Predicting the redox state and secondary structure of cysteine residues in proteins using NMR chemical shifts

2006 ◽  
Vol 63 (1) ◽  
pp. 219-226 ◽  
Author(s):  
Ching-Cheng Wang ◽  
Jui-Hung Chen ◽  
Shih-His Yin ◽  
Woei-Jer Chuang
Keyword(s):  
Secondary Structure ◽  
Redox State ◽  
Chemical Shifts ◽  
Cysteine Residues ◽  
Nmr Chemical Shifts

scholarly journals CSI 3.0: a web server for identifying secondary and super-secondary structure in proteins using NMR chemical shifts

2015 ◽  
Vol 43 (W1) ◽  
pp. W370-W377 ◽  
Author(s):  
Noor E. Hafsa ◽  
David Arndt ◽  
David S. Wishart
Keyword(s):  
Secondary Structure ◽  
Chemical Shifts ◽  
Web Server ◽  
Nmr Chemical Shifts

scholarly journals CSI-LSTM: A Web Server to Predict Protein Secondary Structure Using Bidirectional Long Short Term Memory and NMR Chemical Shifts

2021 ◽  
Author(s):  
Zhiwei Miao ◽  
Qianqian Wang ◽  
Xiongjie Xiao ◽  
Linhong Song ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Secondary Structure Prediction ◽  
Chemical Shifts ◽  
Short Term ◽  
Term Memory ◽  
Nmr Chemical Shifts ◽  
Long Short Term Memory

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.

scholarly journals CSI-LSTM: A Web Server to Predict Protein Secondary Structure Using Bidirectional Long Short Term Memory and NMR Chemical Shifts

2021 ◽  
Author(s):  
Zhiwei Miao ◽  
Qianqian Wang ◽  
Xiongjie Xiao ◽  
Linhong Song ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Secondary Structure Prediction ◽  
Chemical Shifts ◽  
Short Term ◽  
Term Memory ◽  
Nmr Chemical Shifts ◽  
Long Short Term Memory

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.

Relativistic and Dynamic effects are needed to correctly model the 1H NMR Chemical Shifts of an Iridium Polyhydride Cluster

2020 ◽  
Author(s):  
Abril C. Castro ◽  
David Balcells ◽  
Michal Repisky ◽  
Trygve Helgaker ◽  
Michele Cascella
Keyword(s):  
1H Nmr ◽  
Chemical Shifts ◽  
Dynamic Effects ◽  
Nmr Chemical Shifts
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