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Metallomics. 2019 Mar 20;11(3):696-706. doi: 10.1039/c8mt00342d.

High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureus.

Author information

1
Department of Medicine, Division of Infectious Diseases, University of Alabama at Birmingham, BBRB 562, 845 19th St S, Birmingham, AL 35294, USA. dalecki@uab.edu.

Abstract

One potential source of new antibacterials is through probing existing chemical libraries for copper-dependent inhibitors (CDIs), i.e., molecules with antibiotic activity only in the presence of copper. Recently, our group demonstrated that previously unknown staphylococcal CDIs were frequently present in a small pilot screen. Here, we report the outcome of a larger industrial anti-staphylococcal screen consisting of 40 771 compounds assayed in parallel, both in standard and in copper-supplemented media. Ultimately, 483 had confirmed copper-dependent IC50 values under 50 μM. Sphere-exclusion clustering revealed that these hits were largely dominated by sulfur-containing motifs, including benzimidazole-2-thiones, thiadiazines, thiazoline formamides, triazino-benzimidazoles, and pyridinyl thieno-pyrimidines. Structure-activity relationship analysis of the pyridinyl thieno-pyrimidines generated multiple improved CDIs, with activity likely dependent on ligand/ion coordination. Molecular fingerprint-based Bayesian classification models were built using Discovery Studio and Assay Central, a new platform for sharing and distributing cheminformatic models in a portable format, based on open-source tools. Finally, we used the latter model to evaluate a library of FDA-approved drugs for copper-dependent activity in silico. Two anti-helminths, albendazole and thiabendazole, scored highly and are known to coordinate copper ions, further validating the model's applicability.

PMID:
30839007
PMCID:
PMC6467072
[Available on 2020-03-20]
DOI:
10.1039/c8mt00342d

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