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Rhodopsin and the others: A historical perspective on structural studies of G protein-coupled receptors

机译:视紫红质和其他:G蛋白偶联受体的结构研究的历史观点。

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The role of rhodopsin as a structural prototype for the study of the whole superfamily of G protein-coupled receptors (GPCRs) is reviewed in an historical perspective. Discovered at the end of the nineteenth century, fully sequenced since the early 1980s, and with direct three-dimensional information available since the 1990s, rhodopsin has served as a platform to gather indirect information on the structure of the other superfamily members. Recent breakthroughs have elicited the solution of the structures of additional receptors, namely the β1- and β2-adrenergic receptors and the A2A adenosine receptor, now providing an opportunity to gauge the accuracy of homology modeling and molecular docking techniques and to perfect the computational protocol. Notably, in coordination with the solution of the structure of the A2A adenosine receptor, the first "critical assessment of GPCR structural modeling and docking" has been organized, the results of which highlighted that the construction of accurate models, although challenging, is certainly achievable. The docking of the ligands and the scoring of the poses clearly emerged as the most difficult components. A further goal in the field is certainly to derive the structure of receptors in their signaling state, possibly in complex with agonists. These advances, coupled with the introduction of more sophisticated modeling algorithms and the increase in computer power, raise the expectation for a substantial boost of the robustness and accuracy of computer-aided drug discovery techniques in the coming years.
机译:从历史的角度回顾了视紫红质作为结构原型研究G蛋白偶联受体(GPCR)的整个超家族的作用。视紫红质发现于19世纪末,自1980年代初开始完全测序,并于1990年代开始提供直接的三维信息,视紫红质作为收集其他超家族成员结构的间接信息的平台。最近的突破引起了对其他受体(即β1-和β2-肾上腺素受体)和A2A腺苷受体的结构的解决方案,现在提供了一个机会来评估同源性建模和分子对接技术的准确性并完善计算方案。值得注意的是,与A2A腺苷受体结构的解决方案相协调,已组织了首次“ GPCR结构建模和对接的关键评估”,其结果突显出,尽管具有挑战性,但准确的模型构建无疑是可以实现的。配体的对接和姿势的评分显然是最困难的部分。该领域的另一个目标当然是推导处于其信号传导状态的受体的结构,可能与激动剂复合。这些进展,再加上更先进的建模算法的引入以及计算机功能的增强,提高了人们对在未来几年中大幅提高计算机辅助药物发现技术的鲁棒性和准确性的期望。

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