Almost every aspect of the human body – from our senses to our moods – depends, in one way or another, on a large family of proteins called G-protein-coupled receptors. These receptor proteins, known as GPCRs for short, detect signals from outside the cell and trigger activity within the cell. This allows cells to gather information from their surroundings and to communicate with each other. Importantly, since GPCRs regulate many processes in the body that are involved in disease, it is perhaps unsurprising that over a third of all approved drugs target these receptors.

Like all proteins, GPCRs are long chain-like molecules with a repetitive backbone and short branches called sidechains. Each sidechain has its own chemical properties and electrical charge, which can affect how different parts of the chain interact with each other and what shape the protein can adopt. This in turn can influence how strongly a drug or other molecule can bind to a receptor protein.

Protein crystallography is one technique that has been used to better understand how the different GPCRs are built and how they work. The technique involves growing crystals from pure samples of the protein; this locks millions of copies of the protein in place and provides a snapshot of its shape. However, GPCRs – and especially their sidechains – are flexible and can adopt different shapes, which cannot be seen fully by only looking at protein crystals.

Now, Clark, Dikiy et al. used another technique called nuclear magnetic resonance spectroscopy, or NMR for short, to understand how drugs affect the fast moving sidechains within a GPCR. First, genetically modified yeast was used to create samples of a GPCR called the adenosine receptor A_2A that were labelled with specific markers which made it easier to measure the structure and flexibility of the protein by NMR.

This approach revealed that too much sodium in the sample’s solution supresses the large structural changes that occur in the A_2A receptor when it binds to a drug. Moreover, it showed that the sidechains of several regions on the receptor move in different ways depending on whether the receptor binds to an activating drug or an inhibiting drug.

These findings lay the groundwork for understanding how the movements of sidechains help to activate or inhibit GPCRs, and will complement on-going studies using protein crystals. Moreover, the new approach to producing labelled proteins could be applied to other types of proteins that until now could not be studied with NMR due to practical limitations. In future, this may help scientists to better understand how drugs affect these proteins and to develop new treatments for a whole range of diseases.

Our understanding of the molecular underpinnings of GPCR function has been greatly advanced over the past two decades through a combination of X-ray crystal structures, computational simulations, and spectroscopic studies of protein dynamics. Crystals of bovine rhodopsin provided the first high-resolution picture of a GPCR’s architecture (Palczewski et al., 2000; Li et al., 2004), and structures of photoactivation intermediates (Nakamichi and Okada, 2006; Salom et al., 2006) and retinal-free opsin (Park et al., 2008; Scheerer et al., 2008) further documented the structural transitions involved in rhodopsin activation. For GPCRs activated by diffusible ligands, crystal structures of the human β₂ adrenergic receptor (β₂AR) with inverse agonists (Rosenbaum et al., 2007; Cherezov et al., 2007), agonists (Rosenbaum et al., 2011; Rasmussen et al., 2011a; Warne et al., 2011), and bound G protein (Rasmussen et al., 2011b) provided a molecular basis for understanding how diffusible agonist binding can promote structural changes in a receptor to enhance signaling. Subsequent crystal structures have revealed the ligand binding pockets for GPCRs of diverse function, responding to biogenic amines, purines, lipids, peptides, and proteins. While the sequences of these GPCRs and their orthosteric ligand binding pockets are highly diverse, the overall structures are remarkably similar, with Cα rmsd values between unrelated receptors in the 2–3 Å range. How these diverse ligands activate signaling by a small number of G proteins and arrestins through a common structural scaffold remains a central problem for the GPCR field. Contributing to this problem is the fact that most of the existing X-ray GPCR structures are of antagonist-bound receptors locked in inactive conformations. For the few GPCRs that have been captured in both inactive and active conformations, common structural changes include inward movements of transmembrane (TM) helices at the orthosteric pocket, rearrangements of common core residues through the helical bundle, and rigid-body outward movements of TM6 at the cytoplasmic surface to expose G protein binding epitopes (Katritch et al., 2013).

Beyond the static pictures of GPCR conformations seen in these crystal structures, experimental evidence for complex dynamic behavior has emerged from spectroscopic studies on purified receptors. Unlike rhodopsin, which exhibits an efficient and ordered photon-induced transition from the dark state conformation to metarhodopsin II (Choe et al., 2011a; Choe et al., 2011b), ligand-activated GPCRs generally show considerable basal activity that is reduced by inverse agonists. ¹⁹F NMR and EPR experiments on the β₂AR (Liu et al., 2012a; Manglik et al., 2015) and A_2AR (Ye et al., 2016) provided evidence for at least four different conformational states populated by these receptors, which are differentially stabilized by ligands of different efficacy. ¹H/¹³C 2D NMR studies on ¹³Cε-methionine-labeled β₂AR (Nygaard et al., 2013; Kofuku et al., 2014) showed that a high free-energy barrier exists between inactive and active conformations (with exchange on the millisecond timescale), and agonist binding alone is only capable of weakly populating the active conformation. These studies, in addition to similar measurements on μ-opioid receptor chemically modified to introduce NMR-active ¹³C-methyl labels onto lysine residues (Sounier et al., 2015), suggest that ligand-activated GPCRs are weakly coupled allosteric systems in which multiple energetic inputs (i.e. agonist and G protein binding) are required to predominantly populate the active conformation.

