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Molecular Statistics and Bioinformatics Section, Biometric Research Branch, National Cancer Institute, and
Neuroimmunology Branch, National Institute of Neurological Disorder and Stroke, National Institutes of Health, Bethesda, MD 20892;
Torrey Pines Institute for Molecular Studies and Mixture Sciences, San Diego, CA 92121; and
Clinical Neuroimmunology Group, Department of Neurology, Philipps-University Marburg, Marburg, Germany
The interaction of TCRs with MHC peptide ligands can be highly flexible, so that many different peptides are recognized by the same TCR in the context of a single restriction element. We provide a quantitative description of such interactions, which allows the identification of T cell epitopes and molecular mimics. The response of T cell clones to positional scanning synthetic combinatorial libraries is analyzed with a mathematical approach that is based on a model of independent contribution of individual amino acids to peptide Ag recognition. This biometric analysis compares the information derived from these libraries composed of trillions of decapeptides with all the millions of decapeptides contained in a protein database to rank and predict the most stimulatory peptides for a given T cell clone. We demonstrate the predictive power of the novel strategy and show that, together with gene expression profiling by cDNA microarrays, it leads to the identification of novel candidate autoantigens in the inflammatory autoimmune disease, multiple sclerosis.
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