Modeling the adaptive immune system: predictions and simulations

Lundegaard, Claus; Lund, Ole; Kesmir, Can; Brunak, Søren; Nielsen, Morten

Successful prediction of the proteasome cleavage site specificity should provide valuable additional information useful in the design of treatments based on CTL responses. However, the complexity of proteasomal enzymatic specificity complicates such predictions. The proteasome have a highly stochastic element, exemplified by the observation that only $80% of the cleavage sites observed in one in vitro experiment can be verified in a second identical experiment (Hansjo¨rg Schild, personal communication). It is thus expected that the accuracy for prediction of proteasomal activity will be relatively low when compared to that of methods for MHC peptide binding. FragPredict, which is publicly available as a part of MAPPP service (http://www.mpiibberlin.mpg.de/MAPPP/), combines proteasomal cleavage predictions with MHC-and TAP-binding predictions. FragPredict consists of two algorithms. The first algorithm uses a statistical analysis of cleavage-enhancing and -inhibiting amino acid motifs to predict potential proteasomal cleavage sites (Holzhutter et al., 1999) . The second algorithm, which uses the results of the first algorithm as an input, predicts which fragments are most likely to be generated. This model takes the time-dependent degradation into account based on a kinetic model of the 20S proteasome (Holzhutter and Kloetzel, 2000) . At the moment, FragPredict is the only method that can predict fragments, instead of only possible cleavage sites.