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Structural and molecular analyses of functional epitopes and escape mutants in Japanese encephalitis virus envelope protein domain III.

Urmi Roy
Published in: Immunologic research (2021)
The Japanese encephalitis virus (JEV) is one of the vector borne causes of encephalitis found in southeastern Asia. This positive single-stranded RNA virus is a member of the Flaviviridae family, which notably includes dengue, tick-borne, West Nile, Zika as well as yellow fever, and transmits to humans by infected mosquitos. The main site of interactions for antibodies against this virus is the envelope protein domain III (ED3). The present report investigates the time-dependent structural and conformational changes of JEV ED3 functional epitopes and escape mutants by computer simulations. The results indicate the presence of significant structural differences between the functional epitopes and the escape mutants. Mutation-induced structural/conformational instabilities of this type can decrease the antibody neutralization activity. Among the different escape mutants studied here, Ser40Lys/Asp41Arg appear to be most unstable, while Ser40Glu/Asp41Leu exhibit the lowest structural variations. The highest level of escape mutation observed in Ser40Lys is linked to the relatively higher values of root mean square deviation/fluctuation found in the molecular dynamics simulation of this protein. Secondary-structure deviations and depletion of H bonding are other contributing factors to the protein's increased instability. Overall, the proteins with residue 41 mutations are found to be structurally more ordered than those with residue 40 mutations. The detailed time-based structural assessment of the mutant epitopes described here may contribute to the development of novel vaccines and antiviral drugs necessary to defend against future outbreaks of JEV escape mutants.
Keyphrases
  • molecular dynamics simulations
  • amino acid
  • emergency department
  • molecular dynamics
  • wild type
  • zika virus
  • protein protein
  • binding protein
  • machine learning
  • aedes aegypti
  • dengue virus
  • disease virus