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DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra

Author

Listed:
  • Da-Wei Li

    (The Ohio State University)

  • Alexandar L. Hansen

    (The Ohio State University)

  • Chunhua Yuan

    (The Ohio State University)

  • Lei Bruschweiler-Li

    (The Ohio State University)

  • Rafael Brüschweiler

    (The Ohio State University
    The Ohio State University
    The Ohio State University)

Abstract

The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems. Here, we introduce DEEP Picker, a deep neural network (DNN)-based approach for peak picking and spectral deconvolution which semi-automates the analysis of two-dimensional NMR spectra. DEEP Picker includes 8 hidden convolutional layers and was trained on a large number of synthetic spectra of known composition with variable degrees of crowdedness. We show that our method is able to correctly identify overlapping peaks, including ones that are challenging for expert spectroscopists and existing computational methods alike. We demonstrate the utility of DEEP Picker on NMR spectra of folded and intrinsically disordered proteins as well as a complex metabolomics mixture, and show how it provides access to valuable NMR information. DEEP Picker should facilitate the semi-automation and standardization of protocols for better consistency and sharing of results within the scientific community.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25496-5
    DOI: 10.1038/s41467-021-25496-5
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    Cited by:

    1. 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.
    2. 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.

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