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In-silico design of envelope based multi-epitope vaccine candidate against Kyasanur forest disease virus

  • 1.

    Work, T. H., A new virus disease in India. Summary of preliminary report on investigations of the Virus Research Center on an epidemic disease affecting forest villagers and wild monkeys of Shimoga Districk, Mysore. Indian J. Med. Sci. 11, 341–342 (1957).

    CAS 
    PubMed 

    Google Scholar 

  • 2.

    Sreenivasanh, M. A., Bhat, R. & Rajagopalan, P. K. The epizootics of kyasanur forest disease in wild monkeys during 1964 to 1973. Trans. R. Soc. Trop. Med. Hyg. 80, 810–814 (1986).

    Article 

    Google Scholar 

  • 3.

    Sreejith, K. A. B. K. N. K. Kyasanur forest disease virus breaking the endemic barrier: An investigation into ecological effects on disease emergence and future outlook. 1–8. https://doi.org/10.1111/zph.12349 (2017).

  • 4.

    Yadav, P. D. et al. Phylogeography of Kyasanur Forest Disease virus in India (1957–2017) reveals evolution and spread in the Western Ghats region. Sci. Rep. 1–12. https://doi.org/10.1038/s41598-020-58242-w (2020).

  • 5.

    Naren Babu, N. et al. Spatial distribution of Haemaphysalis species ticks and human Kyasanur Forest Disease cases along the Western Ghats of India, 2017–2018. Exp. Appl. Acarol. 77, 435–447 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 6.

    Dodd, K. A. et al. Ancient ancestry of KFDV and AHFV revealed by complete genome analyses of viruses isolated from ticks and Mammalian hosts. PLoS Negl. Trop. Dis. 5, 1–7 (2011).

    Article 
    CAS 

    Google Scholar 

  • 7.

    Gritsun, D. J., Jones, I. M., Gould, E. A. & Gritsun, T. S. Molecular archaeology of Flaviviridae untranslated regions: Duplicated RNA structures in the replication enhancer of flaviviruses and pestiviruses emerged via convergent evolution. PLoS One 9, 1–11 (2014).

    Article 
    CAS 

    Google Scholar 

  • 8.

    Chakraborty, S., Andrade, F. C. D., Ghosh, S., Uelmen, J. & Ruiz, M. O. Historical expansion of Kyasanur Forest Disease in India from 1957 to 2017: A retrospective analysis. GeoHealth 3, 44–55 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 9.

    Gurav, Y. K. et al. Kyasanur Forest Disease prevalence in Western Ghats proven and confirmed by recent outbreak in Maharashtra, India, 2016. Vector-Borne Zoonotic Dis. 18, 164–172 (2018).

    PubMed 
    Article 

    Google Scholar 

  • 10.

    Mehendale, S. et al. Kyasanur Forest Disease outbreak and vaccination strategy, Shimoga District, India 2013–2014. Emerg. Infect. Dis. 21, 2013–2014 (2019).

    Google Scholar 

  • 11.

    Kasabi, G. S., Murhekar, M. V., Sandhya, V. K. & Raghunandan, R. Coverage and effectiveness of Kyasanur Forest Disease (KFD) vaccine in Karnataka, South India, 2005–10. PLoS Negl. Trop. Dis. 7, 13–16 (2013).

    Article 

    Google Scholar 

  • 12.

    Shil, P., Yadav, P. D., Patil, A. A., Balasubramanian, R. & Mourya, D. T. Bioinformatics characterization of envelope glycoprotein from Kyasanur Forest disease virus. Indian J. Med. Res. https://doi.org/10.4103/ijmr.IJMR (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 13.

    de Sousa, C. B. P., da Soares, I. S. & Rosa, D. S. Editorial: Epitope discovery and synthetic vaccine design. Front. Immunol. 9, 9–11 (2018).

    Article 
    CAS 

    Google Scholar 

  • 14.

    Ali, M., Pandey, R. K., Khatoon, N., Narula, A. & Mishra, A. Exploring dengue genome to construct a multi-epitope based subunit vaccine by utilizing immunoinformatics approach to battle against dengue infection. Sci. Rep. https://doi.org/10.1038/s41598-017-09199-w (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 15.

