doi.org/10.1093/nar/gku339.
[45]
Mackenzie-Dyck, S., Kovacs-Nolan, J., Snider, M., Babiuk, L.A., van Drunen Littel-van, den and Hurk, S. 2014. Inclusion of the bovine neutrophil beta-defensin 3 with glycoprotein D of bovine herpesvirus 1 in a DNA vaccine modulates immune responses of mice and cattle. Clinical and Vaccine Immunology 21 (4): 463–477. https://doi.org/10.1128/CVI.00696-13.
[46]
Magnan, C.N., A. Randall, and P. Baldi. 2009. SOLpro: Accurate sequence-based prediction of protein solubility. Bioinformatics 25 (17): 2200–2207. https://doi.org/10.1093/bioinformatics/btp386.
[47]
Majee, P., N. Jain, and A. Kumar. 2021. Designing of a multiepitope vaccine candidate against Nipah virus by in silico approach: a putative prophylactic solution for the deadly virus. Journal of Biomolecular Structure and Dynamics 39 (4): 1461–1480. https://doi.org/10.1080/07391102.2020.1734088.
[48]
María, R.R., Arturo, C.J. Alicia, J., Paulina, M.G. and Gerardo, A.O. 2017. The impact of bioinformatics on vaccine design and development. In Vaccines. InTech. https://doi.org/10.5772/intechopen.69273.
[49]
Molteni, M., S. Gemma, and C. Rossetti. 2016. The role of toll-like receptor 4 in infectious and noninfectious inflammation. Mediators of Inflammation 2016: 6978936. https://doi.org/10.1155/2016/6978936.
[50]
Murray, G.M., O'Neill, R.G., More, S.J., McElroy, M.C., Earley, B. and Cassidy, J.P. 2016. Evolving views on bovine respiratory disease: An appraisal of selected control measures – part 2. Veterinary Journal 217: 78–82. https://doi.org/10.1016/j.tvjl.2016.09.013.
[51]
Mustafa, A.S. 2013. In silico analysis and experimental validation of Mycobacterium tuberculosis-specific proteins and peptides of Mycobacterium tuberculosis for immunological diagnosis and vaccine development. Medical Principles and Practice 22 (Suppl. 1): 43–51. https://doi.org/10.1159/000354206.
[52]
Naz, A., Shahid, F., Butt, T.T., Awan, F.M., Ali, A. and Malik, A. 2020. Designing multi-epitope vaccines to combat emerging coronavirus disease 2019 (COVID-19) by employing immuno-informatics approach. Frontiers in Immunology 11: 1–13. https://doi.org/10.3389/fimmu.2020.01663.
[53]
Nielsen, M., Lundegaard, C., Blicher, T., Peters, B., Sette, A., Justesen, S., Buus, S. and Lund, O. 2008. Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. PLoS Computational Biology 4 (7): 1–10. https://doi.org/10.1371/journal.pcbi.1000107.
[54]
Pandey, R.K., Ojha, R., Aathmanathan, V.S., Krishnan, M. and Prajapati, V.K. 2018. Immunoinformatics approaches to design a novel multiepitope subunit vaccine against HIV infection. Vaccine 36 (17): 2262–2272. https://doi.org/10.1016/j.vaccine.2018.03.042.
[55]
Pathak, R.K., Lim, B., Kim, D.Y. and Kim, J.M. 2022. Designing multiepitope-based vaccine targeting surface immunogenic protein of Streptococcus agalactiae using immunoinformatics to control mastitis in dairy cattle. BMC Veterinary Research 18 (1): 1–17. https://doi.org/10.1186/s12917-022-03432-z.
[56]
Patra, B., Panigrahi, M., Kumar, H., Kaisa, K., Dutt, T. and Bhushan, B. 2023. Molecular and phylogenetic analysis of MHC class I exons 7–8 in a variety of cattle and buffalo breeds. Animal Biotechnology 34 (4): 1655–1661. https://doi.org/10.1080/10495398.2021.1999969.
