4.3 Article

FID-Net: A versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling

期刊

JOURNAL OF BIOMOLECULAR NMR
卷 75, 期 4-5, 页码 179-191

出版社

SPRINGER
DOI: 10.1007/s10858-021-00366-w

关键词

NMR; Deep learning; Non-uniform sampling; Virtual decoupling; Spectral reconstruction

资金

  1. BBSRC [BB/R000255/1]
  2. Wellcome Trust [101569/z/13/z, FC010233]
  3. Cancer Research UK [FC010233]
  4. UK Medical Research Council [FC010233]
  5. Biotechnology and Biological Sciences Research Council (BBSRC) [BB/T011831/1]
  6. Wellcome Trust [101569/Z/13/Z] Funding Source: Wellcome Trust
  7. BBSRC [BB/R000255/1, BB/T011831/1] Funding Source: UKRI

向作者/读者索取更多资源

The article introduces a deep neural network architecture called FID-Net, inspired by WaveNet, for analyzing time domain NMR data and demonstrates its effectiveness in reconstructing NUS biomolecular NMR spectra.
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 C-13(alpha)-C-13(beta) 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.

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