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Vaccines and Immunoinformatics for Vaccine Design

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Translational Informatics

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1368))

Abstract

The host immune system recognizes and responds to the selective antigens or epitopes (immunome) of the intruding pathogen over an entire organism. The immune response so generated is ample to confer the desired immunity and protection to the host. This led to the conception of immunome-derived vaccines that exploit selective genome-derived antigens or epitopes from the pathogen’s immunome and not its entire genome or proteome. These are designed to elicit the required immune response and confer protection against future invasions by the same pathogen. Immunoinformatics through its epitope mapping tools allows direct selection of antigens from a pathogen’s genome or proteome, which is critical for the generation of an effective vaccine. This paved way for novel vaccine design strategies based on the mapped epitopes for translational applications that includes prophylactic, therapeutic, and personalized vaccines. In this chapter, various Immunoinformatics tools for epitope mapping are presented along with their applications. The methodology for immunoinformatics-assisted vaccine design is also outlined.

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References

  1. Terry FE, Moise L, Martin RF et al (2015) Time for T? Immunoinformatics addresses vaccine design for neglected tropical and emerging infectious diseases. Expert Rev Vaccines 14:21–35

    Article  CAS  PubMed  Google Scholar 

  2. Poland GA, Whitaker JA, Poland CM et al (2016) Vaccinology in the third millennium: scientific and social challenges. Curr Opin Virol 17:116–125

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Servín-Blanco R, Zamora-Alvarado R, Gevorkian G et al (2016) Antigenic variability: obstacles on the road to vaccines against traditionally difficult targets. Hum Vaccin Immunother 12:2640–2648

    Article  PubMed  PubMed Central  Google Scholar 

  4. De Groot AS, Martin W (2003) From immunome to vaccine: epitope mapping and vaccine design tools. Novartis Found Symp 254:57–72

    PubMed  Google Scholar 

  5. Doytchinova IA, Taylor P, Flower DR (2003) Proteomics in vaccinology and immunobiology: an informatics perspective of the immunone. J Biomed Biotechnol 2003:267–290

    Article  PubMed  PubMed Central  Google Scholar 

  6. Jongeneel V (2001) Towards a cancer immunome database. Cancer Immun 1:3

    CAS  PubMed  Google Scholar 

  7. Klade CS (2002) Proteomics approaches towards antigen discovery and vaccine development. Curr Opin Mol Ther 4:216–223

    CAS  PubMed  Google Scholar 

  8. Pederson T (1999) The immunome. Mol Immunol 36:1127–1128

    Article  CAS  PubMed  Google Scholar 

  9. Petrovsky N, Brusic V (2002) Computational immunology: the coming of age. Immunol Cell Biol 80:248–254

    Article  PubMed  Google Scholar 

  10. Rappuoli R (2001) Reverse vaccinology, a genome-based approach to vaccine development. Vaccine 19:2688–2691

    Article  CAS  PubMed  Google Scholar 

  11. Etz H, Minh DB, Henics T et al (2002) Identification of in vivo expressed vaccine candidate antigens from Staphylococcus aureus. Proc Natl Acad Sci U S A 99:6573–6578

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Weichhart T, Horky M, Söllner J et al (2003) Functional selection of vaccine candidate peptides from Staphylococcus aureus whole-genome expression libraries in vitro. Infect Immun 71:4633–4641

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Adamou JE, Heinrichs JH, Erwin AL et al (2001) Identification and characterization of a novel family of pneumococcal proteins that are protective against sepsis. Infect Immun 69:949–958

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Tettelin H, Masignani V, Cieslewicz MJ et al (2002) Complete genome sequence and comparative genomic analysis of an emerging human pathogen, serotype V Streptococcus agalactiae. Proc Natl Acad Sci U S A 99:12391–12396

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ross BC, Czajkowski L, Hocking D et al (2001) Identification of vaccine candidate antigens from a genomic analysis of Porphyromonas gingivalis. Vaccine 19:4135–4142

