相关实验视频

Updated: Jun 28, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

通过自我监督的深度学习来克服冷EM中首选的导向问题

Yun-Tao Liu^1,2, Hongcheng Fan^1,2, Jason J Hu^1,2,3

¹Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, USA.

bioRxiv : the preprint server for biology

|April 22, 2024

概括

spIsoNet是一个新的深度学习软件,通过计算解决了冷电子显微镜中首选的定向问题. 它可以提高3D重建的准确性和同位素性,而无需复杂的样本准备.

科学领域:

结构生物学是结构生物学.
生物物理学的生物物理.
计算生物学是一种计算生物学.

背景情况:

单粒子冷电子显微镜 (cryo-EM) 实现了宏分子复合体的原子分辨率.
粒子定向偏差,或首选的定向问题,使冷EM中的3D重建变得复杂.
目前的解决方案涉及复杂的生化和物理样本操纵.

结论:

spIsoNet为冷EM中首选方向问题提供了一个通用的计算解决方案.
消除了需要额外的,复杂的样本准备程序.
增强地图同变性和粒子对齐,以偏向导向的分子.

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相关概念视频

Cryo-electron Microscopy

Cryo-electron Microscopy

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...