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

By Ellen D. Zhong, Tristan Bepler, Bonnie Berger, Joseph H. Davis. Nature Methods, 2021

578 citations recorded by the source corpus.

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.

Subfields: Materials Chemistry, Radiation, Structural Biology

View Paper: https://www.semanticscholar.org/paper/35d5105282402946c131a1f9e53f53071dfa20a9

How researchers see this paper

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In Materials Chemistry

1. Thomas Mulvaney
2. J. Beton
3. Maya Topf
4. Tristan Cragnolini
5. Manaz Kaleel
6. Aaron Sweeney

In Structural Biology

1. Tristan Cragnolini
2. J. Beton
3. Maya Topf
4. Thomas Mulvaney
5. Manaz Kaleel
6. Aaron Sweeney

Citations in the corpus

• Integrating model simulation tools and cryo‐electron microscopy (2022, methodology): “43 52 The output 3D classes are normally subjected to further refinement, to boost their resolution.”