The present study searched for a fast and reliable approach to estimate the structural diversity of medium-large training datasets for general fold discovery. We considered three protein secondary structure predictors (PSSP) Jpred4, PEP2D, PSIPRED and the 3D structure predictor AlphaFold2 (batch mode) in combination with STRIDE for secondary structure annotation. We benchmarked the four PSP methods comparing 261 curated and experimentally known PDBe structures with their predicted 3-state secondary structures (H, E, C). PEP2D predictions were the closest to the experimental distributions across the three states among the PSSP methods. Our results also revealed that the AlphaFold2+STRIDE approach provided more accurate predictions of helical and stranded/β-sheet structures, but PSSP methods performed better for coiled structures. The protein secondary structure predictor PEP2D is fast, and its results were comparable to those of AlphaFold2+STRIDE to estimate the structural landscape of sequential datasets with less than 50 residues. The coupled method represents an excellent alternative to PEP2D, particularly for peptide or protein sequences with 50 or more residues.