Clustering. The first step of our strategy is to obtain clusters of similar peptides. In particular we exploited the scoring matrix computed by aligning every sequence with all the others with the Smith-Waterman algorithm (local alignment). We chose local alignment because the results of the main challenge showed that it has higher predictive performance than the global one (see Results). We obtained a distance matrix by subtracting each element of the normalized scoring matrix to one. Then, we applied hierarchical clustering with complete linkage and we used a cut-off value equal to 0.7 to generate the clusters.