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A method for restoring signals and revealing individual macromolecule states in cryo-ET, REST

Author

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  • Haonan Zhang

    (CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Yan Li

    (CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences)

  • Yanan Liu

    (CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Dongyu Li

    (CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Lin Wang

    (CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences)

  • Kai Song

    (CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences)

  • Keyan Bao

    (CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences)

  • Ping Zhu

    (CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

Abstract

Cryo-electron tomography (cryo-ET) is widely used to explore the 3D density of biomacromolecules. However, the heavy noise and missing wedge effect prevent directly visualizing and analyzing the 3D reconstructions. Here, we introduced REST, a deep learning strategy-based method to establish the relationship between low-quality and high-quality density and transfer the knowledge to restore signals in cryo-ET. Test results on the simulated and real cryo-ET datasets show that REST performs well in denoising and compensating the missing wedge information. The application in dynamic nucleosomes, presenting either in the form of individual particles or in the context of cryo-FIB nuclei section, indicates that REST has the capability to reveal different conformations of target macromolecules without subtomogram averaging. Moreover, REST noticeably improves the reliability of particle picking. These advantages enable REST to be a powerful tool for the straightforward interpretation of target macromolecules by visual inspection of the density and of a broad range of other applications in cryo-ET, such as segmentation, particle picking, and subtomogram averaging.

Suggested Citation

  • Haonan Zhang & Yan Li & Yanan Liu & Dongyu Li & Lin Wang & Kai Song & Keyan Bao & Ping Zhu, 2023. "A method for restoring signals and revealing individual macromolecule states in cryo-ET, REST," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38539-w
    DOI: 10.1038/s41467-023-38539-w
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    References listed on IDEAS

    as
    1. Yun-Tao Liu & Heng Zhang & Hui Wang & Chang-Lu Tao & Guo-Qiang Bi & Z. Hong Zhou, 2022. "Isotropic reconstruction for electron tomography with deep learning," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    2. Philipp S. Erdmann & Zhen Hou & Sven Klumpe & Sagar Khavnekar & Florian Beck & Florian Wilfling & Jürgen M. Plitzko & Wolfgang Baumeister, 2021. "In situ cryo-electron tomography reveals gradient organization of ribosome biogenesis in intact nucleoli," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    3. Florent Waltz & Thalia Salinas-Giegé & Robert Englmeier & Herrade Meichel & Heddy Soufari & Lauriane Kuhn & Stefan Pfeffer & Friedrich Förster & Benjamin D. Engel & Philippe Giegé & Laurence Drouard &, 2021. "How to build a ribosome from RNA fragments in Chlamydomonas mitochondria," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    4. Tristan Bepler & Kotaro Kelley & Alex J. Noble & Bonnie Berger, 2020. "Topaz-Denoise: general deep denoising models for cryoEM and cryoET," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    5. Yaser Hashem & Amedee des Georges & Jie Fu & Sarah N. Buss & Fabrice Jossinet & Amy Jobe & Qin Zhang & Hstau Y. Liao & Robert A. Grassucci & Chandrajit Bajaj & Eric Westhof & Susan Madison-Antenucci &, 2013. "High-resolution cryo-electron microscopy structure of the Trypanosoma brucei ribosome," Nature, Nature, vol. 494(7437), pages 385-389, February.
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    Cited by:

    1. Simon Wiedemann & Reinhard Heckel, 2024. "A deep learning method for simultaneous denoising and missing wedge reconstruction in cryogenic electron tomography," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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