    Can, H., Köseoğlu, A. E., Alak, S. E., Güvendi, M. & Döşkaya, M. In silico discovery of antigenic proteins and epitopes of SARS-CoV-2 for the development of a vaccine or a diagnostic approach for COVID-19. Sci. Rep. https://doi.org/10.1038/s41598-020-79645-9 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 16.

    Kar, T. et al. A candidate multi-epitope vaccine against SARS-CoV-2. Sci. Rep. https://doi.org/10.1038/s41598-020-67749-1 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 17.

    Behmard, E., Soleymani, B., Najafi, A. & Barzegari, E. Immunoinformatic design of a COVID-19 subunit vaccine using entire structural immunogenic epitopes of SARS-CoV-2. Sci. Rep. https://doi.org/10.1038/s41598-020-77547-4 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 18.

    Bibi, S., Ullah, I., Zhu, B., Adnan, M. & Liaqat, R. In silico analysis of epitope-based vaccine candidate against tuberculosis using reverse vaccinology. Sci. Rep. https://doi.org/10.1038/s41598-020-80899-6 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 19.

    Klimka, A. et al. Epitope-specific immunity against Staphylococcus aureus coproporphyrinogen III oxidase. NPJ Vaccines https://doi.org/10.1038/s41541-020-00268-2 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 20.

    Palanisamy, N., Akaberi, D., Lennerstrand, J. & Lundkvist, Å. Comparative genome analysis of Alkhumra hemorrhagic fever virus with Kyasanur forest disease and tick-borne encephalitis viruses by the in silico approach. Pathog. Glob. Health 112, 1–17 (2018).

    Article 
    CAS 

    Google Scholar 

  • 21.

    Devadiga, S., McElroy, A. K., Prabhu, S. G. & Arunkumar, G. Dynamics of human B and T cell adaptive immune responses to Kyasanur forest disease virus infection. Sci. Rep. 10, 1–9 (2020).

    Article 
    CAS 

    Google Scholar 

  • 22.

    Liu, G. et al. Immunogenicity and efficacy of flagellin-envelope fusion dengue. Clin. Vaccine Immunol. 22, 516–525 (2015).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 23.

    Bauer, A. et al. Preferential targeting of conserved gag regions after vaccination with a heterologous DNA prime-modified vaccinia virus Ankara boost HIV-1 vaccine regimen. J. Virol. 91, e00730-17 (2017).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 24.

    Rajaiah, P. Kyasanur Forest Disease in India: Innovative options for intervention. Hum. Vaccines Immunother. 15, 2243–2248 (2019).

    Article 

    Google Scholar 

  • 25.

    Sette, A. et al. Definition of epitopes and antigens recognized by vaccinia specific immune responses: Their conservation in variola virus sequences, and use as a model system to study complex pathogens. Vaccine 27(Suppl 6), G21–G26 (2009).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 26.

    Mackay, L. K. et al. T Cell Detection of a B-Cell Tropic Virus Infection: Newly-Synthesised Versus Mature Viral Proteins as Antigen Sources for CD4 and CD8 Epitope Display. 5, (2009).

  • 27.

    De Gregorio, E., Caproni, E. & Ulmer, J. B. Vaccine adjuvants: Mode of action. Front. Immunol. 4, 1–6 (2013).

    Article 
    CAS 

    Google Scholar 

  • 28.

    Tani, K. et al. Defensins act as potent adjuvant taht promote cellular and humoral immune response in mice to a lymphhoma idiotype and carrier antigents. Int. Immunol. 12, 691–700 (2000).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 29.

    Barton, G. M. Viral recognition by Toll-like receptors. Semin Immunol. 19, 33–40 (2007).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 30.

    Zheng, M. et al. TLR2 senses the SARS-CoV-2 envelope protein to produce inflammatory cytokines. Nat. Immunol. https://doi.org/10.1038/s41590-021-00937-x (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 31.

    Xagorari, A. & Chlichlia, K. Toll-like receptors and viruses: Induction of innate antiviral immune responses. Open Microbiol. J. 2, 49–59 (2008).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 32.

    Pauling, L., Corey, R. B. & Branson, H. R. The structure of proteins: two hydrogen-bonded helical configurations of the polypeptide chain. Proc. Natl. Acad. Sci. 37, 205–211 (1951).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 33.

