Empirical evaluation of a dynamic experiment design method for prediction of MHC class I-binding peptides.

The ability to predict MHC-binding peptides remains limited despite ever expanding demands for specific immunotherapy against cancers, infectious diseases, and autoimmune disorders. Previous analyses revealed position-specific preference of amino acids but failed to detect sequence patterns. Efforts to use computational analysis to identify sequence patterns have been hampered by the insufficiency of the number/quality of the peptide binding data. We propose here a dynamic experiment design to search for sequence patterns that are common to the MHC class I-binding peptides. The method is based on a committee-based framework of query learning using hidden Markov models as its component algorithm. It enables a comprehensive search of a large variety (20(9)) of peptides with a small number of experiments. The learning was conducted in seven rounds of feedback loops, in which our computational method was used to determine the next set of peptides to be analyzed based on the results of the earlier iterations. After these training cycles, the algorithm enabled a real number prediction of MHC binding peptides with an accuracy surpassing that of the hitherto best performing positional scanning method.