In silico identification of novel vaccine candidates against multidrug-resistant pseudomonas aeruginosa using subtractive proteomics and immunoinformatics.

Pseudomonas aeruginosa, a notorious pathogen, frequently presents in hospitals and poses numerous health issues due to its drug resistance, making treatment challenging. The rise of multidrug-resistant microorganisms emphasizes the crucial need for novel therapeutic approaches, such as vaccines. This work employed immunoinformatics and subtractive proteomics to find possible universal vaccine candidates. After analyzing the proteome of P. aeruginosa (PAO1 strain), three pathogenic or antigenic proteins were identified as potential targets for vaccines. T and B cell epitopes were predicted from vaccine candidate proteins using various immunoinformatics techniques. A vaccine was developed using specific epitopes (three Helper T lymphocyte (HTL), four Linear B lymphocyte (LBL), and seven Cytotoxic T lymphocyte (CTL) epitopes) and linkers, and a Cholera toxin subunit B adjuvant was added to enhance the immune response. The vaccine binds to and interacts with MHC molecules and TLR4 using molecular dynamics simulations and docking studies. After cloning, we reverse-translated the vaccine in E. coli to optimize protein production. Our approach offers a promising strategy for developing effective, long-lasting vaccines against Pseudomonas aeruginosa infections, which could help address the growing problems of antibiotic resistance in this bacterium.