    Article  CAS  PubMed  Google Scholar 

  16. Montigiani S, Falugi F, Scarselli M et al (2002) Genomic approach for analysis of surface proteins in Chlamydia pneumoniae. Infect Immun 70:368–379

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Mathiassen S, Lauemøller SL, Ruhwald M et al (2001) Tumor-associated antigens identified by mRNA expression profiling induce protective anti-tumor immunity. Eur J Immunol 31:1239–1246

    Article  CAS  PubMed  Google Scholar 

  18. Robinson WH, Garren H, Utz PJ et al (2002) Millennium award. Proteomics for the development of DNA tolerizing vaccines to treat autoimmune disease. Clin Immunol 103:7–12

    Article  CAS  PubMed  Google Scholar 

  19. De Groot AS (2004) Immunome-derived vaccines. Expert Opin Biol Ther 4:767–772

    Article  PubMed  Google Scholar 

  20. Ortutay C, Vihinen M (2009) Immunome knowledge base (IKB): an integrated service for immunome research. BMC Immunol 10:3

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Chaplin DD (2010) Overview of the immune response. J Allergy Clin Immunol 125:S3–S23

    Article  PubMed  PubMed Central  Google Scholar 

  22. Sarkander J, Hojyo S, Tokoyoda K (2016) Vaccination to gain humoral immune memory. Clin Transl Immunology 5:e120

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Altuvia Y, Margalit H (2004) A structure-based approach for prediction of MHC-binding peptides. Methods 34:454–459

    Article  CAS  PubMed  Google Scholar 

  24. Bian H, Hammer J (2004) Discovery of promiscuous HLA-II-restricted T cell epitopes with TEPITOPE. Methods 34:468–475

    Article  CAS  PubMed  Google Scholar 

  25. Brusic V, Bajic VB, Petrovsky N (2004) Computational methods for prediction of T-cell epitopes—a framework for modelling, testing, and applications. Methods 34:436–443

    Article  CAS  PubMed  Google Scholar 

  26. De Groot AS, Bishop EA, Khan B et al (2004) Engineering immunogenic consensus T helper epitopes for a cross-clade HIV vaccine. Methods 34:476–487

    Article  PubMed  CAS  Google Scholar 

  27. Doytchinova IA, Guan P, Flower DR (2004) Quantitative structure-activity relationships and the prediction of MHC supermotifs. Methods 34:444–453

    Article  CAS  PubMed  Google Scholar 

  28. Sung MH, Simon R (2004) Candidate epitope identification using peptide property models: application to cancer immunotherapy. Methods 34:460–467

    Article  CAS  PubMed  Google Scholar 

  29. Singh H, Raghava GP (2001) ProPred: prediction of HLA-DR binding sites. Bioinformatics 17:1236–1237

    Article  CAS  PubMed  Google Scholar 

  30. Zhang GL, Khan AM, Srinivasan KN et al (2005) MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides. Nucleic Acids Res 33:W172–W179

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Guan P, Hattotuwagama CK, Doytchinova IA et al (2006) MHCPred 2.0: an updated quantitative T-cell epitope prediction server. Appl Bioinforma 5:55–61

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Larsen MV, Lundegaard C, Lamberth K et al (2007) Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinformatics 8:424

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Peters B, Sidney J, Bourne P et al (2005) The immune epitope database and analysis resource: from vision to blueprint. PLoS Biol 3:e91

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Lefranc MP, Giudicelli V, Ginestoux C et al (2009) IMGT, the international ImMunoGeneTics information system. Nucleic Acids Res 37:D1006–D1012

    Article  CAS  PubMed  Google Scholar 

  36. Schubert B, Brachvogel HP, Jürges C et al (2015) EpiToolKit—a web-based workbench for vaccine design. Bioinformatics 31:2211–2213

