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冷冻电镜单颗粒分析工作流程的基准测试

Benchmarking cryo-EM Single Particle Analysis Workflow.

作者信息

Kim Laura Y, Rice William J, Eng Edward T, Kopylov Mykhailo, Cheng Anchi, Raczkowski Ashleigh M, Jordan Kelsey D, Bobe Daija, Potter Clinton S, Carragher Bridget

机构信息

Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, United States.

出版信息

Front Mol Biosci. 2018 Jun 4;5:50. doi: 10.3389/fmolb.2018.00050. eCollection 2018.

DOI:10.3389/fmolb.2018.00050
PMID:29951483
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6009202/
Abstract

Cryo electron microscopy facilities running multiple instruments and serving users with varying skill levels need a robust and reliable method for benchmarking both the hardware and software components of their single particle analysis workflow. The workflow is complex, with many bottlenecks existing at the specimen preparation, data collection and image analysis steps; the samples and grid preparation can be of unpredictable quality, there are many different protocols for microscope and camera settings, and there is a myriad of software programs for analysis that can depend on dozens of settings chosen by the user. For this reason, we believe it is important to benchmark the entire workflow, using a standard sample and standard operating procedures, on a regular basis. This provides confidence that all aspects of the pipeline are capable of producing maps to high resolution. Here we describe benchmarking procedures using a test sample, rabbit muscle aldolase.

摘要

运行多台仪器并为不同技能水平的用户提供服务的冷冻电子显微镜设施,需要一种强大且可靠的方法来对其单颗粒分析工作流程的硬件和软件组件进行基准测试。该工作流程很复杂,在样品制备、数据采集和图像分析步骤存在许多瓶颈;样品和网格制备的质量可能不可预测,显微镜和相机设置有许多不同的协议,并且有大量用于分析的软件程序,这些程序可能取决于用户选择的数十种设置。因此,我们认为定期使用标准样品和标准操作程序对整个工作流程进行基准测试很重要。这让人相信该流程的所有方面都能够生成高分辨率的图谱。在这里,我们描述了使用测试样品兔肌肉醛缩酶的基准测试程序。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7d7/6009202/27c2e5608250/fmolb-05-00050-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7d7/6009202/4de49094da93/fmolb-05-00050-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7d7/6009202/27c2e5608250/fmolb-05-00050-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7d7/6009202/4de49094da93/fmolb-05-00050-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7d7/6009202/27c2e5608250/fmolb-05-00050-g0002.jpg

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本文引用的文献

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Achieving better-than-3-Å resolution by single-particle cryo-EM at 200 keV.在200 keV下通过单颗粒冷冻电镜实现优于3 Å的分辨率。
Nat Methods. 2017 Nov;14(11):1075-1078. doi: 10.1038/nmeth.4461. Epub 2017 Oct 9.
2
Single-protein detection in crowded molecular environments in cryo-EM images.在冷冻电镜图像的拥挤分子环境中进行单蛋白检测。
Elife. 2017 May 3;6:e25648. doi: 10.7554/eLife.25648.
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MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy.MotionCor2:用于改进冷冻电子显微镜的束流诱导运动的各向异性校正
小分子 LEA 蛋白可减轻在 plunge 冷冻过程中脆弱的 cryo-EM 样品在气-液界面处的损伤。
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Multi-species cryoEM calibration and workflow verification standard.多物种冷冻电镜校准与工作流程验证标准。
bioRxiv. 2024 Aug 5:2024.08.05.606612. doi: 10.1101/2024.08.05.606612.
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VitroJet: new features and case studies.VitroJet:新功能与案例研究。
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CryoTransformer: A Transformer Model for Picking Protein Particles from Cryo-EM Micrographs.低温变压器:一种用于从冷冻电镜显微照片中挑选蛋白质颗粒的变压器模型。
bioRxiv. 2023 Oct 23:2023.10.19.563155. doi: 10.1101/2023.10.19.563155.
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Accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and specialized U-Net.通过整合基础人工智能图像分割模型和专门的U-Net进行准确的冷冻电镜蛋白质颗粒挑选。
bioRxiv. 2024 Mar 14:2023.10.02.560572. doi: 10.1101/2023.10.02.560572.
8
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