Before our study, Kozic and co-workers conducted the large-scale Rosetta ab initio modelling of 184 AMPs containing between 20 and 120 residues . The authors measured PSIPRED secondary structure predictions and clustered all 184 peptides into one of 4 structural classes; all-α, all-β, αβ and coil. About half were predicted to fold into α-helices, supporting our general observation that helical structures dominate fold spaces of the benchmarking dataset and GRAMPA (structural classes 1 and 2, Table ). In addition, stranded/β-sheets structures represented about 15% of their dataset in agreement with GRAMPA (structural classes 4 and 5). These percentages were higher in the benchmarking dataset for PDBe structures and PEP2D predictions. Unlike our study, the authors indicated few coiled structures -i.e., 0.5% vs. 17.6% (structural class 6) -and many αβ structures -i.e., 33.7% vs. 3.1% (structural classes 3 and 7). The differences in the number of peptide sequences, ranges in sequence length, PSSP predictions and disulfide-rich structures between the three datasets might explain these variations in percentages (Table ). In Figure , PSIPRED was less successful than PEP2D in predicting the 3-state secondary structures (H, E, C) for the 261 AMPs.