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

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Abstract
Specificities of three mouse major histocompatibility complex (MHC) class I molecules, K, D, and L, were analyzed by positional scanning using combinatorial peptide libraries. The result of the analysis was used to create a scoring program to predict MHC-binding peptides in proteins. The capacity of the scoring was then challenged with a number of peptides by comparing the prediction with the experimental binding. The score and the experimental binding exhibited a linear correlation but with substantial deviations of data points. Statistically, for approximately 80% of randomly chosen peptides, MHC-binding capacity could be predicted within one log concentration of peptides for a half-maximal binding. Known cytotoxic T-lymphocyte epitope peptides could be predicted, with a few exceptions. In addition, frequent findings of MHC-binding peptides with incomplete or no anchor amino acid(s) suggested a substantial bias introduced by natural antigen processing in peptide selection by MHC class I molecules.

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