Division of Life Science and Engineering, School of Science and Engineering, Tokyo Denki University (TDU), Tokyo, Japan.
Asubio Pharma Co., Ltd., Tokyo, Japan.
J Recept Signal Transduct Res. 2020 Aug;40(4):348-356. doi: 10.1080/10799893.2020.1734821. Epub 2020 Mar 8.
G protein-coupled receptors (GPCRs) can form homodimers, heterodimers, or higher-order molecular complexes (oligomers). The reports on the change of functions through the oligomerization have been accumulated. Inhibition of GPCR oligomerization without affecting the protomer's overall structure would clarify the oligomer-specific functions although inhibition experiments are costly and require accurate information about the interface location. Unfortunately, the number of experimentally determined interfaces is limited. The precise prediction of the oligomerization interfaces is, therefore, useful for inhibition experiments to examine the oligomer-specific functions, which would accelerate investigations of the GPCR signaling. However, interface prediction for GPCR oligomerization is difficult because different GPCR subtypes belonging to the same subfamily often use different structural regions as their interfaces. We previously developed a high-performance method to predict the interfaces for GPCR oligomerization, by identifying the conserved surfaces with the sequence and structure information. Then, the structural characteristic of a GPCR structure is regarded to be a thick-tube like conformation that is approximately perpendicular to the membrane plane. Our method had successfully predicted all of the interfaces available on that day. We had launched a web server for our interface prediction of GPCRs (GRIP). We have improved the previous version of GRIP server and enhanced its usability. First, we discarded the approximation of the GPCR structure as the thick-tube-like conformation. This improvement increased the number of structures for the prediction. Second, the FUGUE-based template recommendation service was introduced to facilitate the choice of an appropriate structure for the prediction. The new prediction server is available at http://grip.b.dendai.ac.jp/∼grip/.
G 蛋白偶联受体 (GPCR) 可以形成同源二聚体、异源二聚体或更高阶的分子复合物 (寡聚体)。关于通过寡聚化改变功能的报道已经积累了很多。尽管抑制实验成本高昂且需要关于界面位置的准确信息,但抑制 GPCR 寡聚化而不影响单体的整体结构,将阐明寡聚体特异性功能。不幸的是,实验确定的界面数量有限。因此,精确预测寡聚化界面对于抑制实验检查寡聚体特异性功能很有用,这将加速 GPCR 信号转导的研究。然而,由于属于同一亚家族的不同 GPCR 亚型通常使用不同的结构区域作为其界面,因此预测 GPCR 寡聚化的界面具有挑战性。我们之前开发了一种基于识别序列和结构信息的保守表面来预测 GPCR 寡聚化界面的高性能方法。然后,将 GPCR 结构的结构特征视为与膜平面大致垂直的厚管状构象。我们的方法成功预测了当时可用的所有界面。我们已经推出了一个用于 GPCR 界面预测的网络服务器(GRIP)。我们改进了之前版本的 GRIP 服务器并增强了其可用性。首先,我们放弃了将 GPCR 结构近似为厚管状构象的假设。这种改进增加了可用于预测的结构数量。其次,引入了基于 FUGUE 的模板推荐服务,以方便选择适当的结构进行预测。新的预测服务器可在 http://grip.b.dendai.ac.jp/∼grip/ 获得。