PREDICTION OF MHC CLASS II-BINDING PEPTIDES USING AN EVOLUTIONARY ALGORITHM AND ARTIFICIAL NEURAL-NETWORK

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed , and thereby facilitate the identification of potential T-cell epitop es. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combi ned with an evolutionary algorithm (EA) and an artificial neural netwo rk (ANN): binding data extraction --> peptide alignment --> ANN traini ng and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B10401). The respecti ve positive predictive values of PERUN predictions of high-, moderate- , low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0 .8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental bind ing. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immu notheraaeutic peptides.