Intelligent cryo-electron microscopy of G protein-coupled receptors

G 蛋白偶联受体的智能冷冻电子显微镜

• 批准号: 22H02554
• 负责人: Danev Radostin
• 金额: $ 9.98万
• 依托单位: The University of Tokyo
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2022
• 资助国家: 日本
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22H02554/
• 关键词: cryo-EM; automation; sample screening; data acquisition; deep learning

In the first year of the project, we made good progress in this research. We established the main research platform for carrying out the project. This included selection and procurement of a powerful GPU workstation for the machine learning tasks related to automated sample screening and AI-based data acquisition. It was installed in March 2023 at the Kashiwa 2 campus where two new cryo-electron microscopes were also installed in the first half of 2022. The GPU machine is now connected to the microscope network and can communicate directly with the microscope and the data storage server. After installation, we began initial performance and stability testing and installation of the necessary software packages, which are still ongoing.From the beginning of the project period, we started the development of the software platform for the intelligent cryo-electron microscopy system. It is written in Python and already has capabilities for analyzing sample maps, including identification and quantification of grid squares, accurate localization of support film holes, measurement of various quantitative parameters for each hole and enumeration of all holes and their properties in a global database for each grid. We also began testing the YOLO deep learning framework for applications in the GridNet neural network for classification of grid squares, support film holes, contaminants etc. The results are very encouraging, and we can accurately detect and classify grid squares. We also began testing linear and non-linear image analysis for quantification of the sample condition.

在项目的第一年，我们在这项研究中取得了很好的进展。我们为项目的实施建立了主要的研究平台。这包括选择和采购功能强大的GPU工作站，用于与自动化样品筛选和基于AI的数据采集相关的机器学习任务。它于2023年3月安装在柏和2校区，并于2022年上半年安装了两台新的冷冻电子显微镜。GPU机器现在连接到显微镜网络，可以直接与显微镜和数据存储服务器通信。安装完成后，我们开始了初步的性能和稳定性测试，并安装了必要的软件包，这些测试仍在进行中。从项目开始，我们就开始了智能冷冻电子显微镜系统软件平台的开发。它是用Python编写的，已经具有分析样本地图的功能，包括网格正方形的识别和量化，支持膜孔的准确定位，每个孔的各种定量参数的测量以及每个网格的全局数据库中所有孔及其属性的枚举。我们还开始测试YOLO深度学习框架在GridNet神经网络中的应用，用于网格正方形、支撑膜孔、污染物等的分类。结果非常令人鼓舞，我们可以准确地检测和分类网格正方形。我们还开始测试线性和非线性图像分析，以量化样品条件。

项目成果

Optimizing cryo-EM for GPCRs and Parallel cryo electron tomography on in situ lamellae

优化 GPCR 的冷冻电镜和原位片层并行冷冻电子断层扫描

• 发表时间: 2022
• 作者: Eisenstein Fabian;Yanagisawa Haruaki;Kashihara Hiroka;Kikkawa Masahide;Tsukita Sachiko;Danev Radostin;Radostin Danev;Radostin Danev;Radostin Danev
• 通讯作者: Radostin Danev

Pushing the performance limits of cryo-EM for membrane proteins

突破膜蛋白冷冻电镜的性能极限

• 发表时间: 2022
• 作者: Eisenstein Fabian;Yanagisawa Haruaki;Kashihara Hiroka;Kikkawa Masahide;Tsukita Sachiko;Danev Radostin;Radostin Danev
• 通讯作者: Radostin Danev

Optimizing cryo-EM for GPCRs and first steps in cryo-tomography

优化 GPCR 的冷冻电镜和冷冻断层扫描的第一步

• 发表时间: 2022
• 作者: Eisenstein Fabian;Yanagisawa Haruaki;Kashihara Hiroka;Kikkawa Masahide;Tsukita Sachiko;Danev Radostin;Radostin Danev;Radostin Danev
• 通讯作者: Radostin Danev

Parallel cryo electron tomography on in situ lamellae

• DOI: 10.1038/s41592-022-01690-1
• 发表时间: 2022-12-01
• 期刊: NATURE METHODS
• 影响因子: 48
• 作者: Eisenstein, Fabian;Yanagisawa, Haruaki;Danev, Radostin
• 通讯作者: Danev, Radostin