Prediction of chemical-protein interactions network with weighted network-based inference method.
Prediction of chemical-protein interactions network with weighted network-based inference method.
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利用基于加权网络的推理方法预测化学-蛋白质相互作用网络
DOI：
10.1371/journal.pone.0041064
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Tang Y
中科院分区：
文献类型：
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作者：
Cheng F;Zhou Y;Li W;Liu G;Tang Y
化学-蛋白质相互作用(CPI)是靶标识别和药物发现的中心课题。然而，CPI的大规模测定对于体外或体内实验是一个巨大的挑战，而在电子预测中，由于低成本和高精度而显示出巨大的优势。在我们以前的基于网络推理(NBI)的药物-靶点相互作用预测方法的基础上，我们进一步发展了节点加权和边加权NBI方法来预测CPI。两个完整的CPI二部网络被用来评估这些方法，一个包含4,741个化合物和97个G蛋白偶联受体之间的17,111个CPI对，另一个包含2,827个化合物和206个蛋白偶联受体之间的13,648个CPI对。外部验证集的受试者工作特性曲线下面积范围为0.73~0.83，证实了预测的可靠性。采用边加权NBI方法对CPI网络中的弱相互作用假说进行了验证。此外，为了验证方法，对五种已获批准的药物，即伊马替尼、达沙替尼、舍替多尔、奥氮平和齐拉西酮，预测了几个候选靶点。进一步为这些预测提供了分子假说和实验证据。这些结果证实了我们的方法在理解药物多元药理学的分子基础方面具有潜在的价值，并将有助于药物的重新定位。
Chemical-protein interaction (CPI) is the central topic of target identification and drug discovery. However, large scale determination of CPI is a big challenge for in vitro or in vivo experiments, while in silico prediction shows great advantages due to low cost and high accuracy. On the basis of our previous drug-target interaction prediction via network-based inference (NBI) method, we further developed node- and edge-weighted NBI methods for CPI prediction here. Two comprehensive CPI bipartite networks extracted from ChEMBL database were used to evaluate the methods, one containing 17,111 CPI pairs between 4,741 compounds and 97 G protein-coupled receptors, the other including 13,648 CPI pairs between 2,827 compounds and 206 kinases. The range of the area under receiver operating characteristic curves was 0.73 to 0.83 for the external validation sets, which confirmed the reliability of the prediction. The weak-interaction hypothesis in CPI network was identified by the edge-weighted NBI method. Moreover, to validate the methods, several candidate targets were predicted for five approved drugs, namely imatinib, dasatinib, sertindole, olanzapine and ziprasidone. The molecular hypotheses and experimental evidence for these predictions were further provided. These results confirmed that our methods have potential values in understanding molecular basis of drug polypharmacology and would be helpful for drug repositioning.
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影响因子：
56.9
作者：
Campillos, Monica;Kuhn, Michael;Bork, Peer
通讯作者：
Bork, Peer
影响因子：
7.3
作者：
Bamborough, Paul;Drewry, David;Schneider, Klaus
通讯作者：
Schneider, Klaus
影响因子：
13.8
作者：
Csermely, P
通讯作者：
Csermely, P
影响因子：
14.9
作者：
Chen, X;Ji, ZL;Chen, YZ
通讯作者：
Chen, YZ
DOI：
10.1038/nrg2918
发表时间：
2011-01
期刊：
Nature reviews. Genetics
影响因子：
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作者：
通讯作者：
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