## 7 Future Work  
While the proposed approach demonstrates strong performance in both Q3 and Q8 classification tasks, several directions remain open for future exploration. * **Integration with Pretrained 3D Models:** Future work can explore the integration of the proposed framework with state-of-the-art 3D structure predictors such as AlphaFold2 or ESMFold. Leveraging tertiary structure outputs as additional input features may enhance secondary structure prediction accuracy and offer multi-level structural consistency. * **Attention-Based Interpretability:** Incorporating attention mechanisms into Bi-LSTM or replacing it with Transformer-based architectures could provide interpretability, enabling the identification of sequence regions critical for structural transitions. * **Multi-task Learning:** Extending the model to jointly predict related structural features--such as solvent accessibility, contact maps, or intrinsically disordered regions--could improve generalization and model robustness across protein families. * **Cross-platform and Edge Deployment:** Given the model's compact design with dimensionality-reduced embeddings, deploying optimized variants on mobile or embedded platforms could open avenues for real-time protein structure analysis in portable lab devices. * **Data Augmentation and Self-Supervised Pretraining:** Utilizing contrastive learning or masked amino acid prediction on large unlabeled protein datasets can improve feature richness, especially in low-resource scenarios where labeled data are limited. * **Dataset Diversity:** The curated PISCES dataset, while high-quality, may suffer from structural bias--particularly the under-representation of membrane proteins. This may limit the model's generalizability to under-represented structural classes. Future efforts should consider incorporating more diverse protein families, including membrane-associated proteins (e.g., GPCRs) and intrinsically disordered proteins (IDPs), to enhance structural diversity and real-world applicability. * **Dynamic Subsequence Lengths:** In this study, we adopted fixed-length subsequences (25, 50, 75, and 100 residues) for computational consistency. However, proteins naturally exhibit domain structures of varying lengths. Future research could investigate dynamic or adaptive subsequence segmentation strategies--possibly guided by domain prediction tools or sequence entropy--to better capture structural boundaries and improve biological relevance. These directions aim to enhance the biological relevance, interpretability, and computational accessibility of secondary structure prediction systems. The proposed framework offers a solid foundation for these future extensions, particularly in applications requiring scalability and adaptability across different protein domains. Conceptualization, I.K., H.G. and Y.N.H.A.-S.; methodology, I.K., H.G. and Y.N.H.A.-S.; software, I.K. and Y.N.H.A.-S.; validation, I.K., H.G., O.F.Y. and Y.N.H.A.-S.; formal analysis, I.K., H.G. and Y.N.H.A.-S.; investigation, Y.N.H.A.-S. and I.K. ; resources, I.K. ; data curation, Y.N.H.A.-S. and i.K. ; writing--original draft preparation, I.K., H.G., O.F.Y. and Y.N.H.A.-S.; writing--review and editing, I.K., H.G., O.F.Y. and Y.N.H.A.-S.; visualization, Y.N.H.A.-S.; supervision, I.K. and H.G. ; project administration, I.K. ; funding acquisition, I.K. All authors have read and agreed to the published version of the manuscript. This study was supported by the Kocaeli University Scientific Research Projects Coordination Unit under project number FBA-2023-3043. Publicly available datasets were analyzed in this study. This data can be found here: [[https://www.kaggle.com/datasets/kirkdc/protein-secondary-structure-202/data](https://www.kaggle.com/datasets/kirkdc/protein-secondary-structure-202/data)]([https://www.kaggle.com/datasets/kirkdc/protein-secondary-structure-202/data](https://www.kaggle.com/datasets/kirkdc/protein-secondary-structure-202/data)).