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MPicker: visualizing and picking membrane proteins for cryo-electron tomography

Author

Listed:
  • Xiaofeng Yan

    (Tsinghua University
    Tsinghua University
    Tsinghua-Peking Joint Center for Life Sciences
    Beijing Frontier Research Center for Biological Structure)

  • Shudong Li

    (Tsinghua University
    Tsinghua University)

  • Weilin Huang

    (Tsinghua University
    Tsinghua University
    Tsinghua-Peking Joint Center for Life Sciences
    Beijing Frontier Research Center for Biological Structure)

  • Hao Wang

    (Tsinghua University
    Tsinghua University
    Tsinghua-Peking Joint Center for Life Sciences
    Beijing Frontier Research Center for Biological Structure)

  • Tianfang Zhao

    (Tsinghua University
    Tsinghua University
    Tsinghua-Peking Joint Center for Life Sciences
    Beijing Frontier Research Center for Biological Structure)

  • Mingtao Huang

    (Tsinghua University
    Tsinghua University
    Tsinghua-Peking Joint Center for Life Sciences
    Beijing Frontier Research Center for Biological Structure)

  • Niyun Zhou

    (Tsinghua University)

  • Yuan Shen

    (Tsinghua University
    Tsinghua University)

  • Xueming Li

    (Tsinghua University
    Tsinghua University
    Tsinghua-Peking Joint Center for Life Sciences
    Beijing Frontier Research Center for Biological Structure)

Abstract

Advancements in cryo-electron tomography (cryoET) allow the structure of macromolecules to be determined in situ, which is crucial for studying membrane protein structures and their interactions in the cellular environment. However, membranes are often highly curved and have a strong contrast in cryoET tomograms, which masks the signals from membrane proteins. These factors pose difficulties in observing and revealing the structures of membrane proteins in situ. Here, we report a membrane-flattening method and the corresponding software, MPicker, designed for the visualization, localization, and orientation determination of membrane proteins in cryoET tomograms. This method improves the visualization of proteins on and around membranes by generating a flattened tomogram that eliminates membrane curvature and reduces the spatial complexity of membrane protein analysis. In MPicker, we integrated approaches for automated particle picking and coarse alignment of membrane proteins for sub-tomogram averaging. MPicker was tested on tomograms of various cells to evaluate the method for visualizing, picking, and analyzing membrane proteins.

Suggested Citation

  • Xiaofeng Yan & Shudong Li & Weilin Huang & Hao Wang & Tianfang Zhao & Mingtao Huang & Niyun Zhou & Yuan Shen & Xueming Li, 2025. "MPicker: visualizing and picking membrane proteins for cryo-electron tomography," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55767-w
    DOI: 10.1038/s41467-024-55767-w
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    References listed on IDEAS

    as
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    3. Xinyu Zhang & Tianfang Zhao & Jiansheng Chen & Yuan Shen & Xueming Li, 2022. "EPicker is an exemplar-based continual learning approach for knowledge accumulation in cryoEM particle picking," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    4. Xin You & Xing Zhang & Jing Cheng & Yanan Xiao & Jianfei Ma & Shan Sun & Xinzheng Zhang & Hong-Wei Wang & Sen-Fang Sui, 2023. "In situ structure of the red algal phycobilisome–PSII–PSI–LHC megacomplex," Nature, Nature, vol. 616(7955), pages 199-206, April.
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