bioRxiv Biochemistry
bioRxiv Biochemistry
5 years ago

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

Cianfrocco, M. A., K., Schawinski, M., Su, H., C., Zhang
Cryo-electron microscopy (cryo-EM) is a powerful structural biology technique capable of determining atomic-resolution structures of biological macromolecules. However, the process of optimizing sample preparation and data collection requires expert-level users many months to years in order to find amenable conditions to collect and determine high-resolution structures due to the low signal-to-noise (SNR) ratio of the raw cryo-EM micrographs. To help address this problem, we have trained and tested generative adversarial networks, a form of artificial intelligence, to denoise and CTF-correct 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.

Publisher URL: http://biorxiv.org/cgi/content/short/256792v1

DOI: 10.1101/256792

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.