Prediction of Putative Epitope Peptides against BaeR Associated with TCS Adaptation in <i>Acinetobacter baumannii</i> Using an In Silico Approach

Abstract

Background and Objectives: The BaeR protein is involved in the adaptation system of A. baumannii and is associated with virulence factors responsible for systemic infections in hospitalized patients. This study was conducted to characterize putative epitope peptides for the design of vaccines against BaeR protein, using an immune-informatic approach. Materials and Methods: FASTA sequences of BaeR from five different strains of A. baumannii were retrieved from the UNIPROT database and evaluated for their antigenicity, allergenicity and vaccine properties using BepiPred, Vaxijen, AlgPred, AntigenPro and SolPro. Their physio-chemical properties were assessed using the Expasy Protparam server. Immuno-dominant B-cell and T-cell epitope peptides were predicted using the IEDB database and MHC cluster server with a final assessment of their interactions with TLR-2. Results: A final selection of two peptide sequences (36aa and 22aa) was made from the 38 antigenic peptides. E1 was considered a soluble, non-allergenic antigen, and possessed negative GRAVY values, substantiating the hydrophilic nature of the proteins. Further analysis on the T-cell epitopes, class I immunogenicity and HLA allele frequencies yielded T-cell immuno-dominant peptides. The protein–peptide interactions of the TLR-2 receptor showed good similarity scores in terms of the high number of hydrogen bonds compared to other protein-peptide interactions. Conclusions: The two epitopes predicted from BaeR in the present investigation are promising vaccine candidates for targeting the TCS of A. baumannii in systemic and nosocomial infections. This study also demonstrates an alternative strategy to tackling and mitigating MDR strains of A. baumannii and provides a useful reference for the design and construction of novel vaccine candidates against this bacteria.

Keywords

• nosocomial infections
• bioinformatics
• BaeR
• epitope peptides
• in silico