Breaking Intrinsic Antibiotic Resistance

Abstract

Antimicrobial resistance (AMR) poses a critical and escalating global health threat. Gram-negative bacteria present a significant challenge due to intrinsic resistance mechanisms mediated by their outer membrane. This study employed a novel strategy targeting outer membrane permeabilization to overcome this resistance. Bioinformatics-driven selection identified 14 candidate peptides, five of which demonstrated significant potentiation of erythromycin, rifampicin, and other Gram-positive antibiotics against Gram-negative pathogens. Among them, the permeabilizing efficacy of peptide OMPP13 exceeded that of the established agent polymyxin B nonapeptide (PMBN). Strong synergistic interactions, confirmed by fractional inhibitory concentration index (FICI) measurements , highlight the therapeutic potential of these peptides. Furthermore, my analysis indicates that hydrophobic antibiotics with higher molecular mass (around 800 da) derive greater benefit from outer membrane disruption. This project provides a dataset to further machine learning studies of permeabilizing peptides.