Abstract
ABSTRACTAdvances in cryo-electron tomography (cryo-ET) have produced new opportunities to visualize the structures of dynamic macromolecular machinery in native cellular environments. Here, we describe a machine learning approach that can reconstruct the structural landscape and dynamics of biomolecular complexes present in cryo-ET subtomograms. This method, cryoDRGN-ET, learns a deep generative model of 3D density maps directly from subtomogram tilt series images and can capture states diverse in both composition and conformation. We use this approach to reconstruct thein situtranslation dynamics of prokaryotic ribosomes, and we reveal the distribution of functional states during translation elongation populated byS. cerevisiaeribosomes inside cells.
Publisher
Cold Spring Harbor Laboratory
Cited by
2 articles.
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