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

2021; Nature Portfolio; Volume: 18; Issue: 2 Linguagem: Inglês

10.1038/s41592-020-01049-4

1548-7105

Ellen D. Zhong, Tristan Bepler, Bonnie Berger, Joseph H. Davis,

Enzyme Structure and Function

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 . CryoDRGN is an unsupervised machine learning algorithm that reconstructs continuous distributions of three-dimensional density maps from heterogeneous single-particle cryo-EM data.