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

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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.