CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks

被引:296
作者
Zhong, Ellen D. [1 ,2 ]
Bepler, Tristan [1 ,2 ]
Berger, Bonnie [2 ,3 ]
Davis, Joseph H. [1 ,4 ]
机构
[1] MIT, Computat & Syst Biol, 77 Massachusetts Ave, Cambridge, MA 02139 USA
[2] MIT, Comp Sci & Artificial Intelligence Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USA
[3] MIT, Dept Math, Cambridge, MA 02139 USA
[4] MIT, Dept Biol, Cambridge, MA 02139 USA
基金
美国国家科学基金会;
关键词
VISUALIZATION; MICROSCOPY;
D O I
10.1038/s41592-020-01049-4
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Cryo-electron microscopy (cryo-EM) single-particle analysis has proven powerful in determining the structures of rigid macromolecules. However, many imaged protein complexes exhibit conformational and compositional heterogeneity that poses a major challenge to existing three-dimensional reconstruction methods. Here, we present cryoDRGN, an algorithm that leverages the representation power of deep neural networks to directly reconstruct continuous distributions of 3D density maps and map per-particle heterogeneity of single-particle cryo-EM datasets. Using cryoDRGN, we uncovered residual heterogeneity in high-resolution datasets of the 80S ribosome and the RAG complex, revealed a new structural state of the assembling 50S ribosome, and visualized large-scale continuous motions of a spliceosome complex. CryoDRGN contains interactive tools to visualize a dataset's distribution of per-particle variability, generate density maps for exploratory analysis, extract particle subsets for use with other tools and generate trajectories to visualize molecular motions. CryoDRGN is open-source software freely available at http://cryodrgn.csail.mit.edu.
引用
收藏
页码:176 / +
页数:26
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