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

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 c

SUBMITTER: Liu G 

PROVIDER: S-EPMC11258139 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature