Computational design of a new multi-epitope vaccine using immunoinformatics approach against mastitis disease

Mastitis disease causes significant economic losses in dairy farms by reducing milk production, increasing production costs, and reducing milk quality. Streptococcus agalactiae continues to be a major cause of mastitis in dairy cattle. To date, there has been no approved multi-epitope vaccine against this pathogen in the market. In the present study, an efficient multi-epitope vaccine against S. agalactiae, the causative agent of mastitis, was designed using various immonoinformtics approaches. Potential epitopes were selected from Sip protein to improve vaccine immunogenicity. The designed vaccine is more antigenic in nature. Then, linkers and profilin adjuvant were added to enhance the immunity of vaccines. The designed vaccine was evaluated in terms of molecular weight, PI, immunogenicity, Toxicity, and allergenicity. Prediction of three-dimensional (3 D) structure of multi-epitope vaccine, followed by refinement and validation, was conducted to obtain a high-quality 3 D structure of the designed multi-epitope vaccine. The designed vaccine was then subjected to molecular docking with Toll-like receptor 11 (TLR11) receptor to evaluate its binding efficiency followed by dynamic simulation for stable interaction. In silico cloning approach was carried out to improve the expression of the vaccine construct. These analyses indicate that the designed multi-epitope vaccine may produce particular immune responses against S. agalactiae and may be further helpful to control mastitis after in vitro and in vivo immunological assays.