期刊
NATURE METHODS
卷 18, 期 2, 页码 176-+出版社
NATURE PORTFOLIO
DOI: 10.1038/s41592-020-01049-4
关键词
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资金
- National Science Foundation Graduate Research Fellowship Program
- NIH [R01-GM081871, R00-AG050749]
- NVIDIA-GPU
- MIT J-Clinic for Machine Learning and Health
cryoDRGN is an algorithm that uses 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. It can uncover residual heterogeneity in high-resolution datasets and visualize large-scale continuous motions of protein complexes, while also offering interactive tools for dataset visualization and analysis.
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.
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