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疾病药物治疗的主要范式之一。为了达到预期的效果，应该详细了解药物对一个或多个靶蛋白的特异性。最大的靶蛋白家族是G蛋白偶联受体（gpcr），一种传递信号的跨膜蛋白。对于这些受体，不仅需要回答特定分子靶向哪些受体的问题，还需要回答调节剂发挥何种功效（激动、拮抗或逆激动）的问题。在这个项目中，基于结构的计算工具将用于开发稳定增长的gpcr可用x射线结构的数量，以预测结合和功效的分子特性。将深入研究两种受体：β -肾上腺素能受体和毒蕈碱乙酰胆碱受体M3。我们将重点关注两点：第一，通过对接非活性构象和活性构象中各自受体的x射线结构来正确预测配体的功效。其次，分析G蛋白和β - arrestin介导的途径之间配体功效的比例（偏差）。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相互作用的变化起着重要作用。为了在实验上验证我们的预测，我们将建立一个基于细胞的实验，这将使我们能够测量几种信号通路的刺激。该试验也可用于中等通量。为了精确量化偏倚，我们将与<s:1>内曼实验室合作使用fret标记的受体和效应蛋白。因此，我们将能够在高时间和空间分辨率下跟踪相互作用。

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.