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BMC Bioinformatics. 2017 Jul 21;18(1):348. doi: 10.1186/s12859-017-1757-y.

A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.

Zhu Y1, Ouyang Q1,2,3, Mao Y4,5,6.

Author information

1
Center for Quantitative Biology, Peking University, Beijing, 100871, China.
2
State Key Laboratory for Artificial Microstructure and Mesoscopic Physics, Peking University, Institute of Condensed Matter Physics, School of Physics, Beijing, 100871, China.
3
Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
4
Center for Quantitative Biology, Peking University, Beijing, 100871, China. youdong_mao@dfci.harvard.edu.
5
State Key Laboratory for Artificial Microstructure and Mesoscopic Physics, Peking University, Institute of Condensed Matter Physics, School of Physics, Beijing, 100871, China. youdong_mao@dfci.harvard.edu.
6
Intel Parallel Computing Center for Structural Biology, Department of Microbiology and Immunobiology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02115, USA. youdong_mao@dfci.harvard.edu.

Abstract

BACKGROUND:

Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands of single-particle images. Particle extraction from experimental micrographs thus can be laborious and presents a major practical bottleneck in cryo-EM structural determination. Existing computational methods for particle picking often use low-resolution templates for particle matching, making them susceptible to reference-dependent bias. It is critical to develop a highly efficient template-free method for the automatic recognition of particle images from cryo-EM micrographs.

RESULTS:

We developed a deep learning-based algorithmic framework, DeepEM, for single-particle recognition from noisy cryo-EM micrographs, enabling automated particle picking, selection and verification in an integrated fashion. The kernel of DeepEM is built upon a convolutional neural network (CNN) composed of eight layers, which can be recursively trained to be highly "knowledgeable". Our approach exhibits an improved performance and accuracy when tested on the standard KLH dataset. Application of DeepEM to several challenging experimental cryo-EM datasets demonstrated its ability to avoid the selection of un-wanted particles and non-particles even when true particles contain fewer features.

CONCLUSIONS:

The DeepEM methodology, derived from a deep CNN, allows automated particle extraction from raw cryo-EM micrographs in the absence of a template. It demonstrates an improved performance, objectivity and accuracy. Application of this novel method is expected to free the labor involved in single-particle verification, significantly improving the efficiency of cryo-EM data processing.

KEYWORDS:

Convolutional neural network; Cryo-EM; Deep learning; Particle recognition; Single-particle reconstruction

PMID:
28732461
PMCID:
PMC5521087
DOI:
10.1186/s12859-017-1757-y
[Indexed for MEDLINE]
Free PMC Article

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