2DCSi: identification of protein secondary structure and redox state using 2D cluster analysis of NMR chemical shifts

2007 ◽  
Vol 38 (1) ◽  
pp. 57-63 ◽  
Author(s):  
Ching-Cheng Wang ◽  
Jui-Hung Chen ◽  
Wen-Chung Lai ◽  
Woei-Jer Chuang
Keyword(s):  
Cluster Analysis ◽  
Secondary Structure ◽  
Redox State ◽  
Chemical Shifts ◽  
Protein Secondary Structure ◽  
Nmr Chemical Shifts

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 Infrared Spectroscopic Identification of β-Thalassemia

2003 ◽  
Vol 49 (7) ◽  
pp. 1125-1132 ◽  
Author(s):  
Kan-Zhi Liu ◽  
Kam Sze Tsang ◽  
Chi Kong Li ◽  
R Anthony Shaw ◽  
Henry H Mantsch
Keyword(s):  
Cluster Analysis ◽  
Secondary Structure ◽  
Ir Spectroscopy ◽  
Protein Secondary Structure ◽  
Thalassemia Major ◽  
Ir Absorption ◽  
Linear Discriminant ◽  
Unsupervised Cluster ◽  
Unsupervised Cluster Analysis ◽  
Hemoglobin Spectra

Abstract Background: The aim of this study was to investigate the potential of infrared (IR) spectroscopy as a fast and reagent-free adjunct tool in the diagnosis and screening of β-thalassemia. Methods: Blood was obtained from 56 patients with β-thalassemia major, 1 patient with hemoglobin H disease, and 35 age-matched controls. Hemolysates of blood samples were centrifuged to remove stroma. IR absorption spectra were recorded for duplicate films dried from 5 μL of hemolysate. Differentiation between the two groups of hemoglobin spectra was by two statistical methods: an unsupervised cluster analysis and a supervised linear discriminant analysis (LDA). Results: The IR spectra revealed changes in the secondary structure of hemoglobin from β-thalassemia patients compared with that from controls, in particular, a decreased α-helix content, an increased content of parallel and antiparallel β-sheets, and changes in the tyrosine ring absorption band. The hemoglobin from β-thalassemia patients also showed an increase in the intensity of the IR bands from the cysteine −SH groups. The unsupervised cluster analysis, statistically separating spectra into different groups according to subtle IR spectral differences, allowed separation of control hemoglobin from β-thalassemia hemoglobin spectra, based mainly on differences in protein secondary structure. The supervised LDA method provided 100% classification accuracy for the training set and 98% accuracy for the validation set in partitioning control and β-thalassemia samples. Conclusion: IR spectroscopy holds promise in the clinical diagnosis and screening of β-thalassemia.

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.

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