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

Quantitative structural analysis of influenza virus by cryo-electron tomography and convolutional neural networks.
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DOI:
10.1016/j.str.2022.02.014
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发表时间:
2022-05-05
期刊:
影响因子:
5.7
通讯作者:
Somasundaran, Mohan
Somasundaran, Mohan
中科院分区:
生物学2区
文献类型:
--
作者:
Huang, Qiuyu J.;Song, Kangkang;Xu, Chen;Bolon, Daniel N. A.;Wang, Jennifer P.;Finberg, Robert W.;Schiffer, Celia A.;Somasundaran, Mohan

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流感病毒对全球公共卫生构成严重威胁。流感病毒在病毒蛋白的形状、大小和组织方面具有广泛的多形性。对流感病毒形态和超微结构的分析有助于阐明病毒结构-功能关系,并有助于治疗和疫苗开发。虽然冷冻电子断层扫描(cryoET)可以描绘多形性流感的3D组织,但cryoET固有的低信噪比和病毒异质性阻碍了流感病毒的详细表征。在这份报告中,我们利用卷积神经网络(CNN)和cryoET来表征A/波多黎各/8/34(H1N1)流感病毒株的形态结构。我们的管道提高了cryoET分析的吞吐量,并准确地识别了断层图像中的病毒成分。使用这种方法,我们成功地表征了流感病毒的形态学、糖蛋白密度,并对流感病毒糖蛋白进行了亚断层扫描平均。这种处理管道的应用不仅可以帮助流感病毒的结构表征,还可以帮助其他多形性病毒和感染细胞的结构表征。
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 (CNNs) 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.
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