Exploring staphylococcal superantigens to design a potential multi-epitope vaccine against Staphylococcus aureus: an in-silico reverse vaccinology approach

Abstract

Staphylococcus aureus is a horrifying bacteria capable of causing millions of deaths yearly across the globe. A major contribution to the success of S. aureus as an ESKAPE pathogen is the abundance of virulence factors that can manipulate the innate and adaptive immune system of the individual. Currently, no vaccine is available to treat S. aureus-mediated infections. In this study, we present in-silico approaches to design a stable, safe and immunogenic vaccine that could help to control the infections associated with the bacteria. Three vital pathogenic secreted toxins of S. aureus, such as staphylococcal enterotoxin A (SEA), staphylococcal enterotoxin B (SEB), Toxic-shock syndrome toxin (TSST-1), were selected using the reverse vaccinology approach to design the multi-epitope vaccine (MEV). Linear B-lymphocyte, cytotoxic T-lymphocyte (CTL) and helper T-lymphocyte (HTL) epitopes were predicted from these selected proteins. For designing the multi-epitope vaccine (MEV), B-cell epitopes were joined with the KK linker, CTL epitopes were joined with the AAY linker, and HTL epitopes were joined with the GPGPG linker. Finally, to increase the immune response to the vaccine, a human β-defensin-3 (hBD-3) adjuvant was added to the N-terminus of the MEV construct. The final MEV was found to be antigenic and non-allergen in nature. In-silico immune simulation and cloning analysis predicted the immune-stimulating potential of the designed MEV construct along with the cloning feasibility in the pET28a(+) vector with the E. coli expression system. This immunoinformatics study provides a platform for designing a suitable, safe and effective vaccine against S. aureus.

Communicated by Ramaswamy H. Sarma

Keywords:

• Staphylococcus aureus
• superantigen
• multi-epitope vaccine
• SEA
• TSST-1

Acknowledgements

SR acknowledges the Indian Institute of Technology Kharagpur (IIT KGP) for individual fellowship. The authors wish to thank PARAM Shakti (IIT KGP)-National Supercomputing Mission, Government of India (GoI), for their computational resources. SR also wishes to acknowledge Rituparna Saha, Department of Biotechnology, IIT KGP, for critical reviews.

Author contribution

SR conceptualized the study, and SR and KS performed analysis and wrote the draft manuscript. AKD supervised the work and improvised the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research received no specific grant from the public, commercial, or not-for-profit funding agencies.