Structures of Echovirus 30 in complex with its receptors inform a rational prediction for enterovirus receptor usage

Kang Wang, Ling Zhu, Yao Sun, Minhao Li, Xin Zhao, Lunbiao Cui, Li Zhang, George F. Gao, Weiwei Zhai, Fengcai Zhu (), Zihe Rao and Xiangxi Wang ()
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Kang Wang: Institute of Biophysics, Chinese Academy of Sciences
Ling Zhu: Institute of Biophysics, Chinese Academy of Sciences
Yao Sun: Institute of Biophysics, Chinese Academy of Sciences
Minhao Li: Institute of Zoology, Chinese Academy of Sciences
Xin Zhao: Institute of Microbiology, Chinese Academy of Sciences
Lunbiao Cui: Jiangsu Provincial Center for Disease Control and Prevention
Li Zhang: Jiangsu Provincial Center for Disease Control and Prevention
George F. Gao: Institute of Microbiology, Chinese Academy of Sciences
Weiwei Zhai: Institute of Zoology, Chinese Academy of Sciences
Fengcai Zhu: Jiangsu Provincial Center for Disease Control and Prevention
Zihe Rao: Institute of Biophysics, Chinese Academy of Sciences
Xiangxi Wang: Institute of Biophysics, Chinese Academy of Sciences

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract Receptor usage that determines cell tropism and drives viral classification closely correlates with the virus structure. Enterovirus B (EV-B) consists of several subgroups according to receptor usage, among which echovirus 30 (E30), a leading causative agent for human aseptic meningitis, utilizes FcRn as an uncoating receptor. However, receptors for many EVs remain unknown. Here we analyzed the atomic structures of E30 mature virion, empty- and A-particles, which reveals serotype-specific epitopes and striking conformational differences between the subgroups within EV-Bs. Of these, the VP1 BC loop markedly distinguishes E30 from other EV-Bs, indicative of a role as a structural marker for EV-B. By obtaining cryo-electron microscopy structures of E30 in complex with its receptor FcRn and CD55 and comparing its homologs, we deciphered the underlying molecular basis for receptor recognition. Together with experimentally derived viral receptor identifications, we developed a structure-based in silico algorithm to inform a rational prediction for EV receptor usage.

Date: 2020
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DOI: 10.1038/s41467-020-18251-9

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