[57]
Pavitrakar, D.V., Atre, N.M., Tripathy, A.S. and Shil, P. 2022. Design of a multiepitope peptide vaccine candidate against chandipura virus: An immuno-informatics study. Journal of Biomolecular Structure & Dynamics 40 (2): 648–659. https://doi.org/10.1080/07391102.2020.1816493.
[58]
Pei, H., Liu, J., Cheng, Y., Sun, C., Wang, C., Lu, Y., Ding, J., Zhou, J. and Xiang, H. 2005. Expression of SARS-coronavirus nucleocapsid protein in Escherichia coli and Lactococcus lactis for serodiagnosis and mucosal vaccination. Applied Microbiology and Biotechnology 68 (2): 220–227. https://doi.org/10.1007/s00253-004-1869-y.
[59]
Pritam, M., Singh, G., Swaroop, S., Singh, A.K., Pandey, B. and Singh, S.P. 2020. A cutting-edge immunoinformatics approach for design of multiepitope oral vaccine against dreadful human malaria. International Journal of Biological Macromolecules 158: 159–179. https://doi.org/10.1016/j.ijbiomac.2020.04.191.
[60]
Pyasi, S., Sharma, V., Dipti, K., Jonniya, N.A. and Nayak, D. 2021. Immunoinformatics approach to design multiepitope-subunit vaccine against bovine ephemeral fever disease. Vaccines 9 (8): 1–20. https://doi.org/10.3390/vaccines9080925.
[61]
Rana, A., and Y. Akhter. 2016. A multisubunit based, thermodynamically stable model vaccine using combined immunoinformatics and protein structure based approach. Immunobiology 221 (4): 544–557. https://doi.org/10.1016/j.imbio.2015.12.004.
[62]
Rawal, K., Sinha, R., Abbasi, B.A., Chaudhary, A., Nath, S.K., Kumari, P., Preeti, P., Saraf, D., Singh, S., Mishra, K. et al. 2021. Identification of vaccine targets in pathogens and design of a vaccine using computational approaches. Scientific Reports 11 (1): 17626. https://doi.org/10.1038/s41598-021-96863-x.
[63]
Reynisson, B., Alvarez, B., Paul, S., Peters, B. and Nielsen, M. 2021. NetMHCpan-4.1 and NetMHCIIpan-4.0: Improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Research 48 (W1): W449–W454. https://doi.org/10.1093/NAR/GKAA379.
[64]
Safavi, A., Kefayat, A., Mahdevar, E., Abiri, A. and Ghahremani, F. 2020. Exploring the out of sight antigens of SARS-CoV-2 to design a candidate multiepitope vaccine by utilizing immunoinformatics approaches. Vaccine 38 (48): 7612–7628. https://doi.org/10.1016/j.vaccine.2020.10.016.
[65]
Saha, S., and G.P.S. Raghava. 2006. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins: Structure, Function and Genetics 65 (1): 40–48. https://doi.org/10.1002/prot.21078.
[66]
Saif, L.J. 2010. Bovine respiratory coronavirus. Veterinary Clinics of North America - Food Animal Practice 26 (2): 349–364. https://doi.org/10.1016/j.cvfa.2010.04.005.
[67]
Santos Junior, M.N., Santos, R.S., Neves, W.S., Fernandesn J.M., de Brito Guimar?esn, B.C., Barbosa, M.S., Silva, L.S.C., Gomes, C.P., Rezende, I.S., Oliveira, C.N.T. et al. 2020. Immunoinformatics and analysis of antigen distribution of Ureaplasma diversum strains isolated from different Brazilian states. BMC Veterinary Research 16 (1): 1–16. https://doi.org/10.1186/s12917-020-02602-1.
[68]
Shruthi, S., Mohan, V., Maradana, M.R. and Aravindhan, V. 2019. In silico identification and wet lab validation of novel cryptic B-cell epitopes in ZnT8 zinc transporter autoantigen. International Journal of Biological Macromolecules 127: 657–664. https://doi.org/10.1016/j.ijbiomac.2019.01.198.
