Stejskal, Lenka and Lees, William and Moss, David and Palor, M. and Bingham, R. and Shepherd, Adrian J. and Grove, J. (2020) Flexibility and intrinsic disorder are conserved features of hepatitis C virus E2 glycoprotein. PLoS Computational Biology , ISSN 1553-7358.
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Abstract
The glycoproteins of hepatitis C virus, E1E2, are unlike any other viral fusion machinery yet described, and are the current focus of immunogen design in HCV vaccine development; thus, making E1E2 both scientifically and medically important. We used pre-existing, but fragmentary, structures to model a complete ectodomain of the major glycoprotein E2 from three strains of HCV. We then performed molecular dynamic simulations to explore the conformational landscape of E2, revealing a number of important features. Despite high sequence divergence, and subtle differences in the models, E2 from different strains behave similarly, possessing a stable core flanked by highly flexible regions, some of which perform essential functions such as receptor binding. Comparison with sequence data suggest that this consistent behaviour is conferred by a network of conserved residues that act as hinge and anchor points throughout E2. The variable regions (HVR-1, HVR-2 and VR-3) exhibit particularly high flexibility, and bioinformatic analysis suggests that HVR-1 is a putative intrinsically disordered protein region. Dynamic cross-correlation analyses demonstrate intramolecular communication and suggest that specific regions, such as HVR-1, can exert influence throughout E2. To support our computational approach we performed small-angle X-ray scattering with purified E2 ectodomain; this data was consistent with our MD experiments, suggesting a compact globular core with peripheral flexible regions. This work captures the dynamic behaviour of E2 and has direct relevance to the interaction of HCV with cell-surface receptors and neutralising antibodies.
Metadata
Item Type: | Article |
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School: | Birkbeck Faculties and Schools > Faculty of Science > School of Natural Sciences |
Research Centres and Institutes: | Data Analytics, Birkbeck Institute for |
Depositing User: | Adrian Shepherd |
Date Deposited: | 15 Apr 2020 10:21 |
Last Modified: | 02 Aug 2023 17:58 |
URI: | https://eprints.bbk.ac.uk/id/eprint/31125 |
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