Skip to main content

Considerations for Vaccine Design in the Postgenomic Era

  • Chapter
  • First Online:
Molecular Vaccines

Abstract

The foundations of vaccination were laid in the eighteenth century by Edward Jenner and in the nineteenth century by Louis Pasteur. During the 1930s and 1940s, live attenuated and inactivated vaccines dominated the field. This was followed by the purification of antigens from pathogens grown in culture using biochemical methods, bringing the era of subunit vaccines to the fore. With the explosion in next-generation sequencing technologies and the availability first genomes, the field of reverse vaccinology alongside the associated “omics”-revolutions became of age. This allowed for the identification of promising antigens, not previously exploited for their protective abilities. By combining the latter technologies with immunogenetics and immunogenomics, insight into the immune response during infection/vaccination is providing a global picture of the various factors involved in protective immunity. In this post-genomic era, vaccine development is moving away from a trial-and-error approach to a knowledge-based vaccine development approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Powell, M.F., Newman, M.J.: Vaccine Design – The Subunit and Adjuvant Approach, vol. 6, 1st edn. Plenum Press, New York (1995)

    Google Scholar 

  2. Jones, D.: Reverse vaccinology on the cusp. Nat. Rev Drug Discov. 11, 175–176 (2012). doi:10.1038/nrd3679

    PubMed  CAS  Google Scholar 

  3. Adu-Bobie, J., Arico, B., Giuliani, M.M.: Serruto, D., Chapter 9: The first vaccine obtained through reverse vaccinology: the serogroup B Meningococcus Vaccine.In: Rappuoli, R., Serruto, D., Rappuoli, R. (eds.). Vaccine Design – Innovative and Novel Strategies, vol. 1, pp. 225–241. Caister Academic. Press, Norfolk (2011)

    Google Scholar 

  4. Luciani, F., Bull, R.A., Lloyd, A.R.: Next generation deep sequencing and vaccine design: today and tomorrow. Trends Biotechnol. 30, 443–452 (2012)

    PubMed  CAS  Google Scholar 

  5. Seib, K.L., Zhao, X., Rappuoli, R.: Developing vaccines in the era of genomics: a decade of reverse vaccinology. Clin. Microbiol. Infect. 18, 1–8 (2012)

    Google Scholar 

  6. Cheng, H., Chan, W.S., Wang, D., Liu, S., Zhou, Y.: Small open reading frames: current prediction techniques and future prospect. Curr. Protein Pept Sci. 12, 503–507 (2011)

    PubMed  CAS  Google Scholar 

  7. Yandell, M., Ence, D.: A beginner’s guide to eukaryotic genome annotation. Nat. Rev. 13, 329–342 (2012)

    CAS  Google Scholar 

  8. Delcher, A.L., Bratke, K.A., Powers, E.C., Salzberg, S.L.: Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics 23, 673–679 (2007). doi:10.1093/bioinformatics/btm009

    PubMed  CAS  Google Scholar 

  9. Lukashin, A.V., Borodovsky, M.: GeneMark.hmm: new solutions for gene finding. Nucleic Acids Res. 26, 1107–1115 (1998). doi:10.1093/nar/26.4.1107

    PubMed  CAS  Google Scholar 

  10. Hyatt, D., et al.: Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010)

    PubMed  Google Scholar 

  11. Pinheiro, C.S., et al.: Computational vaccinology: an important strategy to discover new potential S. mansoni vaccine candidates. J. Biomed. Biotechnol. 2011, 503068 (2011). doi:10.1155/2011/503068

    PubMed  Google Scholar 

  12. Ecker, J.R., et al.: Genomics: ENCODE explained. Nature 489, 52–55 (2012). doi:10.1038/489052a

    PubMed  CAS  Google Scholar 

  13. Cantarel, B.L., et al.: MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes. Genome Res. 18, 188–196 (2008). doi:10.1101/gr.6743907

    PubMed  CAS  Google Scholar 

  14. Keller, O., Kollmar, M., Stanke, M., Waack, S.: A novel hybrid gene prediction method employing protein multiple sequence alignments. Bioinformatics (2011). doi:10.1093/bioinformatics/btr010

    Google Scholar 

  15. Smandi, S., et al.: Methodology optimizing SAGE library tag-to-gene mapping: application to Leishmania. BMC Res. Notes 5, 74 (2012). doi:10.1186/1756-0500-5-74

