We set to benchmark multiple protein secondary structure prediction (PSSP) methods to establish a fast and reliable approach to estimating the structural diversity of medium-large training datasets. Several PSSP methods lacked maintenance or provided the structural prediction for a single query each time. We selected three publicly available -Jpred4 46 , PEP2D , and PSIPRED (using the version included in the PHYRE2 suite) . We subjected the 261 GRAMPA sequences to these three PSSP methods and compared their performances across the assessment of the three secondary structure states (H, E, C). Their performances were measured using the Jensen-Shannon distance (JSD) between the secondary structure probability distributions (predictions) of each PSSP method and the experimental reference. JSD values range from 0 (both distributions are identical) to 1 (for completely different distributions). In short, the closer one H/E/C distribution is to the one made by experimental reference, the smaller the JSD is. The best structural predictor would be the one with the smallest Jensen-Shannon distance. Figure illustrates the predictions of each PSSP method (cyan) versus the experimental distributions (blue, ground truth) for the three-state secondary structures -H: helices, E: extended strand/β-sheets and C: coils. Overall, we observed that PEP2D predictions were the closest to the experimental distributions across the three states. We also noted that, for all three PSSP methods, the predicted distributions for β-sheet motifs were the furthest from the experimental distribution (largest JSD values), which could suggest that these models have more difficulty in predicting this secondary structure class. PEP2D underwent training with consideration given to the imbalance of secondary structures . Indeed, its training dataset was heavily populated with coiled motifs, leading to a skewed outcome towards this particular secondary structure. To address this issue, the authors employed a balancing technique, where the weight each secondary structure holds in the prediction was adjusted based on the ratio between the most abundant secondary structure and the one that needed to be balanced. This could account for its good performance in predicting the three secondary structure states. In addition, PEP2D was solely trained using peptide sequences unlike Jpred4 and PSIPRED. As a result, we anticipated that PEP2D would perform better when tested with sequences of similar length distributions that it was trained on. Finally, Figure displayed the distribution of PEP2D secondary structure predictions (cyan) compared to the ground truth (blue). The six structures mentioned above also depicted the differences between PEP2D predictions and experimental references. The lines indicated the error between the three-state secondary structures (H, E, C) measured between PEP2D predictions and corresponding experimental references. For example, the small lines for (1) pepG1 (7NS1), ( ) termicin (1MM0), and (4) kalata B1[W23WW] (2MN1) suggested minor errors. In contrast, the lines for (3) arenicin-3 analogue (5V11), (5) circulin A (1BH4) and ( ) pleurocidin-like peptide 1a-1 (6RY9) were wider suggesting more noticeable prediction errors. However, these errors remained local as the predictions and the experimental structures were predominantly in the same structural "regions''.