    Pauling, L. & Corey, R. B. The pleated sheet, a new layer configuration of polypeptide chains. Proc. Natl. Acad. Sci. U. S. A. 37, 251–256 (1951).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 34.

    Carsetti, R. The role of memory B cells in immunity after vaccination. Paediatr. Child Health 19, S160–S162 (2009).

    Article 

    Google Scholar 

  • 35.

    Palm, A. E. & Henry, C. Remembrance of things past: Long-term B cell memory after infection and vaccination. Front. Immunol. 10, 1–13 (2019).

    CAS 
    Article 

    Google Scholar 

  • 36.

    Cox, R. J. & Brokstad, K. A. Not just antibodies: B cells and T cells mediate immunity to COVID-19. Nat. Rev. Immunol. https://doi.org/10.1038/s41577-020-00436-4 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 37.

    Rosano, G. L. & Ceccarelli, E. A. Recombinant protein expression in Escherichia coli: Advances and challenges. Front. Microbiol. 5, 1–17 (2014).

    Google Scholar 

  • 38.

    Kim, Y., Sidney, J., Pinilla, C., Sette, A. & Peters, B. Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior. BMC Bioinform. 10, 1–11 (2009).

    Google Scholar 

  • 39.

    Nielsen, M. et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 12, 1007–1017 (2003).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 40.

    Lundegaard, C., Lund, O. & Nielsen, M. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. Bioinformatics 24, 1397–1398 (2008).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 41.

    Peters, B. & Sette, A. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinform. 6, 1–9 (2005).

    Article 
    CAS 

    Google Scholar 

  • 42.

    Sidney, J. et al. Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries. Immunome Res. 4, 1–14 (2008).

    Article 
    CAS 

    Google Scholar 

  • 43.

    Tenzer, S. et al. Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding. Cell. Mol. Life Sci. 62, 1025–1037 (2005).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 44.

    Peters, B., Bulik, S., Tampe, R., van Endert, P. M. & Holzhütter, H.-G. Identifying MHC class I epitopes by predicting the TAP transport efficiency of epitope precursors. J. Immunol. 171, 1741–1749 (2003).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 45.

    Hakenberg, J. et al. MAPPP: MHC class I antigenic peptide processing prediction. Appl. Bioinform. 2, 155–158 (2003).

    CAS 

    Google Scholar 

  • 46.

    Lin, H. H., Ray, S., Tongchusak, S., Reinherz, E. L. & Brusic, V. Evaluation of MHC class I peptide binding prediction servers: Applications for vaccine research. BMC Immunol. 9, 1–13 (2008).

    Article 
    CAS 

    Google Scholar 

  • 47.

    Wang, P. et al. Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinform. 11, 568 (2010).

    Article 
    CAS 

    Google Scholar 

  • 48.

    Wang, P. et al. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput. Biol. 4, e1000048 (2008).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 49.

    Lata, S., Bhasin, M. & Raghava, G. P. S. Application of machine learning techniques in predicting MHC binders. In Immunoinformatics: Predicting Immunogenicity In Silico (ed. Flower, D. R.) 201–215 (Humana Press, 2007). https://doi.org/10.1007/978-1-60327-118-9_14.

    Chapter 

    Google Scholar 

  • 50.

    Saha, S. & Raghava, G. P. S. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65, 40–48 (2006).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 51.

    Chen, J., Liu, H., Yang, J. & Chou, K. C. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids 33, 423–428 (2007).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 52.

    El-Manzalawy, Y., Dobbs, D. & Honavar, V. Predicting linear B-cell epitopes using string kernels. J. Mol. Recognit. 21, 243–255 (2008).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 53.

    Yao, B., Zhang, L., Liang, S. & Zhang, C. SVMTriP: A method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity. PLoS One 7, 5–9 (2012).

    Google Scholar 

  • 54.

    Nezafat, N., Ghasemi, Y., Javadi, G., Khoshnoud, M. J. & Omidinia, E. A novel multi-epitope peptide vaccine against cancer: An in silico approach. J. Theor. Biol. 349, 121–134 (2014).

    ADS 
    MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 

    Google Scholar 

  • 55.

