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丙型肝炎病毒包膜蛋白复合物是异二聚体的二聚体。

The hepatitis C virus envelope protein complex is a dimer of heterodimers.

机构信息

Copenhagen Hepatitis C Program (CO-HEP), Department of Infectious Diseases, Copenhagen University Hospital, Hvidovre, Denmark.

Copenhagen Hepatitis C Program (CO-HEP), Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.

出版信息

Nature. 2024 Sep;633(8030):704-709. doi: 10.1038/s41586-024-07783-5. Epub 2024 Sep 4.

DOI:10.1038/s41586-024-07783-5
PMID:39232163
Abstract

Fifty-eight million individuals worldwide are affected by chronic hepatitis C virus (HCV) infection, a primary driver of liver cancer for which no vaccine is available. The HCV envelope proteins E1 and E2 form a heterodimer (E1/E2), which is the target for neutralizing antibodies. However, the higher-order organization of these E1/E2 heterodimers, as well as that of any Hepacivirus envelope protein complex, remains unknown. Here we determined the cryo-electron microscopy structure of two E1/E2 heterodimers in a homodimeric arrangement. We reveal how the homodimer is established at the molecular level and provide insights into neutralizing antibody evasion and membrane fusion by HCV, as orchestrated by E2 motifs such as hypervariable region 1 and antigenic site 412, as well as the organization of the transmembrane helices, including two internal to E1. This study addresses long-standing questions on the higher-order oligomeric arrangement of Hepacivirus envelope proteins and provides a critical framework in the design of novel HCV vaccine antigens.

摘要

全球有 5800 万人受到慢性丙型肝炎病毒 (HCV) 感染的影响,而 HCV 是导致肝癌的主要原因,但目前尚无针对该病毒的疫苗。HCV 的包膜蛋白 E1 和 E2 形成异二聚体 (E1/E2),是中和抗体的靶标。然而,这些 E1/E2 异二聚体的高级结构,以及任何 Hepacivirus 包膜蛋白复合物的高级结构,仍然未知。在这里,我们确定了两种以同源二聚体形式排列的 E1/E2 异二聚体的低温电子显微镜结构。我们揭示了同源二聚体如何在分子水平上建立,并深入了解 HCV 如何通过 E2 基序(如高变区 1 和抗原位点 412)以及跨膜螺旋的组织来逃避中和抗体和膜融合,包括 E1 内的两个。这项研究解决了 Hepacivirus 包膜蛋白高级别寡聚体排列的长期存在的问题,并为新型 HCV 疫苗抗原的设计提供了关键框架。

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Improvement of cryo-EM maps by simultaneous local and non-local deep learning.
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