Initially, it was noted that the relationship between hydrophobic side chains could lead to α-helix occurrence . In Fig. 4, the forecast of the α-helix is based on four rules according to four patterns, namely, IKLW, IKLC, YACD, and YVM. In rule 1, the IKLW pattern achieved 100% accuracy for α-helix prediction due to isoleucine I, lysine K, leucine L, and tryptophan W displays at the first, second, third, and fourth locations, respectively. Both amino acids I and W are hydrophobic, and their presence at location i, i + 3 referred to a helix manifestation . In rule 2, the IKLC pattern confirmed that I and C are hydrophobic and indicated helix stabilization . In rule 3, the YACD pattern achieved 100% accuracy for α-helix prediction. In rule 4, both amino acids Y and M are hydrophobic, and their occurrence at two locations during the sequence leads to α-helix construction. Valine V has a low rate of helix occurrence .
In Fig. 5, the forecast of the β-strand is based on seven rules according to seven patterns, namely, HIKLW, RTWYC, CGNPPR, DHQWHE, CGCSA, HCTW, and VWCD. In rule 1, the HIKLW pattern achieved 100% accuracy for β-strand prediction due to histidine H, isoleucine I, lysine K, leucine L, and tryptophan W displays at the first, second, third, fourth, and fifth locations, respectively. In rule 2, the RTWYC pattern achieved 79% accuracy due to arginine R, threonine T, tryptophan W, tyrosine Y, and cysteine C displays at the first, second, third, fourth, and fifth locations, respectively. The amino acids T, R, and D are employed as N-terminal β-breakers, while S and G are employed as C-terminal β-breakers . Additionally, these patterns, namely, CGNPPR, CGCSA, and HCTW, achieved 100% accuracy for β-strand prediction.
The strengthening of protein structure and protein regulation is related to the appearance of specific amino acids in the loop structure. Proline P and glycine G are considered the most important amino acids in the loop structure. The high load proclivities are achieved when there are nearest to Proline P . On the other hand, low load proclivities are achieved when cysteine C, isoleucine I, leucine L, tryptophan W, and valine V are present .
In Fig. 6, the forecast of the coil structure is based on seven rules according to seven patterns, namely, EFG, PEH, RYGSVY, TMPA, DTMPV, PTE, and LRKL. In rules 2 and 3, the occurrence of coil structure referred to high load proclivities due to the presence of amino acids P and G. In rule 1, the EFG pattern achieved 90% accuracy for coil prediction. This confirmed that E is considered hydrophilic, while F and G are hydrophobic amino acids. In rule 2, the PEH pattern achieved 100% accuracy. In rule 4, it was confirmed that T is hydrophilic, while M, P, and A are hydrophobic amino acids. In rule 6, the PTE pattern achieved 67% accuracy due to proline P occurrence with threonine T and glutamic E during the series. In rule 7, the LRKL pattern achieved 100% accuracy due to the arginine R display at a location with lysine K and leucine L at the first, third, and fourth locations, respectively, through the series.
For comparative analysis, the recent algorithm  based on convolutional, residual, and recurrent neural network (CRRNN) showed 71.4% accuracy for DSSP. This indicated that our algorithm is more accurate than that in . On the other hand, the quality of protein structure prediction can affect poor alignments, protein misfolding, few similarity rates between known sequences, evolution theory, and machine learning performance .
For results analysis, instead of taking the three binary classifiers: (H/~H), (E/~E), (C/~C) into PSSP account , we compared the proposed algorithm with previous studies based on six classifiers: (H/~H), (H/~E), (E/~E), (E/~C), (C/~C), (H/~C) for PSSP as in Table 3 and predict the residue identity of each position one by one. It also found that the PSSP_SVMCP model has shown superior accuracy rather than the proposed model in terms of H/~H, C/~C, H/~E, and H/~C classifiers.