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dc.contributor.authorHuang, Qiu Yu Judy
dc.contributor.authorSong, KangKang
dc.contributor.authorXu, Chen
dc.contributor.authorBolon, Daniel N.
dc.contributor.authorWang, Jennifer P.
dc.contributor.authorFinberg, Robert W.
dc.contributor.authorSchiffer, Celia A.
dc.contributor.authorSomasundaran, Mohan
dc.date2022-08-11T08:10:52.000
dc.date.accessioned2022-08-23T17:23:14Z
dc.date.available2022-08-23T17:23:14Z
dc.date.issued2022-03-14
dc.date.submitted2022-06-28
dc.identifier.citation<p>Huang QJ, Song K, Xu C, Bolon DNA, Wang JP, Finberg RW, Schiffer CA, Somasundaran M. 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. PMID: 35290796. <a href="https://doi.org/10.1016/j.str.2022.02.014">Link to article on publisher's site</a></p>
dc.identifier.issn0969-2126 (Linking)
dc.identifier.doi10.1016/j.str.2022.02.014
dc.identifier.pmid35290796
dc.identifier.urihttp://hdl.handle.net/20.500.14038/48911
dc.description.abstractInfluenza 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.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=35290796&dopt=Abstract">Link to Article in PubMed</a></p>
dc.relation.urlhttps://doi.org/10.1016/j.str.2022.02.014
dc.subjectconvolutional neural networks
dc.subjectcryo-electron tomography
dc.subjectcryoET
dc.subjecthemagglutinin
dc.subjectinfluenza
dc.subjecttomography
dc.subjectvirus glycoprotein
dc.subjectvirus ultrastructure
dc.subjectAmino Acids, Peptides, and Proteins
dc.subjectBiochemistry
dc.subjectMedicinal Chemistry and Pharmaceutics
dc.subjectMedicinal-Pharmaceutical Chemistry
dc.subjectMolecular Biology
dc.subjectStructural Biology
dc.subjectVirology
dc.subjectViruses
dc.titleQuantitative structural analysis of influenza virus by cryo-electron tomography and convolutional neural networks
dc.typeJournal Article
dc.source.journaltitleStructure (London, England : 1993)
dc.source.volume30
dc.source.issue5
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/schiffer/52
dc.identifier.contextkey29957996
html.description.abstract<p>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.</p>
dc.identifier.submissionpathschiffer/52
dc.contributor.departmentGraduate School of Biomedical Sciences
dc.contributor.departmentSchiffer Lab
dc.contributor.departmentDepartment of Medicine
dc.contributor.departmentDepartment of Biochemistry and Molecular Biotechnology
dc.source.pages777-786.e3


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