Quantitative structural analysis of influenza virus by cryo-electron tomography and convolutional neural networks

Structure. 2022 May 5;30(5):777-786.e3. doi: 10.1016/j.str.2022.02.014. Epub 2022 Mar 14.


Influenza viruses pose severe public health threats globally. Influenza viruses are extensively pleomorphic, in shape, size, and organization of viral proteins. Analysis of influenza morphology and ultrastructure can help elucidate viral structure-function relationships and aid in therapeutics and vaccine development. While cryo-electron tomography (cryoET) can depict the 3D organization of pleomorphic influenza, the low signal-to-noise ratio inherent to cryoET and viral heterogeneity have precluded detailed characterization of influenza viruses. In this report, we leveraged convolutional neural networks and cryoET to characterize the morphological architecture of the A/Puerto Rico/8/34 (H1N1) influenza strain. Our pipeline improved the throughput of cryoET analysis and accurately identified viral components within tomograms. Using this approach, we successfully characterized influenza morphology, glycoprotein density, and conducted subtomogram averaging of influenza glycoproteins. Application of this processing pipeline can aid in the structural characterization of not only influenza viruses, but other pleomorphic viruses and infected cells.

Keywords: convolutional neural networks; cryo-electron tomography; cryoET; hemagglutinin; influenza; tomography; virus glycoprotein; virus ultrastructure.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, N.I.H., Extramural

MeSH terms

  • Cryoelectron Microscopy / methods
  • Electron Microscope Tomography / methods
  • Hemagglutinin Glycoproteins, Influenza Virus
  • Humans
  • Influenza A Virus, H1N1 Subtype*
  • Influenza, Human*
  • Neural Networks, Computer


  • Hemagglutinin Glycoproteins, Influenza Virus