Within the slow millisecond-timescale global interconversion between functional GPCR conformations (Vilardaga et al., 2003), local structural rearrangements of sidechains in the receptor (referred to here as ‘microswitches’) have been observed in comparisons of inactive and active GPCR crystal structures (Katritch et al., 2013), NMR data (Liu et al., 2012a; Manglik et al., 2015; Ye et al., 2016; Bokoch et al., 2010) and MD simulations (Vanni et al., 2009; Dror et al., 2011a). The importance of repacking of sidechains from TM3, TM5, and TM6 beneath the ligand binding pocket, known as the ‘conserved core triad,’ was identified by comparison of structures of β₂AR (Rosenbaum et al., 2011; Rasmussen et al., 2011a) and the μ-opioid receptor (Huang et al., 2015). Further down the TM bundle, an activated microswitch near the site of G protein binding has been found to represent a common feature of GPCRs that have been crystallized in both active and inactive conformations (Venkatakrishnan et al., 2016). A working hypothesis for GPCR activation is that a set of loosely coupled microswitches connecting the orthosteric pocket and G protein binding site are activated by agonists in a non-concerted fashion, and stabilizing subsets of these rearrangements can lead to alternate overall conformations that have different signaling properties.

One of the most powerful ways to probe sidechain dynamics and their contributions to biological processes is provided by NMR-based studies of isotopically-labeled methyl groups (Ruschak and Kay, 2010). To apply these techniques to study the sidechains in these microswitches, one approach would entail isoleucine/leucine/valine (ILV) labeling (Goto et al., 1999; Gardner et al., 1998) and perdeuteration of a wild-type GPCR, to generate proteins with ¹H/¹³C-labeled methyl groups within an otherwise ²H/¹²C-labeled background. Such samples are ideally suited for acquiring ¹H/¹³C methyl TROSY-based (Tugarinov et al., 2003a; Ollerenshaw et al., 2003) relaxation NMR data, allowing the quantitative determination of methyl order parameters and relative motion of sidechains on the ps-ns timescale (Tugarinov et al., 2005; Ishima and Torchia, 2000). Advances in labeling methodology and pulse sequences have enabled such measurements on large macromolecular systems, such as the 80 kDa enzyme malate synthase (Tugarinov and Kay, 2003b; Sandala et al., 2007), the 670 kDa archaeal proteasome core particle (Sprangers and Kay, 2007; Religa et al., 2010), and the 1 MDa GroEL/GroES complex (Fiaux et al., 2002; Horst et al., 2005). NMR spectroscopic interrogation of methyl groups has also been used to dissect the energetic contribution of sidechain entropy to important biological phenomena such as protein-protein interactions (Marlow et al., 2010) and transcription factor binding to double-stranded DNA (Tzeng and Kalodimos, 2012). These previous applications suggest that characterization of sidechain dynamics using methyl NMR approaches should be feasible for a GPCR purified in a detergent micelle, with an aggregate molecular weight in the 100–150 kDa range.

A major hurdle in carrying out such experiments is that perdeuteration and ILV labeling conventionally rely on expression in E. coli (Goto et al., 1999), while wild-type GPCRs typically require a eukaryotic host to ensure proper folding, glycosylation, trafficking, and stability in the plasma membrane. GPCR NMR studies have primarily used supplementation with labeled amino acids (Nygaard et al., 2013; Kofuku et al., 2014; Okude et al., 2015; Isogai et al., 2016; Kofuku et al., 2012) or covalent modification (Liu et al., 2012a; Sounier et al., 2015; Bokoch et al., 2010; Eddy et al., 2016) to incorporate isotopic probes into protein from Sf9 cells, and perdeuteration with incorporation of labeled ILV methyl probes is largely intractable for this established eukaryotic system. Another limitation is that purified wild-type GPCRs are typically prone to aggregation and are not highly thermostable, limiting acquisition times and sample concentration. While high quality 2D NMR spectra of GPCRs have been obtained for receptors with thermostabilizing mutations (Isogai et al., 2016), such mutations can affect the receptor’s dynamics and functional properties, including basal activity and maximal G protein stimulation.