    PubMed  CAS  Google Scholar 

  16. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990)

    PubMed  CAS  Google Scholar 

  17. Barker, W.C., et al.: The protein information resource (PIR). Nucleic Acids Res. 28, 41–44 (2000)

    PubMed  CAS  Google Scholar 

  18. Punta, M., et al.: The Pfam protein families database. Nucleic Acids Res. 40, D290–D301 (2012). doi:10.1093/nar/gkr1065

    PubMed  CAS  Google Scholar 

  19. Boeckmann, B., et al.: The SWISS-PROT protein knowledgebase and its supplement TrEMBL in. Nucleic Acids Res. 31, 365–370 (2003). doi:10.1093/nar/gkg095 (2003)

    PubMed  CAS  Google Scholar 

  20. Magrane, M., Consortium, U.: UniProt Knowledgebase: a hub of integrated protein data. Database (2011). doi:10.1093/database/bar009 (2011)

    PubMed  Google Scholar 

  21. Tatusov, R.L., Galperin, M.Y., Natale, D.A., Koonin, E.V.: The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33–36 (2000). doi:10.1093/nar/28.1.33

    PubMed  CAS  Google Scholar 

  22. Gene Ontology Consortium: The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 32, D258–D261 (2004). doi:10.1093/nar/gkh036

    Google Scholar 

  23. Yon Rhee, S., Wood, V., Dolinski, K., Draghici, S.: Use and misuse of the gene ontology annotations. Nat. Rev. Genet. 9, 509–515 (2008)

    Google Scholar 

  24. Gomez, A., et al.: Gene ontology function prediction in mollicutes using protein-protein association networks. BMC Syst. Biol. 5, 49 (2011). doi:10.1186/1752-0509-5-49

    PubMed  Google Scholar 

  25. Zhang, Q.C., et al.: Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature 490, 556–560 (2012)

    PubMed  CAS  Google Scholar 

  26. Wass, M.N., Barton, G., Sternberg, M.J.E.: CombFunc: predicting protein function using heterogeneous data sources. Nucleic Acids Res. 40, W466–W470 (2012). doi:10.1093/nar/gks489

    PubMed  CAS  Google Scholar 

  27. Fu, Y., et al.: Novel insights into the transcriptome of Dirofilaria immitis. PLoS One 7, e41639 (2012). doi:10.1371/journal.pone.0041639

    PubMed  CAS  Google Scholar 

  28. Hedman, A.K., Li, M.S., Langford, P.R., Kroll, J.S.: Transcriptional profiling of serogroup B Neisseria meningitidis growing in human blood: an approach to vaccine antigen discovery. PLoS One 7, e39718 (2012). doi:10.1371/journal.pone.0039718

    PubMed  CAS  Google Scholar 

  29. Amiruddin, N., et al.: Characterisation of full-length cDNA sequences provides insights into the Eimeria tenella transcriptome. BMC Genomics 13, 21 (2012). doi:10.1186/1471-2164-13-21

    PubMed  CAS  Google Scholar 

  30. Maritz-Olivier, C., van Zyl, W., Stutzer, C.: A systematic, functional genomics and reverse vaccinology approach to the identification of vaccine candidates in the cattle tick. Rhipicephalus microplus. Ticks Tick Borne Dis. 3, 179–189 (2012)

    PubMed  Google Scholar 

  31. Savas, J.N., Stein, B.D., Wu, C.C., Yates, J.R.: Mass spectrometry accelerates membrane protein analysis. Trends Biochem. Sci. 36, 388–396 (2011). doi:10.1016/j.tibs.2011.04.005

    PubMed  CAS  Google Scholar 

  32. Haralambieva, I.H., Poland, G.A.: Vaccinomics, predictive vaccinology and the future of vaccine development. Future Microbiol. 5, 1757–1760 (2010). doi:10.2217/fmb.10.146

    PubMed  CAS  Google Scholar 

  33. Acharya, P., et al.: Clinical proteomics of the neglected human malarial parasite Plasmodium vivax. PLoS One 6, e26623 (2011). doi:10.1371/journal.pone.0026623

    PubMed  CAS  Google Scholar 

  34. Minning, T.A., Weatherly, D.B., Atwood, J., Orlando, R., Tarleton, R.L.: The steady-state transcriptome of the four major life-cycle stages of Trypanosoma cruzi. BMC Genomics 10, 370 (2009). doi:10.1186/1471-2164-10-370

