N The results of the classifiers are in general quite good. This shows that the generated features contain useful information to predict the peptide reactivity. Each cluster of peptide is described by its multiple alignments (on the right top of each sub-figure) and by its representation through sequence logo [49] . This graphical representation displays the conservation of the amino acids in each position of the multi-alignment by their one-letter code. Different residues at the same position are scaled according to their frequency. In particular the height of the entire stack of residues is the information measured in bits (y-axis). doi:10.1371/journal.pone.0023616.g002 N Linear regression and logistic regression show the highest performance, even if the difference between the results of the different classifiers is not statistically significant.