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Issue 59, 2020
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Design and optimization of a subunit vaccine targeting COVID-19 molecular shreds using an immunoinformatics framework

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Abstract

COVID-19 has been declared as a global health emergency and exposed the world to a deadly virus, which has dramatically changed the lives of humans for an unknown period of time. In the battleground with the virus, we have employed an immunoinformatics framework to design a robust vaccine as an insurance plan for the future. The pathogenic sequence with cryptic epitope taken from patients in Wuhan, China, was harnessed to design a promiscuous cytotoxic T-lymphocyte, helper T-lymphocyte, and B-cell epitope based subunit vaccine, engineered with adjuvants and conformational linkers. The reported vaccine has high antigenicity and immunogenicity profiles with potential TAP affinity, which ensures elevated antigen processing capability. It has strong binding with major histocompatibility complex (MHC) receptors (MHC-1 and MHC-2) and virus-specific membrane receptor TLR-2, with scores of −1010.7, −1035.7, and −1076.3 kcal mol−1, respectively. Molecular dynamics simulation analysis was used to assess the stable binding with TLR-2 with minimal atomic motions through a deformation plot, covariance matrix, and elastic network. Importantly, an in silico immunization assay showed the reliable elicitation of key players in terms of immune cells together with memory cells to evoke adaptive immune responses upon administration of the construct. In view of favorable outcomes, we also propose a plausible vaccine mechanism to elicit an immune response to fight coronavirus.

Graphical abstract: Design and optimization of a subunit vaccine targeting COVID-19 molecular shreds using an immunoinformatics framework

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Supplementary files

Article information


Submitted
08 Aug 2020
Accepted
07 Sep 2020
First published
30 Sep 2020

This article is Open Access

RSC Adv., 2020,10, 35856-35872
Article type
Paper

Design and optimization of a subunit vaccine targeting COVID-19 molecular shreds using an immunoinformatics framework

N. Kumar, D. Sood and R. Chandra, RSC Adv., 2020, 10, 35856
DOI: 10.1039/D0RA06849G

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material and it is not used for commercial purposes.

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    [Original citation] - Published by The Royal Society of Chemistry.

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