| 引用本文: | 许伟明,王晓锋,林娟,蔡伟文,鄢仁祥.G蛋白偶联受体计算研究的进展和前瞻[J].生物信息学,2016,14(1):31-38. |
| XU Weiming,Wang Xiaofeng,Lin Juan,CAI Weiwen,YAN Renxiang.Progresses and prospects of computational study on G protein-coupled receptors[J].Chinese Journal of Bioinformatics,2016,14(1):31-38. |
|
| 本文已被：浏览 7686次 下载 3866次 |
码上扫一扫！ |
|
|
| G蛋白偶联受体计算研究的进展和前瞻 |
|
许伟明1,王晓锋2,林娟1,3,蔡伟文1,鄢仁祥1,3
|
|
(1.福州大学生物科学与工程学院,福州 350108;2.山西师范大学数学与计算机科学学院,山西 临汾 041004;3.福建省海洋酶工程重点实验室,福州 350108)
|
|
| 摘要: |
| G蛋白偶联受体(G protein-coupled receptor, GPCR)是含有七个跨膜螺旋的一类重要蛋白,是迄今为止发现的最大的多药物靶标受体超蛋白家族。例如,目前上市药物中有超过30%是以GPCR为靶点的。然而,与GPCR重要性形成强烈反差的是科学界对于其结构与功能的了解非常贫乏,主要原因是通过实验手段来获得GPCR的结构与功能信息极其困难。利用生物信息学方法从基因组规模的数据中识别GPCR并预测三维结构是可行途径之一。基于生物信息学的GPCR研究将为新型药物靶标的筛选和药物的开发提供一定的帮助。本文论述了几种较为典型的GPCR计算方法,并基于已有研究提出可能的创新性研究策略来解决GPCR蛋白识别、跨膜区定位、以及结构和功能预测等问题。 |
| 关键词: G蛋白偶联受体 GPCR识别 蛋白结构预测 跨膜区预测 药物配体 |
| DOI：10.3969/j.issn.1672-5565.2016.01.06 |
| 分类号:Q51 |
| 文献标识码:A |
| 基金项目: |
|
| Progresses and prospects of computational study on G protein-coupled receptors |
|
XU Weiming1, Wang Xiaofeng2,Lin Juan1,3, CAI Weiwen1,YAN Renxiang1,3
|
|
(1.College of Biological Sciences and Engineering, Fuzhou University, Institute of Applied Genomics, Fuzhou 350108, China; 2. College of Mathematics and Computer Science,Shanxi Normal University,Linfen 041004,China;3.Fujian Key Laboratory of of Marine Enzyme Engineering,Fuzhou 350108,China)
|
| Abstract: |
| G protein coupled receptors (GPCR), a general designation of a large class of membrane proteins, contain seven transmembrane helices in its three-dimensional structure, which currently are the drug targets more than 30% in the market. In contrast to the importance of GPCR, the knowledge of scientific community to understand its structure and function is very limited. The main reason is the difficulty to obtain the structure and function of GPCR information by wet experiment. Now, it is feasible to use bioinformatics methods to identify and predict the 3D structure of GPCR. Research on GPCR based on bioinformatics is beneficial to novel drug targets screening and new drugs developing. This paper discusses some typical bioinformatics methods. In addition, several possible new research strategies are presented to address the identification of GPCR proteins from a genome scale database, position its transmembrane region and predict the three-dimensional structure of GPCR and drug ligand binding mode. |
| Key words: G protein-coupled receptors GPCR recognition Protein structure prediction Transmembrane region prediction Drug ligand |
