The challenge of predicting which peptide sequences bind to which major histocompatibility complex (MHC) molecules has been met with various computational techniques. Scoring matrices, hidden Markov models, and artificial neural networks are examples of algorithms that have been successful in MHC–peptide-binding prediction. Because these algorithms are based on a limited amount of experimental peptide-binding data, prediction is only possible for a small fraction of the thousands of known MHC proteins. In the primary field of application for such algorithms—vaccine design—the ability to make predictions for the most frequent MHC alleles may be sufficient. However, emerging applications of leukemia-specific T cells require a patient-specific MHC–peptide-binding prediction. The modular model of MHC presented here is an attempt to maximize the number of predictable MHC alleles, based on a limited pool of experimentally determined peptide-binding data.