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

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

Karunanithy, G; Hansen, DF; (2021) FID-Net: A versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling. Journal of Biomolecular NMR 10.1007/s10858-021-00366-w. (In press). Green open access

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Abstract

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 13Cα-13Cβ 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.

Type:	Article
Title:	FID-Net: A versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling
Location:	Netherlands
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1007/s10858-021-00366-w
Publisher version:	https://doi.org/10.1007/s10858-021-00366-w
Language:	English
Additional information:	This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords:	Deep learning, NMR, Non-uniform sampling, Spectral reconstruction, Virtual decoupling
UCL classification:	UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Structural and Molecular Biology
URI:	https://discovery.ucl.ac.uk/id/eprint/10127318