Computational identification of conserved B- and T-cell epitopes for multi-epitope peptide vaccine design against bovine leukemia virus.

Bovine leukosis continues to impose significant economic losses on the global cattle industry due to its persistent infection and widespread prevalence worldwide. Although control strategies such as test-and-cull programs have been implemented in some countries, an effective and safe vaccine remains unavailable. In this study, an immunoinformatics-based approach combined with reverse vaccinology was applied to design a multi-epitope peptide (MEP) vaccine candidate against Bovine leukemia virus (BLV). Conserved protein regions were analyzed to identify potential immune targets, resulting in the selection of five epitopes, including three cytotoxic T-lymphocyte (CTL) epitopes, one helper T-lymphocyte (Th) epitope, and one linear B-cell epitope derived from the highly conserved BLV Gag and RT-IN proteins. These epitopes were assembled using AAY and GPGPG linkers to generate a 67-amino-acid MEP construct. In silico evaluation indicated that the designed construct possesses favorable immunological properties, including high predicted antigenicity (VaxiJen score: 0.6511, which is notably higher than the baseline score of 0.4784 for the native viral proteins; Pr44: 0.4784, RT-IN: 0.4214), non-allergenic characteristics, and absence of predicted toxic motifs. Homology analysis against the Bos taurus proteome revealed no significant similarity, suggesting a low risk of host cross-reactivity. Structural analyses using PSIPRED and AlphaFold 3 predicted localized secondary structural elements within an overall flexible peptide architecture; this flexibility is highly advantageous for optimal proteasomal cleavage, efficient antigen processing, and MHC presentation. Taken together, these findings provide a rational framework for the development of a BLV MEP vaccine candidate and support further experimental validation to evaluate its predicted immunogenic potential.