Predicting the Antigenic Relationship of Foot-and-Mouth Disease Virus for Vaccine Selection Through a Computational Model

Foot-and-mouth disease virus (FMDV) is an antigenic-variable RNA virus that is responsible for the recurrence of foot-and-mouth disease in livestock and can be prevented and controlled using a vaccine with broad-spectrum protection. Current anti-genicity evaluation methods, which involve animal immunity experiments and serum preparation, are unable to fulfill the needs of high-throughput antigenicity measurements. This study designed an antigenicity scoring model to rapidly predict the antigenicity of FMDV. Antigenic-dominant sites were initially determined on the VP1 protein, a position-specific scoring matrix and physical chemical indexes were integrated to generate antigenicity descriptors. Independent tests showed a high accuracy of 0.848 and an AUC value of 0.889, indicating the good performance of the model in antigenicity measurement. When applying this model to historical data, annual antigenicity coverage of widely used vaccine strains was successfully evaluated, this was also supported by previous experiments. Furthermore, the utility of this model was extended to select potential broad-spectrum vaccines among 1,201 historical non-redundant strains to recommend potential univalent, bivalent and trivalent vaccine candidates. The results suggested that the computational model designed in this study could be used for the high-throughput antigenicity measurement of FMDV and could aid in vaccine development for preventing FMDV epidemics.