    PubMed  Google Scholar 

  35. Jagusztyn-Krynicka, E.K., Roszczenko, P., Grabowska, A.: Impact of proteomics on anti-Mycobacterium tuberculosis (MTB) vaccine development. Pol. J. Microbiol. 58, 281–287 (2009)

    Google Scholar 

  36. Lawrence, E.: Henderson’s Dictionary of Biology. Pearson Education Limited, Harlow (2005)

    Google Scholar 

  37. Lillehoj, H.S., Kim, C.H., Keeler, C.L., Zhang, S.: Immunogenomic approaches to study host immunity to enteric pathogens. Poult. Sci. 86, 1491–1500 (2007)

    PubMed  CAS  Google Scholar 

  38. Ohara, O.: From transcriptome analysis to immunogenomics: current status and future direction. FEBS Lett. 583, 1662–1667 (2009)

    PubMed  CAS  Google Scholar 

  39. Snoep, J.L., Bruggeman, F., Olivier, B.G., Westerhoff, H.V.: Towards building the silicon cell: a modular approach. Biosystems 83, 207–216 (2006)

    PubMed  CAS  Google Scholar 

  40. Tjalsma, H., Schaeps, R.M.J., Swinkels, D.W.: Immunoproteomics: from biomarker discovery to diagnostic applications. Proteomics Clin. Appl. 2, 167–180 (2008)

    PubMed  CAS  Google Scholar 

  41. Poland, G.A., Ovsyannikova, I.G., Jacobson, R.M., Smith, D.I.: Heterogeneity in vaccine immune response: the role of immunogenetics and the emerging field of vaccinomics. Clin. Pharmacol. Ther. 82, 653–664 (2007). doi:10.1038/sj.clpt.6100415

    PubMed  CAS  Google Scholar 

  42. Rappuoli, R.: Reverse vaccinology. Curr. Opin. Microbiol. 3, 445–450 (2000)

    PubMed  CAS  Google Scholar 

  43. Buonaguro, L., Wang, E., Tornesello, M.L., Buonaguro, F.M., Marincola, F.M.: Systems biology applied to vaccine and immunotherapy development. BMC Syst. Biol. 5, 146–157 (2011)

    PubMed  CAS  Google Scholar 

  44. Heng, T.S., Painter, M.W.: The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008). doi:10.1038/ni1008-1091

    PubMed  CAS  Google Scholar 

  45. Abbas, A.R., et al.: Immune response in silico (IRIS): immune-specific genes identified from a compendium of microarray expression data. Genes Immun. 6, 319–331 (2005). doi:10.1038/sj.gene.6364173

    PubMed  CAS  Google Scholar 

  46. Banchereau, J., et al.: Harnessing human dendritic cell subsets to design novel vaccines. Ann. N Y Acad. Sci. 1174, 24–32 (2009). doi:10.1111/j.1749-6632.2009.04999.x

    PubMed  CAS  Google Scholar 

  47. Ovsyannikova, I.G., Poland, G.A.: Vaccinomics: current findings, challenges and novel approaches for vaccine development. AAPS J. 13, 438–444 (2011). doi:10.1208/s12248-011-9281-x

    PubMed  CAS  Google Scholar 

  48. Poland, G.A., Kennedy, R.B., Ovsyannikova, I.G.: Vaccinomics and personalized vaccinology: is science leading us toward a new path of directed vaccine development and discovery? PLoS Pathog. 7, e1002344 (2011). doi:10.1371/journal.ppat.1002344

    PubMed  CAS  Google Scholar 

  49. Bernstein, A., Pulendran, B., Rappuoli, R.: Systems vaccinomics: the road ahead for vaccinology. OMICS 15, 529–531 (2011). doi:10.1089/omi.2011.0022

    PubMed  CAS  Google Scholar 

  50. Kennedy, R.B., Poland, G.A.: The top five “game changers” in vaccinology: toward rational and directed vaccine development. OMICS 15, 533–537 (2011). doi:10.1089/omi.2011.0012

    PubMed  CAS  Google Scholar 

  51. Zhang, W., Li, F., Nie, L.: Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies. Microbiology 156, 287–301 (2010). doi:10.1099/mic.0.034793-0

    PubMed  CAS  Google Scholar 

  52. Yang, D., et al.: RNA-seq liver transcriptome analysis reveals an activated MHC-I pathway and an inhibited MHC-II pathway at the early stage of vaccine immunization in zebrafish. BMC Genomics 13, 319 (2012). doi:10.1186/1471-2164-13-319

