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利用 SmartScope 对冷冻电镜标本进行自动化系统评估。

Automated systematic evaluation of cryo-EM specimens with SmartScope.

机构信息

Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences, Research Triangle Park, United States.

Department of Computer Science, Duke University, Durham, United States.

出版信息

Elife. 2022 Aug 23;11:e80047. doi: 10.7554/eLife.80047.

Abstract

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.

摘要

寻找稳定大分子靶标进行成像的条件仍然是通过冷冻电子显微镜(cryo-EM)确定其结构的最关键障碍。虽然自动化已经显著提高了数据收集的速度,但标本仍然需要手动筛选,这是一项费力且主观的任务,通常决定了项目的成败。在这里,我们提出了 SmartScope,这是第一个用于简化、标准化和自动化 cryo-EM 标本评估的框架。SmartScope 采用基于深度学习的目标检测来识别和分类适合成像的特征,从而能够以完全自动化的方式进行彻底的标本筛选。一个网络界面提供了对显微镜自动操作的实时远程控制,并提供了图像和注释工具的访问。可以使用手动注释来重新训练特征识别模型,从而提高性能。我们用于系统评估标本的自动化工具简化了结构确定过程,降低了 cryo-EM 的采用门槛。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da7d/9398423/9ce3ce2abc05/elife-80047-fig1.jpg

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