We have addressed many of these practical issues with our generation (Clark et al., 2015) of highly-deuterated ¹H/¹³C Ile δ1-methyl labeled proteins in Pichia pastoris, a methylotrophic yeast (Cereghino and Cregg, 2000) that is adaptable to D₂O-based minimal media, will utilize the Ile precursor α-ketobutyrate, and enables expression of GPCRs at levels sufficient for crystallography (Shimamura et al., 2011; Hino et al., 2012; Yurugi-Kobayashi et al., 2009). Here we apply this labeling method to the wild-type human A_2AR, an important regulator of vascular function with a well-developed pharmacology including the natural hormone adenosine and high-affinity synthetic agonists and antagonists (Preti et al., 2015; de Lera Ruiz et al., 2014). A_2AR has been crystallized in inactive (Jaakola et al., 2008; Liu et al., 2012b), intermediate (Lebon et al., 2011; Xu et al., 2011) and active (Carpenter et al., 2016) conformations, and has been studied by ¹⁹F NMR to characterize its conformational equilibria (Ye et al., 2016). We expressed and purified milligram quantities of labeled wild-type human A_2AR and were able to resolve 20 out of 29 expected peaks in the Ile δ1 region in ¹H/¹³C TROSY HMQC experiments. Through structure-guided mutagenesis, we assigned four of these signals to specific functionally important residues, including I92^3.40 of the conserved core triad (Huang et al., 2015) and I292 at the cytoplasmic surface. We collected spectra in the presence of the high-affinity full agonist NECA and the inverse agonist ZM241385, and also measured the effects of monovalent cations (Na⁺ versus K⁺) on these spectra. We further carried out a modified triple-quantum (3Q) relaxation experiment (Sun et al., 2011) to quantify the relative fast ps-ns motions of these sidechains while the receptor is bound to ligands of opposing efficacy. These data represent an important first step towards understanding how agonists can activate the fast motions of specific sidechains to facilitate conformational changes of a GPCR.

The first challenge in collecting methyl NMR spectra on a wild-type GPCR was achieving high-level expression and milligram scale purification from Pichia pastoris. We initially cloned several GPCRs (including A_2AR) into a modified methanol-inducible expression vector with the wild-type MFα signal sequence at the N-terminus to direct incorporation into the membrane. Several receptors, such as the wild-type M2 muscarinic receptor and A_2AR, showed reliable and reproducible expression, however much of the expressed proteins were present in the immature unprocessed form (without MFα cleavage) indicating that they had not been properly localized to the plasma membrane (Figure 1—figure supplement 1). To improve the efficiency of production of mature, folded receptors, we made several modifications to the MFα sequence, based on biochemical and genetic precedents (Rakestraw et al., 2009; Lin-Cereghino et al., 2013) for improved processing efficiency (Figure 1A, Materials and methods). To validate that Pichia-expressed A_2AR is functional, we carried out radioligand binding assays measuring [³H]-ZM241385 saturation and NECA competition on membranes after cell wall disruption and zymolyase treatment. The K_d for [³H]-ZM241385 was 0.42 nM in NaCl-containing buffer, while the K_i for NECA was 210 nM in low ionic strength and 420 nM in KCl-containing buffer, in agreement with previous binding studies (Xu et al., 2011; Carpenter et al., 2016; Bertheleme et al., 2013) on heterologously expressed A_2AR (Figure 1—figure supplement 2).

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We developed a purification protocol for A_2AR from Pichia-derived membranes, in which we can solubilize and purify ~0.5 mg of monodisperse biochemically pure wild-type A_2AR in dodecylmaltoside (DDM) detergent from 1 L of culture (Figure 1—figure supplement 1). Coupling this protocol with our previously-described labeling method (Clark et al., 2015), we were able to generate samples of wild-type A_2AR that are highly deuterated both within the protein (²H at non-exchangeable sites except Ile δ1 methyl groups) and the surrounding buffer (99% D₂O and deuterated DDM). We prioritized the use of a wild-type A_2AR construct to report on the dynamics of a physiologically and functionally relevant GPCR sample. To confirm that the high level of deuteration did not alter the receptor’s functional properties, we reconstituted the perdeuterated purified A_2AR together with purified G_s heterotrimer in phospholipid vesicles and carried out activation assays by [³⁵S]GTPγS binding. Saturating the receptor with the full agonist NECA led to an 8-fold stimulation of [³⁵S]GTPγS binding, and the inverse agonist ZM241385 produced a 50% reduction in basal activity, similar to values measured for protonated A_2AR (Figure 1B). These data show that the perdeuterated A_2AR is capable of activating G_s at wild-type levels in vitro, and can serve as a valid model for GPCR dynamics.

