ABSTRACT
Whooping cough is a disease caused by Bordetella pertussis, whose morbidity has increased, motivating the improvement of current vaccines. Reverse vaccinology is a strategy that helps identify proteins with good characteristics fast and with fewer resources. In this work, we applied reverse vaccinology to study the B. pertussis proteome and pangenome with several in-silico tools. We analyzed the B. pertussis Tohama I proteome with NERVE software and compared 234 proteins with B. parapertussis, B. bronchiseptica, and B. holmessi. VaxiJen was used to calculate an antigenicity value; our threshold was 0.6, selecting 84 proteins. The candidates were depurated and grouped in eight family proteins to select representative candidates, according to bibliographic information and their immunological response predicted with ABCpred, Bcepred, IgPred, and C-ImmSim. Additionally, a pangenome study was conducted with 603 B. pertussis strains and PanRV software, identifying 3421 core proteins that were analyzed to select the best candidates. Finally, we selected 15 proteins from the proteome study and seven proteins from the pangenome analysis as good vaccine candidates.