Epitope based peptide vaccine against SARS-COV2: an immune-informatics approach

Abstract

World is witnessing exponential growth of SARS-CoV2 and fatal outcomes of COVID 19 has proved its pandemic potential already by claiming more than 3 lakhs deaths globally. If not controlled, this ongoing pandemic can cause irreparable socio-economic and psychological impact worldwide. Therefore a safe and effective vaccine against COVID 19 is exigent. Recent advances in immunoinformatics approaches could potentially decline the attrition rate and accelerate the process of vaccine development in these unprecedented times. In the present study, a multivalent subunit vaccine targeting S2 subunit of the SARS-CoV2 S glycoprotein has been designed using open source, immunoinformatics tools. Designed construct comprises of epitopes capable of inducing T cell, B cell (Linear and discontinuous) and Interferon γ. physiologically, vaccine construct is predicted to be thermostable, antigenic, immunogenic, non allergen and non toxic in nature. According to population coverage analysis, designed multiepitope vaccine covers 99.26% population globally. 3D structure of vaccine construct was designed, validated and refined to obtain high quality structure. Refined structure was docked against Toll like receptors to confirm the interactions between them. Vaccine peptide sequence was reverse transcribed, codon optimized and cloned in pET vector. Our in-silico study suggests that proposed vaccine against fusion domain of virus has the potential to elicit an innate as well as humoral immune response in human and restrict the entry of virus inside the cell. Results of the study offer a framework for in-vivo analysis that may hasten the process of development of therapeutic tools against COVID 19.

Communicated by Ramaswamy H. Sarma

Keywords:

• SARS-COV2
• COVID 19
• epitope mapping
• population coverage
• insilico cloning

Acknowledgements

The authors acknowledge Centre for Medical Biotechnology, Maharshi Dayanand University, Rohtak for providing research work platform.

Disclosure statement

The authors report no declarations of interest.

Ethical approval

Not applicable.

Informed consent

Not applicable.