ArticleJournal of biomolecular NMR2021
CSI-LSTM: a web server to predict protein secondary structure using bidirectional long short term memory and NMR chemical shifts.
Article in Journal of biomolecular NMR, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Protein structure prediction via deep learning: an in-depth review.Frontiers in pharmacology · 2025Review
- Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Computational insight into stability-enhanced systems of anthocyanin with protein/peptide.Food chemistry. Molecular sciences · 2023Article
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Authors and funding
10 authors.
Funding
Abstract
Protein secondary structure provides rich structural information, hence the description and understanding of protein structure relies heavily on it. Identification or prediction of secondary structures therefore plays an important role in protein research. In protein NMR studies, it is more convenient to predict secondary structures from chemical shifts as compared to the traditional determination methods based on inter-nuclear distances provided by NOESY experiment. In recent years, there was a significant improvement observed in deep neural networks, which had been applied in many research fields. Here we proposed a deep neural network based on bidirectional long short term memory (biLSTM) to predict protein 3-state secondary structure using NMR chemical shifts of backbone nuclei. While comparing with the existing methods the proposed method showed better prediction accuracy. Based on the proposed method, a web server has been built to provide protein secondary structure prediction service.
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Registered trials
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