Computational prediction of GPCR oligomerization.
Computational prediction of GPCR oligomerization.
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DOI：
10.1016/j.sbi.2019.04.005
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发表时间：
2019-04
影响因子：
6.8
通讯作者：
A. Townsend-Nicholson;N. Altwaijry;Andrew Potterton;Iñaki Morao;Alexander Heifetz
中科院分区：
文献类型：
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作者：
A. Townsend-Nicholson;N. Altwaijry;Andrew Potterton;Iñaki Morao;Alexander Heifetz
GPCR二聚体是一种动态物质，其二聚化界面在受体激活和失活过程中发生变化。计算方法是从存在的少数二聚体结构中提取信息的有价值的手段。基于TM螺旋的整体计算模拟可以用于预测GPCR二聚体界面。FMO是一种量子力学方法，可以用来分析GPCR二聚体中TM之间相互作用的化学性质。最近，GPCR晶体结构的数量有了大量的扩展：以活性和非活性形式，以及与配体、与G蛋白以及彼此之间的复合物。尽管如此，在这些不同的生物学相关状态期间，关于GPCR寡聚体的精确构型的实验信息相对较少。虽然有可能确定优先以特定结构构象结晶GPCR所需的实验条件，但计算方法提供了一种可能更易于处理的方法来描述受体二聚体和更高级低聚体形成的可能性。基于结构确定的二聚体的基于集合的计算方法，结合使用量子力学方法分析GPCR二聚体界面处分子相互作用的化学性质的计算工作流程，将产生预测以前未鉴定的GPCR二聚体所需的可重复和准确的预测，并告知我们理解和开始精确操纵生物学中GPCR寡聚体的能力的未来进展。系统.它还可以提供实现实验确定的寡聚GPCR结构的数量增加所需的信息。
HighlightsGPCR dimers are a dynamic species with a changing dimerization interface that shifts during receptor activation and inactivation.Computational methodologies are a valuable means of extracting information from those few dimer structures that exist.Ensemble-based computational simulations of TM helices can be used to predict GPCR dimer interfaces.FMO, a quantum mechanically-informed approach, can be used to analyse the chemical nature of the interactions between TMs in a GPCR dimer.There has been a recent and prolific expansion in the number of GPCR crystal structures being solved: in both active and inactive forms and in complex with ligand, with G protein and with each other. Despite this, there is relatively little experimental information about the precise configuration of GPCR oligomers during these different biologically relevant states. While it may be possible to identify the experimental conditions necessary to crystallize a GPCR preferentially in a specific structural conformation, computational approaches afford a potentially more tractable means of describing the probability of formation of receptor dimers and higher order oligomers. Ensemble-based computational methods based on structurally determined dimers, coupled with a computational workflow that uses quantum mechanical methods to analyze the chemical nature of the molecular interactions at a GPCR dimer interface, will generate the reproducible and accurate predictions needed to predict previously unidentified GPCR dimers and to inform future advances in our ability to understand and begin to precisely manipulate GPCR oligomers in biological systems. It may also provide information needed to achieve an increase in the number of experimentally determined oligomeric GPCR structures.