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• W2979303901 endingPage "2104" @default.
• W2979303901 startingPage "2096" @default.
• W2979303901 abstract "Crossing the Gram-negative bacterial membrane poses a major barrier to antibiotic development, as many small molecules that can biochemically inhibit key bacterial processes are rendered microbiologically ineffective by their poor cellular uptake. The outer membrane is the major permeability barrier for many drug-like molecules, and the chemical properties that enable efficient uptake into mammalian cells fail to predict bacterial uptake. We have developed a computational method for accurate prospective prediction of outer membrane uptake of drug-like molecules, which we combine with a new medium-throughput experimental assay of outer membrane vesicle swelling. Parallel molecular dynamics simulations of compound uptake through Escherichia coli (E. coli) OmpF are used to successfully and quantitatively predict experimental permeabilities measured via either outer membrane swelling or prior liposome-swelling measurements. These simulations are analyzed using an inhomogeneous solubility-diffusion model to yield predictions of permeability. For most polar molecules we test, outer membrane permeability also correlates well with whole-cell uptake. The ability to accurately predict and measure outer membrane uptake of a wide variety of small molecules will enable simpler determination of which molecular scaffolds and which derivatives are most promising prior to extensive chemical synthesis. It will also assist in formulating a more systematic understanding of the chemical determinants of outer membrane permeability." @default.
• W2979303901 created "2019-10-18" @default.
• W2979303901 creator A5072472177 @default.
• W2979303901 creator A5081016259 @default.
• W2979303901 date "2019-10-08" @default.
• W2979303901 modified "2023-10-01" @default.
• W2979303901 title "Antibiotic Uptake Across Gram-Negative Outer Membranes: Better Predictions Towards Better Antibiotics" @default.
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• W2979303901 doi "https://doi.org/10.1021/acsinfecdis.9b00201" @default.
• W2979303901 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31593635" @default.
• W2979303901 hasPublicationYear "2019" @default.
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