RT Journal Article
SR Electronic
T1 Generative adversarial networks as a tool to recover structural information from cryo-electron microscopy data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 256792
DO 10.1101/256792
A1 Su, Min
A1 Zhang, Hantian
A1 Schawinski, Kevin
A1 Zhang, Ce
A1 Cianfrocco, Michael A.
YR 2018
UL http://biorxiv.org/content/early/2018/02/12/256792.abstract
AB Cryo-electron microscopy (cryo-EM) is a powerful structural biology technique capable of determining atomic-resolution structures of biological macromolecules. Despite this ability, the low signal-to-noise ratio of cryo-EM data continues to remain a hurdle for assessing raw cryo-EM micrographs and subsequent image analysis. To help address this problem, we have performed proof-of-principle studies with generative adversarial networks, a form of artificial intelligence, to denoise individual particles. This approach effectively recovers global structural information for both synthetic and real cryo-EM data, facilitating per-particle assessment from noisy raw images. Our results suggest that generative adversarial networks may be able to provide an approach to denoise raw cryo-EM images to facilitate particle selection and raw particle interpretation for single particle and tomography cryo-EM data.