| Autor: |
Gurvic D; Computational Biology, School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, United Kingdom., Leach AG; Division of Pharmacy and Optometry, University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom.; Medchemica Limited, Mereside, Alderley Park, Macclesfield, SK10 4TG, United Kingdom., Zachariae U; Computational Biology, School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, United Kingdom. |
| Abstrakt: |
The complex cell envelope of Gram-negative bacteria creates a formidable barrier to antibiotic influx. Reduced drug uptake impedes drug development and contributes to a wide range of drug-resistant bacterial infections, including those caused by extremely resistant species prioritized by the World Health Organization. To develop new and efficient treatments, a better understanding of the molecular features governing Gram-negative permeability is essential. Here, we present a data-driven approach, using matched molecular pair analysis and machine learning on minimal inhibitory concentration data from Gram-positive and Gram-negative bacteria to uncover chemical features that influence Gram-negative bioactivity. We find recurring chemical moieties, of a wider range than previously known, that consistently improve activity and suggest that this insight can be used to optimize compounds for increased Gram-negative uptake. Our findings may help to expand the chemical space of broad-spectrum antibiotics and aid the search for new antibiotic compound classes. |
