id9b00295_si_003.xlsx (12.48 kB)
Intrabacterial Metabolism Obscures the Successful Prediction of an InhA Inhibitor of Mycobacterium tuberculosis
dataset
posted on 2019-11-05, 20:33 authored by Xin Wang, Alexander L. Perryman, Shao-Gang Li, Steve D. Paget, Thomas P. Stratton, Alex Lemenze, Arthur J. Olson, Sean Ekins, Pradeep Kumar, Joel S. FreundlichTuberculosis,
caused by Mycobacterium tuberculosis (M. tuberculosis), kills 1.6 million people
annually. To bridge the gap between structure- and cell-based drug
discovery strategies, we are pioneering a computer-aided discovery
paradigm that merges structure-based virtual screening with ligand-based,
machine learning methods trained with cell-based data. This approach
successfully identified N-(3-methoxyphenyl)-7-nitrobenzo[c][1,2,5]oxadiazol-4-amine (JSF-2164) as an inhibitor of
purified InhA with whole-cell efficacy versus in vitro cultured M. tuberculosis. When the intrabacterial
drug metabolism (IBDM) platform was leveraged, mechanistic studies
demonstrated that JSF-2164 underwent a rapid F420H2-dependent biotransformation within M. tuberculosis to afford intrabacterial nitric oxide and two amines, identified
as JSF-3616 and JSF-3617. Thus, metabolism of JSF-2164 obscured the
InhA inhibition phenotype within cultured M. tuberculosis. This study demonstrates a new docking/Bayesian computational strategy
to combine cell- and target-based drug screening and the need to probe
intrabacterial metabolism when clarifying the antitubercular mechanism
of action.
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intrabacterial drug metabolismMycobacterium tuberculosis TuberculosisIBDM. tuberculosiInhA inhibition phenotypeM . tuberculosisJSF -2164intrabacterial nitric oxideF 420 H 2target-based drug screeningstrategyprobe intrabacterial metabolismcell-based drug discovery strategiesIntrabacterial Metabolism Obscures
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