Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
Proc Natl Acad Sci U S A. 2010 Jan 19;107(3):1082-7. doi: 10.1073/pnas.0909181107. Epub 2010 Jan 5.
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.
基因组分析、网络生物学和计算化学的进展有可能通过将药物靶点的系统水平鉴定与能够调节其活性的小分子的原子建模相结合,从而彻底改变药物发现。为了证明这种发现途径的有效性,我们通过识别其代谢网络中共同的组织特异性或普遍必需的代谢反应,推断出大肠杆菌和金黄色葡萄球菌中的常见抗生素靶标。然后,我们通过虚拟筛选预测了这些反应中的几种酶的数十种潜在抑制剂,并通过实验表明,这些抑制剂中的一部分可同时抑制体外酶活性和细菌细胞活力。该蓝图适用于具有高质量代谢重建的任何测序生物体,并为针对菌株的抗感染治疗提出了一般策略。