Computational Design of Peptide Vaccine against Acinetobacter Baumannii Infection using Comparative Genomic Approach

Abstract

The bacterial species Acinetobacter baumannii is a major cause of hospital acquired infection throughout the world and it is increasing public health concern. Infection caused by multidrug resistant A. baumannii is currently among the most difficult to treat due to propensity to acquire mobile genetic element. To date there is no vaccine or specific drug available for its treatment, this necessitate the need for the identification of therapeutic target enzyme and vaccine. Pharmacogenomic and computational biology represent an attractive alternative approach for the identification of common drug target and peptide-vaccine candidates in the pathogen. Vaccine designing is shifted from entire pathogen or whole antigen to peptide or epitope based-vaccines that are specific, safe and easy to produce. Comparative genomic approach was used to identify conserved protein signatures among five genomes. Three outer membrane proteins conserved among the genomes with high vaxijen scores were used to produce both B-cell and T-cell mediated immunity. Propred and propred1 were used to predict promiscuos helper T-Lymphocytes (HTL), Cytotoxic T-Lymphocyte (CTL) epitopes and MHCPred for their binding affinity.Three T-cell epitopes derived from identified B-cells bind to maximum number of MHC class I and class II alleles and specifically bind to HLA alleles such as DRB1*0101 and DRB1*0401.The epitopes are YEKLAAGPS, FYTSQPEDS and YVTGNPLGL with high potential to induce humoral and cell mediated immune responses. These predicted epitopes (small peptide) might be promising candidates for vaccine design against A. baumannii infection, though experimental validation.