Tuberculosis (TB) is a chronic
infectious disease, considered as the second leading cause of death worldwide, caused by Mycobacterium tuberculosis. The limited efficacy of the bacillus Calmette-Guérin (
BCG) vaccine against pulmonary TB and the emergence of multidrug-resistant TB warrants the need for more efficacious
vaccines. Reverse vaccinology uses the entire
proteome of a pathogen to select the best
vaccine antigens by in silico approaches. M.
tuberculosis H37Rv
proteome was analyzed with NERVE (New Enhanced Reverse Vaccinology Environment) prediction software to identify potential
vaccine targets; these 331
proteins were further analyzed with VaxiJen for the determination of their antigenicity value. Only candidates with values ≥0.5 of antigenicity and 50% of adhesin probability and without homology with human
proteins or transmembrane regions were selected, resulting in 73
antigens. These
proteins were grouped by families in seven groups and analyzed by amino acid sequence alignments, selecting 16 representative
proteins. For each candidate, a search of the literature and
protein analysis with different bioinformatics tools, as well as a simulation of the immune response, was conducted. Finally, we selected six novel
vaccine candidates, EsxL, PE26, PPE65, PE_PGRS49, PBP1, and Erp, from M.
tuberculosis that can be used to improve or design new TB
vaccines.