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Topaz-Denoise: general deep denoising models for cryoEM and cryoET

Author

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  • Tristan Bepler

    (Computational and Systems Biology, MIT
    Computer Science and Artificial Intelligence Laboratory, MIT)

  • Kotaro Kelley

    (National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center)

  • Alex J. Noble

    (National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center)

  • Bonnie Berger

    (Computer Science and Artificial Intelligence Laboratory, MIT
    Department of Mathematics, MIT)

Abstract

Cryo-electron microscopy (cryoEM) is becoming the preferred method for resolving protein structures. Low signal-to-noise ratio (SNR) in cryoEM images reduces the confidence and throughput of structure determination during several steps of data processing, resulting in impediments such as missing particle orientations. Denoising cryoEM images can not only improve downstream analysis but also accelerate the time-consuming data collection process by allowing lower electron dose micrographs to be used for analysis. Here, we present Topaz-Denoise, a deep learning method for reliably and rapidly increasing the SNR of cryoEM images and cryoET tomograms. By training on a dataset composed of thousands of micrographs collected across a wide range of imaging conditions, we are able to learn models capturing the complexity of the cryoEM image formation process. The general model we present is able to denoise new datasets without additional training. Denoising with this model improves micrograph interpretability and allows us to solve 3D single particle structures of clustered protocadherin, an elongated particle with previously elusive views. We then show that low dose collection, enabled by Topaz-Denoise, improves downstream analysis in addition to reducing data collection time. We also present a general 3D denoising model for cryoET. Topaz-Denoise and pre-trained general models are now included in Topaz. We expect that Topaz-Denoise will be of broad utility to the cryoEM community for improving micrograph and tomogram interpretability and accelerating analysis.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18952-1
    DOI: 10.1038/s41467-020-18952-1
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    Cited by:

    1. 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.
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    15. Fengfeng Niu & Lingxuan Li & Lei Wang & Jinman Xiao & Shun Xu & Yong Liu & Leishu Lin & Cong Yu & Zhiyi Wei, 2024. "Autoinhibition and activation of myosin VI revealed by its cryo-EM structure," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    16. Lu Xue & Tiancai Chang & Zimu Li & Chenchen Wang & Heyu Zhao & Mei Li & Peng Tang & Xin Wen & Mengmeng Yu & Jiqin Wu & Xichen Bao & Xiaojun Wang & Peng Gong & Jun He & Xinwen Chen & Xiaoli Xiong, 2024. "Cryo-EM structures of Thogoto virus polymerase reveal unique RNA transcription and replication mechanisms among orthomyxoviruses," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
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    18. Kevin M. Knight & Brian E. Krumm & Nicholas J. Kapolka & W. Grant Ludlam & Meng Cui & Sepehr Mani & Iya Prytkova & Elizabeth G. Obarow & Tyler J. Lefevre & Wenyuan Wei & Ning Ma & Xi-Ping Huang & Jona, 2024. "A neurodevelopmental disorder mutation locks G proteins in the transitory pre-activated state," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
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    20. J. Josephine Botsch & Roswitha Junker & Michèle Sorgenfrei & Patricia P. Ogger & Luca Stier & Susanne Gronau & Peter J. Murray & Markus A. Seeger & Brenda A. Schulman & Bastian Bräuning, 2024. "Doa10/MARCH6 architecture interconnects E3 ligase activity with lipid-binding transmembrane channel to regulate SQLE," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    21. Kotaro Kelley & Ashleigh M. Raczkowski & Oleg Klykov & Pattana Jaroenlak & Daija Bobe & Mykhailo Kopylov & Edward T. Eng & Gira Bhabha & Clinton S. Potter & Bridget Carragher & Alex J. Noble, 2022. "Waffle Method: A general and flexible approach for improving throughput in FIB-milling," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    22. Hongcheng Fan & Bo Wang & Yan Zhang & Yun Zhu & Bo Song & Haijin Xu & Yujia Zhai & Mingqiang Qiao & Fei Sun, 2021. "A cryo-electron microscopy support film formed by 2D crystals of hydrophobin HFBI," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    23. Leishu Lin & Jiayuan Dong & Shun Xu & Jinman Xiao & Cong Yu & Fengfeng Niu & Zhiyi Wei, 2024. "Autoinhibition and relief mechanisms for MICAL monooxygenases in F-actin disassembly," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
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    25. Kathryn H. Gunn & Saskia B. Neher, 2023. "Structure of dimeric lipoprotein lipase reveals a pore adjacent to the active site," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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