Machine learning in mass spectrometry: A MALDI-TOF MS approach to phenotypic antibacterial screening
Machine learning techniques can be applied to MALDI-TOF mass spectral data of drug-treated cells to obtain classification models which assign the mechanism of action of drugs. Here, we present an example application of this concept to the screening of antibacterial drugs that act at the major bacter...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
March 19, 2020
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| In: |
Journal of medicinal chemistry
Year: 2020, Jahrgang: 63, Heft: 16, Pages: 8849-8856 |
| ISSN: | 1520-4804 |
| DOI: | 10.1021/acs.jmedchem.0c00040 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1021/acs.jmedchem.0c00040
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| Verfasserangaben: | Luuk N. van Oosten, Christian D. Klein |
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| 520 | |a Machine learning techniques can be applied to MALDI-TOF mass spectral data of drug-treated cells to obtain classification models which assign the mechanism of action of drugs. Here, we present an example application of this concept to the screening of antibacterial drugs that act at the major bacterial target sites such as the ribosome, penicillin-binding proteins, and topoisomerases in a pharmacologically relevant phenotypic setting. We show that antibacterial effects can be identified and classified in a label-free, high-throughput manner using wild-type Escherichia coli and Staphylococcus aureus cells at variable levels of target engagement. This phenotypic approach, which combines mass spectrometry and machine learning, therefore denoted as PhenoMS-ML, may prove useful for the identification and development of novel antibacterial compounds and other pharmacological agents. | ||
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