DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM

被引:126
|
作者
Wang, Feng [3 ]
Gong, Huichao [2 ]
Liu, Gaochao [1 ,4 ]
Li, Meijing [1 ,4 ]
Yan, Chuangye [1 ,5 ]
Xia, Tian [3 ]
Li, Xueming [1 ,4 ,5 ]
Zeng, Jianyang [2 ]
机构
[1] Tsinghua Univ, Sch Life Sci, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Inst Interdisciplinary Informat Sci, Beijing 100084, Peoples R China
[3] Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan 430074, Peoples R China
[4] Tsinghua Univ, Beijing Adv Innovat Ctr Struct Biol, Beijing 100084, Peoples R China
[5] Tsinghua Univ, Tsinghua Peking Joint Ctr Life Sci, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
Cryo-EM; Particle picking; Automation; Deep learning; NEURAL-NETWORKS; SELECTION; MACHINE; SYSTEM;
D O I
10.1016/j.jsb.2016.07.006
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:325 / 336
页数:12
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