Automated systematic evaluation of cryo-EM specimens with SmartScope

被引:26
|
作者
Bouvette, Jonathan [1 ]
Huang, Qinwen [2 ]
Riccio, Amanda A. [1 ]
Copeland, William C. [1 ]
Bartesaghi, Alberto [2 ,3 ,4 ]
Borgnia, Mario J. [1 ]
机构
[1] NIEHS, Genome Integr & Struct Biol Lab, POB 12233, Res Triangle Pk, NC 27709 USA
[2] Duke Univ, Dept Comp Sci, Durham, NC 27706 USA
[3] Duke Univ, Dept Elect & Comp Engn, Durham, NC 27708 USA
[4] Duke Univ, Dept Biochem, Sch Med, Durham, NC 27708 USA
来源
ELIFE | 2022年 / 11卷
关键词
cryo-electron microscopy; automation; machine learning; deep learning; object recognition; software platform; Human; VISUALIZATION;
D O I
10.7554/eLife.80047
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Finding the conditions to stabilize a macromolecular target for imaging remains the most critical barrier to determining its structure by cryo-electron microscopy (cryo-EM). While automation has significantly increased the speed of data collection, specimens are still screened manually, a laborious and subjective task that often determines the success of a project. Here, we present SmartScope, the first framework to streamline, standardize, and automate specimen evaluation in cryo-EM. SmartScope employs deep-learning-based object detection to identify and classify features suitable for imaging, allowing it to perform thorough specimen screening in a fully automated manner. A web interface provides remote control over the automated operation of the microscope in real time and access to images and annotation tools. Manual annotations can be used to re-train the feature recognition models, leading to improvements in performance. Our automated tool for systematic evaluation of specimens streamlines structure determination and lowers the barrier of adoption for cryo-EM.
引用
收藏
页数:18
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