Back

The role of secondary structures of peptide polymers on antimicrobial efficacy and antibiotic potentiation

Barman, S.; Abiodun, A.; Hossain, M. W.; Parris, A.; Chandrasseril, A. B.; Older, E.; Li, J.; Decho, A.; Tang, C.

2024-11-20 microbiology
10.1101/2024.11.19.623429 bioRxiv
Show abstract

The rise of antibiotic resistance, biofilm formation, and dormant bacterial populations poses serious global health threats. Synthetic antimicrobial peptide (AMP) mimics offer promising alternatives, though the impact of secondary structures in polymeric AMP mimics on antimicrobial efficacy is underexplored. This study investigates chirality-controlled -peptide polymers (D-PP and DL-PP), synthesized via ring-opening polymerization of allylglycine N-carboxy anhydrides and post-polymerization modification through thiol-ene click chemistry. D-PP adopts a stable helical structure under biomimetic conditions, whereas DL-PP remains random. This helical structure enhanced D-PPs antibacterial and antibiotic potentiation activities, amplifying antibiotic efficacy by 2- to 256-fold across various classes--including tetracyclines, ansamycins, fusidanes, macrolides, cephalosporins, and monobactams--against multidrug-resistant Gram-negative pathogens, while maintaining low hemolytic activity and high protease stability. Mechanistic investigations revealed that D-PP exhibited greater membrane interaction. D-PP and antibiotic combinations eradicated dormant bacterial populations and disrupted biofilms with minimal antimicrobial resistance development. This study paves the way for the rational design of polypeptide-based antimicrobial agents, harnessing chirality and secondary structural features to enhance the efficacy of synthetic antimicrobial peptide mimics.

Matching journals

The top 15 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.