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Reverse Vaccinology: The Pathway from Genomes and Epitope Predictions to Tailored Recombinant Vaccines

  • Marcin Michalik
  • Bardya Djahanshiri
  • Jack C. Leo
  • Dirk Linke
Part of the Methods in Molecular Biology book series (MIMB, volume 1403)

Abstract

In this chapter, we review the computational approaches that have led to a new generation of vaccines in recent years. There are many alternative routes to develop vaccines based on the technology of reverse vaccinology. We focus here on bacterial infectious diseases, describing the general workflow from bioinformatic predictions of antigens and epitopes down to examples where such predictions have been used successfully for vaccine development.

Keywords

Major Histocompatibility Complex Prediction Tool Major Histocompatibility Complex Molecule Peptide Epitope Major Histocompatibility Complex Allele 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Janeway CAJ, Travers P, Walport M et al (2001) Immunobiology. Garland Science, New YorkGoogle Scholar
  2. 2.
    Alberts B, Johnson A, Walter P et al (2007) Molecular biology of the cell. Taylor & Francis, New YorkGoogle Scholar
  3. 3.
    Neumann J (2008) Immunbiologie. Springer-Lehrbuch, Berlin, HeidelbergCrossRefGoogle Scholar
  4. 4.
    Saha B (2001) Encyclopedia of life sciences. Wiley, Chichester, UKGoogle Scholar
  5. 5.
    WHO UNICEF World Bank (2009) State of the world’s vaccines and immunization. World Health Organization, GenevaGoogle Scholar
  6. 6.
    Flower DR (2009) Bioinformatics for vaccinology. Wiley, Chichester, UKGoogle Scholar
  7. 7.
    Rinaudo CD, Telford JL, Rappuoli R et al (2009) Vaccinology in the genome era. J Clin Invest 119:2515–2525CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Seib KL, Zhao X, Rappuoli R (2012) Developing vaccines in the era of genomics: a decade of reverse vaccinology. Clin Microbiol Infect 18:109–116CrossRefPubMedGoogle Scholar
  9. 9.
    Pizza M, Scarlato V, Masignani V et al (2000) Identification of vaccine candidates against serogroup B meningococcus by whole-genome sequencing. Science 287:1816–1820CrossRefPubMedGoogle Scholar
  10. 10.
    Medicinal products and human use. Bexsero. Technical report, European Medicines Agency. http://www.ema.europa.eu/docs/en_GB/document_library/EPAR_
  11. 11.
    Tettelin H, Masignani V, Cieslewicz MJ et al (2005) Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial pan-genome. Proc Natl Acad Sci USA 102:13950–13955CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Vernikos G, Medini D, Riley DR et al (2014) Ten years of pan-genome analyses. Curr Opin Microbiol 23C:148–154Google Scholar
  13. 13.
    Hiller NL, Janto B, Hogg JS et al (2007) Comparative genomic analyses of seventeen Streptococcus pneumoniae strains: insights into the pneumococcal supragenome. J Bacteriol 189:8186–8195CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Thein M, Sauer G, Paramasivam N et al (2010) Efficient subfractionation of Gram-negative bacteria for proteomics studies. J Proteome Res 9:6135–6147CrossRefPubMedGoogle Scholar
  15. 15.
    Emanuelsson O, Brunak S, von Heijne G et al (2007) Locating proteins in the cell using TargetP, SignalP and related tools. Nat Protoc 2:953–971CrossRefPubMedGoogle Scholar
  16. 16.
    Punta M, Forrest LR, Bigelow H et al (2007) Membrane protein prediction methods. Methods 41:460–474CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Su EC-Y, Chiu H-S, Lo A et al (2007) Protein subcellular localization prediction based on compartment-specific features and structure conservation. BMC Bioinformatics 8:330CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Yu NY, Wagner JR, Laird MR et al (2010) PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26:1608–1615CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Yu C-S, Chen Y-C, Lu C-H et al (2006) Prediction of protein subcellular localization. Proteins 64:643–651CrossRefPubMedGoogle Scholar
  20. 20.
    Rashid M, Saha S, Raghava GP (2007) Support vector machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs. BMC Bioinformatics 8:337CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Chou KC, Shen HB (2006) Large-scale predictions of gram-negative bacterial protein subcellular locations. J Proteome Res 5:3420–3428CrossRefPubMedGoogle Scholar
  22. 22.
    Paramasivam N, Linke D (2011) Clubsub-P: cluster-based subcellular localization prediction for gram-negative bacteria and archaea. Front Microbiol 2:218CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Dunston CR, Herbert R, Griffiths HR (2015) Improving T cell-induced response to subunit vaccines: opportunities for a proteomic systems approach. J Pharm Pharmacol 67(3):290–9CrossRefPubMedGoogle Scholar
  24. 24.
