Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy.
Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy.
复制标题
定量和系统药理学。
DOI：
10.1021/acs.jcim.7b00216
复制
发表时间：
2017-11-27
影响因子：
5.6
通讯作者：
Cheng F
中科院分区：
文献类型：
--
作者：
Fang J;Wu Z;Cai C;Wang Q;Tang Y;Cheng F
具有不同化学支架的天然产物已被认为是药物发现和开发中化合物的宝贵来源。然而，通过各种实验测定在人类蛋白质组水平上系统地鉴定天然产物的药物靶标是非常昂贵和耗时的。在这项研究中，我们提出了一个系统药理学基础设施，预测新的药物靶点和天然产物的抗癌适应症。具体来说，我们重建了一个全球药物-靶标网络，其中包含7，314种相互作用，连接了751个靶标和2，388种天然产物，并通过基于平衡子结构-药物-靶标网络的推理方法建立了预测网络模型。在交叉验证过程中，预测天然产物新靶标的受试者工作特征曲线下面积为0.96。天然产物的新预测目标（例如，白藜芦醇、染料木黄酮和山奈酚）的高得分，通过各种文献研究进行验证。我们进一步建立了统计网络模型，通过整合实验验证和计算预测的天然产物与已知癌症蛋白的药物-靶标相互作用，用于识别天然产物的新抗癌适应症。我们表明，多种天然产物（例如，柚皮素、双硫仑和二甲双胍）与新的作用机制通过各种已发表的实验证据进行了验证。总之，这项研究提供了强大的计算系统药理学方法和工具，通过利用天然产物的多药理学来开发新的靶向癌症治疗。
Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.
登录
查看更多内容
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
影响因子：
3.7
作者：
Cheng F;Zhou Y;Li W;Liu G;Tang Y
通讯作者：
Tang Y
DOI：
10.1136/amiajnl-2013-002512
发表时间：
2014-10-01
影响因子：
6.4
作者：
Cheng, Feixiong;Zhao, Zhongming
通讯作者：
Zhao, Zhongming
影响因子：
3.2
作者：
Goettert, Marcia;Schattel, Verena;Laufer, Stefan
通讯作者：
Laufer, Stefan
DOI：
10.1021/ci0003101
发表时间：
2001-03-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
He, M;Yan, XJ;Xie, GR
通讯作者：
Xie, GR
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y