Abstract
Neospora caninum (NC) is a protozoan infection causing neosporosis, a disease that leads to substantial economic loss in livestock, especially in cattle, sheep, and goats. Unfortunately, there is presently no viable vaccination or authorized therapy available. Our research applied a bioinformatics approach to design a multi-epitope peptide (MEP) vaccine aimed at the pathogenic proteins GRA2 and Nc-p43 of NC, which are critical in parasite-mediated antigenicity and host interactions. Consequently, our research employs an in-silico methodology, including protein sequence retrieval, epitope prediction, vaccine design, structural analysis, molecular docking, molecular dynamics simulations, immunological simulation, and codon optimization using in silico cloning. We conducted analyses of antigenicity, allergenicity, toxicity, topology, and immunogenicity using multiple bioinformatics methods and identified 4 CTL, 4 HTL, and 2 B-cell epitopes. The vaccine design was created by integrating an adjuvant and a PADRE sequence to enhance immunogenicity, along with linkers (AAY, GPGPG, KK) to facilitate appropriate structural assembly, which was then analyzed for optimal complete profiles. Molecular docking with the Bos taurus (cattle) TLR9 receptor demonstrated a robust binding affinity, scoring - 1183.4, while molecular dynamics (MD) simulations over 50 ns validated persistent interactions between the vaccine and immune receptors. Moreover, immunological models forecasted a robust adaptive immune response, marked by an increase in the production of cytokines and the establishment of memory T-lymphocytes. Ultimately, codon optimization and in silico cloning validated the capacity for effective expression in E. coli. The results demonstrate that the developed vaccine has considerable immunogenic potential against NC. Nonetheless, more in vitro and in vivo studies are necessary to confirm its efficiency.