Immunoinformatics-Guided Computational Design and In Silico Validation of Multi-Epitope Vaccine Candidates Targeting Canine and Feline Parvoviruses.

Parvovirus infection causes severe diseases in both feline and canine species. It primarily affects adult cats and dogs but poses a higher risk to kittens and puppies. This virus is highly contagious and is easily transmitted through contaminated food, shared shelter environments, as well as the hands and clothing of people. The recovered species may continue to shed parvovirus in their feces for an extended period, leading to severe environmental contamination. There is no universal vaccine available that protects dogs and cats against parvovirus infections. The main goal of this study is to design a pan-parvovirus multiepitope-based vaccine that could be administered to dogs and cats. We utilized AI-machine learning-incorporated server tools such as IEDB and NetMHCpan to predict B-cell and T-cell epitopes. VaxiJen and ToxinPred were used to analyze immune characteristic features and docking with feline alleles using the HADDOCK server. Following this, the immune response and stability of the vaccine construct were confirmed with disulfide engineering, normal mode analysis, and molecular docking performed with toll-like receptors of both feline and canine (TLR4 and TLR5), and molecular dynamics simulation was performed for 10 ns. The triggered immune response was determined with immuno-simulation (ImmSim), and their activity in a biological environment was reinforced with in silico cloning. The B-cell epitopes (NS1-9, NS2-4, VP1-12 and VP2-9) predicted with the IEDB database were subjected to antigenicity prediction. MHC class I and IFN prediction and MHC class II and IL-4 prediction were performed with IEDB and NetMHCpan. The T-cell epitopes showed high binding affinities with the feline alleles. The final vaccine was designed by combining the top-ranked B-cell epitopes and T-cell epitopes, filtered for high antigenicity, non-allergic, non-toxic, and good solubility, and with the better binding affinity score of the structural and non-structural proteins (NS1, NS2, VP1, and VP2) of feline and canine parvoviruses through linkers and adjuvants. The disulfide bond prediction and normal mode analysis showed that our vaccine construct is stable and flexible. The molecular docking analysis was performed between the designed vaccine epitopes and the TLRs (TLR4-feline and TLR5-canine) with Biovia Discovery Studio using Zdock; it showed better binding interactions with a value of 22.26 (Zdock score), -47.409 (Zrank score) for feline and 16.54 (Zdock score), -134.295 (Zrank score) for canine. A pan-multi-epitope-based vaccine based on the two structural and non-structural proteins (NS1, NS2, VP1, and VP2) was designed and constructed to provide dual protection against parvovirus in both feline and canine species. The molecular docking and molecular dynamics simulation analysis showed higher binding affinities and stable conformations with canine (TLR5) and feline (TLR4) toll-like receptors. Although computational analysis supports the prediction of top-ranked epitopes and their immunogenic properties with greater precision, further experimental validation is required before they can be used against these viruses.