Machine learning in mass spectrometry

• Verfasst von: Oosten, Luuk N. van [VerfasserIn]
• Verfasst von: Klein, Christian D. [VerfasserIn]
• Titel: Machine learning in mass spectrometry
• Titelzusatz: A MALDI-TOF MS approach to phenotypic antibacterial screening
• Verf.angabe: Luuk N. van Oosten, Christian D. Klein
• E-Jahr: 2020
• Jahr: March 19, 2020
• Umfang: 8 S.
• Fussnoten: Gesehen am 08.10.2020
• Titel Quelle: Enthalten in: Journal of medicinal chemistry
• Ort Quelle: Washington, DC : ACS, 1959
• Jahr Quelle: 2020
• Band/Heft Quelle: 63(2020), 16, Seite 8849-8856
• ISSN Quelle: 1520-4804
• DOI: doi:10.1021/acs.jmedchem.0c00040
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1735189057

Abstract

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.