Our first NMR characterization of A_2AR used ¹H/¹³C TROSY HMQC spectra of the protein in the presence of different ligands or G_s. We initially expressed and solubilized the receptor with the low-affinity antagonist theophylline, and exchanged other ligands onto the receptor during washing in the Co²⁺-affinity and gel filtration steps (Materials and methods). In this way we were able to purify A_2AR samples with the inverse agonist ZM241385, the full agonist NECA, or no ligand. The resulting 2D HMQC spectra, requiring ~100 μM GPCR and a minimum of 2 hr acquisition at 30°C on a cryoprobe-equipped 800 MHz spectrometer, are shown in Figure 2. Overall, in a liganded sample, we could resolve 20 peaks in the Ile δ1 region of the spectrum, out of 29 Ile residues present in our construct (28 from the receptor and one from the C-terminal Protein C tag). The chemical shifts of the agonist-bound and inverse agonist-bound A_2AR are similar (Figure 2A,B), showing subtle changes that are comparable in magnitude to those observed previously for ¹³C-methyl-methionine probes in β₂AR (Kofuku et al., 2014; Kofuku et al., 2012). The spectrum of the unliganded (apo) A_2AR shows a loss of most of the well-dispersed peaks present in the ligand-bound spectra (Figure 2C), which could result from protein instability or conformational exchange on the μs-ms timescale. Lastly, we formed and isolated a high-affinity complex between purified NECA-bound A_2AR and purified G_s heterotrimer in the absence of GDP or GTP (Figure 2—figure supplement 1, Materials and methods). Like the apo spectrum, the G protein complex spectrum had fewer well-dispersed Ile peaks than receptor/ligand complexes (Figure 2D), consistent with the increase in molecular weight of the complex. We also note the appearance of a few distinct peaks (most prominently at 0.85 ppm ¹H, 11.8 ppm ¹³C) in the G protein complex, consistent with the conformation of the complexed receptor being distinguishable from the apo receptor (Rasmussen et al., 2011b; Carpenter et al., 2016).

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Assignment of ¹H/¹³C peaks in the Ile δ1 region by NMR was hampered by the modest dispersion of our spectra and the limited stability of A_2AR during data acquisition. Therefore, we took a site-specific mutagenesis approach, wherein we collected HMQC spectra of samples containing point mutations at a small subset of functionally important Ile sites (Figure 2 and Figure 2—figure supplement 2). This process was complicated by the sensitivity of the receptor to mutations at several of these Iles: changes to Val, Leu, or Met often drastically reduced purification yields and/or led to significantly altered spectra; for an example, see the I106V spectra in Figure 2—figure supplement 2. Three of the assignments (I92^3.40, I238^6.40, and I292) could be made primarily through observation of ZM241385-bound mutant spectra. By comparing WT and I92V spectra in the presence of ZM241385, we see a clear loss of signal at ~0.68ppm/13.6ppm (¹H/¹³C) that we can assign to the δ1 methyl of Ile92. The NECA-bound I92V spectrum is missing a subset of peaks compared to the ZM241385-bound spectrum, possibly due to compromised affinity of NECA for the I92V mutant (Figure 1—figure supplement 2D). Ile238 and Ile292 are well-dispersed peaks, allowing for unambiguous assignment through loss of peaks at 0.72 ppm/10.2 ppm and 1.12 ppm/12.7 ppm, respectively (¹H/¹³C). The mutant spectra of I238^6.40 and I292 have reciprocal effects on each other, where I238V causes a full peak-width shift in the I292 signal (upfield in ¹H), and I292L causes a ~ 0.5 peak-width shift in the I238 signal (upfield in ¹³C). The reciprocity between these mutant spectra is consistent with the proximity of these residues in the A_2AR structure. Mutation of I274^7.39 in the ligand binding pocket (within van der Waals contact of the ligands in the respective crystal structures) to Val or Met led to a disappearance of some peaks, reminiscent of the apo spectrum. However, comparison of ZM241385-bound I274M and I274V spectra narrowed the choice of the assignment of I274^7.39 to one of two peaks, with the greater solvent accessibility as revealed by solvent PRE effect (see below and Figure 4) of one peak serving as the deciding factor of assignment at 0.55 ppm/14.1 ppm (¹H/¹³C). As well as disappearance of many peaks, the spectrum of I274M A_2AR in the presence of ZM241385 displays several apparent peak doublings, suggesting that the mutant protein is undergoing slow conformational exchange, potentially related to the slow interconversion between different inactive states of the receptor (Ye et al., 2016). In addition, we assigned a peak to the Ile residue of the C-terminal Protein C tag in our construct by comparing spectra between differently tagged constructs (not shown). The remaining unassigned peaks are referred to as a-o in the rest of this work (Figure 3A). The chemical shift changes between ZM241385-bound and NECA-bound A_2AR are small overall (Figure 2A,B). In the A_2AR-G_s complex spectrum (Figure 2D), the peak for I92^3.40 appears to shift and simultaneously drop in intensity, strongly suggesting that I92^3.40 is involved in conformational changes on several timescales while the receptor is bound to G_s.

