Study on human GPCR-inhibitor interactions by proteochemometric modeling
Study on human GPCR-inhibitor interactions by proteochemometric modeling
复制标题
通过蛋白质化学计量学模型研究人类 GPCR-抑制剂相互作用
DOI：
10.1016/j.gene.2012.11.061
复制
发表时间：
2013-04-10
期刊：
影响因子：
3.5
通讯作者：
He, Yuan
中科院分区：
文献类型：
--
作者：
Gao, Jun;Huang, Qi;He, Yuan
G蛋白偶联受体（GPCR）是制药行业中最常见的药物靶点。然而，在GPCR药物设计中实现高安全性和有效性是相当具有挑战性的，因为它们的一级氨基酸序列显示出相当高的同源性。对抑制剂与多种人GPCR相互作用光谱的系统研究，将为设计针对不同GPCR相关疾病的抑制剂提供理论依据。为了实现这一目标，我们采用两种统计学习技术，分别基于两种蛋白质描述符、两种配体描述符和一种配体-受体交叉项的不同组合，构建了几种蛋白质化学计量学模型，结果表明，对于大多数描述符组合，支持向量回归（SVR）模型的性能优于高斯过程（GP）模型。跨膜身份描述符比Z尺度描述符在GPCR的表征中具有更强的能力。此外，我们的PCM模型的性能并没有通过引入交叉项得到改善。最后，基于TM Identity描述符和28维类药指数，分别建立了GP和SVR的两个最佳PCM模型（GP-S-DLI：R-2 = 0.9345，Q（test）（2）= 0.7441; SVR-S-DLI：R-2 = 1.0000，Q（test）（2）= 0.7423）。当使用外部测试集时，ROC曲线的面积为0.8940，这表明我们的PCM模型获得了预测GPCR与配体之间的新相互作用的强大能力，我们的结果表明，所推导的最佳模型对人类GPCR-抑制剂相互作用具有较高的预测能力。它可能用于发现新的多靶点或特异性GPCR抑制剂，具有更高的疗效和更少的副作用。(c)2012 Elsevier B. V.保留所有权利。
G protein-coupled receptors (GPCRs) are the most frequently addressed drug targets in the pharmaceutical industry. However, achieving highly safety and efficacy in designing of GPCR drugs is quite challenging since their primary amino acid sequences show fairly high homology. Systematic study on the interaction spectra of inhibitors with multiple human GPCRs will shed light on how to design the inhibitors for different diseases which are related to GPCRs. To reach this goal, several proteochemometric models were constructed based on different combinations of two protein descriptors, two ligand descriptors and one ligand-receptor cross-term by two kinds of statistical learning techniques.Our results show that support vector regression (SVR) performs better than Gaussian processes (GP) for most combinations of descriptors. The transmembrane (TM) identity descriptors have more powerful ability than the z-scale descriptors in the characterization of GPCRs. Furthermore, the performance of our PCM models was not improved by introducing the cross-terms. Finally, based on the TM Identity descriptors and 28-dimensional drug-like index, two best PCM models with GP and SVR (GP-S-DLI: R-2 = 0.9345, Q(test)(2) = 0.7441; SVR-S-DLI: R-2 = 1.0000, Q(test)(2) = 0.7423) were derived respectively. The area of ROC curve was 0.8940 when an external test set was used, which indicates that our PCM model obtained a powerful capability for predicting new interactions between GPCRs and ligands.Our results indicate that the derived best model has a high predictive ability for human GPCR-inhibitor interactions. It can be potentially used to discover novel multi-target or specific inhibitors of GPCRs with higher efficacy and fewer side effects. (c) 2012 Elsevier B.V. All rights reserved.