Li Yilai, Cash Jennifer N, Tesmer John J G, Cianfrocco Michael A
Life Sciences Institute, Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA.
Departments of Biological Sciences and of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, USA.
Structure. 2020 Jul 7;28(7):858-869.e3. doi: 10.1016/j.str.2020.03.008. Epub 2020 Apr 14.
Single-particle cryoelectron microscopy (cryo-EM) continues to grow into a mainstream structural biology technique. Recent developments in data collection strategies alongside new sample preparation devices herald a future where users will collect multiple datasets per microscope session. To make cryo-EM data processing more automatic and user-friendly, we have developed an automatic pipeline for cryo-EM data preprocessing and assessment using a combination of deep-learning and image-analysis tools. We have verified the performance of this pipeline on a number of datasets and extended its scope to include sample screening by the user-free assessment of the qualities of a series of datasets under different conditions. We propose that our workflow provides a decision-free solution for cryo-EM, making data preprocessing more generalized and robust in the high-throughput era as well as more convenient for users from a range of backgrounds.
单颗粒冷冻电子显微镜技术（cryo-EM）持续发展成为一种主流的结构生物学技术。数据采集策略的最新进展以及新型样品制备设备预示着一个未来，即用户在每次显微镜观察期间将收集多个数据集。为了使冷冻电镜数据处理更加自动化且用户友好，我们开发了一种利用深度学习和图像分析工具相结合的冷冻电镜数据预处理及评估自动流程。我们已在多个数据集上验证了该流程的性能，并扩展其范围，通过对一系列不同条件下数据集的质量进行无需用户干预的评估来实现样品筛选。我们提出，我们的工作流程为冷冻电镜提供了一种无需决策的解决方案，使数据预处理在高通量时代更加通用和稳健，同时也为来自不同背景的用户提供了更大便利。