    Zhang H, Lund O, Nielsen M (2009) The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding. Bioinformatics 25:1293–1299CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Karosiene E, Lundegaard C, Lund O et al (2012) NetMHCcons: a consensus method for the major histocompatibility complex class I predictions. Immunogenetics 64:177–186CrossRefPubMedGoogle Scholar
  26. 26.
    Wang P, Sidney J, Dow C et al (2008) A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput Biol 4, e000048Google Scholar
  27. 27.
    Zhang L, Udaka K, Mamitsuka H et al (2012) Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools. Brief Bioinform 13:350–364CrossRefPubMedGoogle Scholar
  28. 28.
    Bui H-H, Sidney J, Peters B et al (2005) Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. Immunogenetics 57:304–314CrossRefPubMedGoogle Scholar
  29. 29.
    Sidney J, Assarsson E, Moore C et al (2008) Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries. Immunome Res 4:2CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Nielsen M, Lundegaard C, Worning P et al (2003) Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 12:1007–1017CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Peters B, Sette A (2005) Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics 6:132CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Kim Y, Sidney J, Pinilla C et al (2009) Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior. BMC Bioinformatics 10:394CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Moutaftsi M, Peters B, Pasquetto V et al (2006) A consensus epitope prediction approach identifies the breadth of murine T(CD8+)-cell responses to vaccinia virus. Nat Biotechnol 24:817–819CrossRefPubMedGoogle Scholar
  34. 34.
    Nielsen M, Lundegaard C, Blicher T et al (2007) NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS One 2, e796CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Nielsen M, Lund O (2009) NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinformatics 10:296CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Nielsen M, Lundegaard C, Lund O (2007) Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics 8:238CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Nielsen M, Lundegaard C, Blicher T et al (2008) Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. PLoS Comp Biol 4, e1000107CrossRefGoogle Scholar
  38. 38.
    Sturniolo T, Bono E, Ding J et al (1999) Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol 17:555–561CrossRefPubMedGoogle Scholar
  39. 39.
    Paul S, Weiskopf D, Angelo MA et al (2013) HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity. J Immunol 191:5831–5839CrossRefPubMedGoogle Scholar
  40. 40.
    Doytchinova IA, Guan P, Flower DR (2004) Identifying human MHC supertypes using bioinformatic methods. J Immunol 172:4314–4323CrossRefPubMedGoogle Scholar
  41. 41.
    Sidney J, Peters B, Frahm N et al (2008) HLA class I supertypes: a revised and updated classification. BMC Immunol 9:1CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Doytchinova IA, Flower DR (2005) In silico identification of supertypes for class II MHCs. J Immunol 174:7085–7095CrossRefPubMedGoogle Scholar
  43. 43.
    Ponomarenko JV, van Regenmortel MHV (2009) B-cell epitope prediction. In: Gu J, Bourne PE (eds) Structural bioinformatics. Wiley-Blackwell, New YorkGoogle Scholar
  44. 44.
    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, e1002829CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Larsen JEP, Lund O, Nielsen M (2006) Improved method for predicting linear B-cell epitopes. Immunome Res 2:2CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Saha S, Raghava GPS (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65:40–48CrossRefPubMedGoogle Scholar
  47. 47.
    Gao J, Faraggi E, Zhou Y et al (2012) BEST: improved prediction of B-cell epitopes from antigen sequences. PLoS One 7, e40104CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Ponomarenko J, Bui H-H, Li W et al (2008) ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics 9:514CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Kunik V, Ashkenazi S, Ofran Y (2012) Paratome: an online tool for systematic identification of antigen-binding regions in antibodies based on sequence or structure. Nucleic Acids Res 40:W521–W524CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Moreau V, Fleury C, Piquer D et al (2008) PEPOP: computational design of immunogenic peptides. BMC Bioinformatics 9:71CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Lin SY, Cheng C, Su EC (2013) Prediction of B-cell epitopes using evolutionary information and propensity scales. BMC Bioinformatics 14:S10CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Kim Y, Ponomarenko J, Zhu Z et al (2012) Immune epitope database analysis resource. Nucleic Acid Res 40:W525–W530CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Patronov A, Doytchinova I (2013) T-cell epitope vaccine design by immunoinformatics. Open Biol 3:120139CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Shimizu H, Thorley B, Paladin FJ et al (2004) Circulation of type 1 vaccine-derived poliovirus in the Philippines in 2001. J Virol 78:13512–13521CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Moyle PM (2015) Progress in vaccine development. Curr Protoc Microbiol 36:1–17PubMedGoogle Scholar
  56. 56.
    Centers for Disease Control and Prevention (2012) Epidemiology and prevention of vaccine-preventable diseases. Public Health Foundation, Washington DCGoogle Scholar
  57. 57.
