AI Insight
Researchers used artificial intelligence-driven molecular design to develop Y-11, a new antibiotic compound that targets MsbA, a critical protein in bacterial cell wall synthesis. Y-11 demonstrated effective antibacterial activity against Acinetobacter baumannii, including carbapenem-resistant strains, with a minimum inhibitory concentration of 0.5 μg/mL and showed reduced toxicity compared to the parent compound. The molecule works by inhibiting lipooligosaccharide transport and disrupting outer membrane formation, and successfully reduced bacterial loads in infected mice.
Why it matters
This discovery addresses the urgent need for new antibiotics against drug-resistant Gram-negative bacteria, particularly multidrug-resistant A. baumannii, which is a major healthcare threat. The successful application of AI-driven drug design demonstrates a promising approach for accelerating antibiotic discovery and exploring novel chemical spaces that traditional methods might miss.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Antibiotics with new mechanisms are highly pursued to address the threat of infections caused by drug-resistant Gram-negative bacteria. Targeting MsbA, a key protein of the lipopolysaccharide biosynthesis pathway, represents a promising strategy to discover new classes of antibiotics. However, currently available MsbA-targeted molecules either lack sufficient potency or have unfavorable properties, necessitating expansion of chemical space. In this study, we chose the most promising cerastecin Cpd 4 as the template, and used two Artificial Intelligence (AI)-based tools, i.e. Link-INVENT and AutoMolDesigner for molecular design, performed chemical derivatization and antibacterial activity evaluation, which led to the discovery of Y-11 (MIC for A. baumannii: 0.5 g/mL). Encouragingly, Y-11 showed equivalent potency to Cpd4 for carbapenem-resistant A. baumannii, and less cytotoxicity and hemolysis as well as lower spontaneous resistance frequency. In vivo efficacy study demonstrated that Y-11 could effectively reduce bacterial loads in the mice infected by A. baumannii. The following mechanism study including molecular dynamics simulation, biochemical assay, and transmission electron microscope (TEM) analysis suggested that Y-11 inhibited the transport of lipooligosaccharide and impaired the formation of outer membrane, probably by competitively binding to the substrate binding site of MsbA and modulating ATPase activity. Taken together, we have discovered a MsbA-targeted small molecule Y-11 via AI-driven drug design, which provides a foundation for future antibiotic development.