Modelling and simulation of biased agonism dynamics at a G protein-coupled receptor.
Modelling and simulation of biased agonism dynamics at a G protein-coupled receptor.
复制标题
DOI：
10.1016/j.jtbi.2018.01.010
复制
发表时间：
2018-04-07
影响因子：
2
通讯作者：
Ladds G
中科院分区：
文献类型：
--
作者：
Bridge LJ;Mead J;Frattini E;Winfield I;Ladds G
提出了一种新的G蛋白偶联受体（GPCR）有偏激动动力学的常微分方程模型。该模型在G蛋白和活性受体的数量上具有普遍性。数值模拟揭示了活性G蛋白动力学中的新现象，包括所观察到的配体效应（激动剂到反向激动剂）的相互转换。该模型概括了新的实验数据的细胞和配体被认为表现出偏见激动。G蛋白偶联受体（GPCR）浓度-反应关系的理论模型通常假设激动剂通过受体的单一活性状态产生单一功能反应。这些模型在很大程度上是假设稳态条件进行分析的。现在有很多实验证据表明，许多GPCR可以存在于多种受体构象，并引发许多功能性反应，配体具有激活不同信号通路的潜力，以不同的程度-一个概念称为偏激动，功能选择性或多维功效。此外，最近的实验结果表明时间依赖性偏差的明确可能性，其中激动剂相对于不同途径的偏差可能动态变化。通过表征和量化配体对多种途径的影响，了解时间偏差的影响，将有助于扩展当前的平衡结合和偏置激活模型，包括G蛋白激活动力学。在这里，我们提出了一个新的模型的时间依赖性偏激动，基于常微分方程的多个立方三元复合物激活模型与G蛋白循环动力学。该模型首次允许在活性G蛋白（αGTP）水平上模拟和分析单个受体的多途径激活偏倚动力学，以分析动态功能反应。该模型一般适用于NG G蛋白和N* 活性受体状态的系统。数值模拟揭示了新的见解系统参数（包括协同性，配体和受体浓度）对偏置动力学的影响，突出了新的现象，包括偏置方向的动态相互转换。此外，我们适合这个模型的“湿”实验数据的两个竞争G蛋白（Gi和Gs），成为激活后的腺苷A1受体与腺苷衍生物化合物的刺激。最后，我们表明，我们的模型可以定性地描述这种竞争G蛋白激活的时间动态。
A new ordinary differential equation model for biased agonism dynamics at a G protein-coupled receptor (GPCR) is presented. The model is general in the number of G proteins and active receptor. Numerical simulations reveal new phenomena in active G protein dynamics, including inter-conversion of the observed ligand effect (agonist to inverse agonist). The model recapitulates new experimental data for cells and ligands which are believed to exhibit biased agonism. Theoretical models of G protein-coupled receptor (GPCR) concentration-response relationships often assume an agonist producing a single functional response via a single active state of the receptor. These models have largely been analysed assuming steady-state conditions. There is now much experimental evidence to suggest that many GPCRs can exist in multiple receptor conformations and elicit numerous functional responses, with ligands having the potential to activate different signalling pathways to varying extents–a concept referred to as biased agonism, functional selectivity or pluri-dimensional efficacy. Moreover, recent experimental results indicate a clear possibility for time-dependent bias, whereby an agonist’s bias with respect to different pathways may vary dynamically. Efforts towards understanding the implications of temporal bias by characterising and quantifying ligand effects on multiple pathways will clearly be aided by extending current equilibrium binding and biased activation models to include G protein activation dynamics. Here, we present a new model of time-dependent biased agonism, based on ordinary differential equations for multiple cubic ternary complex activation models with G protein cycle dynamics. This model allows simulation and analysis of multi-pathway activation bias dynamics at a single receptor for the first time, at the level of active G protein (αGTP), towards the analysis of dynamic functional responses. The model is generally applicable to systems with NG G proteins and N* active receptor states. Numerical simulations for reveal new insights into the effects of system parameters (including cooperativities, and ligand and receptor concentrations) on bias dynamics, highlighting new phenomena including the dynamic inter-conversion of bias direction. Further, we fit this model to ‘wet’ experimental data for two competing G proteins (Gi and Gs) that become activated upon stimulation of the adenosine A1 receptor with adenosine derivative compounds. Finally, we show that our model can qualitatively describe the temporal dynamics of this competing G protein activation.
登录
查看更多内容
影响因子：
7.3
作者：
Knight, Anthony;Hemmings, Jennifer L.;Ladds, Graham
通讯作者：
Ladds, Graham
影响因子：
4.3
作者：
Donato, M;Gelpi, RJ
通讯作者：
Gelpi, RJ
影响因子：
16.6
作者：
Klein Herenbrink C;Sykes DA;Donthamsetti P;Canals M;Coudrat T;Shonberg J;Scammells PJ;Capuano B;Sexton PM;Charlton SJ;Javitch JA;Christopoulos A;Lane JR
通讯作者：
Lane JR
影响因子：
3.5
作者：
Chen, CY;Cordeaux, Y;King, JR
通讯作者：
King, JR
影响因子：
7.3
作者：
LEFF, P;MARTIN, GR;MORSE, JM
通讯作者：
MORSE, JM