Identification of novel bacterial histidine biosynthesis inhibitors using docking, ensemble rescoring, and whole-cell assays

Publication: Research - peer-reviewJournal article – Annual report year: 2010

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The rapid spread on multidrug-resistant strains of Staphylococcus aureus requires not just novel treatment options, but the development of faster methods for the identification of new hits for drug development. The exponentially increasing speed of computational methods makes a more extensive use in the early stages of drug discovery attractive if sufficient accuracy can be achieved. Computational target identification using systems-level methods suggested the histidine biosynthesis pathway as an attractive target against S. aureus. Potential inhibitors for the pathway were identified through docking, followed by ensemble rescoring, that is sufficiently accurate to justify immediate testing of the identified compounds by whole-cell assays, avoiding the need for time-consuming and often difficult intermediary enzyme assays. This novel strategy is demonstrated for three key enzymes of the S. aureus histidine biosynthesis pathway, which is predicted to be essential for bacterial biomass productions. Virtual screening of a library of similar to 10(6) compounds identified 49 potential inhibitors of three enzymes of this pathway. Eighteen representative compounds were directly tested on three S. aureus-and two Escherichia coli strains in standard disk inhibition assays. Thirteen compounds are inhibitors of some or all of the S. aureus strains, while 14 compounds weakly inhibit growth in one or both E. coli strains. The high hit rate obtained from a fast virtual screen demonstrates the applicability of this novel strategy to the histidine biosynthesis pathway.
Original languageEnglish
JournalBioorganic & Medicinal Chemistry
Publication date2010
Volume18
Issue14
Pages5148-5156
ISSN0968-0896
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 8

Keywords

  • Virtual screening, Antibiotics, Histidine biosynthesis, Systems biology
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