Modified Association Rule Mining Approach for the MHC-Peptide Binding Problem

  • Galip Gürkan Yardımcı
  • Alper Küçükural
  • Yücel Saygın
  • Uğur Sezerman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4263)


Computational approach to predict peptide binding to major histocompatibility complex (MHC) is crucial for vaccine design since these peptides can act as a T-Cell epitope to trigger immune response. There are two main branches for peptide prediction methods; structural and data mining approaches. These methods can be successfully used for prediction of T-Cell epitopes in cancer, allergy and infectious diseases. In this paper , association rule mining methods are implemented to generate rules of peptide selection by MHCs. To capture the binding characteristics, modified rule mining and data transformation methods are implemented in this paper. Peptides are known to bind to the same MHC show sequence variability, to capture this characteristic, we used a reduced amino acid alphabet by clustering amino acids according to their physico-chemical properties. Using the classification of amino acids and the OR-operator to combine the rules to reflect that different amino acid types and positions along the peptide may be responsible for binding are the innovations of the method presented. We can predict MHC Class-I binding with 75-97% coverage and 76-100% accuracy.


Peptides MHC Class-I Association rule mining reduced amino acid alphabet data mining 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. 1993 ACM-SIGMOD Int. Conf: Management of Data (SIGMOD 1993), Washington, DC, May 1993, pp. 207–216 (1993)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. 1994 Int. Conf. Very Large Data Bases (VLDB 1994), Santiago, Chile, September 1994, pp. 487–499 (1994)Google Scholar
  3. 3.
    Bhasin, M., Singh, H., Raghava, G.P.S.: MHCBN: A Comprehensive Database of MHC Binding and Non-Binding Peptides. Nucleic Acids Research 19(5), 665–666 (2002)Google Scholar
  4. 4.
    Brusic, V., Bajica, V.B., Petrovsky, N.: Computational methods for prediction of T-cell epitopes—a framework for modelling, testing, and applications. Methods 34(4), 436–443 (2004)CrossRefGoogle Scholar
  5. 5.
    Brusic, V., Flower, D.R.: Bioinformatics tools for identifying T-cell epitopes. DDT: BIOSILICO 2(1), 18–23 (2004)CrossRefGoogle Scholar
  6. 6.
    Brusic, V., Rudy, G., Harrison, L.C.: Prediction of MHC Binding Peptides Using Artificial Neural Networks. Complexity International 2 (1995)Google Scholar
  7. 7.
    Dönnes, P., Elofsson, A.: Prediction of MHC class I binding peptides, using SVMHC. Bioinformatics 3, 25 (2002)CrossRefGoogle Scholar
  8. 8.
    Gulukota, K., Sidney, J., Sette, A., DeLisi, C.: Two complementary methods for predicting peptides binding major histocompatibility complex molecules. J Mol Biol 267, 1258–1267 (1997)CrossRefGoogle Scholar
  9. 9.
    Kloetzel, P.M.: The proteasome and MHC class I antigen processing. Biochimica et Biophysica Acta 1695, 217–225 (2004)Google Scholar
  10. 10.
    Milledge, T., Zheng, G., Narasimhan, G.: An Application Of Association Rule Mining to Hla-A*0201 Epitope Prediction. In: ICBA 2004 (2004)Google Scholar
  11. 11.
    Rammensee, H.G., Bachmann, J., Emmerich, N.P.N., Bachor, O.A., Stevanovic, S.: SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50(3-4), 213–219 (1999)CrossRefGoogle Scholar
  12. 12.
    Rammensee, H.G., Friede, T., Stevanovic, S.: MHC ligands and peptide motifs: 1st listing. Immunogenetics 41, 178–228 (1995)CrossRefGoogle Scholar
  13. 13.
    Reche, P.A., Glutting, J.P., Reinherz, E.L.: Prediction of MHC Class I Binding Peptides Using Profile Motifs. Hum. Immunol. 63, 701–709 (2002)CrossRefGoogle Scholar
  14. 14.
    Sezerman, O.U., Islamaj, R., Alpaydin, E.: Three dimensional representation of amino acid characteristics. IEEE EMBC 3, 2903–2906 (2001)Google Scholar
  15. 15.
    Singh, H., Raghava, G.P.S.: ProPred1: prediction of promiscuous MHC Class-I binding sites. Bioinformatics 19(8), 1009–1014 (2003)CrossRefGoogle Scholar
  16. 16.
    Udaka, K., Mamitsuka, H., Nakaseko, Y., Abe, N.: Empirical Evaluation of a Dynamic Experiment Design Method for Prediction of MHC Class I-Binding Peptides. The Journal of Immunology 169, 5744–5753 (2002)Google Scholar
  17. 17.
    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, 816–828 (2000)Google Scholar
  18. 18.
    Zeng, J., Treutlein, H.R., Rudy, G.B.: Predicting sequences and structures of MHC-binding peptides: a computational combinatorial approach. Journal of Computer-Aided Molecular Design, 573–576 (2001)Google Scholar
  19. 19.
    Zhang, C., Anderson, A., DeLisi, C.: Structural principles that govern the peptide-binding motifs of class I MHC molecules. J. Mol Biol, 929–947 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Galip Gürkan Yardımcı
    • 1
  • Alper Küçükural
    • 1
  • Yücel Saygın
    • 1
  • Uğur Sezerman
    • 1
  1. 1.Faculty of Engineering and Natural SciencesSabanci UniversityTurkey

Personalised recommendations