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.

项目概要 冷冻电子显微镜（cryo-EM）现在是一种广泛建立的、不可或缺的方法，用于确定 生物医学重要分子的高分辨率结构。鉴于经常获取数千张图像 在几天的时间里，需要获得这样的结构，自动化软件发挥了关键作用 科学界大规模采用这种方法的作用。在过去的五年里，冷冻电镜 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。冷冻电镜的广泛采用 最近促使 NIH 通过变革性高分辨率冷冻技术投资三个国家中心 电子显微镜计划，为世界各地的生物学家提供免费的高端电子显微镜使用机会 国家。冷冻电镜的普及度呈指数级增长，带来了数量惊人的发展 在样品制备方法和图像处理算法方面，这些改进可实现 单粒子重建的分辨率。但在优化方面取得的进展相对较小 所收集的冷冻电镜数据的质量。开创性的软件包 Leginon 和 Appion 分别展示了自动数据采集和实时处理的强大功能，并且有 现在有许多用于自动数据采集和实时处理的程序。尽管取得了进展 自动化，从 EM 样本中最佳地提取最高质量的数据仍然需要手动参与 电子显微镜专家。运行适当的映像需要用户干预和专业知识 分析、解释结果，并就处理结果与正在进行的结果的关系做出明智的决策 数据收集。然而，即使是专家也必须满足于“最佳网格区域”差异巨大的事实 从一个样本到另一个样本，目前还没有成熟的工具来自动、快速地评估样本的质量 样品在电磁网格的各种微环境中。鉴于不断增加的合并 当冷冻电镜融入实验室的研究计划时，必须简化数据收集和处理，以 满足结构社区不断增长的需求。我们建议开发第二代 Leginon/Appion 软件包“Magellon”克服了现有瓶颈并提供了一条途径 完全自动化的数据采集，无需在数据收集期间进行用户输入。重要的是，这 软件将支持计算基础设施，使实时图像处理结果能够告知 并通过了解在何处获取区域中的图像来修改正在进行的数据收集制度，从而产生 最高分辨率的结构。我们将开发并整合新的、快速的图像评估程序，同时 提供应用程序编程接口以实现扩展和插件的合并 社区中的开发者。此外，Magellon 将实现直接、无缝的数据导入和导出 从其数据库中获取任何区域或国家冷冻电镜中心的远程数据。

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.