Considering the rapid implementation of PEP2D to estimate secondary structures of our benchmarking dataset, we chose to apply this tool to the entire GRAMPA repository. After eliminating sequences with unusual amino acids and length greater than 50 residues, we submitted 5,980 GRAMPA sequences to PEP2D for secondary structure prediction. The results are shown in Figure , where each dot represents the PEP2D prediction of a GRAMPA sequence across the 3-state secondary structures (H, E, C). We also depicted the density of structures present in GRAMPA using a colour gradient (green "low"-red "high"). Our analysis showed that the peptide dataset is heavily populated with sequences that are likely forming helices and sequences with a fully extended structure (coils). The abundance of helical structures may be due to evolutionary forces that have preferred them, but it may also stem from certain studies that purposely skew their predictive and generative models towards these AMP structures . We noticed that specific dots followed each other forming straight lines. They corresponded to PEP2D predictions that presented similar or identical values for two of the 3-state secondary structures (H, E, C); they were not homologous sequences. To ease readership of GRAMPA structures based on their secondary structure composition, we created a classification system using a graphical representation of the percentage of structural composition. The segmentation was done by dividing the ternary plot into three triangles and assigning a structural class to each intersection of their edges (Figure ). The resulting seven classes were: helical and coiled structures (1), mostly helical structures (2), helical and stranded/β-sheet structures (3), mostly βsheet structures (4), stranded/β-sheet and coiled structures (5), mixed structures (7), and mostly coiled structures (7). Here, we report the largest structural prediction of AMPs with 5,980 sequences. Our segmented analysis further confirmed that the GRAMPA repository is dominated by helical and coiled structures (1), with three-four times more sequences than the second most represented classification, mostly coiled structures (6), i.e. 65.1% versus 17.8% (Table ). In contrast, structural classes (2), ( ) and ( ) were disregarded due to the few structures they contained (< 2%). Finally, stranded and coiled structures (5) and mixed structures (7) represent about 15% of the entire dataset.