    Saha, S. & Raghava, G. P. S. AlgPred: Prediction of allergenic proteins and mapping of IgE epitopes. Nucl. Acids Res. 34, 202–209 (2006).

    Article 
    CAS 

    Google Scholar 

  • 56.

    Doytchinova, I. A. & Flower, D. R. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform. 8, 1–7 (2007).

    Article 
    CAS 

    Google Scholar 

  • 57.

    Gasteiger, E. et al. Protein identification and analysis tools in the ExPASy server (ed. Walker, J. M.) 571–607 (Humana Press Inc, 2005).

    Google Scholar 

  • 58.

    Smialowski, P., Doose, G., Torkler, P., Kaufmann, S. & Frishman, D. PROSO II—A new method for protein solubility prediction. FEBS J. 279, 2192–2200 (2012).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 59.

    Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. E. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10, 845–858 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 60.

    Xu, D. & Zhang, Y. Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys. J. 101, 2525–2534 (2011).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 61.

    Shin, W.-H., Lee, G. R., Heo, L., Lee, H. & Seok, C. Prediction of protein structure and interaction by GALAXY protein modeling programs. Bio Des. 2, 1–11 (2014).

    Google Scholar 

  • 62.

    Ko, J., Park, H., Heo, L. & Seok, C. GalaxyWEB server for protein structure prediction and refinement. Nucl. Acids Res. 40, 294–297 (2012).

    Article 
    CAS 

    Google Scholar 

  • 63.

    Carugo, O. & Djinović-Carugo, K. A proteomic Ramachandran plot (PRplot). Amino Acids 44, 781–790 (2013).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 64.

    Wiederstein, M. & Sippl, M. J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucl. Acids Res. 35, 407–410 (2007).

    Article 

    Google Scholar 

  • 65.

    Sippl, M. J. Recognition of errors in three-dimensional structures of proteins. Proteins Struct. Funct. Bioinform. 17, 355–362 (1993).

    CAS 
    Article 

    Google Scholar 

  • 66.

    Vajda, S. et al. New additions to the ClusPro server motivated by CAPRI. Proteins Struct. Funct. Bioinform. 85, 435–444 (2017).

    CAS 
    Article 

    Google Scholar 

  • 67.

    Schneidman-duhovny, D., Inbar, Y., Nussinov, R. & Wolfson, H. J. PatchDock and SymmDock: Servers for rigid and symmetric docking. Nucl. Acids Res. 33, 363–367 (2005).

    Article 
    CAS 

    Google Scholar 

  • 68.

    Mashiach, E., Schneidman-Duhovny, D., Andrusier, N., Nussinov, R. & Wolfson, H. J. FireDock: A web server for fast interaction refinement in molecular docking. Nucl. Acids Res. 36, W229–W232 (2008).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 69.

    Andrusier, N., Nussinov, R. & Wolfson, H. J. FireDock: Fast interaction refinement in molecular docking. Proteins 69, 139–159 (2007).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 70.

    Wallace, A. C., Laskowski, R. A. & Thornton, J. M. LIGPLOT: A program to generate schematic diagrams of protein–ligand interactions. Protein Eng. 8, 127–134 (1995).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 71.

    Lopéz-blanco, J. R., Garzón, J. I. & Chacón, P. iMod: Multipurpose normal mode analysis in internal coordinates. Bioinformatics 27, 2843–2850 (2011).

    PubMed 
    Article 
    CAS 

    Google Scholar 

  • 72.

    Aliaga, I., Quintana-ort, E. S. & Chac, P. iMODS: Internal coordinates normal mode analysis server. Nucl. Acids Res. 42, 271–276 (2014).

    Article 
    CAS 

    Google Scholar 

  • 73.

    Kovacs, J. A., Chaco, P. & Abagyan, R. Predictions of protein flexibility: First-order measures. Proteins Struct. Funct. Bioinform. 668, 661–668 (2004).

    Article 
    CAS 

    Google Scholar 

  • 74.

    Grote, A. et al. JCat: A novel tool to adapt codon usage of a target gene to its potential expression host. Nucl. Acids Res. 33, 526–531 (2005).

    ADS 
    Article 
    CAS 

    Google Scholar 

  • 75.

    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, e9862 (2010).

    MATH 

    Google Scholar 

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