7C9X image
Deposition Date 2020-06-08
Release Date 2020-08-12
Last Version Date 2024-03-27
Entry Detail
PDB ID:
7C9X
Keywords:
Title:
Echovirus 3 F-particle
Biological Source:
Source Organism:
Echovirus E3 (Taxon ID: 47516)
Method Details:
Experimental Method:
Resolution:
3.40 Å
Aggregation State:
PARTICLE
Reconstruction Method:
SINGLE PARTICLE
Macromolecular Entities
Polymer Type:polypeptide(L)
Molecule:VP1
Chain IDs:A
Chain Length:283
Number of Molecules:1
Biological Source:Echovirus E3
Polymer Type:polypeptide(L)
Molecule:VP2
Chain IDs:B
Chain Length:261
Number of Molecules:1
Biological Source:Echovirus E3
Polymer Type:polypeptide(L)
Molecule:VP3
Chain IDs:C
Chain Length:238
Number of Molecules:1
Biological Source:Echovirus E3
Polymer Type:polypeptide(L)
Molecule:VP4
Chain IDs:D
Chain Length:68
Number of Molecules:1
Biological Source:Echovirus E3
Primary Citation
Structures of Echovirus 30 in complex with its receptors inform a rational prediction for enterovirus receptor usage.
Nat Commun 11 4421 4421 (2020)
PMID: 32887891 DOI: 10.1038/s41467-020-18251-9

Abstact

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.

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Primary Citation of related structures