An automated prediction of MHC class I-binding peptides based on positional scanning with peptide libraries

Specificities of three mouse major histocompatibility complex (MHC) class I molecules, K-b, D-b, and L-d, were analyzed by positional scanning using c ombinatorial peptide libraries. The result of the analysis was used to crea te a scoring program to predict MHC-binding peptides in proteins. The capac ity of the scoring was then challenged with a number of peptides by compari ng the prediction with the experimental binding. The score and the experime ntal binding exhibited a linear correlation but with substantial deviations of data points. Statistically, for approximately 80% of randomly chosen pe ptides, MHC-binding capacity could be predicted within one log concentratio n of peptides for a half-maximal binding. Known cytotoxic T-lymphocyte epit ope peptides could be predicted, with a few exceptions. Tn addition, freque nt findings of MHC-binding peptides with incomplete or no anchor amino acid (s) suggested a substantial bias introduced by natural antigen processing i n peptide selection by MHC class I molecules.