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PROJECT SUMMARY/ABSTRACT The percent of Gram-negative bacterial infections that are resistant to common antibiotics has increased at an alarming rate over the last decade, and there is now an acute need for the discovery of novel antibiotics effective against multidrug-resistant Gram-negative pathogens. The standard method of antibacterial discovery – whole- cell screening of compound collections – has met with repeated failure for Gram-negatives, and these failures have been traced to the fact that very few compounds in standard collections can penetrate the Gram-negative cell membranes and accumulate in these pathogens. Unfortunately, there has been scant information about the types of compounds that are competent for accumulation in Gram-negatives. Excitingly, we recently assessed a unique collection of >180 diverse compounds for their ability to accumulate in E. coli, trained a random forest classification model to analyze the results, and from this data we identified physicochemical properties important for accumulation and developed predictive guidelines for compound accumulation in E. coli. We then showed the utility of these guidelines by converting a Gram-positive-only antibiotic into a broad-spectrum agent. We now propose to develop tools that will allow us to fully define the physicochemical traits that enable compounds accumulation in three of the most concerning Gram-negative bacteria, carbapenem-resistant Enterobacteriaceae (CRE), drug-resistant Acinetobacter, and drug-resistant P. aeruginosa (to be referred to collectively as EAP pathogens). Specifically, we seek to develop novel tools in the area of chemical probes (compound collections), bacterial strains, and computational models. Using these tools in conjunction with our well-validated compound accumulation assay, we intend to define the physicochemical traits needed for compound accumulation in the EAP pathogens, including assessment of the influence of porins and efflux pumps, and the relative contribution of the outer and inner-membranes to blocking compound penetrance. Our predictive guidelines will be utilized to convert several high-value Gram-positive-only compounds into broad- spectrum antibiotics.
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DOI: 10.1021/acsinfecdis.0c00715
发表时间: 2021-01-08
期刊: ACS infectious diseases
影响因子: 5.3
作者: [Perlmutter SJ, Geddes EJ, Drown BS, Motika SE, Lee MR, Hergenrother PJ]
通讯作者: Hergenrother PJ
DOI: 10.1021/acsinfecdis.0c00869
发表时间: 2021-02-12
期刊: ACS infectious diseases
影响因子: 5.3
作者: [Garcia Chavez M, Garcia A, Lee HY, Lau GW, Parker EN, Komnick KE, Hergenrother PJ]
通讯作者: Hergenrother PJ
DOI: 10.1021/acs.accounts.0c00895
发表时间: 2021-03-16
期刊: Accounts of chemical research
影响因子: 18.3
作者: [Muñoz KA, Hergenrother PJ]
通讯作者: Hergenrother PJ
An LC-MS/MS assay and complementary web-based tool to quantify and predict compound accumulation in E. coli.
LC-MS/MS 测定和基于网络的补充工具,用于量化和预测大肠杆菌中的化合物积累。
DOI: 10.1038/s41596-021-00598-y
发表时间: 2021-10
期刊: Nature protocols
影响因子: 14.8
作者: [Geddes EJ, Li Z, Hergenrother PJ]
通讯作者: Hergenrother PJ
Developing a Suite of Targeted Anticancer Drugs
FabI Inhibitors as Potent, Gut Microbiome-Sparing Antibiotics
A Novel Therapeutic Strategy for Ovarian Cancer
A Novel Therapeutic Strategy for Ovarian Cancer
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