iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.
iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.
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iGPCR-Drug：用于预测蜂窝网络中 GPCR 和药物之间相互作用的 Web 服务器
DOI：
10.1371/journal.pone.0072234
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Chou KC
中科院分区：
文献类型：
--
作者：
Xiao X;Min JL;Wang P;Chou KC
G蛋白偶联受体（GPCR）与癌症、糖尿病、神经退行性疾病、炎症和呼吸系统疾病等许多疾病有关，是治疗药物最常见的靶点之一。纯粹通过实验技术来确定药物和 GPCR 是否在细胞网络中相互作用是耗时且昂贵的。尽管基于蛋白质 3D（维度）结构的知识在这方面开发了一些计算方法，但不幸的是，它们的用途相当有限，因为大多数 GPCR 的 3D 结构仍然未知。为了克服这种情况，开发了一种称为“iGPCR-药物”的基于序列的分类器来预测细胞网络中 GPCR 和药物之间的相互作用。在预测器中，药物化合物由 256D 向量的 2D（维）指纹配制而成，GPCR 由灰色模型理论生成的 PseAAC（伪氨基酸组成）组成，预测引擎由模糊 K 最近邻算法操作。此外，还建立了一个用户友好的iGPCR-drug网络服务器：http://www.jci-bioinfo.cn/iGPCR-Drug/。为了方便大多数实验科学家，提供了有关如何使用网络服务器获得所需结果的分步指南，而无需遵循本文中提出的复杂数学方程，只是为了保证其完整性。 iGPCR-drug 通过折刀测试实现的总体成功率为 85.5%，这明显高于 2010 年开发的现有同行方法的成功率，尽管尚未为其建立 Web 服务器。预计 iGPCR-Drug 可能成为基础研究和药物开发的有用的高通量工具，并且这里提出的方法也可以扩展到研究其他药物-靶点相互作用网络。
Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called “iGPCR-drug”, was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional) fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition) generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug – target interaction networks.
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影响因子：
4.4
作者：
Chou, KC
通讯作者：
Chou, KC
影响因子：
2
作者：
Chen, Yen-Kuang;Li, Kuo-Bin
通讯作者：
Li, Kuo-Bin
影响因子：
3
作者：
Chou, KC
通讯作者：
Chou, KC
影响因子：
64.5
作者：
Call, Matthew E.;Schnell, Jason R.;Wucherpfennig, Kai W.
通讯作者：
Wucherpfennig, Kai W.
DOI：
10.1021/ci9803381
发表时间：
1999-07-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Butina, D
通讯作者：
Butina, D