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Solution-state methyl NMR spectroscopy of large non-deuterated proteins enabled by deep neural networks

Author

Listed:
  • Gogulan Karunanithy

    (Division of Biosciences, University College London)

  • Vaibhav Kumar Shukla

    (Division of Biosciences, University College London
    The Francis Crick Institute)

  • D. Flemming Hansen

    (Division of Biosciences, University College London
    The Francis Crick Institute)

Abstract

Methyl-TROSY nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for characterising large biomolecules in solution. However, preparing samples for these experiments is demanding and entails deuteration, limiting its use. Here we demonstrate that NMR spectra recorded on protonated, uniformly 13C labelled samples can be processed using deep neural networks to yield spectra that are of similar quality to typical deuterated methyl-TROSY spectra, potentially providing information for proteins that cannot be produced in bacterial systems. We validate the methodology experimentally on three proteins with molecular weights in the range 42–360 kDa. We further demonstrate the applicability of our methodology to 3D NOESY spectra of Escherichia coli Malate Synthase G (81 kDa), where observed NOE cross-peaks are in good agreement with the available structure. The method represents an advance in the field of using deep learning to analyse complex magnetic resonance data and could have an impact on the study of large biomolecules in years to come.

Suggested Citation

  • Gogulan Karunanithy & Vaibhav Kumar Shukla & D. Flemming Hansen, 2024. "Solution-state methyl NMR spectroscopy of large non-deuterated proteins enabled by deep neural networks," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49378-8
    DOI: 10.1038/s41467-024-49378-8
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    References listed on IDEAS

    as
    1. Da-Wei Li & Alexandar L. Hansen & Chunhua Yuan & Lei Bruschweiler-Li & Rafael Brüschweiler, 2021. "DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. Ruth B. Pritchard & D. Flemming Hansen, 2019. "Characterising side chains in large proteins by protonless 13C-detected NMR spectroscopy," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    3. Remco Sprangers & Lewis E. Kay, 2007. "Quantitative dynamics and binding studies of the 20S proteasome by NMR," Nature, Nature, vol. 445(7128), pages 618-622, February.
    4. Piotr Klukowski & Roland Riek & Peter Güntert, 2022. "Rapid protein assignments and structures from raw NMR spectra with the deep learning technique ARTINA," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    5. Nicolas D. Werbeck & Vaibhav Kumar Shukla & Micha B. A. Kunze & Havva Yalinca & Ruth B. Pritchard & Lucas Siemons & Somnath Mondal & Simon O. R. Greenwood & John Kirkpatrick & Charles M. Marson & D. F, 2020. "A distal regulatory region of a class I human histone deacetylase," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
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