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: 10317907
• 负责人: Gabriel C Lander
• 金额: $ 65.5万
• 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/10317907
• 关键词: 2019-nCoV; Adoption; Algorithms; Automation; Automobile Driving; Biophysics; Buffers; Bypass; COVID-19; Code; Communities; Computer software; Country; Cryoelectron Microscopy; Data; Data Collection; Data Set; Databases; Development; Disease; Electron Microscope; Electron Microscopy; Electrons; Feedback; Foundations; Generations; Goals; Hand; Heterogeneity; Ice; Image; Image Analysis; Institution; Intelligence; Intervention; Learning; Maintenance; Manuals; Methodology; Methods; Microscope; Molecular; Molecular Conformation; Output; Particle Size; Pathologic; Planets; Play; Preparation; Process; Research; Resolution; Role; Running; Sampling; Series; Site; Specimen; Structure; Thick; Time; Training; United States National Institutes of Health; Update; Vaccines; Virus; Work; application programming interface; base; biological systems; 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 现在，冷冻电子显微镜（Cryo-EM）是一种广泛建立的，必不可少的方法，用于确定该方法 生物医学重要分子的高分辨率结构。 在严重的日子里，需要获得此类结构，自动化软件发挥了关键 在过去五年中，在大型科学界的大规模采用中 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。 最近促使NIH投资三个国家中心 电子显微镜计划，为您提供免费的高端电子显微镜访问您的生物学家 国家。 在样品制备方法和图像处理算法中，这些算法已改善了attainabe 单个粒子重建的分辨率。 收集的冷冻EM数据的质量。 证明了自动数据获取和实时处理（尊重）的力量，此处 现在，尽管进步了，但许多程序进行了自动化数据和实时处理 自动化，最佳地从EM AN EM AN -AR -RES -REM -REM -REM -AR -AR -AR -AR -AR -AL -AR -AR -AR -AR -AR -AR -AR -AR -AR -AR -AL -AL -AN -AR -AL -AL -AN -AR -AL -AN -AR -AL -AN -AR -AL -AN -REM -AL -AL -AL -AN -AR -REM -REM -RESTING中介入 专业的电子显微镜专家。 分析，解释结果，并就处理后的结果如何与正在进行的结果做出明智的决定 但是，数据收集。 从样本到样本，以及无稳定的工具可自动，快速评估的质量 EM电网各种微环境的样品。 Cryo-Em进入实验室的研究计划，IS是IS是IS IS IS IS IS IS IS IS IS收集和处理也必须简化 符合结构社区的日益增长的需求。 Leginon/Appion/Appion软件包“ Magellon”，以克服销售范围并为途径提供途径 完全自动化的数据采集绕过用户输入数据收集的需求。 软件将支持计算基础架构以启用实时图像处理结果，以告知Onform onform onform onform onform onform onform 并通过学习获取区域中的图像来修改正在进行的数据收集制度将产生 最高的分辨率结构。 提供一个应用程序编程接口，以将扩展名和插件合并 社区中的开发人员。 从其数据库适应远程数据采集区域或国家冷冻EM中心。