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ss-TEA:基于熵的 A 类 GPCR 多重序列比对中受体特异性配体结合残基的鉴定。

ss-TEA: Entropy based identification of receptor specific ligand binding residues from a multiple sequence alignment of class A GPCRs.

机构信息

Computational Drug Discovery Group, Radboud University NijmegenMedical Centre, Geert Grooteplein, Nijmegen, The Netherlands.

出版信息

BMC Bioinformatics. 2011 Aug 10;12:332. doi: 10.1186/1471-2105-12-332.

DOI:10.1186/1471-2105-12-332
PMID:21831265
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3162937/
Abstract

BACKGROUND

G-protein coupled receptors (GPCRs) are involved in many different physiological processes and their function can be modulated by small molecules which bind in the transmembrane (TM) domain. Because of their structural and sequence conservation, the TM domains are often used in bioinformatics approaches to first create a multiple sequence alignment (MSA) and subsequently identify ligand binding positions. So far methods have been developed to predict the common ligand binding residue positions for class A GPCRs.

RESULTS

Here we present 1) ss-TEA, a method to identify specific ligand binding residue positions for any receptor, predicated on high quality sequence information. 2) The largest MSA of class A non olfactory GPCRs in the public domain consisting of 13324 sequences covering most of the species homologues of the human set of GPCRs. A set of ligand binding residue positions extracted from literature of 10 different receptors shows that our method has the best ligand binding residue prediction for 9 of these 10 receptors compared to another state-of-the-art method.

CONCLUSIONS

The combination of the large multi species alignment and the newly introduced residue selection method ss-TEA can be used to rapidly identify subfamily specific ligand binding residues. This approach can aid the design of site directed mutagenesis experiments, explain receptor function and improve modelling. The method is also available online via GPCRDB at http://www.gpcr.org/7tm/.

摘要

背景

G 蛋白偶联受体(GPCRs)参与许多不同的生理过程,其功能可以被结合在跨膜(TM)域中的小分子调节。由于它们的结构和序列保守性,TM 域经常用于生物信息学方法来首先创建多个序列比对(MSA),并随后识别配体结合位置。到目前为止,已经开发了方法来预测 A 类 GPCR 常见的配体结合残基位置。

结果

在这里,我们提出了 1)ss-TEA,这是一种基于高质量序列信息识别任何受体特定配体结合残基位置的方法。2)在公共领域中包含 13324 个序列的 A 类非嗅觉 GPCR 的最大 MSA,涵盖了人类 GPCR 集合的大多数物种同源物。从 10 种不同受体的文献中提取的一组配体结合残基位置表明,与另一种最先进的方法相比,我们的方法对这 10 种受体中的 9 种具有最佳的配体结合残基预测。

结论

大规模多物种比对和新引入的残基选择方法 ss-TEA 的结合可用于快速识别亚家族特异性配体结合残基。这种方法可以辅助设计定点突变实验,解释受体功能并改善建模。该方法也可通过 GPCRDB 在 http://www.gpcr.org/7tm/ 在线获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/14a31055a772/1471-2105-12-332-8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/277718ab4948/1471-2105-12-332-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/d070623e4746/1471-2105-12-332-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/8612d42811e4/1471-2105-12-332-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/a4b502d24728/1471-2105-12-332-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/a305b50448bc/1471-2105-12-332-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/7ec24dc448ce/1471-2105-12-332-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/040a30f4c2d8/1471-2105-12-332-7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/14a31055a772/1471-2105-12-332-8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/277718ab4948/1471-2105-12-332-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/d070623e4746/1471-2105-12-332-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/8612d42811e4/1471-2105-12-332-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/a4b502d24728/1471-2105-12-332-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/a305b50448bc/1471-2105-12-332-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/7ec24dc448ce/1471-2105-12-332-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/040a30f4c2d8/1471-2105-12-332-7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7b1/3162937/14a31055a772/1471-2105-12-332-8.jpg

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