Binding of peptides to MHC class I molecules is highly specific, with only 1-5% of a set of random natural peptides binding to any given MHC molecule [26] . Moreover, in the vast majority of cases only peptides with length 8-10 amino acids can fit in the binding pocket of MHC class I molecules. The predictive performance of NNAlign on 12 human MHC class I alleles from data by Peters et al. [27] is shown in Table 1 (see the table footnote for the parameters used). The benchmark data sets contain quantitative binding data of a given length (9 amino acids) covering the whole spectrum from non-binding to strong-binding peptides, hence serving as a perfect illustration of the strength of the NNAlign method to handle pre-aligned peptide data. The overall performances of the three methods are comparable demonstrating that NNAlign competes with state-of-the-art methods designed specifically for MHC class I prediction.