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.

项目总结

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.