Choosing the optimal motif length Different positions in a binding motif can be more or less informative, and the ends of a motif can often not be clearly delineated. This prompts the question of how many positions are necessary and sufficient to represent a given motif and how the length of a motif is defined. NNAlign allows searching for the optimal motif length in a quantitative peptide data set. Here, the best motif length is the one that yields, in a cross-validation experiment, the lowest root mean square error (RMSE) between For MHC class I no significant difference is found in predicted performance between the NNAlign, SMM and ANN method (p.0.5, binomial test). The values for the SMM and ANN methods were taken from Peters et al. [27] . The method was trained using a fixed motif length of 9 corresponding to the peptide length, and constructing a network ensemble with multiple architectures using respectively 2,3,4,5 and 7 hidden neurons. Performance was measured in cross-validation, training each network for a fixed number of 500 iterations per sequence. The different MHC class II prediction methods are NN-align [20] , SMM-align [19] , and Propred [31, 32] . NNAlign server is the method described here. Performance values for first 4 methods are taken from [25] . NNAlign was trained with a motif length of 9, flanking regions of 3 amino acids, Blosum encoding including peptide length and flanking region length, and an ensemble of 2, 3, 5, 9 and 12 hidden neurons for each of 10 initial random configurations. In bold is highlighted the best performing method for each MHC allele. The column # gives the number of the peptides in the data set for the given allele. doi:10.1371/journal.pone.0026781.t001