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Bhasin M, Raghava GP (2003) Prediction of promiscuous and high-affinity mutated MHC binders. Hybrid Hybridomics 22:229–234

    Article  CAS  PubMed  Google Scholar 

  38. Rammensee H, Bachmann J, Emmerich NP et al (1999) SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50:213–219

    Article  CAS  PubMed  Google Scholar 

  39. Ponomarenko J, Bui HH, Li W et al (2008) ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics 9:514

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  41. Saxová P, Buus S, Brunak S et al (2003) Predicting proteasomal cleavage sites: a comparison of available methods. Int Immunol 15:781–787

    Article  PubMed  CAS  Google Scholar 

  42. Odorico M, Pellequer JL (2003) BEPITOPE: predicting the location of continuous epitopes and patterns in proteins. J Mol Recognit 16:20–22

    Article  CAS  PubMed  Google Scholar 

  43. Kringelum JV, Lundegaard C, Lund O et al (2012) Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS Comput Biol 8:e1002829

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Jespersen MC, Peters B, Nielsen M et al (2017) BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res 45:W24–W29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Mayrose I, Penn O, Erez E et al (2007) Pepitope: epitope mapping from affinity-selected peptides. Bioinformatics 23:3244–3246

    Article  CAS  PubMed  Google Scholar 

  46. El-Manzalawy Y, Dobbs D, Honavar V (2008) Predicting flexible length linear B-cell epitopes. Comput Syst Bioinformatics Conf 7:121–132

    Article  PubMed  PubMed Central  Google Scholar 

  47. van Endert PM, Tampé R, Meyer TH et al (1994) A sequential model for peptide binding and transport by the transporters associated with antigen processing. Immunity 1:491–500

    Article  PubMed  Google Scholar 

  48. Keşmir C, Nussbaum AK, Schild H et al (2002) Prediction of proteasome cleavage motifs by neural networks. Protein Eng 15:287–296

    Article  PubMed  Google Scholar 

  49. Nussbaum AK, Kuttler C, Hadeler KP et al (2001) PAProC: a prediction algorithm for proteasomal cleavages available on the WWW. Immunogenetics 53:87–94

    Article  CAS  PubMed  Google Scholar 

  50. Bhasin M, Raghava GP (2004) Analysis and prediction of affinity of TAP binding peptides using cascade SVM. Protein Sci 13:596–607

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Dönnes P, Kohlbacher O (2005) Integrated modeling of the major events in the MHC class I antigen processing pathway. Protein Sci 14:2132–2140

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Zhang GL, Petrovsky N, Kwoh CK et al (2006) PRED(TAP): a system for prediction of peptide binding to the human transporter associated with antigen processing. Immunome Res 2:3

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Petrovsky N, Brusic V (2004) Virtual models of the HLA class I antigen processing pathway. Methods 34:429–435

    Article  CAS  PubMed  Google Scholar 

  54. Gomez-Perosanz M, Ras-Carmona A, Reche PA (2020) PCPS: a web server to predict proteasomal cleavage sites. Methods Mol Biol 2131:399–406

    Article  PubMed  Google Scholar 

  55. Bhasin M, Raghava GP (2005) Pcleavage: an SVM based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences. Nucleic Acids Res 33:W202–W207

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Kalita JK, Chandrashekar K, Hans R et al (2006) Computational modelling and simulation of the immune system. Int J Bioinforma Res Appl 2:63–88

    Article  CAS  Google Scholar 

  57. Pizza M, Scarlato V, Masignani V et al (2000) Identification of vaccine candidates against serogroup B meningococcus by whole-genome sequencing. Science 287:1816–1820

    Article  CAS  PubMed  Google Scholar 

  58. Gallimore A, Hengartner H, Zinkernagel R (1998) Hierarchies of antigen-specific cytotoxic T-cell responses. Immunol Rev 164:29–36

