PropertyValue
?:abstract
  • Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging health concern due to its high mortality rate of 35%. At present, no vaccine is available to protect against MERS-CoV infections. Therefore, an in silico search for potential antigenic epitopes in the non-redundant proteome of MERS-CoV was performed herein. First, a subtractive proteome-based approach was employed to look for the surface exposed and host non-homologous proteins. Following, immunoinformatics analysis was performed to predict antigenic B and T cell epitopes that were used in the design of a multi-epitopes peptide. Molecular docking study was carried out to predict vaccine construct affinity of binding to Toll-like receptor 3 (TLR3) and understand its binding conformation to extract ideas about its processing by the host immune system. We identified membrane protein, envelope small membrane protein, non-structural protein ORF3, non-structural protein ORF5, and spike glycoprotein as potential candidates for subunit vaccine designing. The designed multi-epitope peptide then linked to ß-defensin adjuvant is showing high antigenicity. Further, the sequence of the designed vaccine construct is optimized for maximum expression in the Escherichia coli expression system. A rich pattern of hydrogen and hydrophobic interactions of the construct was observed with the TLR3 allowing stable binding of the construct at the docked site as predicted by the molecular dynamics simulation and MM-PBSA binding energies. We expect that the panel of subunit vaccine candidates and the designed vaccine construct could be highly effective in immunizing populations from infections caused by MERS-CoV and could possible applied on the current pandemic COVID-19.
is ?:annotates of
?:creator
?:journal
  • J_Mol_Liq
?:license
  • unk
?:publication_isRelatedTo_Disease
is ?:relation_isRelatedTo_publication of
?:source
  • WHO
?:title
  • Towards a novel peptide vaccine for Middle East respiratory syndrome coronavirus and its possible use against pandemic COVID-19
?:type
?:who_covidence_id
  • #912505
?:year
  • 2020

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