Immunoinformatics-based design of a multi-epitope vaccine candidate targeting VP5 and VP7 of colorado tick fever virus

Background Colorado tick fever virus is an understudied tick-borne pathogen for which no preventive vaccine is currently available. It is a double-stranded RNA virus of the genus Coltivirus within the family Spinareoviridae and is primarily transmitted by infected Dermacentor andersoni ticks. This study aimed to design a multi-epitope vaccine candidate against the virus through an immunoinformatics-based strategy. Methods Conserved B-cell, helper T-cell, and cytotoxic T-cell epitopes were selected from VP5 and VP7 and integrated into a 228-amino-acid construct containing β-defensin-3 and appropriate linker sequences. The construct was evaluated for antigenicity, allergenicity, solubility, structural characteristics, population coverage, receptor docking, molecular dynamics behavior, binding-energy estimates, simulated immune responses, codon optimization, and bacterial cloning compatibility. Results The construct showed predicted antigenicity, no predicted allergenicity, favorable solubility, satisfactory structural characteristics, and an estimated worldwide population coverage of approximately 95%. Protein–protein docking produced ClusPro weighted docking scores of −​1684.5 for vaccine–TLR2 and −​1235.0 for vaccine–TLR3. Molecular dynamics simulations characterized the conformational behavior of both complexes, with lower structural deviations observed for vaccine–TLR2 than for vaccine–TLR3. MM-GBSA calculations provided a comparatively more favorable relative energetic estimate for vaccine–TLR2. Immune simulation predicted humoral and cellular immune-response patterns following repeated in silico administration. Codon optimization and in silico cloning supported the potential compatibility of the optimized sequence with the pET-28a(+) expression system. Conclusions These computational findings present the design of a multi-epitope vaccine candidate against Colorado tick fever virus that requires further experimental validation.