We initially set to benchmark four protein structure prediction methods -Jpred4 46 , PEP2D , PSIPRED and AlphaFold2 49 -using a curated dataset of peptide sequences and their corresponding experimentally known structures from the PDBe database. We collected our benchmarking dataset from the GRAMPA repository (Giant Repository of AMP Activities), including 6,760 peptide sequences . These sequences are associated with 51,345 experimental minimum inhibitory concentration (MIC) values for 766 different bacteria, the most represented being E. coli (n=9,150), S. aureus (n=8,954), and P. aeruginosa (n=4,966). After filtering for sequences with undetermined or non-canonical amino acids, we obtained 6,169 unique sequences and 45,498 associated MIC values for 738 unique bacteria. The sequences against E. coli, S. aureus and P. aeruginosa will be used later in the study to compare the predicted structural landscapes across the three bacterial strains. We obtained 66,805 associations/pairs of a GRAMPA sequence with a PDBe structure. However, these results only comprised 2,143 GRAMPA sequences related to 6,087 unique identifiers. Many of these structures were related to more than one GRAMPA sequence and that, of the total number of sequences, only 34.7% had a related experimental structure (Figure ). After reviewing the most prevalent biomolecules in our results (Figure ), we observed large proteins that were only distantly related to the GRAMPA sequences through a short sequence motif with a significant degree of sequence identity between the two. Therefore, they did not truly represent the structures of the peptides present in GRAMPA. For example, we identified 36 GRAMPA sequences related to 364 PDBe identifiers belonging only to our most represented α-subunit of haemoglobin (Figure ), leading to 10,788 associations or pairs.