Protein Secondary Structure Prediction Using Transformers
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Abstract (arXiv)
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.
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References · 4
- 2017Attention Is All You Needcited by 59,932
- 2020ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computingcited by 162
- 2019Learning protein sequence embeddings using information from structurecited by 50
- 2015Protein secondary structure prediction using deep convolutional neural fieldscited by 12
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