In silico designing of multi-epitope vaccine against canine parvovirus using reverse vaccinology.

Abstract

Canine parvovirus (CPV-2) is a highly contagious virus affecting dogs worldwide, posing a significant threat. The VP2 protein stands out as the predominant and highly immunogenic structural component of CPV-2. Soon after its emergence, CPV-2 was replaced by variants known as CPV-2a, 2b and 2c, marked by changes in amino acid residue 426 of VP2. Additional amino acid alterations have been identified within VP2, with certain modifications serving as signatures of emerging variants. In Brazil, CPV-2 outbreaks persist with diverse VP2 profiles. Vaccination is the main preventive measure against the virus. However, the emergence of substitutions presents challenges to conventional vaccine methods. Commercial vaccines are formulated with strains that usually do not match those currently circulating in the field. To address this, the study aimed to investigate CPV-2 variants in Brazil, predict epitopes, and design an in silico vaccine tailored to local variants employing reverse vaccinology. The methodology involved data collection, genetic sequence analysis, and amino acid comparison between field strains and vaccines, followed by the prediction of B and T cell epitope regions. The predicted epitopes were evaluated for antigenicity, allergenicity and toxicity. The final vaccine construct consisted of selected epitopes linked to an adjuvant and optimized for expression in Escherichia coli. Structural predictions confirmed the stability and antigenicity of the vaccine, while molecular docking demonstrated interaction with the canine toll-like receptor 4. Molecular dynamics simulations indicated a stable complex formation. In silico immune simulations demonstrated a progressive immune response post-vaccination, including increased antibody production and T-helper cell activity. The multi-epitope vaccine design targeted prevalent CPV-2 variants in Brazil and potentially other regions globally. However, experimental validation is essential to confirm our in silico findings.