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Figure 4

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A_2AR contains a well-characterized Na⁺ binding site within the receptor core (Liu et al., 2012b), and Na⁺ has been shown to act as a negative allosteric modulator of A_2AR activation (Gao and Ijzerman, 2000). Due to the presence of 150 mM NaCl in the experiments shown in Figure 2, one possible explanation for the relatively small chemical shift differences between NECA- and ZM241385-bound spectra (Figure 3A,B) is that high [Na⁺] is suppressing structural changes in the agonist-bound receptor. To test this possibility, we acquired HMQC spectra for A_2AR samples with 150 mM KCl substituting for NaCl. The spectrum of ZM241385-bound receptor in KCl is very similar to that in the presence of NaCl (Figure 3C and Figure 3—figure supplement 1B), with the peak assigned to I92^3.40 shifting slightly upfield in the ¹³C dimension. The spectrum of NECA-bound A_2AR, on the other hand, is dramatically different in KCl and NaCl (Figure 3D, Figure 3—figure supplement 1D), with multiple peaks shifting and decreasing in intensity. However, the KCl samples were significantly less stable than those in NaCl, showing markedly decreased spectral quality after 1 (NECA-bound) or 2 (ZM241385-bound) hours of acquisition. Given the weak signal-to-noise and poor stability of the KCl samples, we were only able to perform NMR relaxation experiments (below) on A_2AR in the presence of NaCl.

In addition to ligand-induced chemical shift changes, we used our system to characterize the solvent accessibility of residues within the ZM241385-bound A_2AR (with NaCl) using solvent paramagnetic relaxation enhancements (PREs). To do so, we used the soluble paramagnetic Gd³⁺-DTPA probe (Petros et al., 1990), which enhances the relaxation rates of protons in a distance dependent manner within roughly 15 Å of the protein/solvent interface (Madl et al., 2011). By acquiring ¹H/¹³C TROSY HMQC spectra in the absence and presence of Gd³⁺-DTPA, we could compare peak intensities between the two to establish solvent PRE levels. Examining our data, we see that the average peak intensity in the Gd³⁺-DTPA containing spectrum is about 0.4 of that in the control spectrum, suggesting that the Ile δ1 methyl groups of A_2AR in the DDM micelle are quite close to solvent on average (Figure 4). Of the four most strongly affected peaks, three peaks correspond to assigned residues (I238^6.40, I274^7.39, and I292). The accessibility of I274^7.39 is expected due this amino acid’s presence in the solvent-exposed ligand-binding pocket. Broadening of I292 at the junction between TM7 and Helix 8 is also somewhat expected due to its position at the cytoplasmic surface. The broadening observed for I238^6.40 is more surprising, given that it is further embedded within the membrane and buried within the protein surface of A_2AR. Crystal structures of multiple GPCRs, including A_2AR, β₂AR, μOR, and rhodopsin have all revealed ordered solvent networks within the protein core in contact with the position equivalent to I238^6.40. In ZM241385-bound A_2AR, this residue is also in close proximity to the Na⁺ site (Liu et al., 2012b), one packing layer toward the cytoplasmic surface. The solvent PRE effect seen for this residue could reflect breathing of the structure to expose this region to the Gd³⁺-DTPA complex.

