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

Guole Liu, Tongxin Niu, Mengxuan Qiu, Yun Zhu, Fei Sun () and Ge Yang ()
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Guole Liu: Chinese Academy of Sciences
Tongxin Niu: Chinese Academy of Sciences
Mengxuan Qiu: Chinese Academy of Sciences
Yun Zhu: Chinese Academy of Sciences
Fei Sun: Chinese Academy of Sciences
Ge Yang: Chinese Academy of Sciences

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-46041-0

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