Generating Rules for Predicting MHC Class I Binding Peptide using ANN and Knowledge-based GA

Cytotoxic T cells recognize specific peptides bound to major histocompatibility complex (MHC) class I molecule. Accurate prediction for the binding peptides could be of much use for the design of efficient peptide vaccines, which substantially reduce the cost of synthesizing and testing candidate binders. In this paper, we demonstrated that a machine learning approach can be successfully applied to extract rules to predict MHC class I binding peptides. We introduce a new method using a feed-forward neural network and genetic algorithm, and show that the proposed method outperforms other methods in both quantity and quality of the prediction rules. In order to verify the rules generated by our method, we compared them with the known-rules available in the HLA FactBook. Our method successfully identified most of the known-rules, and found some new additional rules for HLA-A*0204 and HLA-B*2706. We also found new rules for HLA-A*3301 for which no rules have ever been reported before.