Whole proteome screening and identification of potential epitopes of SARS-CoV-2 for vaccine design-an immunoinformatic, molecular docking and molecular dynamics simulation accelerated robust strategy

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the most cryptic pandemic outbreak of the 21st century, has gripped more than 1.8 million people to death and infected almost eighty six million. As it is a new variant of SARS, there is no approved drug or vaccine available against this virus. This study aims to predict some promising cytotoxic T lymphocyte epitopes in the SARS-CoV-2 proteome utilizing immunoinformatic approaches. Firstly, we identified 21 epitopes from 7 different proteins of SARS-CoV-2 inducing immune response and checked for allergenicity and conservancy. Based on these factors, we selected the top three epitopes, namely KAYNVTQAF, ATSRTLSYY, and LTALRLCAY showing functional interactions with the maximum number of MHC alleles and no allergenicity. Secondly, the 3D model of selected epitopes and HLA-A*29:02 were built and Molecular Docking simulation was performed. Most interestingly, the best two epitopes predicted by docking are part of two different structural proteins of SARS-CoV-2, namely Membrane Glycoprotein (ATSRTLSYY) and Nucleocapsid Phosphoprotein (KAYNVTQAF), which are generally target of choice for vaccine designing. Upon Molecular Docking, interactions between selected epitopes and HLA-A*29:02 were further validated by 50 ns Molecular Dynamics (MD) simulation. Analysis of RMSD, Rg, SASA, number of hydrogen bonds, RMSF, MM-PBSA, PCA, and DCCM from MD suggested that ATSRTLSYY is the most stable and promising epitope than KAYNVTQAF epitope. Moreover, we also identified B-cell epitopes for each of the antigenic proteins of SARS CoV-2. Findings of our work will be a good resource for wet lab experiments and will lessen the timeline for vaccine construction.

Communicated by Ramaswamy H. Sarma

Keywords:

• SARS-CoV-2
• T lymphocyte epitope
• immunoinformatics
• molecular docking
• molecular dynamics simulation

Acknowledgements

Authors are thankful to Molecular Modeling Drug-design and Discovery Laboratory, Pharmacology Research Division, BCSIR Laboratories Chattogram, Bangladesh Council of Scientific and Industrial Research, Chattogram, Bangladesh, for providing software supports during perform protein preparation (Schrodinger Suite) and molecular dynamics simulation (YASARA).

Disclosure statement

The authors declare no competing interests

Author contributions

Conceptualization: M.J., and M.M.A.E.; methodology: M.M.A.E., and M.J.; data curation: M.M.A.E., A.N., A.S., S.M.A.N. and M.S.H.; formal analysis & investigation: M.M.A.E., M.J., Y.A., and S.S.A.; validation, M.J., and M.M.A.E.; resources: S.M.Z.H.; writing—original draft preparation: Y.A., M.M.A.E., M.J., A.N., A.S., S.S.A., and S.M.A.N.; writing—review and editing, Y.A., M.M.A.E., and M.J.; visualization, M.J., and M.M.A.E; supervision: M.J.; suggestion for journal selection: M.A.M.; project administration, M.J. and S.M.Z.H. All authors read and approved the final manuscript.