Prediction of Epitope Peptides for PTK Gene of Acinetobacter baumannii

Abstract

Background: Acinetobacter baumannii is a gramnegative bacilli acquiring both intrinsic and adaptive patterns of multi drug resistance and virulence. Immune-informatics approach holds promise to detect putative epitope peptides from vital virulence factors which can be further synthesized and evaluated for their immunological response. Aim: The aim of the study was to predict the immuno-dominant peptides from the ptk gene of A. baumannii. Materials and Methods: Protein retrieval of the Ptk gene using CELLO V.2.5 was done with the evaluation of antigenicity and allergenicity of the predicted epitopes, using Vaxijen V2.0 server and AlgPred servers. Epitope structure prediction and validation by using RAMPAGE revealed the homology peptides. Molecular Docking of epitopes with HLA-alleles using ClusterPro server, and further identification of B cell epitope was performed by using Kolaskar and Tonganokar antigenicity method. Results: A total of 20 epitopes were predicted and 18 peptides were chosen based on antigenicity and stability analysis prediction. The structure predictions were carried out using pepfold server and based on Ramachandran plot analysis 10 epitopes were taken for further analysis. Conclusion: The present finding has detected and evaluated the desirable epitope as LFFSLIAQW using an immune-informatic approach. However, it needs further experimental validation for its immunological response using standard in-vitro studies.