Automated, optimized, intelligent data collection for cryo-EM

冷冻电镜的自动化、优化、智能数据采集

• 批准号: 10649517
• 负责人: Gabriel C Lander
• 金额: $ 55.35万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-22 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10649517
• 关键词: 2019-nCoV; Adoption; Algorithms; Automation; Automobile Driving; Buffers; Bypass; COVID-19; Calibration; Code; Communities; Computer software; Country; Cryoelectron Microscopy; Data; Data Collection; Data Set; Databases; Development; Disease; Electron Microscope; Electron Microscopy; Electron Microscopy Facility; Electrons; Feedback; Generations; Goals; Hand; Heterogeneity; Ice; Image; Image Analysis; Institution; Intelligence; Intervention; Investments; Learning; Maintenance; Manuals; Methodology; Methods; Microscope; Molecular; Molecular Conformation; Output; Particle Size; Pathologic; Planets; Play; Plug-in; Preparation; Process; Pythons; Research; Resolution; Role; Running; Sampling; Series; Site; Specimen; Structure; Thick; Time; Training; United States National Institutes of Health; Update; Vaccines; Virus; Visualization; Work; application programming interface; base; biological systems; biophysical properties; cluster computing; comparative; computer infrastructure; computer program; computerized data processing; convolutional neural network; data acquisition; data mining; data quality; design; fighting; image processing; improved; innovation; migration; neutralizing antibody; next generation; open source; particle; portability; prediction algorithm; preservation; programs; real-time images; reconstruction; structural biology; therapeutic development; tool

Project Summary Cryo-electron microscopy (cryo-EM) is now a widely established and indispensable method for determining the high-resolution structures of biomedically important molecules. Given that thousands of images, often acquired over the course of several days, are required to obtain such structures, automation software has played a critical role in the large-scale adoption of this method by the scientific community. In just the past five years, cryo-EM has revolutionized our understanding of entire biological systems, and in 2020 provided the first molecular descriptions of SARS-CoV-2 interaction with neutralizing antibodies. The widespread adoption of cryo-EM recently prompted the NIH to invest in three National Centers through the Transformative High Resolution Cryo- Electron Microscopy Program, providing free, high-end electron microscope access to biologists across the country. The exponential increase in the popularity of cryo-EM has led to an astonishing number of developments in sample preparation methodologies and image processing algorithms, which have improved attainable resolution of single particle reconstructions. However, comparatively little progress has been made in optimizing the quality of the cryo-EM data being collected. The pioneering software packages Leginon and Appion demonstrated the power of automated data acquisition and real-time processing (respectively), and there are now numerous programs for automated data acquisition and real-time processing. Despite advances in automation, optimally extracting the highest quality data from an EM sample still requires manual involvement of an expert electron microscopist. User intervention and expertise is necessary to run the appropriate image analyses, interpret the results, and make informed decisions on how the processed results relate to the ongoing data collection. However, even experts must content with the fact that the “best grid regions” differ drastically from sample to sample, and there are no established tools for automatically and quickly assessing the quality of the specimen across the various microenvironments of an EM grid. Given the ever-increasing incorporation of cryo-EM into labs’ research programs, it is imperative that data collection and processing be streamlined to match the growing needs of the structural community. We propose to develop a second generation Leginon/Appion software package, “Magellon”, to overcome existing bottlenecks and provide an avenue toward fully automated data acquisition that bypasses need for user input during data collection. Importantly, this software will support the computational infrastructure to enable real-time image processing results to inform on and modify the ongoing data collection regime by learning where to acquire images in regions that will yield the highest resolution structures. We will develop and incorporate new, fast image assessment routines, while also providing an application programming interface to enable the incorporation of extensions and plugins from developers in the community. Further, Magellon will enable straightforward, seamless import and export of data from its database to accommodate remote data acquisition at any of the regional or national cryo-EM centers.

项目摘要 冷冻电子显微镜（Cryo-EM）现在是一种广泛且必不可少的方法来确定 生物医学重要分子的高分辨率结构。鉴于成千上万的图像经常获得 在几天的过程中，需要获得此类结构，自动化软件发挥了关键 科学界在大规模采用这种方法中的作用。在过去的五年中，Cryo-Em 已经彻底改变了我们对整个生物系统的理解，并于2020年提供了第一个分子 SARS-COV-2与中和抗体相互作用的描述。低温em的宽度采用 最近促使NIH通过变革性的高分辨率冷冻 - 电子显微镜计划，为跨整个生物学家提供免费的高端电子显微镜访问 国家。冷冻EM的普及呈指数增长，导致了惊人的发展 在样品制备方法和图像处理算法中，可以改善可达到的 单个粒子重建的分辨率。但是，优化取得的进展相对较少 收集的冷冻EM数据的质量。开创性软件包Leginon和Appion 证明了自动数据获取和实时处理的力量（分别是），并且有 现在，许多用于自动数据获取和实时处理的程序。尽管进步 自动化，最佳从EM样本中提取最高质量数据仍然需要手动参与 专家电子显微镜。用户干预和专业知识对于运行适当的图像是必要的 分析，解释结果，并就处理结果如何与正在进行的结果有关 数据收集。但是，即使是专家也必须满足于“最佳网格区域”不同的事实 从样本到样本，并且没有建立的工具可以自动和快速评估 EM电网各种微环境的样品。考虑到不断增加的收入 Cryo-Em进入实验室的研究计划，必须简化数据收集和处理 符合结构社区不断增长的需求。我们建议发展第二代 Leginon/Appion软件包“ Magellon”，以克服现有的瓶颈，并提供途径 完全自动化的数据采集在数据收集过程中绕开了对用户输入的需求。重要的是，这个 软件将支持计算基础架构以实现实时图像处理结果以告知 并通过学习在哪里获取将产生的区域中获取图像的方法来修改正在进行的数据收集制度 最高分辨率结构。我们将开发并合并新的快速图像评估程序，同时也 提供应用程序编程接口，以实现从 社区中的开发人员。此外，Magellon将使数据直接，无缝导入和数据导出 从其数据库中，可以在任何区域或国家冷冻EM中心容纳远程数据获取。

项目成果

Characterizing the resolution and throughput of the Apollo direct electron detector.

表征 Apollo 直接电子探测器的分辨率和吞吐量。

• DOI: 10.1016/j.yjsbx.2022.100080
• 发表时间: 2023
• 期刊: JOURNAL OF STRUCTURAL BIOLOGY-X
• 影响因子: 2.9
• 作者: Peng, Ruizhi;Fu, Xiaofeng;Mendez, Joshua H.;Randolph, Peter S.;Bammes, Benjamin E.;Stagg, Scott M.
• 通讯作者: Stagg, Scott M.