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We are analyzing https://www.nature.com/articles/s41592-020-01049-4.

Title:
CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks | Nature Methods
Description:
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.
Website Age:
30 years and 10 months (reg. 1994-08-11).

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Custom-built

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🌠 Phenomenal Traffic: 5M - 10M visitors per month


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Keywords {🔍}

article, pubmed, cryodrgn, google, scholar, cryoem, nature, cas, density, data, fig, latent, maps, reconstruction, central, methods, ribosome, structure, structures, generated, particles, neural, davis, singleparticle, empiar, cryosparc, extended, analysis, heterogeneous, state, assembly, biol, fsc, encodings, content, information, zhong, map, complex, structural, particle, molecular, access, learning, images, visualization, space, supplementary, networks, nat,

Topics {✒️}

open-source software freely nature portfolio journals nature portfolio privacy policy atomic-resolution single-particle cryo-em exploratory research single-particle cryo-em data cryo-em single-particle data advertising single-particle cryo-em datasets social media reprints cryo-em data sets single-particle electron cryomicroscopy single-particle cryo-em development author information authors single-particle image processing anti-protozoan drug emetine nature+ nature 546 nature resampled cryo-em images synaptic rag1–rag2 complex nih grant r01-gm081871 nih grant r00-ag050749 continuously heterogeneous hyper-objects rag-mediated dna cleavage cryo-em structure determination auto-encoding variational bayes publishing agreement interactive tools real-time ctf determination single-particle data heterogeneous cryo-em structures gold-standard fsc resolution k-means cluster centers nvidia-gpu grant deep generative models mit-ibm satori team permissions tools ground-truth density maps gaussian mixture models accepted manuscript version springerlink instant access deep neural networks cryo-electron micrographs single-particle analysis cryo-em images

Schema {🗺️}

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         headline:CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks
         description: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.
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      headline:CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks
      description: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.
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         Proteomics
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