    Plotkin S (2014) History of vaccination. Proc Natl Acad Sci U S A 2014:1–5Google Scholar
  58. 58.
    Moyle PM, Toth I (2013) Modern subunit vaccines: development, components, and research opportunities. ChemMedChem 8:360–376CrossRefPubMedGoogle Scholar
  59. 59.
    Purcell AW, McCluskey J, Rossjohn J (2007) More than one reason to rethink the use of peptides in vaccine design. Nat Rev Drug Discov 6:404–414CrossRefPubMedGoogle Scholar
  60. 60.
    Moyle PM, Toth I (2008) Self-adjuvanting lipopeptide vaccines. Curr Med Chem 15:506–516CrossRefPubMedGoogle Scholar
  61. 61.
    Sato Y, Sato H (1999) Development of acellular pertussis vaccines. Biologicals 27:61–69CrossRefPubMedGoogle Scholar
  62. 62.
    Michel M-L, Tiollais P (2010) Hepatitis B vaccines: protective efficacy and therapeutic potential. Pathol Biol 58:288–295CrossRefPubMedGoogle Scholar
  63. 63.
    Cybulski RJ, Sanz P, O’Brien AD (2009) Anthrax vaccination strategies. Mol Aspects Med 30:490–502CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Chun JH, Hong KJ, Cha SH et al (2012) Complete genome sequence of Bacillus anthracis H9401, an isolate from a Korean patient with anthrax. J Bacteriol 194:4116–4117CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Keitel WA (2006) Recombinant protective antigen 102 (rPA102): profile of a second-generation anthrax vaccine. Expert Rev Vaccines 5:417–430CrossRefPubMedGoogle Scholar
  66. 66.
    McKee SJ, Bergot A-S, Leggatt GR (2015) Recent progress in vaccination against human papillomavirus-mediated cervical cancer. Rev Med Virol 25:54–71CrossRefPubMedGoogle Scholar
  67. 67.
    Khallouf H, Grabowska A, Riemer A (2014) Therapeutic vaccine strategies against human papillomavirus. Vaccines 2:422–462CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
  69. 69.
    Vincent J-L (2014) Vaccine development and passive immunization for Pseudomonas aeruginosa in critically ill patients: a clinical update. Future Microbiol 9:457–463CrossRefPubMedGoogle Scholar
  70. 70.
    Westritschnig K, Hochreiter R, Wallner G et al (2014) A randomized, placebo-controlled phase I study assessing the safety and immunogenicity of a Pseudomonas aeruginosa hybrid outer membrane protein OprF/I vaccine (IC43) in healthy volunteers. Hum Vaccin Immunother 10:170–183CrossRefPubMedGoogle Scholar
  71. 71.
    Skwarczynski M, Toth I (2014) Recent advances in peptide-based subunit nanovaccines. Nanomedicine 9:2657–2669CrossRefPubMedGoogle Scholar
  72. 72.
    Sharma M, Dixit A (2015) Identification and immunogenic potential of B cell epitopes of outer membrane protein OmpF of Aeromonas hydrophila in translational fusion with a carrier protein. Applied Microbiol Biotechnol 99(15):6277–91CrossRefGoogle Scholar
  73. 73.
    Weltzin R, Guy B, Thomas WD et al (2000) Parenteral adjuvant activities of Escherichia coli heat-labile toxin and its B subunit for immunization of mice against gastric Helicobacter pylori infection. Infect Immun 68:2775–2782CrossRefPubMedPubMedCentralGoogle Scholar
  74. 74.
    Van Regenmortel MHV (1996) Mapping epitope structure and activity: from one-dimensional prediction to four-dimensional description of antigenic specificity. Methods 9:465–472CrossRefPubMedGoogle Scholar
  75. 75.
    Sette A, Fikes J (2003) Epitope-based vaccines: an update on epitope identification, vaccine design and delivery. Curr Opin Immunol 15:461–470CrossRefPubMedGoogle Scholar
  76. 76.
    Guichard G, Zerbib A, Gal FA et al (2000) Melanoma peptide MART-1(27-35) analogues with enhanced binding capacity to the human class I histocompatibility molecule HLA-A2 by introduction of a β-amino acid residue: implications for recognition by tumor-infiltrating lymphocytes. J Med Chem 43:3803–3808CrossRefPubMedGoogle Scholar
  77. 77.
    Reinelt S, Marti M, Dédier S et al (2001) β-amino acid scan of a class I major histocompatibility complex-restricted alloreactive T-cell epitope. J Biol Chem 276:24525–24530CrossRefPubMedGoogle Scholar
  78. 78.
