Abstract
The cattle tick Rhipicephalus microplus poses a major problem to the livestock industry worldwide, with acaricides resistance presenting an increasing challenge. On other hand, vaccination has been suggested as a better strategy for tick control, and peptide-based vaccines could be developed to target multiple tick antigens. Nevertheless, there are still limitations to the identification of epitopes in tick candidate antigens, as the bioinformatics tools currently available were developed almost exclusively based on mammalian genomes. Therefore, improving the performance of B-cell epitope predictor algorithms is essential to achieve an effective multi-epitope vaccine for tick control. The aim of this study was to reduce costs and increase the efficacy in identifying epitopes in tick antigens. We first evaluated the performance of B-cell epitope predictor algorithms in replicating the results of an in vitro epitope mapping result for the tick salivary serpin RmS-17 as a "benchmark". Then the algorithm with the best performance was employed to predict epitopes for the tick salivary serpin RmS-6, and we screened the candidate epitopes based on predictions that were close to the reactive center loop (RCL), the region of the serpin that interacts with the target protease. Antibodies raised against p1RmS-6 and p3RmS-6 neutralize RmS-6 activity. Using this strategy, we were able to adjust an in silico algorithm predictor based on a Pepscan result to identify epitopes in another serpin. Our strategy offers a cost-effective way to identify neutralizing epitopes in serpins. Furthermore, this strategy can be applied to identify epitopes in serpins and other proteins from other tick species, potentially leading to the development of a peptide-based anti-tick vaccine.