Combinatorial peptide libraries and biometric score matrices permit the quantitative analysis of specific and degenerate interactions between clonotypic TCR and MHC peptide ligands

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 m olecular mimics. The response of T cell clones to positional scanning synth etic combinatorial libraries is analyzed with a mathematical approach that is based on a model of independent contribution of individual amino acids t o 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 p redict the most stimulatory peptides for a given T cell clone. We demonstra te the predictive power of the novel strategy and show that, together with gene expression profiling by cDNA microarrays, it leads to the identificati on of novel candidate autoantigens in the inflammatory autoimmune disease, multiple sclerosis.