    PubMed  CAS  Google Scholar 

  53. Toufeer, M., et al.: Gene expression profiling of dendritic cells reveals important mechanisms associated with predisposition to Staphylococcus infections. PLoS One 6, e22147 (2011). doi:10.1371/journal.pone.0022147

    PubMed  CAS  Google Scholar 

  54. Wang, F., et al.: Deep-sequencing analysis of the mouse transcriptome response to infection with Brucella melitensis strains of differing virulence. PLoS One 6, e28485 (2011). doi:10.1371/journal.pone.0028485

    PubMed  CAS  Google Scholar 

  55. Kaleta, C., de Figueiredo, L.F., Heiland, I., Klamt, S., Schuster, S.: Special issue: integration of OMICs datasets into metabolic pathway analysis. Biosystems 105, 107–108 (2011). doi:10.1016/j.biosystems.2011.05.008

    PubMed  Google Scholar 

  56. Joyce, A.R., Palsson, B.O.: The model organism as a system: integrating ‘omics’ data sets. Nat. Rev. Mol. Cell Biol. 7, 198–210 (2006). doi:10.1038/nrm1857

    PubMed  CAS  Google Scholar 

  57. Myers, C.L., Chiriac, C., Troyanskaya, O.G.: Discovering biological networks from diverse functional genomic data. Methods Mol. Biol. 563, 157–175 (2009). doi:10.1007/978-1-60761-175-2_9

    PubMed  Google Scholar 

  58. Hijikata, A., et al.: Construction of an open-access database that integrates cross-reference information from the transcriptome and proteome of immune cells. Bioinformatics 23, 2934–2941 (2007). doi:10.1093/bioinformatics/btm430

    PubMed  CAS  Google Scholar 

  59. Korb, M., et al.: The Innate Immune Database (IIDB). BMC Immunol. 9, 7 (2008). doi:10.1186/1471-2172-9-7

    PubMed  Google Scholar 

  60. Lesk, V., Taubert, J., Rawlings, C., Dunbar, S., Muggleton, S.: WIBL: Workbench for Integrative Biological Learning. JIB 8, 156 (2011). doi:10.2390/biecoll-jib-2011-156

    PubMed  Google Scholar 

  61. Maneck, M., Schrader, A., Kube, D., Spang, R.: Genomic data integration using guided clustering. Bioinformatics 27, 2231–2238 (2011). doi:10.1093/bioinformatics/btr363

    PubMed  CAS  Google Scholar 

  62. Misra, R.V., Horler, R.S.P., Reindl, W., Goryanin, I.I., Thomas, H.G.: EchoBASE: an integrated post-genomic database for Escherichia coli. Nucleic Acids Res. (2005). doi:10.1093/nar/gki028

    PubMed  Google Scholar 

  63. Le Cao, K.A., Gonzalez, I., Dejean, S.: IntegrOmics: an R package to unravel relationships between two omics datasets. Bioinformatics 25, 2855–2856 (2009). doi:10.1093/bioinformatics/btp515

    PubMed  Google Scholar 

  64. Peterson, E.S., et al.: VESPA: software to facilitate genomic annotation of prokaryotic organisms through integration of proteomic and transcriptomic data. BMC Genomics 13, 131 (2012). doi:10.1186/1471-2164-13-131

    PubMed  CAS  Google Scholar 

  65. Bauch, A., et al.: OpenBIS: a flexible framework for managing and analyzing complex data in biology research. BMC Bioinformatics 12, 468 (2011). doi:10.1186/1471-2105-12-468

    PubMed  Google Scholar 

  66. Fahey, M.E., et al.: GPS-Prot: a web-based visualization platform for integrating host-pathogen interaction data. BMC Bioinformatics 12, 298 (2011). doi:10.1186/1471-2105-12-298

    PubMed  Google Scholar 

  67. Do, L.H., Esteves, F.F., Karten, H.J., Bier, E.: Booly: a new data integration platform. BMC Bioinformatics 11, 513 (2010). doi:10.1186/1471-2105-11-513

    PubMed  Google Scholar 

  68. Yu, E.Z., Burba, A.E.C., Gerstein, M.: PARE: a tool for comparing protein abundance and mRNA expression data. BMC Bioinformatics 8, 309 (2007). doi:10.1186/1471-2105-8-309