To characterize the motions of the Ile sidechains within A_2AR, we sought to carry out a triple quantum (3Q) relaxation experiment developed by Kay, Tugarinov and colleagues (Sun et al., 2011) to quantify sidechain dynamics in large macromolecules. In this experiment, two related series of 2D ¹H/¹³C HMQC-based spectra were acquired, both of which start by generating transverse ¹H magnetization composed of single quantum (SQ) coherences. During a subsequent variable delay period, intra-methyl ¹H−¹H dipolar cross-correlated relaxation mechanisms lead to a portion of these SQ coherences evolving into 3Q coherences; the rate of this conversion (η) is proportional to the S²_axis methyl order parameter. Practically, the degree of SQ->3Q evolution can be quantitated by examining paired ‘forbidden’ and ‘allowed’ spectra, which report on the fraction of SQ coherences which do and do not evolve into 3Q coherences, respectively. As previously reported (Sun et al., 2011), this allows the simple calculation of the η rate for each specific methyl group from the ratio of peak intensities in the forbidden and allowed spectra. Our initial attempts to apply this method to purified A_2AR were hampered by the fact that the wild-type receptor is insufficiently stable at 30°C to permit the measurement of the requisite paired spectra at many relaxation delays and with a high enough signal-to-noise ratio. We therefore explored the possibility of reducing the number of forbidden spectra required to accurately measure η values. Using similarly ²H,¹²C (¹H,¹³C δ1 methyl) labeled maltose binding protein (MBP) as a test case (Materials and methods), we collected 3Q datasets with different numbers of forbidden spectra paired with a constant number of allowed spectra (Figure 5, Figure 5—figure supplement 1). We found that pairing five allowed spectra with a single forbidden spectrum, we were able to faithfully recapitulate the η values measured using five pairs of forbidden and allowed experiments (Figure 5B,C). This η value is proportional to the S²_axis order parameter, which quantifies the amplitude of motion of the methyl group on the ps-ns timescale that is faster than global molecular tumbling.

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With this modified analysis of 3Q relaxation data and our Ile δ1 ¹H/¹³C-methyl-labeled and deuterated GPCR samples, we measured η values for Ile sidechains in A_2AR bound to either ZM241385 or NECA in NaCl. The resulting values for the 20 Ile peaks and relative changes between the samples with different ligands are shown in Figure 6. The error in these measurements is large due to the limited signal-to-noise of our A_2AR spectra, and we did not attempt to convert these η measurements to S²_axis values. However, in comparing the two datasets some features of the Ile dynamics can be discerned. The average η value for the ZM241385-bound sample is higher than for NECA, indicative of greater overall rigidity of sidechains with inverse agonist. At the individual residue level, we observe a diversity of ligand-dependent changes in η, with some Ile residues becoming more rigid with agonist while most become more flexible. As an internal control, the peak we assigned to the C-terminal Protein C tag shows the lowest η value measured (i.e. greatest flexibility) with little ligand-dependence. Among the other assigned peaks, I292 shows the largest difference between ligands, becoming more flexible in the NECA-bound sample. I92^3.40 and I274^7.39 display more modest increases in flexibility with agonist, while the dynamics of I238^6.40 are largely unchanged by ligand (Figure 6B). Interestingly, these differences in fast timescale dynamics occur in the presence of NaCl, in which chemical shifts for the agonist- and inverse agonist-bound states were nearly identical. To our knowledge, these data represent the first reported effort to experimentally quantify site-specific sidechain dynamics in a GPCR or comparable human integral membrane protein.

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While we were able to measure sidechain dynamics in this challenging A_2AR sample, the associated errors are quite large. As such, we turned to molecular dynamics (MD) simulations to provide independent validation of our dynamics measurements. We extracted S²_axis order parameters for Ile δ1 methyl groups from ~80 ns trajectories (post-equilibrium) of ZM241385- and NECA-bound A_2AR in DDM micelles, using overlapping 30-ns windows to estimate the standard deviation in the order parameters (Figure 6—figure supplement 1). The S²_axis order parameters for some methyl groups (for example, I60 and I80 in ZM241385; I106 and I302 in NECA) show significant variations across different windows, suggesting that slower timescale motions involving these residues contribute to the sidechain dynamics measured on the fast timescale. Interestingly, the differences between ZM241385- and NECA-bound dynamics at three of the four assigned sites (I238^6.40, I274^7.39, and I292) are qualitatively similar to what we see by NMR. The fourth site, I92^3.40, shows the opposite trend, with higher S²_axis, i.e. more rigidity, in the NECA-bound state. The simulations were set up with bulk Na⁺ ions, but none specifically occupying the binding site near I92^3.40, which could account for this discrepancy.

Sidechain dynamics represent an important functional component of protein behavior. In cases of protein-ligand (Marlow et al., 2010; Frederick et al., 2007) and protein-DNA (Tzeng and Kalodimos, 2012) interactions that have been intensively studied, changes in entropy arising from modified sidechain dynamics in complexes were found to substantially contribute toward the overall free energy of binding. For GPCRs, changes in sidechain dynamics may play an energetic role in binding to ligands and G proteins, however we have focused on allosteric mechanisms connecting these two functionally important binding sites. To experimentally assess the roles of sidechain dynamics in a wild-type GPCR, we set out to create a labeled sample that would be amenable to relaxation NMR methods. The spectra we obtained for the labeled Ile δ1 methyl groups of perdeuterated A_2AR are comparable or superior to previously published NMR spectra on other wild-type GPCRs, however the limited dispersion and signal-to-noise contributed to significant error in the relaxation values derived from the data (Figure 6A).

