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Virtual Homonuclear Decoupling in Direct Detection Nuclear Magnetic Resonance Experiments Using Deep Neural Networks

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    Virtual Homonuclear Decoupling in Direct Detection Nuclear Magnetic Resonance Experiments Using Deep Neural Networks
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    • Gogulan Karunanithy*
      Gogulan Karunanithy
      Department of Structural and Molecular Biology, Division of Biosciences, University College London, London, United Kingdom WC1E 6BT
      *(G.K.) Email: [email protected]
      More by Gogulan Karunanithy
    • Harold W. Mackenzie
      Harold W. Mackenzie
      Department of Structural and Molecular Biology, Division of Biosciences, University College London, London, United Kingdom WC1E 6BT
      More by Harold W. Mackenzie
    • D. Flemming Hansen*
      D. Flemming Hansen
      Department of Structural and Molecular Biology, Division of Biosciences, University College London, London, United Kingdom WC1E 6BT
      *(D.F.H.) Email: [email protected]
      More by D. Flemming Hansen
      Orcidhttps://orcid.org/0000-0003-0891-220X
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    Journal of the American Chemical Society

    Cite this: J. Am. Chem. Soc. 2021, 143, 41, 16935–16942
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    https://doi.org/10.1021/jacs.1c04010
    Published October 11, 2021
    Copyright © 2021 American Chemical Society
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    Abstract

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    Nuclear magnetic resonance (NMR) experiments are frequently complicated by the presence of homonuclear scalar couplings. For the growing body of biomolecular 13C-detected NMR methods, one-bond 13C–13C couplings significantly reduce sensitivity and resolution. The solution to this problem has typically been to perform virtual decoupling by recording multiple spectra and taking linear combinations. Here, we propose an alternative method of virtual decoupling using deep neural networks, which only requires a single spectrum and gives a significant boost in resolution while reducing the minimum effective phase cycles of the experiments by at least a factor of 2. We successfully apply this methodology to virtually decouple in-phase CON (13CO–15N) protein NMR spectra, 13C–13C correlation spectra of protein side chains, and 13Cα-detected protein 13Cα–13CO spectra where two large homonuclear couplings are present. The deep neural network approach effectively decouples spectra with a high degree of flexibility, including in cases where existing methods fail, and facilitates the use of simpler pulse sequences.

    Copyright © 2021 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/jacs.1c04010.

    • Protein production and purification protocols; parameters used for NMR experiments; protocols for making DNN training data and training DNNs as well as visualization of FID-Net architecture; spectra showing recovery of peaks following virtual decoupling and performance of DNNs for virtually decoupling synthetic data (PDF)

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    This article is cited by 28 publications.

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    Journal of the American Chemical Society

    Cite this: J. Am. Chem. Soc. 2021, 143, 41, 16935–16942
    Click to copy citationCitation copied!
    https://doi.org/10.1021/jacs.1c04010
    Published October 11, 2021
    Copyright © 2021 American Chemical Society
    Request reuse permissions

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