MHC Class I Epitope Binding Prediction Trained on Small Data Sets

  • Claus Lundegaard
  • Morten Nielsen
  • Kasper Lamberth
  • Peder Worning
  • Christina Sylvester-Hvid
  • Søren Buus
  • Søren Brunak
  • Ole Lund
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3239)

Abstract

The identification of potential T-cell epitopes is important for development of new human or vetenary vaccines, both considering single protein/subunit vaccines, and for epitope/peptide vaccines as such. The highly diverse MHC class I alleles bind very different peptides, and accurate binding prediction methods exist only for alleles were the binding pattern have been deduced from peptide motifs. Using empirical knowledge of important anchor positions within the binding peptides dramatically reduces the number of peptides needed for reliable predictions. We here present a general method for predicting peptides binding to specific MHC class I alleles. The method combines advanced automatic scoring matrix generation with empirical position specific differential anchor weighting. The method leads to predictions with a comparable or higher accuracy than other established prediction servers, even in situations where only very limited data are available for training.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Claus Lundegaard
    • 1
  • Morten Nielsen
    • 1
  • Kasper Lamberth
    • 2
  • Peder Worning
    • 1
  • Christina Sylvester-Hvid
    • 2
  • Søren Buus
    • 2
  • Søren Brunak
    • 1
  • Ole Lund
    • 1
  1. 1.Center for Biological Sequence Analysis, BioCentrum, Technical University of DenmarkLyngbyDenmark
  2. 2.Department of Experimental Immunology, Institute of Medical Microbiology and ImmunologyUniversity of Copenhagen DenmarkDenmark

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