Immuno-informatics analysis predicts B and T cell consensus epitopes for designing peptide vaccine against SARS-CoV-2 with 99.82% global population coverage

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Abstract

The current global pandemic due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has taken a substantial number of lives across the world. Although few vaccines have been rolled-out, a number of vaccine candidates are still under clinical trials at various pharmaceutical companies and laboratories around the world. Considering the intrinsic nature of viruses in mutating and evolving over time, persistent efforts are needed to develop better vaccine candidates. In this study, various immuno-informatics tools and bioinformatics databases were deployed to derive consensus B-cell and T-cell epitope sequences of SARS-CoV-2 spike glycoprotein. This approach has identified four potential epitopes which have the capability to initiate both antibody and cell-mediated immune responses, are non-allergenic and do not trigger autoimmunity. These peptide sequences were also evaluated to show 99.82% of global population coverage based on the genotypic frequencies of HLA binding alleles for both MHC class-I and class-II and are unique for SARS-CoV-2 isolated from human as a host species. Epitope number 2 alone had a global population coverage of 98.2%. Therefore, we further validated binding and interaction of its constituent T-cell epitopes with their corresponding HLA proteins using molecular docking and molecular dynamics simulation experiments, followed by binding free energy calculations with molecular mechanics Poisson-Boltzmann surface area, essential dynamics analysis and free energy landscape analysis. The immuno-informatics pipeline described and the candidate epitopes discovered herein could have significant impact upon efforts to develop globally effective SARS-CoV-2 vaccines.
Original languageEnglish
Article numberbbab496
Pages (from-to)1-15
Number of pages15
JournalBriefings in Bioinformatics
Volume23
Issue number1
Early online date27 Dec 2021
DOIs
Publication statusPublished - 1 Jan 2022

Bibliographical note

Funding Information:
UKRI funded National Core Study: Immunity (NCSi4P programme), 'Optimal cellular assays for SARS-CoV-2 T-cell, B-cell and innate immunity' to P.S. and D.S.G.; programme grant jointly from Science Foundation Ireland (SFI), Republic of Ireland and Department for the Economy (DfE), Northern Ireland, UK, 'COVRES: Understanding the host-virus response in patients with mild versus serious disease' to P.S., T.S.R., E.M., D.S.G. and A.J.B.; research grant from the Northern Ireland Public Health Agency (HSC R&D Division), 'COVRES2: Identifying temporal immune responses associated with Covid-19 severity' (Grant No. COM/5631/20) to P.S., T.S.R., E.M., D.S.G. and A.J.B.; research grant from the Northern Ireland Public Health Agency (HSC R&D Division), 'Senescence biomarkers for predicting risk in Covid-19 patients' (Grant No. COM/5618/20) to T.S.R. and D.S.G.; Vice-Chancellor's Research Scholarship (VCRS), Ulster University to B.P.; postgraduate studentship by Department for the Economy (DfE), Northern Ireland to T.R.; programme grant jointly from the European Union (EU) Regional Development Fund (ERDF) EU Sustainable Competitiveness Programme for Northern Ireland, the Northern Ireland Public Health Agency (HSC R&D Division) and Ulster University to A.J.B.; Kelvin-2 (Grant No. EP/T022175/1) by UK Engineering and Physical Sciences Research Council (EPSRC) to A.J.B.

Publisher Copyright:
© The Author(s) 2021. Published by Oxford University Press.

Keywords

  • Immunoinformatics
  • Bioinformatics
  • Vaccine
  • Peptide
  • SARS-CoV-2
  • peptide
  • vaccine
  • immuno-informatics
  • bio-informatics
  • Immuno-informatics
  • Sars-cov-2
  • Bio-informatics

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