Bioinformatics Vol. 19 no. 15 2003
Pages 1978-1984
© 2003 Oxford University Press
Application of support vector machines for T-cell epitopes prediction
1 Biometric Research Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA, 2 Torrey Pines Institute for Molecular Studies, San Diego, CA 92121, USA, 3 Division of Clinical Onco-Immunology, Ludwig Institute for Cancer Research, University Hospital (CHUV), Lausanne, Switzerland and 4 Neuroimmunology Branch, National Institute of Neurological Disorder and Stroke, Bethesda, National Institutes of Health, MD 20892, USA
Received on October 24, 2002
; revised on March 12, 2003
; accepted on April 7, 2003
Motivation: The T-cell receptor, a major histocompatibility complex (MHC) molecule, and a bound antigenic peptide, play major roles in the process of antigen-specific T-cell activation. T-cell recognition was long considered exquisitely specific. Recent data also indicate that it is highly flexible, and one receptor may recognize thousands of different peptides. Deciphering the patterns of peptides that elicit a MHC restricted T-cell response is critical for vaccine development.
Results: For the first time we develop a support vector machine (SVM) for T-cell epitope prediction with an MHC type I restricted T-cell clone. Using cross-validation, we demonstrate that SVMs can be trained on relatively small data sets to provide prediction more accurate than those based on previously published methods or on MHC binding.
Supplementary information: Data for 203 synthesized peptides is available at http://linus.nci.nih.gov/Data/LAU203_Peptide.pdf
Contact: rsimon{at}mail.nih.gov
* To whom correspondence should be addressed.
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