High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureus Article Swipe
Alex G. Dalecki
,
Kimberley M. Zorn
,
Alex M. Clark
,
Sean Ekins
,
Whitney Narmore
,
Nichole A. Tower
,
Lynn Rasmussen
,
Robert Bostwick
,
Olaf Kutsch
,
Frank Wolschendorf
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1039/c8mt00342d
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1039/c8mt00342d
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1039/c8mt00342d
- OA Status
- green
- Cited By
- 32
- References
- 61
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2916672662Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1039/c8mt00342dDigital Object Identifier
- Title
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High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureusWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-02-22Full publication date if available
- Authors
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Alex G. Dalecki, Kimberley M. Zorn, Alex M. Clark, Sean Ekins, Whitney Narmore, Nichole A. Tower, Lynn Rasmussen, Robert Bostwick, Olaf Kutsch, Frank WolschendorfList of authors in order
- Landing page
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https://doi.org/10.1039/c8mt00342dPublisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/6467072Direct OA link when available
- Concepts
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Copper, Chemistry, Combinatorial chemistry, In silico, Cheminformatics, Ligand (biochemistry), Staphylococcus aureus, Computational biology, Biochemistry, Biology, Computational chemistry, Bacteria, Organic chemistry, Receptor, Genetics, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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32Total citation count in OpenAlex
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2024: 2, 2023: 3, 2022: 4, 2021: 10, 2020: 11Per-year citation counts (last 5 years)
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61Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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