Modeling the adaptive immune system: predictions and simulations

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

A number of methods for predicting the binding of peptides to MHC molecules have been developed (Schirle et al., 2001) since the first motif methods were presented (Rothbard and Taylor, 1988; Sette et al., 1989) . The majority of peptides binding to MHC class I molecules have a length of 8-10 amino acids. Position 2 and the C-terminal position have turned out generally to be very important for the binding to most class I MHCs and these positions are referred to as anchor positions (Rammensee et al., 1999) . For some alleles, the binding motifs further have auxiliary anchor positions. Peptides binding to the human HLA-A*0101 allele thus have positions 2, 3 and 9 as anchors (Kondo et al., 1997; Kubo et al., 1994; Rammensee et al., 1999) . The importance of anchor positions for peptide binding and the allele-specific amino acid preference at the anchor positions was first described by Falk et al., 1990 . The discovery of such allele-specific motifs led to the development of the first reasonable accurate algorithms (Pamer et al., 1991; Rotzschke et al., 1991) . In these prediction tools, it is assumed that the amino acids at each position along the peptide sequence contribute a given binding energy, which can independently be added up to yield the overall binding energy of the peptide (Meister et al., 1995; Parker et al., 1994; Stryhn et al., 1996) . Similar types of approaches are used by the EpiMatrix method (Schafer et al., 1998) , the BIMAS method (Parker et al., 1994) , the SYFPEITHI method (Rammensee et al., 1999) , the RANKPEP method (Reche et al., 2002) and the Gibbs sampler method (Nielsen et al., 2004) . Several of these matrix methods use an approach in the development where the method is build using exclusively positive examples defined after certain criteria, like eluted peptides and interferon gamma response data. This data can be used in training as well as affinity binding data defining binding stronger than a certain threshold (usually 500 nM). Other matrix methods, like the SMM method, aim at predicting an actual affinity and thus use exclusively affinity data. As described earlier, matrix-based methods cannot take correlated effects into account, i.e. where the contribution to the binding affinity by a given amino acid at one position is influenced by amino acids at other positions in the peptide. Higher order methods like ANNs and SVMs, on the other hand, are ideally suited to take such correlations into account. These methods can be trained with data either in the format of binder/non-binder classification, or as real affinity data. Some of the recent methods combine the two types of data and prediction methods, either by averaging over predictions made by either (Bhasin and Raghava, 2007) , or by feeding the predictions from the positive data-trained PSSMs to ANNs together with sequence/affinity data (Nielsen et al., 2003) . A study by Yu et al. (2002) clearly shows the influence of having a large dataset on the performance of the resulting method. However, including knowledge of important positions reduce the need for data significantly (Lundegaard et al., 2004) .