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bioRxiv · 10.1101/256792

Generative adversarial networks as a tool to recover structural information from cryo-electron microscopy data

Su, M.·Zhang, H.·Schawinski, K.·Zhang, C.·Cianfrocco, M. A.
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Abstract

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.

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Su, M., Zhang, H., Schawinski, K., Zhang, C., Cianfrocco, M. A.. 2018-02-12. Generative adversarial networks as a tool to recover structural information from cryo-electron microscopy data. https://doi.org/10.1101/256792

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