Intelligent cryo-electron microscopy of G protein-coupled receptors

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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.