Mar 29, 2020

CryoDRGN: Reconstruction of heterogeneous structures from cryo-electron micrographs using neural networks

BioRxiv : the Preprint Server for Biology
E. D. ZhongJoseph H Davis

Abstract

Cryo-EM single-particle analysis has proven powerful in determining the structures of rigid macromolecules. However, many protein complexes are flexible and can change conformation and composition as a result of functionally-associated dynamics. Such dynamics are poorly captured by current analysis methods. Here, we present cryoDRGN, an algorithm that for the first time leverages the representation power of deep neural networks to efficiently reconstruct highly heterogeneous complexes and continuous trajectories of protein motion. We apply this tool to two synthetic and three publicly available cryo-EM datasets, and we show that cryoDRGN provides an interpretable representation of structural heterogeneity that can be used to identify discrete states as well as continuous conformational changes. This ability enables cryoDRGN to discover previously overlooked structural states and to visualize molecules in motion.

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Mentioned in this Paper

Arsa
Embryo
Flow Cytometry
Biochemical Pathway
Primitive Streak
Cell Motility
Entire Embryo
Enhanced S-Cone Syndrome
Epithelium
Motility

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