Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase.

The worldwide spread of the metallo-β-lactamases (MBL), especially New Delhi metallo-β-lactamase-1 (NDM-1), is threatening the efficacy of β-lactams, which are the most potent and prescribed class of antibiotics in the clinic. Currently, FDA-approved MBL inhibitors are lacking in the clinic even though many strategies have been used in inhibitor development, including quantitative high-throughput screening (qHTS), fragment-based drug discovery (FBDD), and molecular docking. Herein, a machine learning-based prediction tool is described, which was generated using results from HTS of a large chemical library and previously published inhibition data.

Elusive structural changes of New Delhi metallo-β-lactamase revealed by ultraviolet photodissociation mass spectrometry.

We use mass spectrometry (MS), under denaturing and non-denaturing solution conditions, along with ultraviolet photodissociation (UVPD) to characterize structural variations in New Delhi metallo-β-lactamase (NDM) upon perturbation by ligands or mutation. Mapping changes in the abundances and distributions of fragment ions enables sensitive detection of structural alterations throughout the protein. Binding of three covalent inhibitors was characterized: a pentafluorphenyl ester, an -aryloxycarbonyl hydroxamate, and ebselen.

Visualizing the Dynamic Metalation State of New Delhi Metallo-β-lactamase-1 in Bacteria Using a Reversible Fluorescent Probe.

New Delhi metallo-β-lactamase (NDM) grants resistance to a broad spectrum of β-lactam antibiotics, including last-resort carbapenems, and is emerging as a global antibiotic resistance threat. Limited zinc availability adversely impacts the ability of NDM-1 to provide resistance, but a number of clinical variants have emerged that are more resistant to zinc scarcity (e.g.