Applying Immunoinformatics Methods to Identify Potential T and B Cell Epitopes in the CagA Protein of Helicobacter pylori

Background and Aim: Helicobacter pylori is not only identified as a leading cause of chronic active gastritis and peptic ulcer disease in humans, but also it is considered as a risk factor for the development of gastric adenocarcinoma and MALT lymphoma. This study aims to predict specific epitopes for the utility of designing peptide vaccine against H. pylori infection by targeting invasive, virulent and membrane associated proteins CagA. Materials and Methods: In the present study, various immunoinformatics approaches have been applied to design a potential epitope-based vaccine against H. pylori infection. For prediction of linear epitopes, the sequence of CagA was submitted to ABCpred, BCPREDS, Bcepred, Bepipred and Ellipro servers. DiscoTope 2.0 and B-pred servers were also used for the prediction of conformational epitopes. In addition, prediction of T-cell epitopes was carried out by CTLPred. Results: The obtained results demonstrated 277 conformational B-Cell epitopes in addition to predicted high score linear B and T cell epitopes in CagA protein. Conclusion: These predicted epitopes might be used to design a vaccine against H. pylori and thus, could be validated in model hosts to verify their efficacy as vaccine.