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