The release of GRAMPA in 2019 has led several research groups to develop generative ML models capable of designing broad-spectrum AMPs and strainspecific AMPs . Most studies described their AI-generated AMPs to share similar physicochemical properties (i.e., hydrophobicity, hydrophobic moment, global charge) and similar amino acid composition (i.e., moderate-high fractions in alanine, valine, glycine, lysine, arginine) to their training sets. In addition, some would the newly generated peptides to fold into α-helices using helical wheel representation and circular dichroism, protein structure predictors, or molecular dynamics simulations . The training sets often consisted of sequences with proteinogenic residues (except cysteine), positively charged, between 10 and 52 residues in length, and potentially amidated on their C-terminus. Their secondary or tertiary structures were often ignored but assumed to be helical. Thus, we explored the structural composition of GRAMPA sequences inhibiting the three bacterial strains E. coli, S. aureus or P. aeruginosa (FASTA sequences, see Data availability). After eliminating sequences with unusual amino acids and lengths greater than 50 residues, we displayed PEP2D secondary structure predictions of the three GRAMPA subsets in Figure -top-left: E. coli N=4,567, top-right: S. aureus N=4,146, and bottom-right: P. aeruginosa N=2,519. In addition, we reported the secondary structures for 3,367 GRAMPA sequences (Figure , bottom-left); the subset is quasi-identical to the 3,280 training sequences used to build PepVAE , showing antimicrobial activity against E.coli. The results, summarised in Table , alluded that the first three subsets would mimic the fold landscape of GRAMPA; where most of the sequences (i.e., 67-69.5%) may fold into α-helices (1) and another 16.4-17.5% would predict as mostly coiled structures (6). The structural classes (2)-(4) were merely observed (<2%). Stranded and coiled structures (5) and mixed structures (7) represented 12.4-13.9% of the subsets.