Bacterial Cellulose Containing Combinations of Antimicrobial Peptides with Various QQ Enzymes as a Prototype of an "Enhanced Antibacterial" Dressing: In Silico and In Vitro Data.
Aysel AslanliIlya LyaginNikolay StepanovDenis E PresnovElena N EfremenkoPublished in: Pharmaceutics (2020)
To improve the action of already in use antibiotics or new antimicrobial agents against different bacteria, the development of effective combinations of antimicrobial peptides (AMPs) with enzymes that can quench the quorum (QQ) sensing of bacterial cells was undertaken. Enzymes hydrolyzing N-acyl homoserine lactones (AHLs) and peptides that are signal molecules of Gram-negative and Gram-positive bacterial cells, respectively, were estimated as "partners" for antibiotics and antimicrobial peptides in newly designed antimicrobial-enzymatic combinations. The molecular docking of six antimicrobial agents to the surface of 10 different QQ enzyme molecules was simulated in silico. This made it possible to choose the best variants among the target combinations. Further, bacterial cellulose (BC) was applied as a carrier for uploading such combinations to generally compose prototypes of effective dressing materials with morphology, providing good absorbance. The in vitro analysis of antibacterial activity of prepared BC samples confirmed the significantly enhanced efficiency of the action of AMPs (including polymyxin B and colistin, which are antibiotics of last resort) in combination with AHL-hydrolyzing enzymes (penicillin acylase and His6-tagged organophosphorus hydrolase) against both Gram-negative and Gram-positive cells.
Keyphrases
- gram negative
- multidrug resistant
- molecular docking
- induced apoptosis
- cell cycle arrest
- staphylococcus aureus
- acinetobacter baumannii
- cell death
- molecular dynamics simulations
- silver nanoparticles
- oxidative stress
- pseudomonas aeruginosa
- endoplasmic reticulum stress
- copy number
- escherichia coli
- ionic liquid
- hydrogen peroxide
- gene expression
- nitric oxide
- hepatitis c virus
- hiv testing
- klebsiella pneumoniae
- machine learning
- men who have sex with men