Computer-aided tailoring of the efficacy profiles of G protein-coupled receptor ligands

用小分子调节蛋白质功能是药物治疗疾病的主要范例之一。为了达到预期效果，应详细了解药物对一种或多种靶蛋白的特异性。最大的靶蛋白家族是G蛋白偶联受体（GPCR），它们是传递信号的跨膜蛋白。对于这些受体，不仅是特定分子靶向哪种受体的问题，而且还涉及哪种有效性的问题。（激动的、拮抗的或反向激动的）调节剂施加的作用需要被回答。在该项目中，基于结构的计算工具将被用来利用稳定增加的可用X-射线结构的GPCR，以预测结合和效力的分子特性。将深入研究两种受体：β 2-肾上腺素能受体和毒蕈碱乙酰胆碱受体M3。我们将集中在两点：第一，正确的配体效力的预测对接到X射线结构的各自的受体在非活性和活性构象。第二，分析G蛋白和β-Arrestin介导的途径之间的配体功效的比率（偏倚）。在第二种情况下，我们也将使用分子动力学模拟，因为随着时间的推移，调节剂和蛋白质之间的相互作用的变化起着重要的作用。为了实验测试我们的预测，我们将建立一个基于细胞的测定，这将使我们能够测量几个信号通路的刺激。该测定也可用于中等通量。为了精确定量偏倚，我们将与Bünemann实验室合作使用FRET标记的受体和效应蛋白。因此，我们将能够以高的时间和空间分辨率跟踪相互作用。

Modulating protein function with small molecules is one of the major paradigms in the pharmacological therapy of disease. To achieve the desired effect, the specificity of a drug towards one or more target proteins should be understood in detail. The biggest target protein family are the G protein-coupled receptors (GPCRs), membrane-spanning proteins that transmit signals. For these receptors, not only the question of which receptor(s) are targeted by a particular molecule, but also the question which efficacy (agonistic, antagonistic, or inverse agonistic) the modulator exerts needs to be answered.Within this project, structure-based computational tools will be used to exploit the steadily increasing number of available X-ray structures of GPCRs in order to predict the molecular properties of binding and efficacy. Two receptors will be investigated in depth: the beta2-adrenergic receptor and the muscarinic acetylcholine receptor M3. We will focus on two points: first, the correct prediction of ligand efficacy by docking to X-ray structures of the respective receptors in inactive and active conformations. Second, the analysis of the ratio (bias) of ligand efficacy between the G protein- and the beta-Arrestin-mediated pathways. In the second case, we will also use molecular dynamics simulations, as the changes in interaction between modulator and protein over time play an important role.In order to experimentally test our predictions, we will establish a cell-based assay, which will allow us to measure stimulation of several signaling pathways. This assay can also be used in medium throughput. For an exact quantification of bias, we will employ FRET-labeled receptors and effector proteins in collaboration with the Bünemann lab. Thereby, we will be able to track the interactions at high temporal and spatial resolution.