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Tytuł pozycji:

DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM.

Tytuł:
DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM.
Autorzy:
Al-Azzawi A; Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA.
Ouadou A; Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA.
Max H; Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA.
Duan Y; Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA.
Tanner JJ; Departments of Biochemistry and Chemistry, University of Missouri, Columbia, MO, 65211-2060, USA.
Cheng J; Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA. .; Informatics Institute, University of Missouri, Columbia, MO, 65211, USA. .
Źródło:
BMC bioinformatics [BMC Bioinformatics] 2020 Nov 09; Vol. 21 (1), pp. 509. Date of Electronic Publication: 2020 Nov 09.
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Original Publication: [London] : BioMed Central, 2000-
MeSH Terms:
Deep Learning*
Cryoelectron Microscopy/*methods
Proteins/*chemistry
Automation ; Cluster Analysis
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Grant Information:
R01GM093123 National Institutes of Health (NIH); R01GM065546 United States GM NIGMS NIH HHS; R01 GM065546 United States GM NIGMS NIH HHS; DBI 1759934 and IIS1763246 National Science Foundation; R01 GM093123 United States GM NIGMS NIH HHS
Contributed Indexing:
Keywords: AutoCryoPicker; Cryo-EM; Deep learning; Intensity based clustering (IBC); Micrograph; Protein structure determination; Singe particle pickling; Super clustering; SuperCryoPicker
Substance Nomenclature:
0 (Proteins)
Entry Date(s):
Date Created: 20201110 Date Completed: 20201123 Latest Revision: 20231104
Update Code:
20240105
PubMed Central ID:
PMC7653784
DOI:
10.1186/s12859-020-03809-7
PMID:
33167860
Czasopismo naukowe
Background: Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps.
Results: Here we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automatically. Results indicate that the DeepCryoPicker compares favorably with semi-automated methods such as DeepEM, DeepPicker, and RELION, with the significant advantage of not requiring human intervention.
Conclusions: Our framework combing supervised deep learning classification with automated un-supervised clustering for generating training data provides an effective approach to pick particles in cryo-EM images automatically and accurately.
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