Correlation Engine 2.0
Clear Search sequence regions

Sizes of these terms reflect their relevance to your search.

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. © 2021. The Author(s), under exclusive licence to Springer Nature B.V.


Zhiwei Miao, Qianqian Wang, Xiongjie Xiao, Ghulam Mustafa Kamal, Linhong Song, Xu Zhang, Conggang Li, Xin Zhou, Bin Jiang, Maili Liu. CSI-LSTM: a web server to predict protein secondary structure using bidirectional long short term memory and NMR chemical shifts. Journal of biomolecular NMR. 2021 Dec;75(10-12):393-400

Expand section icon Mesh Tags

Expand section icon Substances

PMID: 34510297

View Full Text