Mining and disulphide engineering of antimicrobial peptide for enhanced anti MDR Pseudomonas aeruginosa activity

Abstract

Pseudomonas aeruginosa, a motile Gram-negative rod bacterium, is responsible for severe acute and chronic nosocomial infections, particularly in immunocompromised individuals. Some pan-drug resistant strains of P. aeruginosa are unsusceptible to any current antibiotics, increasing mortality rates significantly and complicating clinical treatments. Novel therapeutic agents are hence urgently required to save lives in critical care. Antimicrobial peptides (AMPs) are emerging as potential alternatives or supplements to conventional antibiotics due to their promising effectiveness, rapid action, and wide range of antimicrobial capabilities. The aim of this study is to identify the best AMPs against P. aeruginosa, design novel peptides from these AMPs and create a specialized database for researchers. In this study, amino-acid walking method was applied for in-silico designing of potential AMPs deriving from reported natural peptides. Following the series of screening and analysis steps, we mined, designed and prioritized peptides based on their anti-microbial potential and physicochemical properties. These peptides were then docked against the target proteins i.e., the virulence factors of P. aeruginosa including beta lactamases, efflux-pump structural proteins, Type-4-pilli and those involved in biofilm and quorum sensing; to find the peptides with best binding energies. The docked complexes were further simulated and analyzed for stable binding. Finally, we created a curated database of all the prioritized peptides with antimicrobial structural and physicochemical properties. Initially, 93 important peptides with reported efficacy against P. aeruginosa were curated from the literature and different databases. After in-silico analysis, we derived 21 novel peptides from 8 different antimicrobial peptide sources. The final predicted peptides PAAMP17 and PAAMP10 show promising results in interaction analysis and the therapeutic capabilities of combating antibiotic-resistant P. aeruginosa. Further MD simulations of PAAMP17 were also carried out. Ultimately, we created a specialized database “PaAMPdb” (https://paampdb.mgbio.tech/) which contains 29 distinct entries, supporting details, mutated as well as naturally occurring AMPs. The database will be expanded continuedly as more annotations and analysis are being carried out while the proposed peptides are put forward for in-vitro and in-vivo testing for drug development.