    Webb AI, Dunstone MA, Williamson NA et al (2005) T cell determinants incorporating β-amino acid residues are protease resistant and remain immunogenic in vivo. J Immunol 175:3810–3818CrossRefPubMedGoogle Scholar
  79. 79.
    Brito LA, Malyala P, O’Hagan DT (2013) Vaccine adjuvant formulations: a pharmaceutical perspective. Semin Immunol 25:130–145CrossRefPubMedGoogle Scholar
  80. 80.
    Pulendran B, Ahmed R (2011) Immunological mechanisms of vaccination. Nat Immunol 12:509–517CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Berti F, Adamo R (2013) Recent mechanistic insights on glycoconjugate vaccines and future perspectives. ACS Chem Biol 8:1653–1663CrossRefPubMedGoogle Scholar
  82. 82.
    Plotkin SA (2009) Vaccines: the fourth century. Clin Vaccine Immunol 16:1709–1719CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Azmi F, Fuaad AAHA, Skwarczynski M et al (2014) Recent progress in adjuvant discovery for peptide-based subunit vaccines. Hum Vaccin Immunother 10:778–796CrossRefPubMedGoogle Scholar
  84. 84.
    Lua LHL, Connors NK, Sainsbury F et al (2014) Bioengineering virus-like particles as vaccines. Biotechnol Bioeng 111:425–440CrossRefPubMedGoogle Scholar
  85. 85.
    Wieser A, Magistro G, Nörenberg D et al (2012) First multi-epitope subunit vaccine against extraintestinal pathogenic Escherichia coli delivered by a bacterial type-3 secretion system (T3SS). Int J Med Microbiol 302:10–18CrossRefPubMedGoogle Scholar
  86. 86.
    Bumann D, Hueck C, Aebischer T et al (2000) Recombinant live Salmonella spp. for human vaccination against heterologous pathogens. FEMS Immunol Med Microbiol 27:357–364CrossRefPubMedGoogle Scholar
  87. 87.
    Garmory HS, Leary SEC, Griffin KF et al (2003) The use of live attenuated bacteria as a delivery system for heterologous antigens. J Drug Target 11:471–479CrossRefPubMedGoogle Scholar
  88. 88.
    Demento SL, Siefert AL, Bandyopadhyay A et al (2011) Pathogen-associated molecular patterns on biomaterials: a paradigm for engineering new vaccines. Trends Biotechnol 29:294–306CrossRefPubMedGoogle Scholar
  89. 89.
  90. 90.
  91. 91.
    A service of the U.S. National Institutes of Health. https://clinicaltrials.gov/
  92. 92.
  93. 93.
    El Garch H, Minke JM, Rehder J et al (2008) A West Nile virus (WNV) recombinant canarypox virus vaccine elicits WNV-specific neutralizing antibodies and cell-mediated immune responses in the horse. Vet Immunol Immunopathol 123:230–239CrossRefPubMedGoogle Scholar
  94. 94.
  95. 95.
    NovaDigm Therapeutics. http://www.novadigm.net/
  96. 96.
    Schmidt CS, White CJ, Ibrahim AS et al (2012) NDV-3, a recombinant alum-adjuvanted vaccine for Candida and Staphylococcus aureus, is safe and immunogenic in healthy adults. Vaccine 30:7594–7600CrossRefPubMedPubMedCentralGoogle Scholar
  97. 97.
    Anderson AS, Miller A, Donald RGK et al (2012) Development of a multicomponent Staphylococcus aureus vaccine designed to counter multiple bacterial virulence factors. Hum Vaccin Immunother 8:1585–1594CrossRefPubMedPubMedCentralGoogle Scholar
  98. 98.
    Emergent Biosolutions. http://emergentbiosolutions.com/
  99. 99.
    Raghunandan R, Lu H, Zhou B et al (2014) An insect cell derived respiratory syncytial virus (RSV) F nanoparticle vaccine induces antigenic site II antibodies and protects against RSV challenge in cotton rats by active and passive immunization. Vaccine 32:6485–6492CrossRefPubMedGoogle Scholar
  100. 100.
    Immune Response BioPharma, Inc. http://www.immuneresponsebiopharma.com
  101. 101.
    Wedemeyer H, Schuller E, Schlaphoff V et al (2009) Therapeutic vaccine IC41 as late add-on to standard treatment in patients with chronic hepatitis C. Vaccine 27:5142–5151CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Marcin Michalik
    • 1
    • 2
  • Bardya Djahanshiri
    • 2
    • 3
  • Jack C. Leo
    • 1
  • Dirk Linke
    • 1
    • 2
  1. 1.Department of BiosciencesUniversity of OsloOsloNorway
  2. 2.Department of Protein EvolutionMax Planck Institute for Developmental BiologyTübingenGermany
  3. 3.Department for Applied BioinformaticsGoethe-UniversityFrankfurtGermany

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