    PubMed  Google Scholar 

  69. Dormitzer, P.R., Ulmer, J.B., Rappuoli, R.: Structure-based antigen design: a strategy for next generation vaccines. Trends Biotechnol. 26, 659–667 (2008)

    PubMed  CAS  Google Scholar 

  70. Nuccitelli, A., et al.: Structure-based approach to rationally design a chimeric protein for an effective vaccine against Group B Streptococcus infections. Proc. Natl. Acad. Sci. 108, 10278–10283 (2011). doi:10.1073/pnas.1106590108

    PubMed  CAS  Google Scholar 

  71. Bagnoli, F., et al.: Designing the next generation of vaccines for global public health. OMICS 15, 545–566 (2011)

    PubMed  CAS  Google Scholar 

  72. Fujinami, R.S., Oldstone, M.B., Wroblewska, Z., Frankel, M.E., Koprowski, H.: Molecular mimicry in virus infection: crossreaction of measles virus phosphoprotein or of herpes simplex virus protein with human intermediate filaments. Proc. Natl. Acad. Sci. 80, 2346–2350 (1983)

    PubMed  CAS  Google Scholar 

  73. He, Y., Xiang, Z., Mobley, H.: Vaxign: the first web-based vaccine design program for reverse vaccinology and applications for vaccine development. J. Biomed. Biotechnol. (2010). doi:10.1155/2010/297505 (2010)

    Google Scholar 

  74. Oldstone, M.B.A.: Molecular mimicry and immune-mediated diseases. FASEB J. 12, 1255–1265 (1998)

    PubMed  CAS  Google Scholar 

  75. Schatz, M.M., et al.: Characterizing the N-terminal processing motif of MHC class I ligands. J. Immunol. 180, 3210–3217 (2008)

    PubMed  CAS  Google Scholar 

  76. Moriel, D.G., et al.: Identification of protective and broadly conserved vaccine antigens from the genome of extraintestinal pathogenic Escherichia coli. Proc. Natl. Acad. Sci. 107, 9072–9077 (2010). doi:10.1073/pnas.0915077107

    PubMed  CAS  Google Scholar 

  77. Bui, H.H., 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 8, 361 (2007). doi:10.1186/1471-2105-5-361

    PubMed  Google Scholar 

  78. Bagnoli F., Norauis, N., Ferlenghi, I., Scarselli, M., Danati, C., Savina, S., Barocchi, M.A., Rappuoli, R. Chapter 2: deigning Vaccine in the Era of Genomics.In: Rappuoli, R., Serruto, D., Rappuoli, R. (eds.). Vaccine Design – Innovative and Novel Strategies, vol. 1, pp. 21–53. Caister Academic. Press, Norfolk (2011)

    Google Scholar 

  79. Sollner, J., et al.: Concept and application of a computational vaccinology workflow. Immunome Res. 6(Suppl 2), S7 (2010). doi:10.1186/1745-7580-6-s2-s7

    PubMed  Google Scholar 

  80. Pizza, M., et al.: Identification of vaccine candidates against serogroup B. meningococcus by whole-genome sequencing. Science 287, 1816–1820 (2000). doi:10.1126/science.287.5459.1816

    PubMed  CAS  Google Scholar 

  81. Bowman, B., et al.: Improving reverse vaccinology with a machine learning approach. Vaccine 29, 8156–8164 (2011)

    PubMed  Google Scholar 

  82. Vivona, S., et al.: Computer-aided biotechnology: from immuno-informatics to reverse vaccinology. Trends Biotechnol. 26, 190–200 (2008). doi:10.1016/j.tibtech.2007.12.006

    PubMed  CAS  Google Scholar 

  83. Chou, K.C., Wu, Z.C., Xiao, X.: iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins. PLoS One 6, e18258 (2011). doi:10.1371/journal.pone.0018258

    PubMed  CAS  Google Scholar 

  84. Garg, G., Ranganathan, S.: In silico secretome analysis approach for next generation sequencing transcriptomic data. BMC Genomics 12, 514–524 (2011)

    Google Scholar 

  85. Briesemeister, S., et al.: SherLoc2: a high-accuracy hybrid method for predicting subcellular localization of proteins. J. Proteome Res. 8, 5363–5366 (2009). doi:10.1021/pr900665y

    PubMed  CAS  Google Scholar 

  86. Nakai, K., Horton, P.: PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization. Trends Biochem. Sci. 24, 34–35 (1999)

