Search and Prediction of Epitopes for Bacterial Antigen Binding

This master's thesis presents the development and implementation of a bioinformatic pipeline for the in silico identification of B-cell epitopes exposed on the surface of pathogenic bacteria. Genomic and proteomic sequences of clinically relevant bacterial pathogens associated with human infectious diseases and elements of the normal microbiota were used as input data. Based on annotated protein data, unique surface antigens were selected. To exclude potential cross-reactivity with the human proteome, homology analysis was performed using the BLAST algorithm. Epitope prediction was carried out using BepiPred 2.0 (for linear epitopes) and DiscoTope 3.0 (for conformational epitopes), followed by antigenicity assessment via VaxiJen 2.0. Additionally, the predicted epitopes were compared with experimentally validated data from the IEDB database. The results demonstrate the effectiveness of an integrative approach for identifying promising immunogenic targets. The findings can be applied in the development of epitope-based vaccines and diagnostic reagents against priority bacterial pathogens.