Modeling the adaptive immune system: predictions and simulations

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

Reliable predictions of immunogenic peptides can reduce the experimental effort needed to identify new epitopes, and though reliable predictions of the MHC binding alone can indeed be used to rank the possible epitopes very accurately, even better predictions should be possible if the other steps in the pathway were integrated in the predictions. Accordingly, many attempts have been made to predict the outcome of the steps involved in antigen presentation, MAPP (Hakenberg et al., 2003) , NetCTL (Larsen et al., 2005) , MHCpathway (Tenzer et al., 2005) , epiJen (Doytchinova et al., 2006) and WAPP (Donnes and Kohlbacher, 2005) . All these methods attempt to predict antigen presentation by integrating peptide-MHC binding predictions with one or more of the other events involved in the antigen presentation pathway. To benchmark these, a set of verified epitopes can be used as the positive dataset. Negative examples (peptides that cannot induce an immunologic response) are hard to identify, as it is very hard to determine that a peptide will never be an epitope in any persons with a given HLA haplotype. Instead, epitopes from well-studied pathogens (e. g. HIV) are often used as the positive set, and all other peptides from the genome of the same pathogen that have never been shown to be an epitope are assumed negative as they have a very low probability of being an epitope. Running a large-scale benchmark calculation comparing the predictive performance of several publicly available MHC-I presentation prediction methods evaluated on a large set of known HIV epitopes (http://www.cbs.dtu.dk/suppl/immunology/CTL-1.2/ HIV_dataset) reveals that the updated NetCTL and MHCpathway methods have the highest predictive performance with 475% if the epitopes being within the top 5% peptides with the highest prediction scores (Mette Volby Larsen, personal communication).