MERS virus spike protein HTL-epitopes selection and multi-epitope vaccine design using computational biology

Abstract

MERS-CoV, a zoonotic virus, poses a serious threat to public health globally. Thus, it is imperative to develop an effective vaccination strategy for protection against MERS-CoV. Immunoinformatics and computational biology tools provide a faster and more cost-effective strategy to design potential vaccine candidates. In this work, the spike proteins from different strains of MERS-CoV were selected to predict HTL-epitopes that show affinity for T-helper MHC-class II HTL allelic determinant (HLA-DRB1:0101). The antigenicity and conservation of these epitopes among the selected spike protein variants in different MERS-CoV strains were analyzed. The analysis identified five epitopes with high antigenicity: QSIFYRLNGVGITQQ, DTIKYYSIIPHSIRS, PEPITSLNTKYVAPQ, INGRLTTLNAFVAQQ and GDMYVYSAGHATGTT. Then, a multi-epitope vaccine candidate was designed using linkers and adjuvant molecules. Finally, the vaccine construct was subjected to molecular docking with TLR5 (Toll-like receptor-5). The proposed vaccine construct had strong binding energy of −32.3 kcal/mol when interacting with TLR5.Molecular dynamics simulation analysis showed that the complex of the vaccine construct and TLR5 is stable. Analysis using in silico immune simulation also showed that the prospective multi-epitope vaccine design had the potential to elicit a response within 70 days, with the immune system producing cytokines and immunoglobulins. Finally, codon adaptation and in silico cloning analysis showed that the candidate vaccine could be expressed in the Escherichia coli K12 strain. Here we also designed support vaccine construct MEV-2 by using B-cell and CD8+ CTL epitopes to generate the complete immunogenic effect. This study opens new avenues for the extension of research on MERS vaccine development.

Communicated by Ramaswamy H. Sarma

Keywords:

• MERS-CoV
• immunoinformatics
• molecular docking
• immunosimulation
• multi epitope vaccine

Acknowledgment

All authors are thankful to the School of Bioengineering and Biosciences, Lovely Professional University, and Invertis University for providing sound computational facilities for the conduction of research work.

Author’s contribution

AJ, SB, and NA conducted the research work. VK, NRS designed and guided the research work. All authors equally contributed to manuscript writing and verification.

Disclosure statement

There are no relevant financial or non-financial competing interests to report.

Data availability statement

The results from the NetMHCpan calculations are available at https://github.com/subhomoi/NetMHCpan-R.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.