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DeepETPicker: Fast and accurate 3D particle picking for cryo-electron tomography using weakly supervised deep learning

Author

Listed:
  • Guole Liu

    (Chinese Academy of Sciences
    Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Tongxin Niu

    (Chinese Academy of Sciences)

  • Mengxuan Qiu

    (Chinese Academy of Sciences
    Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Yun Zhu

    (Chinese Academy of Sciences)

  • Fei Sun

    (Chinese Academy of Sciences
    Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory))

  • Ge Yang

    (Chinese Academy of Sciences
    Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

Abstract

To solve three-dimensional structures of biological macromolecules in situ, large numbers of particles often need to be picked from cryo-electron tomograms. However, adoption of automated particle-picking methods remains limited because of their technical limitations. To overcome the limitations, we develop DeepETPicker, a deep learning model for fast and accurate picking of particles from cryo-electron tomograms. Training of DeepETPicker requires only weak supervision with low numbers of simplified labels, reducing the burden of manual annotation. The simplified labels combined with the customized and lightweight model architecture of DeepETPicker and accelerated pooling enable substantial performance improvement. When tested on simulated and real tomograms, DeepETPicker outperforms the competing state-of-the-art methods by achieving the highest overall accuracy and speed, which translate into higher authenticity and coordinates accuracy of picked particles and higher resolutions of final reconstruction maps. DeepETPicker is provided in open source with a user-friendly interface to support cryo-electron tomography in situ.

Suggested Citation

  • Guole Liu & Tongxin Niu & Mengxuan Qiu & Yun Zhu & Fei Sun & Ge Yang, 2024. "DeepETPicker: Fast and accurate 3D particle picking for cryo-electron tomography using weakly supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46041-0
    DOI: 10.1038/s41467-024-46041-0
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    1. Lauren Ann Metskas & Davi Ortega & Luke M. Oltrogge & Cecilia Blikstad & Derik R. Lovejoy & Thomas G. Laughlin & David F. Savage & Grant J. Jensen, 2022. "Rubisco forms a lattice inside alpha-carboxysomes," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
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