Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory coronavirus 2 (SARS-COV-2) is a significant threat to global health security. Till date, no completely effective drug or vaccine is available to cure COVID-19. Therefore, an effective vaccine against SARS-COV-2 is crucially needed. This study was conducted to design an effective multiepitope based vaccine (MEV) against SARS-COV-2. Seven highly antigenic proteins of SARS-COV-2 were selected as targets and different epitopes (B-cell and T-cell) were predicted. Highly antigenic and overlapping epitopes were shortlisted. Selected epitopes indicated significant interactions with the HLA-binding alleles and 99.93% coverage of the world’s population. Hence, 505 amino acids long MEV was designed by connecting 16 MHC class I and eleven MHC class II epitopes with suitable linkers and adjuvant. MEV construct was non-allergenic, antigenic, stable and flexible. Furthermore, molecular docking followed by molecular dynamics (MD) simulation analyses, demonstrated a stable and strong binding affinity of MEV with human pathogenic toll-like receptors (TLR), TLR3 and TLR8. Finally, MEV codons were optimized for its in silico cloning into Escherichia coli K-12 system, to ensure its increased expression. Designed MEV in present study could be a potential candidate for further vaccine production process against COVID-19. However, to ensure its safety and immunogenic profile, the proposed MEV needs to be experimentally validated.
Article activity feed
-
-
SciScore for 10.1101/2020.02.28.970343: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources To visualize the docked complex and draw figures, the PyMOL educational version was used [51]. PyMOLsuggested: (PyMOL, RRID:SCR_000305)Complexes of MEV with TLR3 and TLR8 were simulated at 20 ns using GROMACS 5.1.4 [67] by following the same protocol of our previously published studies [5, 49, 68]. GROMACSsuggested: (GROMACS, RRID:SCR_014565)Coli pET28a(+) vector with SnapGene 4.2 tool (https:/snapgene.com/) to ensure the in vitro expression. SnapGenesuggested: (SnapGene, RRID:SCR_015052)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are …
SciScore for 10.1101/2020.02.28.970343: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources To visualize the docked complex and draw figures, the PyMOL educational version was used [51]. PyMOLsuggested: (PyMOL, RRID:SCR_000305)Complexes of MEV with TLR3 and TLR8 were simulated at 20 ns using GROMACS 5.1.4 [67] by following the same protocol of our previously published studies [5, 49, 68]. GROMACSsuggested: (GROMACS, RRID:SCR_014565)Coli pET28a(+) vector with SnapGene 4.2 tool (https:/snapgene.com/) to ensure the in vitro expression. SnapGenesuggested: (SnapGene, RRID:SCR_015052)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 20 and 21. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
-
-