Screening of Bovine Coronavirus Multiepitope Vaccine Candidates: An Immunoinformatics Approach

Abstract

Bovine coronavirus (BCoV) is a causative agent of enteric and respiratory disease in cattle. BCoV has been reported to cause a variety of animal diseases and is closely related to human coronaviruses; moreover, it has attracted extensive attention from both cattle farmers and researchers. With the rise of BCoV, a vaccine that is prophylactic and immunotherapeutic has to be utilized for a preemptive and adroit therapeutic approach. The aim of this study was to develop a novel multiepitope-based BCoV vaccine that can induce an immune response using a silicon reverse vaccinology approach. In this study, an immunoinformatics approach was employed to identify potential vaccine targets against BCoV, and four candidate antigen proteins were selected to predict B-cell and T-cell epitopes. To identify dominant epitopes, we employed a variety of bioinformatics techniques, including antigenicity prediction, immunogenicity assessment, allergenicity analysis, conservative analysis, and toxicity assessment. Finally, six multiepitope vaccines were developed using dominant epitopes, suitable adjuvants, Pan HLADR—binding epitope (PADRE), and linkers. Then based on the antigenicity score, solubility analysis, allergenicity evaluation, physicochemical property assessment, and tertiary structure verification score, construct 6 was selected as the best candidate vaccine; it was named CY. Molecular modeling and structural validation ensured the high-quality 3D structure of construct CY. The immunogenicity and complex stability of the vaccine were evaluated by molecular docking and kinetic simulation. In silicon clones, the BCoV vaccine had high levels of gene expression in the insect expression system. These results may contribute to the development of experimental BCoV vaccines with higher potency and safety.