Considering the rapid implementation of PEP2D, we explored the structural landscape of GRAMPA, the giant yet vastly uncharted repository of antimicrobial peptides (AMPs). Our analysis showed that most 5,980 peptide sequences would adopt helical structures (65.1%), random coils (17.8%), and β-stranded and mixed structures accounted for the rest. We observed similar structural compositions across three strain-specific GRAMPA subsets against E. coli, S. aureus or P. aeruginosa. Removing cysteine-rich sequences further enriches the subset with helical and coiled structures. Finally, we introduced a new classification system for peptide structures based on their secondary structure composition, which provided a convenient way to visualize and compare the diversity of AMP folds. The abundance of helical structures may be due to evolutionary forces that have preferred them, but it may also stem from specific studies that skew their sequence-based predictive and generative models towards this structural class. Early peptide/protein structure prediction of mediumlarge training datasets becomes crucial prior to building predictive or generative ML models.