[69]
Singh, T., Fakiola, M., Oommen, J., Singh, A.P., Singh, A.K., Smith, N., Chakravarty, J., Sundar, S. and Blackwell, J.M. 2018. Epitope-binding characteristics for risk versus protective DRB1 alleles for visceral leishmaniasis. The Journal of Immunology 200 (8): 2727–2737. https://doi.org/10.4049/jimmunol.1701764.
[70]
Skwarczynski, M., and I. Toth. 2016. Peptide-based synthetic vaccines. Chemical Science 7 (2): 842–854. https://doi.org/10.1039/c5sc03892h.
[71]
Sohail, M.U., A.A. Al Thani, and H.M. Yassine. 2019. Comparative phylogenetic and residue analysis of hepatitis C virus E1 protein from the middle east and North Africa region. Hepatitis Monthly 19 (8): e92437. https://doi.org/10.5812/hepatmon.92437.
[72]
Tahir, U.l., Qamar, M., Rehman, A., Tusleem, K., Ashfaq, U.A., Qasim, M., Zhu, X, Fatima, I., Shahid, F. and Chen, L.L. 2018. Peptide vaccine against chikungunya virus: Immuno-informatics combined with molecular docking approach. Journal of Translational Medicine 16 (1): 1–14. https://doi.org/10.1186/s12967-018-1672-7.
[73]
Tahir, U.l., Qamar, M., Bari, A., Adeel, M.M., Maryam, A., Ashfaq, U.A., Du, X., Muneer, I., Ahmad, H.I. and Wang, J. 2020. Designing of a next generation multiepitope based vaccine (MEV) against SARS-CoV-2: Immunoinformatics and in silico approaches. PLoS One 15 (12): e0244176. https://doi.org/10.1371/journal.pone.0244176. Edited by M.Y.K. Barozai.
[74]
Takeshima, S.N., and Y. Aida. 2006. Structure, function and disease susceptibility of the bovine major histocompatibility complex. Animal Science Journal 77 (2): 138–150. https://doi.org/10.1111/j.1740-0929.2006.00332.x.
[75]
Ullah, M.A., B. Sarkar, and S.S. Islam. 2020. Exploiting the reverse vaccinology approach to design novel subunit vaccines against Ebola virus. Immunobiology 225 (3): 151949. https://doi.org/10.1016/j.imbio.2020.151949.
[76]
Vaure, C., and Y. Liu. 2014. A comparative review of toll-like receptor 4 expression and functionality in different animal species. Frontiers in Immunology 5 (JUL): 1–15. https://doi.org/10.3389/fimmu.2014.00316.
[77]
Vlasova, A.N., and L.J. Saif. 2021. Bovine coronavirus and the associated diseases. Frontiers in Veterinary Science 8: 643220. https://doi.org/10.3389/fvets.2021.643220. Frontiers Media S.A.
[78]
Wilkins, M. R., Gasteiger, E., Bairoch, A., Sanchez, J. C., Williams, K. L., Appel, R. D., and Hochstrasser, D. F. 1999. Protein identification and analysis tools in the ExPASy server. Methods in molecular biology (Clifton, N.J.), 112: 531–552. https://doi.org/10.1385/1-59259-584-7:531.
[79]
Y?lmaz ?olak, ?. 2021. Computational design of a multiepitope vaccine against Clostridium chauvoei: An immunoinformatics approach. International Journal of Peptide Research and Therapeutics 27 (4): 2639–2649. https://doi.org/10.1007/s10989-021-10279-9.
[80]
Ysrafil, Y., Sapiun, Z., Astuti, I., Anasiru, M.A., Slamet, N.S., Hartati, H., Husain, F. and Damiti, S.A. 2022. Designing multiepitope based peptide vaccine candidates against SARS-CoV-2 using immunoinformatics approach. BioImpacts 12 (4): 359–370. https://doi.org/10.34172/bi.2022.23769.
[81]
Yuan, S., H.C.S. Chan, and Z. Hu. 2017. Using PyMOL as a platform for computational drug design. Wiley Interdisciplinary Reviews: Computational Molecular Science 7 (2): e1298. https://doi.org/10.1002/wcms.1298.
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