Deep reconstructing generative networks for visualizing dynamic biomolecules inside cells

Abstract

Advances 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 the in situ translation dynamics of prokaryotic ribosomes, and we reveal the distribution of functional states during translation elongation populated by S. cerevisiae ribosomes inside cells.