    Article  CAS  PubMed  Google Scholar 

  59. Morris S, Kelley C, Howard A et al (2000) The immunogenicity of single and combination DNA vaccines against tuberculosis. Vaccine 18:2155–2163

    Article  CAS  PubMed  Google Scholar 

  60. Zhao B, Sakharkar KR, Lim CS et al (2007) MHC–peptide binding prediction for epitope based vaccine design. Int J Integr Biol 1:127–140

    CAS  Google Scholar 

  61. Florea L, Halldórsson B, Kohlbacher O et al (2003) Epitope prediction algorithms for peptide-based vaccine design. Proc IEEE Comput Soc Bioinform Conf 2:17–26

    PubMed  Google Scholar 

  62. Doytchinova IA, Flower DR (2007) VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 8:4

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Harish N, Gupta R, Agarwal P et al (2006) DyNAVacS: an integrative tool for optimized DNA vaccine design. Nucleic Acids Res 34:W264–W266

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Vivona S, Bernante F, Filippini F (2006) NERVE: new enhanced reverse vaccinology environment. BMC Biotechnol 6:35

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. He Y, Xiang Z, Mobley HL (2010) Vaxign: the first web-based vaccine design program for reverse vaccinology and applications for vaccine development. J Biomed Biotechnol 2010:297505

    PubMed  PubMed Central  Google Scholar 

  66. He Y, Racz R, Sayers S et al (2014) Updates on the web-based VIOLIN vaccine database and analysis system. Nucleic Acids Res 42:D1124–D1132

    Article  CAS  PubMed  Google Scholar 

  67. Gong T, Cai Z (2005) Visual modeling and simulation of adaptive immune system. Conf Proc IEEE Eng Med Biol Soc 2005:6116–6119

    PubMed  Google Scholar 

  68. De Groot AS, Rappuoli R (2004) Genome-derived vaccines. Expert Rev Vaccines 3:59–76

    Article  PubMed  Google Scholar 

  69. Castiglione F, Liso A (2005) The role of computational models of the immune system in designing vaccination strategies. Immunopharmacol Immunotoxicol 27:417–432

    Article  PubMed  Google Scholar 

  70. Bahrami AA, Payandeh Z, Khalili S et al (2019) Immunoinformatics: in silico approaches and computational design of a multi-epitope, immunogenic protein. Int Rev Immunol 38:307–322

    Article  CAS  PubMed  Google Scholar 

  71. Imler JL, Hoffmann JA (2001) Toll receptors in innate immunity. Trends Cell Biol 11:304–311

    Article  CAS  PubMed  Google Scholar 

  72. Enshell-Seijffers D, Denisov D, Groisman B et al (2003) The mapping and reconstitution of a conformational discontinuous B-cell epitope of HIV-1. J Mol Biol 334:87–101

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

This work was supported by the COVID-19 Research Projects of West China Hospital Sichuan University (Grant no. HX-2019-nCoV-057), the Regional Innovation Cooperation between Sichuan and Guangxi Provinces (2020YFQ0019) and the National Natural Science Foundation of China (32070671).

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Author notes

  1. Shikha Joon and Rajeev K. Singla contributed equally to this work.

Authors and Affiliations

  1. Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China

    Shikha Joon, Rajeev K. Singla & Bairong Shen

  2. iGlobal Research and Publishing Foundation, New Delhi, India

    Shikha Joon & Rajeev K. Singla

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  1. Shikha Joon
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  2. Rajeev K. Singla
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  3. Bairong Shen
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Correspondence to Bairong Shen .

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  1. Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China

    Bairong Shen

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Joon, S., Singla, R.K., Shen, B. (2022). Vaccines and Immunoinformatics for Vaccine Design. In: Shen, B. (eds) Translational Informatics. Advances in Experimental Medicine and Biology, vol 1368. Springer, Singapore. https://doi.org/10.1007/978-981-16-8969-7_5

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