MHC Class I Epitope Binding Prediction Trained on Small Data Sets
- Cite this paper as:
- Lundegaard C. et al. (2004) MHC Class I Epitope Binding Prediction Trained on Small Data Sets. In: Nicosia G., Cutello V., Bentley P.J., Timmis J. (eds) Artificial Immune Systems. ICARIS 2004. Lecture Notes in Computer Science, vol 3239. Springer, Berlin, Heidelberg
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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