Abstract (may include machine translation)
Advances in genome analysis, network biology, and computational chemistry have the potential to revolutionize drug discovery by combining system-level identification of drug targets with the atomistic modeling of small molecules capable of modulating their activity. To demonstrate the effectiveness of such a discovery pipeline, we deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. This blueprint is applicable for any sequenced organism with high-quality metabolic reconstruction and suggests a general strategy for strain-specific antiinfective therapy.
Original language | English |
---|---|
Pages (from-to) | 1082-1087 |
Number of pages | 6 |
Journal | Proceedings of the National Academy of Sciences of the United States of America |
Volume | 107 |
Issue number | 3 |
DOIs | |
State | Published - 19 Jan 2010 |
Externally published | Yes |
Keywords
- Antibiotics
- Flux balance analysis
- Virtual screening