Knowledge of immunodominant B-cell epitopes is essential to design powerful diagnostic strategies aiming for antibody detection. Outstanding progress in computational prediction has achieved a significant contribution to the biomedical fields, including immunodiagnosis. In silico analysis may have an even more important role when information concerning antigens from etiologic agents of neglected diseases, such as leprosy, is scarce. The aim of this study was to provide mapping of B-cell epitopes from two Mycobacterium leprae-derived antigens (Ag85B and ML2055), confirm their antigenicity, and to assess the ability of in silico immunoinformatics tools to accurately predict them. Linear B-cell epitopes predicted by ABCpred and SVMTrip servers were compared to antigenic regions of synthetic overlapping peptides that exhibited reactivity to antibodies from patients with leprosy. Our in vitro results identified several immunodominant regions that had also been indicated by in silico prediction, providing agreement between experimental and simulated data. After chemical synthesis, we used enzyme-linked immunosorbent assays to determine the effectiveness of the first identified sequence (GTNVPAEFLENFVHG) which had 72 % sensitivity and 78 % specificity (AUC = 0.79) while the second one (PVSSEAQPGDPNAPS) had 72 % sensitivity and 93.8 % specificity (AUC = 0.85). Using dot blotting, an easy-to-read visual test, both peptides could distinguish sera from patients with leprosy from those with tuberculosis and from sera of healthy volunteers. Our findings suggest that these synthetic peptides, with some refinement, may be useful as serological diagnostic antigens for leprosy. In addition, it was displayed that immunoinformatics provides reliable information for mapping potential B-cell epitopes for development of peptide-based diagnostic assays for neglected diseases.
Copyright © 2021. Published by Elsevier Ltd.