    PubMed  CAS  Google Scholar 

  87. Yu, N.Y., et al.: PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26, 1608–1615 (2010). doi:10.1093/bioinformatics/btq249

    PubMed  CAS  Google Scholar 

  88. Sprenger, J., Fink, J., Teasdale, R.: Evaluation and comparison of mammalian subcellular localization prediction methods. BMC Bioinformatics 7, S3 (2006). doi:10.1186/1471-2105-7-S5-S3

    PubMed  Google Scholar 

  89. Chou, K.-C., Shen, H.-B.: Cell-PLoc: a package of web servers for predicting subcellular localization of proteins in various organisms. Nat. Protoc. 3, 153–162 (2008)

    PubMed  CAS  Google Scholar 

  90. Xiao, X., Wu, Z.-C., Chou, K.-C.: iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. J. Theor. Biol. 284, 42–51 (2011)

    PubMed  CAS  Google Scholar 

  91. Shen, H.-B., Chou, K.-C.: Virus-mPLoc: a fusion classifier for viral protein subcellular location prediction by incorporating multiple sites. J. Biomol. Struct. Dyn. 28, 175–186 (2010)

    PubMed  CAS  Google Scholar 

  92. Bannai, H., Tamada, Y., Maruyama, O., Nakai, K., Miyano, S.: Extensive feature detection of N-terminal protein sorting signals. Bioinformatics 18, 298–305 (2002). doi:10.1093/bioinformatics/18.2.298

    PubMed  CAS  Google Scholar 

  93. Lodish, H., Berk, A., Zipursky, S.L.: Molecular Cell Biology, 4th edn. W. H. Freeman, New York (2000)

    Google Scholar 

  94. Nielsen, H., Engelbrecht, J., Brunak, S., von Heijne, G.: Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng. 10, 1 (1997). doi:10.1093/protein/10.1.1

    PubMed  CAS  Google Scholar 

  95. Choo, K.H., Tan, T.W., Ranganathan, S.: A comprehensive assessment of N-terminal signal peptides prediction methods. BMC Bioinformatics 10, S3 (2009). doi:10.1186/1471-2105-10-S15-S2

    Google Scholar 

  96. Scott, M.S., Oomen, R., Thomas, D.Y., Hallett, M.T.: Predicting the subcellular localization of viral proteins within a mammalian host cell. J. Virol. 3, 24 (2006). doi:10.1186/1743-422X-3-24

    CAS  Google Scholar 

  97. Petersen, T.N., Brunak, S., Von Heijne, G., Nielsen, H.: SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat. Methods 8, 784–786 (2011). doi:10.1038/nmeth.1701

    Google Scholar 

  98. Käll, L., Krogh, A., Sonnhammer, E.L.L.: A combined transmembrane topology and signal peptide prediction method. J. Mol. Biol. 338, 1027–1036 (2004)

    PubMed  Google Scholar 

  99. Käll, L., Krogh, A., Sonnhammer, E.: Advantages of combined transmembrane topology and signal peptide prediction - the Phobius web server. Nucleic Acids Res. 35, W429–W432 (2007). doi:10.1093/nar/gkm256

    PubMed  Google Scholar 

  100. Viklund, H.K., Bernsel, A., Skwark, M., Elofsson, A.: SPOCTOPUS: a combined predictor of signal peptides and membrane protein topology. Bioinformatics 24, 2928–2929 (2008). doi:10.1093/bioinformatics/btn550

    PubMed  CAS  Google Scholar 

  101. Chen, P., Rayner, S., Hu, K.H.: Advances of bioinformatics tools applied in virus epitopes prediction. Virol. Sin. 26, 1–7 (2011). doi:10.1007/s12250-011-3159-4

    PubMed  Google Scholar 

  102. Flower, D.R. Chapter 5: Vaccines: data driven prediction of binders, epitopes and immunogenicity. In: Flower, D.R. (ed.) Bioinformatics for Vaccinology, pages 167–216. Wiley-Blackwell, Chichester (2008)

    Google Scholar 

  103. Iurescia, S., Fioretti, D., Fazio, V.M., Rinaldi, M.: Epitope-driven DNA vaccine design employing immunoinformatics against B-cell lymphoma: a biotech´s challenge. Biotechnol. Adv. 30, 372–383 (2012)

