The dynamic process of ß(2)-adrenergic receptor activation.

G-protein-coupled receptors (GPCRs) can modulate diverse signaling pathways, often in a ligand-specific manner. The full range of functionally relevant GPCR conformations is poorly understood. Here, we use NMR spectroscopy to characterize the conformational dynamics of the transmembrane core of the ß(2)-adrenergic receptor (ß(2)AR), a prototypical GPCR. We labeled ß(2)AR with (13)CH(3)ε-methionine and obtained HSQC spectra of unliganded receptor as well as receptor bound to an inverse agonist, an agonist, and a G-protein-mimetic nanobody. These studies provide evidence for conformational states not observed in crystal structures, as well as substantial conformational heterogeneity in agonist- and inverse-agonist-bound preparations. They also show that for ß(2)AR, unlike rhodopsin, an agonist alone does not stabilize a fully active conformation, suggesting that the conformational link between the agonist-binding pocket and the G-protein-coupling surface is not rigid. The observed heterogeneity may be important for ß(2)AR's ability to engage multiple signaling and regulatory proteins.

Publisher Correction: Structural insights into binding specificity, efficacy and bias of a ß2AR partial agonist.

In the version of this paper originally published, the structure for epinephrine shown in Figure 1a was redrawn with an extra carbon. The structure has been replaced in the HTML and PDF versions of the article. The original and corrected versions of the structure are shown below.

Structural insights into binding specificity, efficacy and bias of a ß2AR partial agonist.

Salmeterol is a partial agonist for the ß2 adrenergic receptor (ß2AR) and the first long-acting ß2AR agonist to be widely used clinically for the treatment of asthma and chronic obstructive pulmonary disease. Salmeterol's safety and mechanism of action have both been controversial. To understand its unusual pharmacological action and partial agonism, we obtained the crystal structure of salmeterol-bound ß2AR in complex with an active-state-stabilizing nanobody. The structure reveals the location of the salmeterol exosite, where sequence differences between ß1AR and ß2AR explain the high receptor-subtype selectivity. A structural comparison with the ß2AR bound to the full agonist epinephrine reveals differences in the hydrogen-bond network involving residues Ser2045.43 and Asn2936.55. Mutagenesis and biophysical studies suggested that these interactions lead to a distinct active-state conformation that is responsible for the partial efficacy of G-protein activation and the limited ß-arrestin recruitment for salmeterol.

Identifying localized changes in large systems: Change-point detection for biomolecular simulations.

Research on change-point detection, the classical problem of detecting abrupt changes in sequential data, has focused predominantly on datasets with a single observable. A growing number of time series datasets, however, involve many observables, often with the property that a given change typically affects only a few of the observables. We introduce a general statistical method that, given many noisy observables, detects points in time at which various subsets of the observables exhibit simultaneous changes in data distribution and explicitly identifies those subsets. Our work is motivated by the problem of identifying the nature and timing of biologically interesting conformational changes that occur during atomic-level simulations of biomolecules such as proteins. This problem has proved challenging both because each such conformational change might involve only a small region of the molecule and because these changes are often subtle relative to the ever-present background of faster structural fluctuations. We show that our method is effective in detecting biologically interesting conformational changes in molecular dynamics simulations of both folded and unfolded proteins, even in cases where these changes are difficult to detect using alternative techniques. This method may also facilitate the detection of change points in other types of sequential data involving large numbers of observables--a problem likely to become increasingly important as such data continue to proliferate in a variety of application domains.

Activation mechanism of the ß2-adrenergic receptor.

A third of marketed drugs act by binding to a G-protein-coupled receptor (GPCR) and either triggering or preventing receptor activation. Although recent crystal structures have provided snapshots of both active and inactive functional states of GPCRs, these structures do not reveal the mechanism by which GPCRs transition between these states. Here we propose an activation mechanism for the ß(2)-adrenergic receptor, a prototypical GPCR, based on atomic-level simulations in which an agonist-bound receptor transitions spontaneously from the active to the inactive crystallographically observed conformation. A loosely coupled allosteric network, comprising three regions that can each switch individually between multiple distinct conformations, links small perturbations at the extracellular drug-binding site to large conformational changes at the intracellular G-protein-binding site. Our simulations also exhibit an intermediate that may represent a receptor conformation to which a G protein binds during activation, and suggest that the first structural changes during receptor activation often take place on the intracellular side of the receptor, far from the drug-binding site. By capturing this fundamental signaling process in atomic detail, our results may provide a foundation for the design of drugs that control receptor signaling more precisely by stabilizing specific receptor conformations.

The role of ligands on the equilibria between functional states of a G protein-coupled receptor.

G protein-coupled receptors exhibit a wide variety of signaling behaviors in response to different ligands. When a small label was incorporated on the cytosolic interface of transmembrane helix 6 (Cys-265), (19)F NMR spectra of the ß2 adrenergic receptor (ß2AR) reconstituted in maltose/neopentyl glycol detergent micelles revealed two distinct inactive states, an activation intermediate state en route to activation, and, in the presence of a G protein mimic, a predominant active state. Analysis of the spectra as a function of temperature revealed that for all ligands, the activation intermediate is entropically favored and enthalpically disfavored. ß2AR enthalpy changes toward activation are notably lower than those observed with rhodopsin, a likely consequence of basal activity and the fact that the ionic lock and other interactions stabilizing the inactive state of ß2AR are weaker. Positive entropy changes toward activation likely reflect greater mobility (configurational entropy) in the cytoplasmic domain, as confirmed through an order parameter analysis. Ligands greatly influence the overall changes in enthalpy and entropy of the system and the corresponding changes in population and amplitude of motion of given states, suggesting a complex landscape of states and substates.

SIGNAL TRANSDUCTION. Structural basis for nucleotide exchange in heterotrimeric G proteins.

G protein-coupled receptors (GPCRs) relay diverse extracellular signals into cells by catalyzing nucleotide release from heterotrimeric G proteins, but the mechanism underlying this quintessential molecular signaling event has remained unclear. Here we use atomic-level simulations to elucidate the nucleotide-release mechanism. We find that the G protein α subunit Ras and helical domains-previously observed to separate widely upon receptor binding to expose the nucleotide-binding site-separate spontaneously and frequently even in the absence of a receptor. Domain separation is necessary but not sufficient for rapid nucleotide release. Rather, receptors catalyze nucleotide release by favoring an internal structural rearrangement of the Ras domain that weakens its nucleotide affinity. We use double electron-electron resonance spectroscopy and protein engineering to confirm predictions of our computationally determined mechanism.