Working Paper
Authors
- Zhiwei Miao ,
- Qianqian Wang ,
- Xiongjie Xiao ,
- Linhong Song ,
- Xu Zhang ,
- Conggang Li ,
- Xin Zhou ,
- Bin Jiang ,
- Maili Liu ,
- 滨 蒋
Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences
Abstract
The description and understanding of protein
structure rely on secondary structure heavily. Secondary structure
determination and prediction are widely used in protein structure related
research. The secondary structure prediction methods based on NMR chemical
shifts are convenient to use, so they are popular in protein NMR research. In
recent years, there is significant improvement in deep neural network, which is
consequently applied in many search 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. Compared with the existing methods of the same sort, the accuracy of
the proposed method was improved. And a web server was built to provide
secondary structure prediction service using this method.
Version notes
version 1.0
Content

Supplementary material

supporting information v1
Supporting Information 2. proteins in training and validation dataset
Supporting Information 3. protein information of test dataset and prediction by 3 methods