The data set of quantitative peptide-MHC class I binding affinity data published by Peters et al. [27] contains data from 48 different human, mouse, macaque and chimpanzee alleles. We selected 12 representative human alleles, and extracted binding data for 9-mer peptides maintaining the subsets of the original benchmark. This allows comparing the performance of NNAlign to the other methods presented in the paper by Peters et al. Figure 5 . Analysing high-density peptide array data with NNAlign. a) Fluorescence microscopy picture of a peptide microarray. The image is a magnified segment of the peptide chip used in the trypsin cleavage analysis. b) Trypsin peptide-chip data. The normalized observed (target) likelihood of cleavage as a function of the prediction score for the trypsin data set. Localizations of peptides containing the pairs of amino acids RP, RA or RR are highlighted in the plot. Proline (P) is known to prevent cleavage after arginine (R), whereas cleavage is observed with other amino acids such as R and A. c) Chymotrypsin peptide-chip data. Correlation plot between predicted and measured (target) data from the chymotrypsin data set. Values are binned by their x,y proximity, so that the scatterplot represents the density of data in each bin. NNAlign was trained with linear rescaling of the quantitative data, a motif length of 4 amino acids without inclusion of PFR encoding, Blosum encoding of peptide sequences, a combination of 3,7,15 hidden neurons, 10 initial seeds, 5-fold exhaustive cross-validation, training was stopped on the best test set performance. doi:10.1371/journal.pone.0026781.g005 MHC class II data set A large set of over 17,000 HLA-peptide binding affinities was published by Wang et al. [25] containing data from several different human alleles including HLA DR, DP and DQ alleles. For each allele, the predictive performance of various methods was estimated on the similarity reduced (SR) data set, where sequence similarity is minimized in order to avoid overlap between crossvalidation subsets. We preserved the same subsets for our crossvalidation, for easy comparison of the results and predictive performances.