Understanding Prediction Systems for HLA-Binding Peptides and T-Cell Epitope Identification

  • Liwen You
  • Ping Zhang
  • Mikael Bodén
  • Vladimir Brusic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)

Abstract

Peptide binding to HLA molecules is a critical step in induction and regulation of T-cell mediated immune responses. Because of combinatorial complexity of immune responses, systematic studies require combination of computational methods and experimentation. Most of available computational predictions are based on discriminating binders from non-binders based on use of suitable prediction thresholds. We compared four state-of-the-art binding affinity prediction models and found that nonlinear models show better performance than linear models. A comprehensive analysis of HLA binders (A*0101, A*0201, A*0301, A*1101, A*2402, B*0702, B*0801 and B*1501) showed that non-linear predictors predict peptide binding affinity with high accuracy. The analysis of known T-cell epitopes of survivin and known HIV T-cell epitopes showed lack of correlation between binding affinity and immunogenicity of HLA-presented peptides. T-cell epitopes, therefore, can not be directly determined from binding affinities by simple selection of the highest affinity binders.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brusic, V., Zeleznikow, J.: Computational binding assays of antigenic peptides. Letters in Peptide Sci. 6, 313–324 (1999)Google Scholar
  2. 2.
    Yewdell, J.W.: Confronting complexity: real-world immunodominance in antiviral CD8+ T cell responses. Immunity 25, 533–543 (2006)CrossRefGoogle Scholar
  3. 3.
    Peters, B., Bui, H.H., Frankild, S., Nielson, M., Lundegaard, C., Kostem, E., Basch, D., Lamberth, K., Harndahl, M., Fleri, W., Wilson, S.S., Sidney, J., Lund, O., Buus, S., Sette, A.: A community resource benchmarking predictions of peptide binding to MHC-I molecules. PLoS Comput. Biol. 2(6), 574–584 (2006)CrossRefGoogle Scholar
  4. 4.
    Rammensee, H.G., Bachmann, J., Emmerich, N.P., Bachor, O.A., Stevanovic, S.: SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50(3-4), 213–219 (1999)CrossRefGoogle Scholar
  5. 5.
    Parker, K.C., Bednarek, M.A., Coligan, J.E.: Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J. Immunol. 152(1), 163–175 (1994)Google Scholar
  6. 6.
    Udaka, K., Wiesmuller, K.H., Kienle, S., Jung, G., Tamamura, H., Yamagishi, H., Okumura, K., Walden, P., Suto, T., Kawasaki, T.: An automated prediction of MHC class I-binding peptides based on positional scanning with peptide libraries. Immunogenetics 51(10), 816–828 (2000)CrossRefGoogle Scholar
  7. 7.
    Guan, P., Doytchinova, I.A., Zygouri, C., Flower, D.R.: MHCPred: bringing a quantitative dimension to the online prediction of MHC binding. Applied Bioinformatics 2(1), 63–66 (2003)Google Scholar
  8. 8.
    Peters, B., Sette, A.: Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics 6(132) (2005)Google Scholar
  9. 9.
    Bui, H.H., Sidney, J., Peters, B., Sathiamurthy, M., Sinichi, A., Purton, K.A., Mothe, B.R., Chisari, F.V., Watkins, D.I., Sette, A.: Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. Immunogenetics 57(5), 304–314 (2005)CrossRefGoogle Scholar
  10. 10.
    Buus, S., Lauemoller, S.L., Worning, P., Kesmir, C., Frimurer, T., Corbet, S., Fomsgaard, A., Hilden, J., Holm, A., Brunak, S.: Sensitive quantitative predictions of peptide-MHC binding by a ‘Query by Committee’ artificial neural network approach. Tissue Antigens 62(5), 378–384 (2003)CrossRefGoogle Scholar
  11. 11.
    Brusic, V., Bucci, K., Schonbach, C., Petrovsky, N., Zeleznikow, J., Kazura, J.W.: Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding. Journal of Molecular Graphics and Modelling 19(5), 405–411 (2001)CrossRefGoogle Scholar
  12. 12.
    Mamitsuka, H.: Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins 33(4), 460–474 (1998)CrossRefGoogle Scholar