The Na⁺ site first seen in the high-resolution structure of A_2AR bound to ZM241285 is conserved throughout most Class A GPCRs, and Na⁺ exerts a negative allosteric effect on A_2AR activation by bridging residues on TM3 and TM7 and stabilizing the inactive conformation (Liu et al., 2012b). This effect can be observed pharmacologically as a NaCl-dependent decrease in agonist affinity (Carpenter et al., 2016; Gao and Ijzerman, 2000). For A_2AR expressed in P. pastoris, we observe a 9-fold decrease in NECA affinity when membranes are incubated in 150 mM NaCl buffer versus 150 mM KCl buffer (Figure 1—figure supplement 2), similar to previously reported values (Carpenter et al., 2016). Several of the Ile residues that we assigned (i.e. I92^3.40 and I238^6.40) are in close proximity to the Na⁺ binding site, which should make for sensitive probes of local structure in this part of the receptor core. Our 2D NMR spectra indicate a strong dependency on combined lack of Na⁺ and binding of agonist to stabilize significant structural changes in the receptor core (Figure 3). This observation is also consistent with the active conformation seen in the crystal structure of the A_2AR/mini-G_s complex (Carpenter et al., 2016), which results in collapse of the Na⁺ site due to inward movements of TM3 and TM7. In contrast to our data, the crystal structure of a NECA-bound thermostabilized A_2AR mutant construct (Lebon et al., 2011) showed significant structural changes relative to the inactive conformation even with a high [Na⁺] beyond its EC₅₀ (~50 mM) for negative allosteric modulation (Gao and Ijzerman, 2000). Unfortunately the weak signal and stability of our samples in KCl precluded measurement of 3Q relaxation dynamics under these conditions.

For the Na⁺-bound A_2AR samples, we can correlate our observations of ligand-dependent changes in the relaxation rate of specific sites to previous structural and biophysical studies of GPCRs. I274^7.39 at the ligand binding site makes direct contact with the adenine or adenine analogue rings of both NECA and ZM241385 in their respective crystal structures (Jaakola et al., 2008; Liu et al., 2012b; Lebon et al., 2011; Carpenter et al., 2016) (Figure 7A). In our relaxation datasets, I274^7.39 was more rigid in terms of its fast sidechain motions with inverse agonist bound, and more flexible with NECA occupying the binding pocket. NMR experiments previously showed different ligand-dependent conformations of the extracellular surface region of β₂AR bound to agonists compared to inverse agonists (Bokoch et al., 2010). In addition, MD simulations on β₂AR predicted greater mobility of agonists relative to inverse agonists in the orthosteric pocket of the inactive-state structure (i.e. not bound to G_s) (Dror et al., 2011b). Our data suggests that the fast dynamics of residues at the orthosteric pockets of Class A GPCRs may be correlated with the functional efficacy of the bound ligand. The residue I92^3.40 is one of the three residues of the conserved core triad in A_2AR, along with P189^5.50 and F242^6.44, which interact at a layer beneath the orthosteric pocket further toward the cytoplasmic surface (Figure 7B). Consistent with the rearrangements of this microswitch region between the inactive and active conformations of multiple GPCRs, we observe a lower η value for I92^3.40 in the NECA dataset versus ZM241385, indicating greater sidechain flexibility when A_2AR is agonist-bound. In several GPCRs, including β₂AR and μOR, the conserved core triad is unchanged at a static structural level with an agonist bound but without a G protein or nanobody to further stabilize the active conformation. In contrast, crystal structures of A_2AR bound to agonists alone (Figure 7B) showed an intermediate active-like conformation in the region surrounding I92^3.40 (Lebon et al., 2011; Xu et al., 2011). Our data indicate that low [Na⁺] is required for NECA alone to stabilize structural rearrangements surrounding I92 (Figure 3, Figure 3—figure supplement 1). However agonist alone (even with high [Na⁺]) is enough to at least promote the fast motions of the sidechains in this microswitch, which may reduce the activation energy for the observed packing rearrangement to the active conformation.

