Tick-borne viruses are a major risk from tick bites, which could result in viral infectious diseases among animals and humans. Bunyavirus causes severe fever with thrombocytopenia syndrome (SFTS), with signs and symptoms including high fever, vomiting, diarrhea, thrombocytopenia (low platelet count), leukopenia (low white blood cell count), elevated liver enzyme levels, multiple organ failure, and has a 6%-30% case-fatality rate. To date no effective drug or vaccines are available thus need urgent research for therapeutics formulation. Hence, in this study, the computational meta-analysis approach was implemented that incorporates immunoinformatics to find potential B-cell, HTL (helper T lymphocytes) and T-cell epitopes derived from antigenic SFTS proteins to design multi-epitopes vaccines for the treatment of SFTS. The predicted T cell, B cell and HTL epitopes were shortlisted and checked for antigenic properties and allergenic features. The best epitopes were then joined together to model of multi-epitopes vaccines for specific proteins (replicase and glycoprotein) and proteome wide. The constructed models were validated using in silico molecular docking approach to evaluate binding potential of the designed best constructs with TLR3 (toll like receptor 3). Following the MEVC (multi-epitopes vaccine construct) injection, the response of the immune system was significantly stimulated, and anti-toxicity of induced antibodies was tremendously enhanced. Before being neutralized, the antigen titers remained high 5-10 days after injection of replicase, glycoprotein and proteome wide constructed vaccines. For each antigenic vaccine, a significant antibody response induction was observed. Further, in vivo trials are required to affirm the effectiveness of the constructed vaccine against SFTS.
Keywords: Docking; Immune simulation; MEVC; SFTS; Vaccine designing; Viral outbreak.
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