As opposed to MHC class I binding, which is mostly limited to peptides of similar length, the MHC class II molecule interacts with peptides of a wide length distribution and high compositional diversity [28] . Binding of a peptide to an MHC class II molecule is primarily determined by a core of normally 9 amino acids, but the composition of the regions flanking the binding core (peptide flanking region, PFR) has been shown to also affect the binding strength of a peptide [29, 30] . Identifying the binding motif and binding register for MHC class II binding peptides is thus a problem that inherently requires simultaneous alignment and binding affinity identification. Here, an MHC class II benchmarking was obtained from the recent publication by Wang et al. [25] . The performance was estimated for each allele using a 5 fold cross validation, where at each step 4/5 of the data were used to train the neural networks, and 1/5 were left out for evaluation. For cross-validation, we preserved the same data partitioning as used in the original publication. In Table 1 , the performance of NNAlign on the Wang set is compared to other publicly available methods for MHC class II prediction. These include SMM-align [19] , ProPred/Tepitope [31, 32] , as well as the original version of the NN-align algorithm [20] . The NN-align-based methods outperform their competitors on all alleles, confirming the ability of the neural networks in dealing with alignment problems. The difference with the original NN-align method, which is due to differences in network architecture, is small and not significant (p.0.2, binomial test). For this example involving unaligned data, the NNAlign server competes with comparable state-of-the-art methods.