DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM

By Feng Wang, Huichao Gong, Gaochao liu, Meijing Li, Chuangye Yan, Tian Xia, Xueming Li, Jianyang Zeng. 2016

194 citations recorded by the source corpus.

Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM). Here we report a deep learning framework, called DeepPicker, to address this problem and fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker employs a novel cross-molecule training strategy to capture common features of particles from previously-analyzed micrographs, and thus does not require any human intervention during particle picking. Tests on the recently-published cryo-EM data of three complexes have demonstrated that our deep learning based scheme can successfully accomplish the human-level particle picking process and identify a sufficient number of particles that are comparable to those picked manually by human experts. These results indicate that DeepPicker can provide a practically useful tool to significantly reduce the time and manual effort spent in single-particle analysis and thus greatly facilitate high-resolution cryo-EM structure determination. DeepPicker is released as an open-source program, which can be downloaded from https://github.com/nejyeah/DeepPicker-python.

Subfields: Materials Chemistry, Structural Biology, Surfaces, Coatings and Films

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Citations in the corpus

Automated Particle Picking in Cryo-Electron Micrographs using Deep Regression (2018, methodology): “DeepPicker [14] is a fully automated particle picking program based on deep neural networks.”
Advances in methods for atomic resolution macromolecular structure determination (2020, background): “: Structure of amyloid-β (20-34) with Alzheimer’s-associated isomerization at Asp23 reveals a distinct protofilament interface.”
DeepEMhancer: a deep learning solution for cryo-EM volume post-processing (2020, result): “10 – 13, our neural network tends to suffer from under ﬁ tting when trained on maps derived from atomic models and thus, the results are blurrier than the ones obtained when using LocScale maps as a target.”