In Silico Design and Structural Validation ofEpitope-Based Vaccine Candidates Against Feline Calicivirus using Immunoinformatics

This research aims to utilize bioinformatics methods to identify and analyze potential vaccine epitopes for feline calicivirus (FCV). FCV is a highly contagious pathogen causing respiratory illnesses in cats, posing significant challenges due to its genetic variability. The project involved retrieving FCV nucleotide sequences, predicting potential vaccine epitopes, and evaluating their antigenicity, toxicity, and allergenicity. The properties of the vaccine peptide sequence were also predicted and analyzed in 3D. There are 4 identified nucleotide sequences and 5 peptide sequences that show promising attributes of being non-antigenic, non-toxic, and non-allergenic. The methodology employed tools like IEDB, Vaxijen, AllerTOP, ToxinPred, ProtParam, Protein-Sol, SWISS-MODEL, MolProbity website, and PDBsum. Despite the genetic diversity of FCV, targeting conserved regions can potentially provide broad protection. However, further empirical validation through T-cell epitope prediction, secondary structure extrapolation, and molecular docking could be done to ensure vaccine efficacy against emerging FCV strains. Collaborative efforts in veterinary research are also emphasized to expedite vaccine development and implementation.