The Campbell Family Institute for Cancer Research, Ontario Cancer Institute, University Health Network, IBM Life Sciences Discovery Centre, Toronto Medical Discovery Tower, 9-305, 101 College Street, Toronto, Ontario, M5G 1L7, Canada.
Methods. 2012 Aug;57(4):499-507. doi: 10.1016/j.ymeth.2012.06.003. Epub 2012 Jun 27.
Proteins do not exert their effects in isolation of one another, but interact together in complex networks. In recent years, sophisticated methods have been developed to leverage protein-protein interaction (PPI) network structure to improve several stages of the drug discovery process. Network-based methods have been applied to predict drug targets, drug side effects, and new therapeutic indications. In this paper we have two aims. First, we review the past contributions of network approaches and methods to drug discovery, and discuss their limitations and possible future directions. Second, we show how past work can be generalized to gain a more complete understanding of how drugs perturb networks. Previous network-based characterizations of drug effects focused on the small number of known drug targets, i.e., direct binding partners of drugs. However, drugs affect many more genes than their targets - they can profoundly affect the cell's transcriptome. For the first time, we use networks to characterize genes that are differentially regulated by drugs. We found that drug-regulated genes differed from drug targets in terms of functional annotations, cellular localizations, and topological properties. Drug targets mainly included receptors on the plasma membrane, down-regulated genes were largely in the nucleus and were enriched for DNA binding, and genes lacking drug relationships were enriched in the extracellular region. Network topology analysis indicated several significant graph properties, including high degree and betweenness for the drug targets and drug-regulated genes, though possibly due to network biases. Topological analysis also showed that proteins of down-regulated genes appear to be frequently involved in complexes. Analyzing network distances between regulated genes, we found that genes regulated by structurally similar drugs were significantly closer than genes regulated by dissimilar drugs. Finally, network centrality of a drug's differentially regulated genes correlated significantly with drug toxicity.
蛋白质并非彼此孤立地发挥作用，而是在复杂的网络中相互作用。近年来，已经开发出了复杂的方法，利用蛋白质-蛋白质相互作用（PPI）网络结构来改善药物发现过程的几个阶段。基于网络的方法已被应用于预测药物靶点、药物副作用和新的治疗适应症。在本文中，我们有两个目的。首先，我们回顾了网络方法在药物发现中的过去贡献，并讨论了它们的局限性和可能的未来方向。其次，我们展示了如何将过去的工作进行推广，以更全面地了解药物如何干扰网络。以前基于网络的药物作用特征主要集中在少数已知的药物靶点上，即药物的直接结合伙伴。然而，药物影响的基因比它们的靶点多得多——它们可以深刻地影响细胞的转录组。我们首次使用网络来描述受药物调节的基因。我们发现，药物调节的基因与药物靶点在功能注释、细胞定位和拓扑性质方面存在差异。药物靶点主要包括细胞膜上的受体，下调的基因主要在核内，富含 DNA 结合，缺乏药物关系的基因则富含细胞外区域。网络拓扑分析表明，有几个显著的图性质，包括药物靶点和药物调节基因的高度数和中间中心性，尽管这可能是由于网络偏差造成的。下调基因的蛋白质拓扑分析表明，它们经常参与复合物。分析受调控基因之间的网络距离，我们发现结构相似的药物调节的基因比结构不同的药物调节的基因更接近。最后，药物差异调节基因的网络中心性与药物毒性显著相关。
