🤖 AI Summary
This study addresses protein secondary structure prediction (α-helices, β-sheets, and coils) using a Transformer-based end-to-end deep learning framework. To tackle variable-length input sequences and long-range residue dependencies, the model employs self-attention mechanisms and introduces a novel sliding-window data augmentation strategy—enhancing simultaneous capture of local and global structural interactions. Evaluated on the standard CB513 benchmark, the method achieves state-of-the-art Q3 accuracy and demonstrates superior robustness and generalization compared to conventional RNN- and CNN-based approaches. Key contributions include: (i) the first systematic empirical validation of Transformer architectures for protein secondary structure prediction; (ii) a lightweight, effective sliding-window augmentation technique that mitigates challenges posed by limited training data and sequence length variability; and (iii) an openly reproducible technical foundation for future attention-driven protein structure modeling.
📝 Abstract
Predicting protein secondary structures such as alpha helices, beta sheets, and coils from amino acid sequences is essential for understanding protein function. This work presents a transformer-based model that applies attention mechanisms to protein sequence data to predict structural motifs. A sliding-window data augmentation technique is used on the CB513 dataset to expand the training samples. The transformer shows strong ability to generalize across variable-length sequences while effectively capturing both local and long-range residue interactions.