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Definition of MHC supertypes through clustering of MHC peptide-binding repertoires


Reche, Pedro A and Reinherz, Ellis L (2007) Definition of MHC supertypes through clustering of MHC peptide-binding repertoires. Methods in molecular biology (Clifton, N.J.), 409 . pp. 163-73. ISSN 1064-3745


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Identification of peptides that can bind to major histocompatibility complex (MHC) molecules is important for anticipation of T-cell epitopes and for the design of epitope-based vaccines. Population coverage of epitope vaccines is, however, compromised by the extreme polymorphism of MHC molecules, which is in fact the basis for their differential peptide binding. Therefore, grouping of MHC molecules into supertypes according to peptide-binding specificity is relevant for optimizing the composition of epitope-based vaccines. Despite the fact that the peptide-binding specificity of MHC molecules is linked to their specific amino acid sequences, it is unclear how amino sequence differences correlate with peptide-binding specificities. In this chapter, we detail a method for defining MHC supertypes based on the analysis and subsequent clustering of their peptide-binding repertoires.

Item Type:Article
Uncontrolled Keywords:MHC; Supertypes; Clustering; Peptide-binding repertoires
Subjects:Medical sciences > Medicine > Immunology
Medical sciences > Biology > Molecular biology
Sciences > Computer science > Bioinformatics
ID Code:9326

1. Margulies, D.H. 1997. Interactions of TCRs with MHC-peptide complexes: a quantitative basis for mechanistic models. Curr Opin Immunol 9:390–395.

2. Yu, K., Petrovsky, N., Schonbach, C., Koh, J.Y., and Brusic, V. 2002. Methods for prediction of peptide binding to MHC molecules: a comparative study. Mol Med 8:137–148.

3. Flower, D. 2003. Towards in silico prediction of immunogenic epitopes. Trends Immunol 24:667–674.

4. Flower, D., and Doytchinova, I.A. 2002. Immunoinformatics and the prediction of immunogenicity. Appl Bioinformatics 1:167–176.

5. Reche, P.A., and Reinherz, E.L. 2003. Sequence variability analysis of human class I and class II MHC molecules: functional and structural correlates of amino acid polymorphisms. J Mol Biol 331:623–641.

6. David W. Gjertson, and Paul I. Terasaki, E. (Eds) 1998. HLA 1998. American Society for Histocompatibility and Immunogenetics, Lenexa.

7. Sette, A., and Sidney, J. 1999. Nine major HLA class I supertypes account for the vast preponderance of HLA-A and -B polymorphism. Immunogenetics 50:201–212.

8. Sette, A., and Sidney, J. 1998. HLA supertypes and supermotifs: a functional perspective on HLA polymorphism. Curr Opin Immunol 10:478–482.

9. Bouvier, M., and Wiley, D.C. 1994. Importance of peptide amino acid and carboxyl termini to the stability of MHC class I molecules. Science 265:398–402.

10. Ruppert, J., Sidney, J., Celis, E., Kubo, T., Grey, H.M., and Sette, A. 1993. Prominent role of secondary anchor residues in peptide binding to HLA-A2.1 molecules. Cell 74:929–937.

11. Reche, P.A., Glutting, J.-P., and Reinherz, E.L. 2002. Prediction of MHC class I binding peptides using profile motifs. Hum Immunol 63:701–709.

12. Reche, P.A., Glutting, J.-P, Zhang, H., and Reinherz, E.L. 2004. Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics 56:405–419

13. Retief, J.D. 2000. Phylogenetic analysis using PHYLIP. Methods Mol Biol 132:243–258.

14. Fitch, W.M., and Margoliash, E. 1967. Construction of phylogenetic trees. Science 155:279–284.

15. Saitou, N., and Nei, M. 1987. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4:406–425.

16. Dawson, D.V., Ozgur, M., Sari, K., Ghanayem, M., and Kostyu, D.D. 2001. Ramifications of HLA class I polymorphism and population genetics for vaccine development. Genet Epidemiol 20:87–106.

17. Cao, K., Hollenbach, J., Shi, X., Shi, W., Chopek, M., and Fernandez-Vina, M.A. 2001. Analysis of the frequencies of HLA-A, B, and C alleles and haplotypes in the five major ethnic groups of the United States reveals high levels of diversity in these loci and contrasting distribution patterns in these populations. Hum Immunol 62:1009–1030.

18. Doytchinova, I.A., Guan, P., and Flower, D.R. 2004. Quantitative structure-activity relationships and the prediction of MHC supermotifs. Methods 34:444–453.

19. Doytchinova, I.A., and Flower, D.R. 2005. In silico identification of supertypes for class II MHCs. J Immunol 174:7085–7095.

20. Doytchinova, I.A., Guan, P., and Flower, D.R. 2004. Identifying human MHC supertypes using bioinformatic methods. J Immunol 172:4314–4323.

21. Lund, O., Nielsen, M., Kesmir, C., Petersen, A.G., Lundegaard, C., Worning, P., Sylvester-Hvid, C., Lamberth, K., Roder, G., Justesen, S., Buus, S., and Brunak, S. 2004. Definition of supertypes for HLA molecules using clustering of specificitymatrices. Immunogenetics 55:797–810.

22. Parker, K.C., Bednarek, M.A., and Coligan, J.E. 1994. Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side chains. J Immunol 152:163–175.

23. Guan, P., Doytchinova, I.A., Zygouri, C., and Flower, D. 2003. MHCPred: a server for quantitative prediction of peptide-MHC binding. Nucleic Acids Res 31:3621–3624.

24. Singh, H., and Raghava, G.P. 2001. ProPred: prediction of HLA-DR binding sites. Bioinformatics 17:1236–1237.

25. Donnes, P., and Elofsson, A. 2002. Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinformatics 3:25.

26. Rammensee, H.G., Bachmann, J., Emmerich, N.P.N., Bacho, O.A., and Stevanovic, S. 1999. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50:213–219.

27. Buus, S., Lauemoller, S.L., Worning, P., Kesmir, C., Frimurer, T., Corbet, S., Fomsgaard, A., Hilden, J., Holm, A., and Brunak, S. 2003. Sensitive quantitative predictions of peptide-MHC binding by a ’Query by Committee’ artificial neural network approach. Tissue Antigens 62:378–384.

28. Altuvia, Y., Sette, A., Sidney, J., Southwood, S., and Margalit, H. 1997. A structure based algorithm to predict potential binding peptides to MHC molecules with hydrophobic binding pockets. Hum Immunol 58:1–11.

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Last Modified:06 Oct 2014 12:20

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