Journal
JOURNAL OF STRUCTURAL BIOLOGY
Volume 195, Issue 3, Pages 325-336Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jsb.2016.07.006
Keywords
Cryo-EM; Particle picking; Automation; Deep learning
Funding
- National Basic Research Program of China [2011CBA00300, 2011CBA00301]
- National Natural Science Foundation of China [61033001, 61361136003, 61472205, 31570730, 61571202]
- China Youth 1000-Talent Program by the State Council of China
- Beijing Advanced Innovation Center for Structural Biology
- Tsinghua-Peking Joint Center for Life Sciences
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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. (C) 2016 Elsevier Inc. All rights reserved.
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