A large expert-curated cryo-EM image dataset for machine learning protein particle picking.
A large expert-curated cryo-EM image dataset for machine learning protein particle picking.
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DOI：
10.1038/s41597-023-02280-2
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发表时间：
2023-06-22
期刊：
影响因子：
9.8
通讯作者：
Cheng, Jianlin
中科院分区：
文献类型：
--
作者：
Dhakal, Ashwin;Gyawali, Rajan;Wang, Liguo;Cheng, Jianlin
冷冻电子显微镜（Cryo-EM）是确定生物学大分子复合物结构的强大技术。从冷冻EM显微照片中挑选单蛋白颗粒是重建蛋白质结构的关键步骤。但是，广泛使用的基于模板的粒子拾取过程是劳动密集型且耗时的。尽管基于机器的学习和人工智能（AI）的粒子采摘可以可能自动化这一过程，但由于缺乏大型，高质量的标签训练数据而阻碍了其发展。为了解决这种瓶颈，我们提出了Cryoppp，这是一种用于蛋白质颗粒采摘和分析的大型，多样的，专家策划的冷冻EM图像数据集。它由从电子显微镜公共图像档案（empiar）中选择的34个代表性蛋白数据集的标记的冷冻显微照片（图像）组成。该数据集为2.6 trabytes，包括带有标记的蛋白质颗粒坐标的9,893个高分辨率显微照片。标记过程通过2D粒子类验证和具有金标准的3D密度图验证对标记过程进行了严格的验证。预计该数据集将极大地促进自动冷冻蛋白蛋白销售的AI和经典方法的开发。
Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.
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