In particular,

we find a preference for acidic amino acids close to or at the C-terminal of the binding motif for three of the six molecules, and generally, the motifs seem rather promiscuous, with several residues allowed in the binding groove AZD2281 mouse of the MHC. In this report, we applied a state-of-the-art neural network-based method, NNAlign, to characterize the binding specificities of five HLA-DP and six HLA-DQ molecules. The allelic variants are among the most common human MHC class II molecules at the two HLA loci DP and DQ, covering a large percentage of the human population.8,9 For what concerns HLA-DP, there appears to be a common pattern in all the five variants under consideration, with primary anchor positions at P1 and P6 with preference for hydrophobic and aromatic residues. Some variants show an additional hydrophobic anchor at

P9 and other minor differences, but in general there appears to be a consistent overlap in the binding specificities of all five molecules. The same cannot be said for HLA-DQ, where most of the molecules have very different anchor positions, anchor spacing and amino acid preferences. Hence, there does not seem to be a supertypical mode of binding for DQ, and each variant appears to be characterized by a distinct binding specificity. The most striking observation for the DQ loci binding motifs is the preference for acidic amino selleck kinase inhibitor acids close to or at the C-terminal of the binding groove. Such an amino acid preference has Cell press not, to the best of our knowledge, previously been described for any HLA class I molecules, and has only sporadically been reported for HLA class II molecules. Binding predictions (including identification of the binding core) for any peptide sequence to all the alleles described in this report can be obtained at the NetMHCII server (http://www.cbs.dtu.dk/services/NetMHCII). The binding motifs described in this work confirm most of the observations brought up by previous studies, but also highlight some interesting differences.

Importantly, the sequence logo representation provides a quantitative measure of the relevance of each position in the binding core, and the relative importance of each amino acid, in determining the specificities of a given molecule, a differentiation that was not obtained in previous studies. The study first and foremost demonstrates the power of the NNalign method to, in a fully automated manner, identify and characterize the receptor-binding motif from a set of peptide-binding data. Second, it underlines the importance of generating such peptide data sets to carry out receptor-binding motif characterizations, gain insights into the peptide-binding repertoire of MHC molecules and reveal details about which amino acids and amino acid positions are critical for binding and, potentially, for peptide immunity.