Quantitative structural analysis of influenza virus by cryo-electron tomography and convolutional neural networks.
Autor: | Huang QJ; Department of Biochemistry and Molecular Biotechnology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA., Song K; Department of Biochemistry and Molecular Biotechnology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA., Xu C; Department of Biochemistry and Molecular Biotechnology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA., Bolon DNA; Department of Biochemistry and Molecular Biotechnology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA., Wang JP; Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA., Finberg RW; Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA., Schiffer CA; Department of Biochemistry and Molecular Biotechnology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA. Electronic address: celia.schiffer@umassmed.edu., Somasundaran M; Department of Biochemistry and Molecular Biotechnology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA. Electronic address: mohan.somasundaran@umassmed.edu. |
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Jazyk: | angličtina |
Zdroj: | Structure (London, England : 1993) [Structure] 2022 May 05; Vol. 30 (5), pp. 777-786.e3. Date of Electronic Publication: 2022 Mar 14. |
DOI: | 10.1016/j.str.2022.02.014 |
Abstrakt: | 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. Competing Interests: Declaration of interests C.A.S. is on the Scientific Advisory Board of Gandeeva Therapeutics. (Copyright © 2022 Elsevier Ltd. All rights reserved.) |
Databáze: | MEDLINE |
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