Early MHC-binding studies identified characteristic amphipathic chemical patterns on the binding peptides [44] , and enhanced versions of these systems continue in use in association with other methods [41, 44, 45] . Today, the most basic MHC-binding prediction methods are based on the identification of specific amino acids commonly found at particular positions, called binding motifs, within peptides that bind to a specific MHC molecule. However, all the amino acids of a peptide bound to an MHC groove (normally 8-10 amino acids for MHC I and 8-14 amino acids for MHC II) can potentially play a positive or negative role in binding, and more complex methods assign positive or negative values for each amino acid at each position of a peptide and combine these values to define scores that predict binding; these ''quantitative-matrix'' approaches have been very successful. One of the limitations of the ''quantitative-matrix'' approach is that it does not take into consideration the influences of interactions between amino acids at different peptide positions of the epitope. The value of these interactions are difficult to measure and have been explored only in a limited fashion by combining pair-wise interactions between two peptide positions [46] . The combination of independent binding calculated by quantitative matrices with coefficients derived from the pair-wise interaction provided better predictions. Moreover, quantitative-matrix scores have been generated for several HLA alleles, and studies using HLA sequence homology have allowed the development of virtual quantitative matrices to be applicable to many more HLA alleles [28, 39] . General methods for MHC-binding prediction systems, such as artificial neural networks, and statistical models such as Hidden Markov, can incorporate nonlinear complex interactions between the MHC molecule and the peptide epitope, and can evolve as more data is included in the training set. These general methods have shown to be potentially superior to the previous ones [27, 37] . The greatest challenge, however, of the general methods is that, to be reliable, they require a larger amount of peptide-binding data. Strategies using query-by-committee approaches to compare predictions from different training sets have been used to identify the most informative peptide-binding data to be determined in the biochemical binding assays in order to more efficiently build a representative training dataset [40] . Recently a collection of more than 48,000 peptide binding affinities of class I molecules were made public [47] . The development of prediction models will be greatly accelerated by this community resource benchmark. We remark that epitope recognition by the immune system involves more than a receptor-ligand problem between the MHC molecule and a peptide, as other biological processes are involved in preparation of the peptide for loading into the MHC molecule. Epitope prediction systems continue to evolve, and many steps in antigen processing and presentation required for development of cognate immune responses are being modeled and combined into rational prediction systems. The greatest current challenge is the development of models incorporating the rules of B cell and T cell receptor engagement and their possible outcomes.