National Institutes of Health/National Institute of General Medical Sciences (NIH/NIGMS)
R01GM080139
United States
Citation
Journal: Nat Methods / Year: 2021 Title: Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability in cryo-EM. Authors: Muyuan Chen / Steven J Ludtke / Abstract: Structural flexibility and/or dynamic interactions with other molecules is a critical aspect of protein function. Cryogenic electron microscopy (cryo-EM) provides direct visualization of individual ...Structural flexibility and/or dynamic interactions with other molecules is a critical aspect of protein function. Cryogenic electron microscopy (cryo-EM) provides direct visualization of individual macromolecules sampling different conformational and compositional states. While numerous methods are available for computational classification of discrete states, characterization of continuous conformational changes or large numbers of discrete state without human supervision remains challenging. Here we present e2gmm, a machine learning algorithm to determine a conformational landscape for proteins or complexes using a three-dimensional Gaussian mixture model mapped onto two-dimensional particle images in known orientations. Using a deep neural network architecture, e2gmm can automatically resolve the structural heterogeneity within the protein complex and map particles onto a small latent space describing conformational and compositional changes. This system presents a more intuitive and flexible representation than other manifold methods currently in use. We demonstrate this method on both simulated data and three biological systems to explore compositional and conformational changes at a range of scales. The software is distributed as part of EMAN2.
History
Deposition
May 24, 2021
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Header (metadata) release
Aug 25, 2021
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Map release
Aug 25, 2021
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Update
Aug 25, 2021
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Current status
Aug 25, 2021
Processing site: RCSB / Status: Released
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Structure visualization
Movie
Surface view with section colored by density value
Name: Pre-catalytic spliceosome / type: complex / ID: 1 / Parent: 0 Details: Re-processing of the dataset EMPIAR-10180 to resolve the continuous movement of the system. Five frames of the first motion mode are attached as additional map files.
Resolution.type: BY AUTHOR / Resolution: 20.0 Å / Resolution method: OTHER / Software - Name: EMAN (ver. 2.91) Details: Each 3D frame of the continuous movement movie includes 4000 particles. The maps are filtered to 20A for visualization. Number images used: 4000
Initial angle assignment
Type: NOT APPLICABLE
Final angle assignment
Type: PROJECTION MATCHING / Software - Name: EMAN (ver. 2.91)
Final 3D classification
Software - Name: EMAN (ver. 2.91) / Details: Deep learning based manifold embedding
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