Propelled by improvements in hardware for data collection and processing, single particle cryo-electron microscopy has rapidly gained relevance in structural biology. Yet, finding the conditions to stabilize a macromolecular target for imaging remains the most critical barrier to determining its structure. Attaining the optimal specimen requires the evaluation of multiple grids in a microscope as conditions are varied. While automation has significantly increased the speed of data collection, optimization is still carried out manually. This laborious process which is highly dependent on subjective assessments, inefficient and prone to error, often determines the success of a project. Here, we present SmartScope, the first framework to streamline, standardize, and automate specimen evaluation in cryo-electron microscopy. 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-electron microscopy. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://docs.smartscope.org/...

Automated systematic evaluation of cryo-EM specimens with SmartScope

10.1101/2022.05.05.490801