The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Bacterial Membranes as Predictors of Antimicrobial Potency
Abstract
A wide range of chemical structures having antimicrobial activity have been studied in an effort to treat the increasing emergence of bacteria that are resistant to traditional antibiotics. These agents have varying degrees of toxicity against different bacterial species. We demonstrate, using members of a novel class of antimicrobial agents, the oligomers of acyllysine, that one cause for the difference in species selectivity is the ability to induce the clustering of anionic lipids, resulting in their segregation into domains. This phenomenon occurs only in bacterial membranes composed of both anionic and zwitterionic lipids and not with bacteria whose membrane lipids are largely anionic. As a consequence it can be predicted which bacterial species will be most affected by antimicrobial agents that function principally by this mechanism. This finding allows for the design of new antibiotics with selective toxicity against different groups of bacteria.
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Citations
Cites
Antimicrobial peptides of multicellular organisms
Zasloff, M. (2002) · Nature · doi:10.1038/415389a · Cited by 8,457
Cited by
Biomimetic antimicrobial polymers: recent advances in molecular design
Ergene, C., Yasuhara, K., & Palermo, E. F. (2018) · Polymer Chemistry · doi:10.1039/c8py00012c · Cited by 312
Metadata
- Authors
- Epand, R. M., Rotem, S., Mor, A., Berno, B., & Epand, R. F.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-10-01
- Volume
- 130
- Issue
- 43
- Pages
- 14346-14352
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W2009939017
- Cited by
- 172
- References
- 1
- FWCI
- 8.25
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.