Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barton GM, Medzhitov R (2002) Toll-like receptors and their ligands. Curr Top Microbiol Immunol 270:81–92
Bhasin M, Raghava GPS (2004a) SVM based method for predicting HLA-DRB1 binding peptides in an antigen sequence. Bioinformatics 20:421–423
Bhasin M, Raghava GPS (2004b) Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine 22:3195–3204
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–W207
Bhasin M, Singh H, Raghava GPS (2003) MHCBN: A comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19:665–666
Fearon DT, Locksley RM (1996) The instructive role of innate immunity in the acquired immune response. Science 272:50
Gordon S (2002) Pattern recognition receptors: Doubling up for the innate immune response. Cell 111:927–930
Gourley TS et al (2004) Generation and maintenance of immunological memory. Semin Immunol 16:323–333
Huang J, Honda W (2006) CED: A conformational epitope database. BMC Immunol 7:7
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):365–368
Iwasaki A, Medzhitov R (2004) Toll-like receptor control of the adaptive immune responses. Nat Immunol 5:987–995
Janeway CA Jr (1989) Approaching the asymptote? Evolution and revolution in immunology. Cold Spring Harbor Symp Quant Biol 54:1–13
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 9:7
Lata S, Raghava GP (2008a) PRRDB: A comprehensive database of pattern-recognition receptors and their ligands. BMC Genomic 9:180
Lata S, Raghava GP (2008b) CytoPred: A server for prediction and classification of cytokines. Protein Eng Des Sel 21(4):279–282
Lata S, Sharma BK, Raghava GPS (2007) Analysis and prediction of antibacterial peptides. BMC Bioinform 8:263
McSparron H et al (2003) JenPep: A novel computational information resource for immunobiology and vaccinology. J Chem Inf Comput Sci 43:1276–1287
Pellequer JL, Westhof E (1993) PREDITOP: A program for antigenicity prediction. J Mol Graph 11:204–210
Peters B et al (2005) The immune epitope database and analysis resource: From vision to blueprint. PLoS Biol 3:e91
Rammensee HG, Bachman J, Stevanovich S (1997) MHC ligands and peptide motifs. Landes Bioscience, Georgetown, pp 1–462
Reche PA, Glutting J, Reinherz EL (2002) Prediction of MHC class I binding peptides using profile motifs. Hum Immunol 63:701–709
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 197–204
Saha S, Raghava GPS (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65(1):40–48
Saha S, Bhasin M, Raghava GPS (2005) Bcipep: A database of B-cell epitopes. BMC Genomics 6:79
Schellack C et al (2006) IC31, a novel adjuvant signaling via TLR9, induces potent cellular and humoral immune responses. Vaccine 24: 5461–5472
Singh H, Raghava GPS (2001) ProPred: Prediction of HLA-DR binding sites. Bioinformatics 17:1236–1237
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Lata, S., Raghava, G.P.S. (2009). Databases and Web-Based Tools for Innate Immunity. In: Flower, D., Davies, M., Ranganathan, S. (eds) Bioinformatics for Immunomics. Immunomics Reviews:, vol 3. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0540-6_6
Download citation
DOI: https://doi.org/10.1007/978-1-4419-0540-6_6
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-0539-0
Online ISBN: 978-1-4419-0540-6
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)