Modeling the adaptive immune system: predictions and simulations

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

Unlike the MHC class I molecules, the binding cleft of MHC class II molecules is open-ended, which allows for the bound peptide to have significant overhangs in both ends. As a result MHC class II binding peptides have a broader length distribution even though the part of the binding peptide that interacts with the MHC (the binding core) still includes only 9 amino acid residues. This complicate binding predictions as identification of the correct alignment of the binding core is a crucial part of identifying the MHC class II binding motif (Nielsen et al., 2004) . The MHC class II binding motifs have relatively weak and often degenerate sequence signals. While some alleles like HLA-DRB1*0405 show a strong preference for certain amino acids at the anchor positions, other alleles like HLA-DRB1*0401 allow basically all amino acids at all positions (Rammensee et al., 1999) . However, there are other issues affecting the predictive performance of most MHC class II binding prediction methods. The majority of these methods take as a fundamental assumption that the peptide-MHC binding affinity is determined solely from the nine amino acids in binding core motif. This is clearly a large oversimplification since it is known that peptide flanking residues (PFR) on both sides of the binding core may contribute to the binding affinity and stability (Godkin et al., 2001) . Some methods for MHC class II binding have attempted to include PFRs indirectly, in terms of the peptide length, in the prediction of binding affinities (Chang et al., 2006) . Recently, Nielsen et al. (2007b) published a method for MHC class II prediction that directly include PFRs and demonstrated that these PFRs improves the prediction accuracy. Most of the methods for MHC class II binding predictions have been trained and evaluated on very limited datasets covering only a single or a few different MHC class II alleles, making it very difficult to compare the different performance values and generality of the methods. Nielsen et al. (2007b) have made available a large-scale benchmark set-up for evaluating MHC class II peptide binding affinity prediction algorithms. The benchmark covers 14 HLA-DR (human MHC) and three mouse H2-IA alleles, and consists of peptide/IC50 affinity data downloaded from the publicly available IEDB database , and could set the start for large-scale unbiased evaluations of novel methods for MHC class II prediction.