  13. 13.
    Dönnes, P., Elofsson, A.: Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinformatics 3(25) (2002)Google Scholar
  14. 14.
    Zhao, Y., Pinilla, C., Valmori, D., Martin, R., Simon, R.: Application of support vector machines for T-cell epitopes prediction. Bioinformatics 19, 1978–1984 (2003)CrossRefGoogle Scholar
  15. 15.
    Riedesel, H., Kolbeck, B., Schmetzer, O., Knapp, E.W.: Peptide binding at class I major histocompatibility complex scored with linear functions and support vector machines. Genome Informatics 15(1), 198–212 (2004)Google Scholar
  16. 16.
    Yang, Z.R., Johnson, F.C.: Prediction of T-cell epitopes using biosupport vector machines. J. Chem. Inf. Model 45(5), 1424–1428 (2005)CrossRefGoogle Scholar
  17. 17.
    Bozic, I., Zhang, G.L., Brusic, V.: Predictive vaccinology: optimisation of predictions using support vector machine classifiers. In: Gallagher, M., Hogan, J.P., Maire, F. (eds.) IDEAL 2005. LNCS, vol. 3578, pp. 375–381. Springer, Heidelberg (2005)Google Scholar
  18. 18.
    Zhang, G.L., Bozic, I., Kwoh, C.K., August, J.T., Brusic, V.: Prediction of supertype-specific HLA class I binding peptides using support vector machines. Journal of Immunological Methods 320(1-2) (2007)Google Scholar
  19. 19.
    Cui, J., Han, L.Y., Lin, H.H., Zhang, H.L., Tang, Z.Q.: Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties. Molecular Immunology 44(5), 866–877 (2007)CrossRefGoogle Scholar
  20. 20.
    Jojic, N., Reyes-Gomez, M., Heckerman, D., Kadie, C., Schueler-Furman, O.: Learning MHC I-peptide binding. Bioinformatics 22(14), e227–235 (2006)CrossRefGoogle Scholar
  21. 21.
    Yu, K., Petrovsky, N., Schonbach, C., Koh, J.Y., Brusic, V.: Methods for prediction of peptide binding to MHC molecules: a comparative study. Molecular Medicine 8(3), 137–148 (2002)Google Scholar
  22. 22.
    Trost, B., Bickis, M., Kusalik, A.: Strength in numbers: achieving greater accuracy in MHC-I binding prediction by combining the results from multiple prediction tools. Immunome Research 3, 5 (2007)CrossRefGoogle Scholar
  23. 23.
    Bachinsky, M.M., Guillen, D.E., Patel, S.R., Singleton, J., Chen, C., Soltis, D.A., Tussey, L.G.: Mapping and binding analysis of peptides derived from the tumor-associated antigen survivin for eight HLA alleles. Cancer Immunity 5, 1–9 (2005)Google Scholar
  24. 24.
    Friedrichs, B., Siegel, S., Andersen, M.H., Schmitz, N., Zeis, M.: Survivin-derived peptide epitopes and their role for induction of antitumor immunity in hematological malignancies. Leukemia & Lymphoma 47(6), 978–985 (2006)CrossRefGoogle Scholar
  25. 25.
    Nielsen, M., Lundegaard, C., Worning, P., Lauemøller, S.L., Lamberth, K., Buus, S., Brunak, S., Lund, O.: Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 12(5), 1007–1017 (2003)CrossRefGoogle Scholar
  26. 26.
    Wobser, M., Keikavoussi, P., Kunzmann, V., Weininger, M., Andersen, M.H., Becker, J.C.: Complete remission of liver metastasis of pancreatic cancer under vaccination with a HLA-A2 restricted peptide derived from the universal tumor antigen survivin. Cancer Immunology and Immunotherapy 55(10), 1294–1298 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Liwen You
    • 1
    • 2
    • 3
  • Ping Zhang
    • 3
  • Mikael Bodén
    • 4
  • Vladimir Brusic
    • 3
    • 5
  1. 1.School of Information Science, Computer and Electrical Engineering, Halmstad University, HalmstadSweden
  2. 2.Department of Theoretical Physics, Lund University, LundSweden
  3. 3.School of Land, Crop, and Food Sciences, and University of Queensland, Brisbane QLDAustralia
  4. 4.School of Information Technology and Electrical Engineering, University of Queensland, Brisbane QLDAustralia
  5. 5.Cancer Vaccine Center, Dana-Farber Cancer Institute, Boston MAUSA

Personalised recommendations