    PubMed  CAS  Google Scholar 

  104. Sirskyj, D., Diaz-Mitoma, F., Golshani, A., Kumar, A., Azizi, A.: Innovative bioinformatic approaches for developing peptide-based vaccines against hypervariable viruses. Immunol. Cell Biol. 89, 81–89 (2011). doi:10.1038/icb.2010.65

    PubMed  CAS  Google Scholar 

  105. Yang, X., Yu, X.: An introduction to epitope prediction methods and software. Rev. Med. Virol. 19, 77–96 (2009)

    PubMed  CAS  Google Scholar 

  106. Yu, K., Petrovsky, N., Schönbach, C., Koh, J., Brusic, V.: Methods for prediction of peptide binding to MHC molecules: a comparative study. Mol. Med. 8, 137–148 (2002)

    PubMed  CAS  Google Scholar 

  107. Davydov, Y.I., Tonevitsky, A.G.: Prediction of linear B-cell epitopes. Mol. Biol. 43, 150–158 (2009)

    CAS  Google Scholar 

  108. Zhang, Q., et al.: Immune epitope database analysis resource (IEDB-AR). Nucleic Acids Res. 36, W513–W518 (2008). doi:10.1093/nar/gkn254

    PubMed  CAS  Google Scholar 

  109. Van Bergen, J., et al.: Get into the groove! Targeting antigens to MHC class II. Immunol. Rev. 172, 87–96 (1999)

    PubMed  Google Scholar 

  110. Tung, C.W., Ziehm, M., Kamper, A., Kohlbacher, O., Ho, S.Y.: POPISK: T-cell reactivity prediction using support vector machines and string kernels. BMC Bioinformatics 12, 446 (2011). doi:10.1186/1471-2105-12-446

    PubMed  CAS  Google Scholar 

  111. Stranzl, T., Larsen, M., Lundegaard, C., Nielsen, M.: NetCTLpan: pan-specific MHC class I pathway epitope predictions. Immunogenetics 62, 357–368 (2010). doi:10.1007/s00251-010-0441-4

    PubMed  CAS  Google Scholar 

  112. Kulkarni-Kale, U., Bhosle, S., Kolaskar, A.S.: CEP: a conformational epitope prediction server. Nucleic Acids Res. 33, W168–W171 (2005). doi:10.1093/nar/gki460

    PubMed  CAS  Google Scholar 

  113. Rubinstein, N.D., Mayrose, I., Pupko, T.: A machine-learning approach for predicting B-cell epitopes. Mol. Immunol. 46, 840–847 (2009)

    PubMed  CAS  Google Scholar 

  114. Liang, S., Zheng, D., Zhang, C., Zacharias, M.: Prediction of antigenic epitopes on protein surfaces by consensus scoring. BMC Bioinformatics 10, 302 (2009). doi:10.1186/1471-2105-10-302

    PubMed  Google Scholar 

  115. Liu, R., Hu, J.: Prediction of discontinuous B-cell epitopes using logistic regression and structural information. J. Proteomics Bioinformatics 4, 010–015 (2011)

    CAS  Google Scholar 

  116. Zhang, W., et al.: Prediction of conformational B-cell epitopes from 3D structures by random forests with a distance-based feature. BMC Bioinformatics 12, 341 (2011). doi:10.1186/1471-2105-12-341

    PubMed  CAS  Google Scholar 

  117. Ryvkin, A., et al.: Deep panning: steps towards probing the IgOme. PLoS One 7, e41469 (2012). doi:10.1371/journal.pone.0041469

    PubMed  CAS  Google Scholar 

  118. Doytchinova, I.A., Flower, D.R.: Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties. Vaccine 25, 856–866 (2007)

    PubMed  CAS  Google Scholar 

  119. Magnan, C.N., et al.: High-throughput prediction of protein antigenicity using protein microarray data. Bioinformatics 26, 2936–2943 (2010). doi:10.1093/bioinformatics/btq551

    PubMed  CAS  Google Scholar 

  120. Grandi, G.: Genomics and proteomics in reverse vaccines. Methods Biochem. Anal. 49, 379–393 (2006)

    PubMed  CAS  Google Scholar 

  121. Vivona, S., Bernante, F., Filippini, F.: NERVE: new enhanced reverse vaccinology environment. BMC Biotechnol. 6, 35 (2006). doi:10.1186/1472-6750-6-35

    PubMed  Google Scholar 

  122. Gardy, J.L., et al.: PSORT-B: improving protein subcellular localization prediction for gram-negative bacteria. Nucleic Acids Res. 31, 3613–3617 (2003). doi:10.1093/nar/gkg602

