## 4 Experiments and Results
### Results
Key Observations:  
* The highest Q3 and Q8 F1 scores are achieved using 1024D features. * Reducing the feature size to 256D preserves 99% of Q3 and 98.5% of Q8 performance. * The 256D-50L configuration offers an optimal trade-off between efficiency and accuracy. * Longer subsequences (75-100) provide more stable training but increase training time. * Q8 classification requires more training epochs to reach convergence due to higher class complexity. Figure 6: Training and validation loss for different subsequence lengths. Longer subsequences show smoother convergence, especially at 75 and 100 residues. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Dimension** & **25** & **50** & **75** & **100** \\ \hline
1024 & 14 & 16 & 14 & 23 \\
512 & 24 & 18 & 28 & 27 \\
256 & 24 & 24 & 34 & 29 \\
128 & 32 & 32 & 36 & 30 \\
64 & 29 & 32 & 30 & 52 \\
32 & 24 & 28 & 15 & 41 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Training epochs required for model convergence. Overall, the results confirm that the proposed framework offers an effective balance between computational cost and predictive accuracy. The 256-dimensional configuration combined with 50-residue subsequences yields efficient and high-performing models for large-scale protein sequence analysis. To contextualize the contributions of our proposed framework, we provide a structured comparison with recent state-of-the-art protein secondary structure prediction methods in Table 7. The compared models include CSI-LSTM [46], DeepACLSTM [47], and MHTAPred-SS [48], all of which have demonstrated competitive performance in the literature. As shown in Table 7, our model is distinguished by its integration of ProtBERT embeddings with a lightweight autoencoder that effectively reduces embedding dimensionality by over 90%, significantly improving computational efficiency. Unlike CSI-LSTM, which requires fixed-length NMR-derived input segments and incurs high preprocessing costs, our approach handles full-length protein sequences directly and operates efficiently on general-purpose hardware. Compared to transformer-based methods such as MHTAPred-SS, our model achieves a more favorable trade-off between model complexity and inference speed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Dimension** & **25** & **50** & **75** & **100** \\ \hline
1024 & 0.6553 & 0.6501 & 0.6504 & 0.6498 \\
512 & 0.6484 & 0.6447 & 0.6457 & 0.6317 \\
256 & 0.6472 & 0.6440 & 0.6416 & 0.6344 \\
128 & 0.6463 & 0.6418 & 0.6402 & 0.6441 \\
64 & 0.6414 & 0.6363 & 0.6332 & 0.6010 \\
32 & 0.6173 & 0.6176 & 0.6267 & 0.6278 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: F1 scores for 8-class (Q8) PSSP classification. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Study** & **Embedding Used** & **Autoencoder Used** &
\begin{tabular}{l} **Handles** \\ **Variable-Length** \\ **Sequences** \\ \end{tabular} \\ \hline Our Model & ProtBERT & \(\checkmark\) (for embedding & \(\checkmark\) \\ CSI-LSTM [46] & NMR Shifts & \(\checkmark\) & \(\checkmark\) \\ DeepACLSTM [47] & AAindex + HHBlits & \(\checkmark\) (attention + & \(\checkmark\) \\  & & \(\checkmark\) autoencoder) & \\ MHTAPred-SS [48] & One-hot + & \(\checkmark\) (hierarchical & \\  & & Evolutionary Info & transformer & \(\checkmark\) \\  & & & autoencoder) & \\ \hline
**Study** & \
\begin{tabular}{l} **Embedding** \\ **Compression** \\ **Efficiency** \\ \end{tabular} &
\begin{tabular}{l} **Computational** \\ **Efficiency** \\ \end{tabular} \\ \hline Our Model & \(\checkmark\) (90\% reduction) & High (fast inference, \\  & & low memory) \\  & & Low (NMR \\ CSI-LSTM [46] & \(\checkmark\) & preprocessing \\  & & overhead) \\ DeepACLSTM [47] & \(\checkmark\) & Moderate (deep + \\  & & attention layers) \\ MHTAPred-SS [48] & \(\checkmark\) & Moderate (transformer cost) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of our proposed model with recent studies on secondary structure prediction.