Discovery of Potential Therapeutic Targets in Multi-Drug Resistant Pseudomonas aeruginosa: An Integrative Computational Analysis Spotlighting Proteins

Background Pseudomonas aeruginosa is an aerobic, gram-negative, non-spore forming, rod shaped bacterium which can infect both immunocompetent and immunocompromised hosts with a wide range of diseases. Aside from its high adaptability, this bacterium is highly resistant, modulates host survival, and affects individuals with compromised immune systems severely. It is imperative to explore new drug targets due to the significant increase in mortality caused by P. aeruginosa infections. Methods Subtractive proteomics was used to identify broad spectrum putative Pseudomonas targets using 5,564 core proteins from P. aeruginosa PAO1 . The process of identifying drug targets began with the identification of proteins not homologous to humans, the identification of essential proteins, the identification of functional pathways, the localization of proteins in cells, the analysis of proteins involved in virulence and resistance, the analysis of protein stability, and the identification of druggable proteins using various computational tools and webservers. Further, we have conducted docking-based inverse virtual screening using Schrodinger's Glide module to find inhibitors against the identified target proteins using 4,64,867 compounds from the VITAS-M laboratory and druggability analysis of hit compounds using Qikprop module. Results This study revealed three novel broad-spectrum druggable targets of pathogenic Pseudomonas species - Preprotein translocase subunit SecD, chemotaxis-specific methylesterase, and imidazole glycerol phosphate synthase subunit HisF2 which are involved in the virulence and multi-drug resistance of the pathogen. Based on the binding affinities, and binding energies of the molecules with the target proteins, we identified 15 hit compounds. The pharmacokinetics analysis revealed that all the 15 compounds as safer inhibitors and could serve as potential therapeutic candidates. Conclusion Herein, we present a computationally based framework for identifying therapeutic targets and their inhibitors. The findings of this study can lead to further wet-lab research that may contribute to the eradication of infections caused by multi-drug resistant Pseudomonas species.