Constant-time NOESY: An aid in the analysis of protein NMR spectra

1992 ◽  
Vol 2 (6) ◽  
pp. 647-653 ◽  
Author(s):  
Jorge Santoro ◽  
Marta Bruix ◽  
Carlos González ◽  
José L. Nieto ◽  
Manuel Rico
Keyword(s):  
Nmr Spectra ◽  
Protein Nmr ◽  
Constant Time

Elimination of13Cα Splitting in Protein NMR Spectra by Deconvolution with Maximum Entropy Reconstruction

2003 ◽  
Vol 125 (9) ◽  
pp. 2382-2383 ◽  
Author(s):  
Nobuhisa Shimba ◽  
Alan S. Stern ◽  
Charles S. Craik ◽  
Jeffrey C. Hoch ◽  
Volker Dötsch
Keyword(s):  
Maximum Entropy ◽  
Nmr Spectra ◽  
Protein Nmr ◽  
Maximum Entropy Reconstruction

Assignment of protein NMR spectra based on projections, multi-way decomposition and a fast correlation approach

2008 ◽  
Vol 42 (2) ◽  
pp. 87-97 ◽  
Author(s):  
Doroteya K. Staykova ◽  
Jonas Fredriksson ◽  
Wolfgang Bermel ◽  
Martin Billeter
Keyword(s):  
Nmr Spectra ◽  
Protein Nmr

Computer-aided analysis of protein NMR spectra

1991 ◽  
Vol 1 (6) ◽  
pp. 1036-1041 ◽  
Author(s):  
Jeffrey C. Hoch ◽  
Christina Redfield ◽  
Alan S. Stern
Keyword(s):  
Nmr Spectra ◽  
Protein Nmr ◽  
Computer Aided Analysis ◽  
Computer Aided

scholarly journals Chemical shifts of aromatic protons in protein NMR spectra

FEBS Letters ◽  
1983 ◽  
Vol 159 (1-2) ◽  
pp. 132-136 ◽  
Author(s):  
Christina Redfield ◽  
Jeffrey C. Hoch ◽  
Christopher M. Dobson
Keyword(s):  
Nmr Spectra ◽  
Chemical Shifts ◽  
Protein Nmr

scholarly journals FID-Net: A Versatile Deep Neural Network Architecture for NMR Spectral Reconstruction and Virtual Decoupling

2020 ◽  
Author(s):  
Gogulan Karunanithy ◽  
Flemming Hansen
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Nmr Spectra ◽  
Deep Neural Network ◽  
Protein Nmr ◽  
Single Shot ◽  
Neural Network Architecture ◽  
Diverse Range ◽  
Nmr Data ◽  
Wide Range

<p>In recent years, the transformative potential of deep neural networks (DNNs) for analysing and interpreting NMR data has clearly been recognised. However, most applications of DNNs in NMR to date either struggle to outperform existing methodologies or are limited in scope to a narrow range of data that closely resemble the data that the network was trained on. These limitations have prevented a widescale uptake of DNNs in NMR. Addressing this, we introduce FID-Net, a deep neural network architecture inspired by WaveNet, for performing analyses on time domain NMR data. We first demonstrate the effectiveness of this architecture in reconstructing non-uniformly sampled (NUS) biomolecular NMR spectra. It is shown that a single network is able to reconstruct a diverse range of 2D NUS spectra that have been obtained with arbitrary sampling schedules, with a range of sweep widths, and a variety of other acquisition parameters. The performance of the trained FID-Net in this case exceeds or matches existing methods currently used for the reconstruction of NUS NMR spectra. Secondly, we present a network based on the FID-Net architecture that can efficiently virtually decouple <sup>13</sup>C<sub>α</sub>-<sup>13</sup>C<sub>β</sub> couplings in HNCA protein NMR spectra in a single shot analysis, while at the same time leaving glycine residues unmodulated. The ability for these DNNs to work effectively in a wide range of scenarios, without retraining, paves the way for their widespread usage in analysing NMR data. </p>

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