Suppr超能文献

G 蛋白偶联受体结构预测的进展:与依替唑仑结合的 D3 受体。

Progress in the structural prediction of G protein-coupled receptors: D3 receptor in complex with eticlopride.

机构信息

Research Programme on Biomedical Informatics, GRIB, IMIM/Universitat Pompeu Fabra, PRBB, Dr Aiguader 88, E-08003 Barcelona, Spain.

出版信息

Proteins. 2011 Jun;79(6):1695-703. doi: 10.1002/prot.23021. Epub 2011 Apr 12.

Abstract

Predicting the three-dimensional structure of ligand-receptor complexes involving G protein-coupled receptors (GPCRs) is still a challenging task in rational drug design. To evaluate the reliability of the GPCR structural prediction, only a couple of community-wide assessments have been carried out. Our participation in the last edition, DOCK2010, involved the blind prediction of the dopaminergic D(3) receptor in complex with the D(2)/D(3) selective antagonist eticlopride for which the crystal structure has been recently released. Here, we describe a methodology that succeeded to produce a correctly predicted eticlopride-D(3) receptor complex out of three submitted models. Ranking the obtained models in the correct order is the main challenge due to subtle structural differences in the complex that are not sufficiently captured by conventional scoring functions. Importantly, our work reveals that a correct ranking is obtained by including a more sophisticated description of conformational ligand energy on binding. All in all, this case study highlights the current progress in modeling GPCR complexes and underlines that in silico modeling can be a valuable complement in GPCR drug discovery.

摘要

预测配体-受体复合物(涉及 G 蛋白偶联受体(GPCRs))的三维结构仍然是合理药物设计中的一项具有挑战性的任务。为了评估 GPCR 结构预测的可靠性,仅进行了几次全社区评估。我们在上一届 DOCK2010 中的参与涉及与多巴胺 D(3)受体的盲测,该受体与多巴胺 D(2)/D(3)选择性拮抗剂埃替克洛平形成复合物,其晶体结构最近已公布。在这里,我们描述了一种成功产生三种提交模型中正确预测的埃替克洛平-D(3)受体复合物的方法。由于复合物中存在细微的结构差异,常规评分函数无法充分捕捉到这些差异,因此以正确的顺序对获得的模型进行排名是主要挑战。重要的是,我们的工作表明,通过在结合时包含对构象配体能量的更复杂描述,可以获得正确的排名。总而言之,本案例研究突出了目前在 GPCR 复合物建模方面的进展,并强调了计算建模可以成为 GPCR 药物发现的有价值的补充。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验