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Updated: Jul 2, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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在冷电磁图中自动建模和蛋白质识别

Kiarash Jamali1, Lukas Käll2, Rui Zhang3

  • 1MRC Laboratory of Molecular Biology, Cambridge, UK. kjamali@mrc-lmb.cam.ac.uk.

Nature
|February 26, 2024
PubMed
概括

ModelAngelo是一个新的机器学习工具,可以在电子冷显微镜 (cryo-EM) 地图中自动构建原子模型. 这种人工智能方法与人类专家的蛋白质质量相匹配,并提高核酸骨干的准确性,加速结构的确定.

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Conventional electron microscopy (EM) involves dehydration, fixation, and staining of biological samples, which distorts the native state of biological molecules and results in several artifacts. Also, the high-energy electron beam damages the sample and makes it difficult to obtain high-resolution images. These issues can be addressed using cryo-EM, which uses frozen samples and gentler electron beams. The technique was developed by Jacques Dubochet, Joachim Frank, and Richard Henderson, for...
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