Databases and Web-Based Tools for Innate Immunity

  • Sneh Lata
  • G. P. S. RaghavaEmail author
Part of the Immunomics Reviews: book series (IMMUN, volume 3)


A number of studies in the past decade have demonstrated that the innate immune system does not merely act as the first line of defense but provides critical signals for the development of specific adaptive immune response. Innate immune system employs a set of receptors called pattern recognition receptors (PRRs) that recognize evolutionarily conserved patterns from pathogens known as pathogen associated molecular patterns (PAMPs). These receptors when stimulated lead to activation of adaptive antigen-recognition receptors subsequently inducing the expression of key co-stimulatory molecules and cytokines as well as maturation and migration of other cells. Though many bioinformatics-based databases and prediction methods have been developed for adaptive immune system, the work in the field of bioinformatics for innate immune system is still in its infancy. Here in this chapter, we describe the few databases that store the detailed information about innate immunity and related molecules and tools that are developed for prediction of important components of innate immune system.


Innate Immunity Innate Immune System Adaptive Immune System Subunit Vaccine Query Protein 
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.


  1. Barton GM, Medzhitov R (2002) Toll-like receptors and their ligands. Curr Top Microbiol Immunol 270:81–92PubMedGoogle Scholar
  2. Bhasin M, Raghava GPS (2004a) SVM based method for predicting HLA-DRB1 binding peptides in an antigen sequence. Bioinformatics 20:421–423CrossRefPubMedGoogle Scholar
  3. Bhasin M, Raghava GPS (2004b) Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine 22:3195–3204CrossRefPubMedGoogle Scholar
  4. Bhasin M, Raghava GPS (2005) Pcleavage: An SVM-based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences. Nucleic Acids Res 33:W202–W207CrossRefPubMedGoogle Scholar
  5. Bhasin M, Singh H, Raghava GPS (2003) MHCBN: A comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19:665–666CrossRefPubMedGoogle Scholar
  6. Fearon DT, Locksley RM (1996) The instructive role of innate immunity in the acquired immune response. Science 272:50CrossRefPubMedGoogle Scholar
  7. Gordon S (2002) Pattern recognition receptors: Doubling up for the innate immune response. Cell 111:927–930CrossRefPubMedGoogle Scholar
  8. Gourley TS et al (2004) Generation and maintenance of immunological memory. Semin Immunol 16:323–333CrossRefPubMedGoogle Scholar
  9. Huang J, Honda W (2006) CED: A conformational epitope database. BMC Immunol 7:7CrossRefPubMedGoogle Scholar
  10. Huang N, Chen H, Sun Z (2005) CTKPred: An SVM-based method for the prediction and classification of the cytokine superfamily. Protein Eng Des Sel 18(8):365–368CrossRefPubMedGoogle Scholar
  11. Iwasaki A, Medzhitov R (2004) Toll-like receptor control of the adaptive immune responses. Nat Immunol 5:987–995CrossRefPubMedGoogle Scholar
  12. Janeway CA Jr (1989) Approaching the asymptote? Evolution and revolution in immunology. Cold Spring Harbor Symp Quant Biol 54:1–13PubMedGoogle Scholar
  13. Korb M, Rust AG, Thorsson V, Battail C, Li B, Hwang D, Kennedy KA, Roach JC, Rosenberger CM, Gilchrist M, Zak D, Johnson C, Marzolf B, Aderem A, Shmulevich I, Bolouri H (2008) The innate immune database (IIDB). BMC Immunol 9:7CrossRefPubMedGoogle Scholar
  14. Lata S, Raghava GP (2008a) PRRDB: A comprehensive database of pattern-recognition receptors and their ligands. BMC Genomic 9:180CrossRefGoogle Scholar
  15. Lata S, Raghava GP (2008b) CytoPred: A server for prediction and classification of cytokines. Protein Eng Des Sel 21(4):279–282CrossRefPubMedGoogle Scholar
  16. Lata S, Sharma BK, Raghava GPS (2007) Analysis and prediction of antibacterial peptides. BMC Bioinform 8:263CrossRefGoogle Scholar
  17. McSparron H et al (2003) JenPep: A novel computational information resource for immunobiology and vaccinology. J Chem Inf Comput Sci 43:1276–1287PubMedGoogle Scholar
  18. Pellequer JL, Westhof E (1993) PREDITOP: A program for antigenicity prediction. J Mol Graph 11:204–210CrossRefPubMedGoogle Scholar
  19. Peters B et al (2005) The immune epitope database and analysis resource: From vision to blueprint. PLoS Biol 3:e91CrossRefPubMedGoogle Scholar
  20. Rammensee HG, Bachman J, Stevanovich S (1997) MHC ligands and peptide motifs. Landes Bioscience, Georgetown, pp 1–462Google Scholar
  21. Reche PA, Glutting J, Reinherz EL (2002) Prediction of MHC class I binding peptides using profile motifs. Hum Immunol 63:701–709CrossRefPubMedGoogle Scholar
  22. Saha S, Raghava GPS (2004) BcePred: Prediction of Continuous B-Cell Epitopes in Antigenic Sequences Using Physico-chemical Properties. In: Nicosia G, Cutello V, Bentley PJ,Timis J (eds) ICARIS, LNCS 3239. Springer, Heidelberg, pp 197–204Google Scholar
  23. Saha S, Raghava GPS (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65(1):40–48CrossRefPubMedGoogle Scholar
  24. Saha S, Bhasin M, Raghava GPS (2005) Bcipep: A database of B-cell epitopes. BMC Genomics 6:79CrossRefPubMedGoogle Scholar
  25. Schellack C et al (2006) IC31, a novel adjuvant signaling via TLR9, induces potent cellular and humoral immune responses. Vaccine 24: 5461–5472Google Scholar
  26. Singh H, Raghava GPS (2001) ProPred: Prediction of HLA-DR binding sites. Bioinformatics 17:1236–1237CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Institute of Microbial TechnologyChandigarhIndia

Personalised recommendations