RT Journal Article
SR Electronic
T1 CryoDRGN: Reconstruction of heterogeneous structures from cryo-electron micrographs using neural networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.03.27.003871
DO 10.1101/2020.03.27.003871
A1 Zhong, Ellen D.
A1 Bepler, Tristan
A1 Berger, Bonnie
A1 Davis, Joseph H.
YR 2020
UL http://biorxiv.org/content/early/2020/03/29/2020.03.27.003871.abstract
AB 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.