Journal Article

CSI-LSTM: a web server to predict protein secondary structure using bidirectional long short term memory and NMR chemical shifts

Wang, Qianqian, Xiao Xiongjie, Song Linhong, Jiang, Bin, Li, Conggang, Kamal Ghulam Mustafa, Miao Zhiwei, Liu, Maili, Zhang, Xu, Zhou, Xin

2021

Overview

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.

• Publisher: Springer Nature B.V
• Subject: Artificial neural networks / Internet / Long short-term memory / Neural networks / NMR / Nuclear magnetic resonance / Predictions / Protein structure / Proteins / Secondary structure / Servers / Short term