Methods corner

Nicola Dijon,

University of Nottingham, UK

Use of NanoBiT complementation technology to investigate ligand signalling and binding kinetics at the human β2 adrenoceptor

The ability to extract fundamental measures of agonist affinity and efficacy from functional data remains a key goal in G protein-coupled receptor (GPCR) pharmacological analysis. Hoare et al (2018) [1] have described a kinetic operational model (kOM) that enables derivation of agonist binding affinities from functional biosensor data. We and others have previously described a NanoBiT luciferase complementation assay to report real time β-arrestin2 recruitment to GPCRs (NanoBiT [2] ), and here we evaluate the application of the kOM to these data to estimate β 1 and β 2 adrenoceptor (β 1 AR/β 2 AR) agonist affinities.

In both β 1 and β 2 AR NanoBiT recruitment assays, β-arrestin2 recruitment by ≥5 representative agonists displayed a transient recruitment profile best analysed by the rise and fall kOM. This obtained estimates of each agonist’s affinity as log K A . At both βAR subtypes these estimates were highly correlated with binding data under whole cell conditions [3] , as well as membrane conditions, with particularly close agreement in actual affinity parameters at the β 2 AR.

The kOM accurately predicted orders of affinity of βAR agonists at multiple βAR subtypes, when applied to real time NanoBiT arrestin assay data. In contrast to other methods, K A estimates are possible from a single agonist concentration response curve experiment, provided timecourses are collected during the assay read. Given the rise and fall kOM assumes instantaneous equilibration of agonist binding to the receptor [1] , this version of the method may be best suited to GPCR agonists with rapid binding kinetics.

[1] Hoare S.R., et al. (2018). J. Theor. Biol., 446:168-204.

[2] Dixon A.S., et al. (2016). ACS Chem. Biol., 11:400-408

[3] Baker J.G., et al (2010). Br. J. Pharmacol., 160(5):1048-1061

Stefan Gahbauer,

University of California, San Francisco, USA

Large-scale virtual docking screens against GPCRs

Computational structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and synthesizable chemical space has expanded, the ability to screen hundreds of millions, and even billions, of molecules has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls that guide careful calibration of the docking model to enhance the likelihood of success. Here we present best practices and a set of control calculations that help to evaluate the performance of a docking model prior to launching a large-scale prospective screen.

The described protocols were employed in a docking campaign targeting the prostaglandin E2 receptor EP4 in search of novel antagonist for treatment of inflammatory pain. We docked more than 400 million compounds against a EP4 crystal structure leading to two novel scaffolds which were optimized to antagonists with low nanomolar potencies. The lead compound demonstrated antiallodynic effects in vivo in different mouse inflammation models.

Maria Dolores Garcia Fernandez,

Institut de Biologie Structurale Grenoble, France

Distinct classes of potassium channels fused to GPCRs as electrical signaling biosensors

For biosensing applications, electrical signals are attractive since they can be recorded by micro- or nano-electronic systems for developing miniaturized detection devices in biomedical or environmental fields. One of the main limiting steps is the design of sensing elements that specifically recognize ligands such as biomarkers or chemical compounds.

Ligand-gated ion channels are natural biosensors generating electrical signals in response to the binding of specific ligands. They have naturally evolved to be finely tuned by regulatory domains or proteins resulting in an appropriate electrical signal. The objective of this work is to leverage these natural biosensors by engineering diverse artificial ion channels that have the desired properties for biosensing or basic research applications. The main challenge is to design de novo allosteric regulations between ion channels and physiologically unrelated membrane proteins.

Previously, different G protein-coupled receptors (GPCR) were successfully coupled to a specific ion channel, Kir6.2. In this work, we extrapolate these design concepts to other channels with different structures and oligomeric states, namely a tetrameric viral Kcv channel and the dimeric mouse TREK-1 channel. After precise engineering of the linker regions, the two ion channels were successfully regulated by a GPCR fused to their N-terminal domain. Two-electrode voltage-clamp recordings showed that Kcv and mTREK-1 fusions were inhibited and activated by GPCR agonists, respectively, and antagonists abolished both effects.

Thus, dissimilar ion channels can be allosterically regulated through their N-terminal domains. These results open avenues for diversifying engineered ligand-gated ion channels

Yulong Li,

Peking University, China

Spying on in vivo neuromodulation by constructing GPCR based fluorescent sensors

Diverse neuromodulators in the brain, such as acetylcholine, monoamines, lipids and neuropeptides, play important roles in a plethora of physiological processes including reward, movement, attention, sleep, learning and memory. Dysfunction of the neuromodulatory system is associated with a range of diseases, such as epilepsy, addition, neurodegenerative and psychiatric diseases. A longstanding yet largely unmet goal is to measure the dynamics of different neuromodulators reliably and specifically with high spatiotemporal resolution, particularly in behaving animals. To achieve this goal, we develop a series of genetically encoded GPCR-activation-based (GRAB) sensors for the detection of acetylcholine, dopamine, norepinephrine, serotonin, histamine, endocannabinoids, adenosine, ATP and neuropeptides, and validate the performance of these sensors in multiple preparations in vitro and in vivo. The GRAB sensor toolbox provides new insights into the dynamics and mechanism of neuromodulatory signaling both in health and disease.