    PubMed  CAS  Google Scholar 

  123. Sachdeva, G., Kumar, K., Jain, P., Ramachandran, S.: SPAAN: a software program for prediction of adhesins and adhesin-like proteins using neural networks. Bioinformatics 21, 483–491 (2005). doi:10.1093/bioinformatics/bti028

    PubMed  CAS  Google Scholar 

  124. Tusnády, G.E., Simon, I.: Principles governing amino acid composition of integral membrane proteins: application to topology prediction. J. Mol. Biol. 283, 489–506 (1998)

    PubMed  Google Scholar 

  125. Doytchinova, I.A., Flower, D.R.: VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 8, 4 (2007). doi:10.1186/1471-2105-8-4

    PubMed  Google Scholar 

  126. Flower, D.R., Macdonald, I.K., Ramakrishnan, K., Davies, M.N., Doytchinova, I.A.: Computer aided selection of candidate vaccine antigens. Immunome Res. 6(Suppl 2), S1 (2010). doi:10.1186/1745-7580-6-s2-s1

    PubMed  Google Scholar 

  127. Plotkin, S.A.: Correlates of protection induced by vaccination. Clin. Vaccine Immunol. 17, 1055–1065 (2010)

    PubMed  CAS  Google Scholar 

  128. Thakur, A., Pedersen, L.E., Jungersen, G.: Immune markers and correlates of protection for vaccine induced immune responses. Vaccine 30, 4907–4920 (2012). doi:10.1016/j.vaccine.2012.05.049

    PubMed  CAS  Google Scholar 

  129. Whelan, M., Ball, G., Beattie, C., Dalgleish, A.: Biomarkers for development of cancer vaccines. Future Med. 3, 79–88 (2006)

    CAS  Google Scholar 

  130. Mou, Z., He, Y., Wu, Y.: Immunoproteomics to identify tumor-associated antigens eliciting humoral response. Cancer Lett. 278, 123–129 (2009). doi:10.1016/j.canlet.2008.09.009

    PubMed  CAS  Google Scholar 

  131. Walzl, G., Ronacher, K., Hanekom, W., Scriba, T.J., Zumla, A.: Immunological biomarkers of tuberculosis. Nat. Rev. Immunol. 11, 343–354 (2011). doi:10.1038/nri2960

    PubMed  CAS  Google Scholar 

  132. Liebenberg, J., et al.: Identification of Ehrlichia ruminantium proteins that activate cellular immune responses using a reverse vaccinology strategy. Vet. Immunol. Immunopathol. 145, 340–349 (2012). doi:10.1016/j.vetimm.2011.12.003

    PubMed  CAS  Google Scholar 

  133. Cardoso, F.C., Roddick, J.S., Groves, P., Doolan, D.L.: Evaluation of approaches to identify the targets of cellular immunity on a proteome-wide scale. PLoS One 6, e27666 (2011). doi:10.1371/journal.pone.0027666

    PubMed  CAS  Google Scholar 

  134. Coffman, R.L., Sher, A., Seder, R.A.: Vaccine adjuvants: putting innate immunity to work. Immunity 33, 492–503 (2010)

    PubMed  CAS  Google Scholar 

  135. Tizard, I.A.: Veterinary Immunology – An Introduction, 8th edn. Philadelphia, Saunders (2008)

    Google Scholar 

  136. Sayers, S., Ulysse, G., Xiang, Z., He, Y.: Vaxjo: a web-based vaccine adjuvant database and its application for analysis of vaccine adjuvants and their uses in vaccine development. J. Biomed. Biotechnol. 2012, 13 (2012). doi:10.1155/2012/831486

    Google Scholar 

  137. Skibinski, D.A.G., O´Hagan, D.T., Chapter 6: Adjuvants. In: Rappuoli, R., Serruto, D., Rappuoli, R. (eds.). Vaccine Design – Innovative and Novel Strategies, vol. 1, pp.139–169. Caister Academic Press, Norfolk (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christine Maritz-Olivier PhD .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Maritz-Olivier, C., Richards, S. (2014). Considerations for Vaccine Design in the Postgenomic Era. In: Giese, M. (eds) Molecular Vaccines. Springer, Cham. https://doi.org/10.1007/978-3-319-00978-0_16

Download citation

Publish with us

Policies and ethics