Figure 7

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The residue I238^6.40 is situated further toward the G protein binding site at a critical region for GPCR activation, where it packs against TM7 and undergoes significant outward movement in the transition to the active conformation (Figure 7C). I238^6.40 is also one helical turn on TM6 above L235^6.37, which participates in a conserved microswitch between inactive and active conformations for multiple GPCRs (Venkatakrishnan et al., 2016). I292 is present at the linker between TM7 and Helix 8, where it packs against Y288^7.53 of the highly conserved NPXXY motif (Katritch et al., 2013) (Figure 7D). NMR studies of ¹³C-dimethyllysine-labeled μOR showed that peak broadening of a lysine probe in Helix 8 (near the position equivalent to I292) was more sensitive to agonist than probes at the cytoplasmic ends of TM5 and TM6 (21). Further, in the structure of A_2AR/mini-G_s (Carpenter et al., 2016), the engineered G_s protein makes direct contact with I292 at the TM7-Helix 8 junction. In our relaxation dataset, the η value of I238^6.40 is low (i.e. more flexible) and essentially independent of the ligand, while I292 undergoes a large change from more rigid to more flexible from ZM241385 to NECA. As mentioned above, the environment surrounding I238^6.40 is loosely packed in A_2AR structures, with an ordered solvent network that may allow for relative freedom of motion for this sidechain. In contrast, despite the solvent exposure of I292 (Figure 4), inverse agonist binding suppresses the fast dynamics of this residue relative to agonist binding (Figure 6). Outward movement of TM6 is one of the hallmarks of GPCR activation seen in crystal structures, and the cytoplasmic ends of TM6 and TM7 are separated from each other in the structures of β₂AR and A_2AR bound to G_s (Rasmussen et al., 2011b; Carpenter et al., 2016). Our data suggest that the responsiveness of sidechain dynamics to ligands in these two regions may be largely uncoupled, and that the inverse agonist activity of ZM241385 could partly arise from its allosteric suppression of dynamics at the cytoplasmic end of TM7. Changes in η for other peaks in our dataset may provide further insights into the regulation of different regions of the receptor by ligands, depending on assignment of the other Ile residues in our spectra.

Beyond our studies of the A_2AR, we can now apply the methods for labeling and NMR spectroscopy described here to other GPCRs and eukaryotic membrane proteins. Since the microswitches discussed above were identified by comparison of different GPCR structures (Huang et al., 2015; Venkatakrishnan et al., 2016), it will be instructive to apply our methods to other receptors to see if the same patterns of ligand-dependent changes apply across the GPCR superfamily or change depending on the cognate ligand or preferred G protein signaling partner. Further, sidechain dynamics may be among the biophysical properties of GPCRs that are altered when receptors are bound to allosteric modulators or biased agonists, classes of ligands that are of increasing value and importance in GPCR pharmacology and drug development. In addition to GPCRs, many other disease-relevant human integral membrane proteins (such as ABC transporters and ion channels) are currently impossible to study by NMR methods, largely due to the challenges of expression, labeling, and perdeuteration in E. coli. Our approach has the potential to make these systems tractable for similar NMR measurements of sidechain dynamics.

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Author details

1. Lindsay D Clark

   1. Department of Biophysics, The University of Texas Southwestern Medical Center, Dallas, United States
   2. Molecular Biophysics Graduate Program, The University of Texas Southwestern Medical Center, Dallas, United States

   Contribution

   Conceptualization, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Igor Dikiy

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6879-6883

2. Igor Dikiy

   Structural Biology Initiative, CUNY Advanced Science Research Center, New York, United States

   Contribution

   Conceptualization, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Lindsay D Clark

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8155-7788

3. Karen Chapman

   Department of Biophysics, The University of Texas Southwestern Medical Center, Dallas, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

4. Karin EJ Rödström

   Department of Neuroscience and Pharmacology, University of Copenhagen, Copenhagen, Denmark

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

5. James Aramini

   Structural Biology Initiative, CUNY Advanced Science Research Center, New York, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

6. Michael V LeVine

   1. Department of Physiology and Biophysics, Weill Cornell Medical College, New York, United States
   2. Institute for Computational Bioscience, Weill Cornell Medical College, New York, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

7. George Khelashvili

   1. Department of Physiology and Biophysics, Weill Cornell Medical College, New York, United States
   2. Institute for Computational Bioscience, Weill Cornell Medical College, New York, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7235-8579

8. Søren GF Rasmussen

   Department of Neuroscience and Pharmacology, University of Copenhagen, Copenhagen, Denmark

   Contribution

   Supervision, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

9. Kevin H Gardner

   1. Structural Biology Initiative, CUNY Advanced Science Research Center, New York, United States
   2. Department of Chemistry and Biochemistry, City College of New York, New York, United States
   3. Biochemistry, Chemistry and Biology PhD Programs, Graduate Center, City University of New York, New York, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   Kevin.Gardner@asrc.cuny.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8671-2556

10. Daniel M Rosenbaum

    1. Department of Biophysics, The University of Texas Southwestern Medical Center, Dallas, United States
    2. Molecular Biophysics Graduate Program, The University of Texas Southwestern Medical Center, Dallas, United States

    Contribution

    Conceptualization, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

    For correspondence

    dan.rosenbaum@utsouthwestern.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7174-4993

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