Wednesday, February 5, 2025
2/5/2025
PROJECT SUMMARY/ABSTRACT This proposal describes efforts to develop, test, and improve a machine learning approach to identify potential metallo-β-lactamase (MBL) inhibitors from large chemical libraries. The MBLs are bacterial enzymes that are becoming more prevalent in the clinic and are leading to more incidents of antibiotic resistance in once easily- treatable infections, including secondary infections in COVID patients. There have been tremendous efforts to identify new MBL inhibitors; however to date, there are no clinical inhibitors of these enzymes. This proposal describes a novel, multidisciplinary approach to identify new MBL inhibitors. In Specific Aim 1, we propose to improve our initial computer model, which currently ranks compounds in chemical libraries on their likelihood of being a potential MBL inhibitor, based on previous inhibition data collected on MBL NDM-1. The improved model will be developed using data sets containing inhibition data from quantitative HTS (qHTS) experiments on MBLs, NDM-1, VIM-2, and IMP-1. The new model will be used to screen the 1.3 million compound-containing ChemBridge chemical library, and qHTS studies with VIM-2, IMP-1, and NDM-1 will be conducted to test the results from the virtual screenings. The final, validated model will be made available to the public on our MBL inhibitor website. In Specific Aim 2, we will perform microbiological, structural, and biochemical studies on the top 500 compounds identified by our model and confirmed with qHTS. In addition to minimal inhibitor concentration values, we propose to determine the mechanism of inhibition and to structurally-characterize the enzyme-inhibitor complexes. It is hoped that this machine learning approach will identify novel, pan MBL inhibitors and compounds that can be further re-designed. In addition, it is hoped that this approach, once developed and tested, can be used to identify inhibitors of other biomedically-important enzymes.
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