Abstract
G-protein-coupled receptors (GPCRs) activate heterotrimeric G proteins by promoting guanine nucleotide exchange. Here, we investigate the coupling of G proteins with GPCRs and describe the events that ultimately lead to the ejection of GDP from its binding pocket in the Gα subunit, the rate-limiting step during G-protein activation. Using molecular dynamics simulations, we investigate the temporal progression of structural rearrangements of GDP-bound Gs protein (Gs·GDP; hereafter GsGDP) upon coupling to the β2-adrenergic receptor (β2AR) in atomic detail. The binding of GsGDP to the β2AR is followed by long-range allosteric effects that significantly reduce the energy needed for GDP release: the opening of α1-αF helices, the displacement of the αG helix and the opening of the α-helical domain. Signal propagation to the Gs occurs through an extended receptor interface, including a lysine-rich motif at the intracellular end of a kinked transmembrane helix 6, which was confirmed by site-directed mutagenesis and functional assays. From this β2AR–GsGDP intermediate, Gs undergoes an in-plane rotation along the receptor axis to approach the β2AR–Gsempty state. The simulations shed light on how the structural elements at the receptor–G-protein interface may interact to transmit the signal over 30 Å to the nucleotide-binding site. Our analysis extends the current limited view of nucleotide-free snapshots to include additional states and structural features responsible for signaling and G-protein coupling specificity.
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Data availability
PDB IDs 3SN6, 6EG8 and 6E67 were used in this work. For sequence analysis, the alignments were downloaded from the GPCRdb90 at https://gpcrdb.org/alignment/targetselection. All shown data is included in the Source data files. Fully atomistic MD trajectories are available online for direct and interactive 3D visualization in web browser as mdsrv74 sessions here: initial model of the β2AR–GsGDP simulations, https://proteinformatics.uni-leipzig.de/mdsrv.html?load=file://_int-n/model.js; representative run (run 2) of the β2AR–GsGDP simulations, https://proteinformatics.uni-leipzig.de/mdsrv.html?load=file://_int-n/b2ar-gsgdp-intermediate.js; representative run (run 5) of the β2AR–GsGDP spawns, https://proteinformatics.uni-leipzig.de/mdsrv.html?load=file://_int-n/b2ar-gsgdp-intermediate-spawn.js; representative run (run 2) of the GsGDP simulations, https://proteinformatics.uni-leipzig.de/mdsrv.html?load=file://_int-n/gsgdp.js; representative run (run 3) of the β2AR–GsGDP ICL3 simulations, https://proteinformatics.uni-leipzig.de/mdsrv.html?load=file://_int-n/icl3.js; representative run (run 3) of the β2AR–GsGDP MUT simulations, https://proteinformatics.uni-leipzig.de/mdsrv.html?load=file://_int-n/triple-k.js. Source data are provided with this paper.
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Acknowledgements
This work was funded by German Research Foundation (DFG) through CRC1423, project number 421152132, subproject C01 (to P.W.H.) and subprojects A01, A05 and Z03 (to P.S.), Stiftung Charité and the Einstein Center Digital for Future to P.W.H. P.S. is further supported through CRC 1078–Project ID 221545957–SFB 1078, subproject B06; through the cluster of excellence ‘UniSysCat‘ (under Germany’s Excellence Strategy-EXC2008/1-390540038 and through the European Union’s Horizon 2020 MSCA Program under grant agreement 956314 (ALLODD). This work was also funded by National Institutes of Health grant R01NS028471 (to B.K.K.), by National Natural Science Foundation of China (Grant 32122041 to X.L.) and by Tsinghua University Initiative Scientific Research Program (to X.L.). We are grateful to A. Inoue (Tohoku University, Japan) for providing the CRISPR–Cas9-edited triple knockout barr1/barr2/β2AR HEK293A cells and to H. Schihada (Philipps-Universität Marburg, Germany) for providing the Gs-CASE sensor DNA material and advice for the BRET dissociation assay. We thank B. Bauer (Charité–Universitätsmedizin Berlin, Germany) for assistance in molecular biology and purifying reagents. B.K.K. and P.W.H. acknowledge the Einstein Foundation and the Berlin Institute of Health for their support. We are grateful to M. Heck (Charité–Universitätsmedizin Berlin, Germany) for advice on the statistical analysis of the BRET 2 assay and M. Heck and K. P. Hofmann (Charité–Universitätsmedizin Berlin, Germany) for helpful discussions. P.F.S. also holds external affiliations with the Institute of Theoretical Chemistry, University of Vienna, Austria, the Universidad Nacional de Colombia, Bogotá, Colombia, the Center for noncoding RNA in Technology and Health at the University of Copenhagen and the Santa Fe Institute, Santa Fe, New Mexico, USA. We gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR project p101ae. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by DFG (440719683).
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H.B. and G.P.H. contributed equally and share first authorship. H.B. refined initial models, performed MD simulations and data analysis, prepared figures and wrote the initial draft. G.P.H. conceptualized and performed data analysis and prepared figures. B.L. and M.S. performed mutation experiments. J.K.S.T. contributed to generation of the starting model and suggested mutations. R.G.G. contributed to generation of the starting model. F.R. provided sequence analysis. P.F.S. supervised sequence analysis. A.K. performed and analyzed the BRET 2 (G-protein dissociation) assay with the support of D.S.; P.S. supervised and analyzed the BRET 2 (G-protein dissociation) assay and helped with development of the manuscript and figures. B.K.K. and X.L. provided initial structural data, suggested mutations and supervised mutation experiments. J.M.M. designed and performed in vitro functional cAMP studies and performed the binding studies on membranes. S.N.R. designed and performed kinetic studies for G-protein activation under the supervision of J.M.M. M.P.S. and G.S. provided advice on structural mechanism and data interpretation. P.W.H. conceptualized the study, supervised the project and wrote the manuscript. All authors contributed to the proof-reading and final approval of the submitted version of the manuscript. H.B., G.P.H. and P.W.H. wrote the manuscript.
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G.S. is a co-founder of, and consultant for, Deep Apple Therapeutics. B.K.K. is a co-founder of, and consultant for, ConfometRx.
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Extended data
Extended Data Fig. 1 Starting model and sample snapshots of the β2AR-GsGDP simulations, together with overview of relevant crystal structures.
A1) In the starting model, inactive GsGDP (PDB ID: 6EG8) is placed below the receptor with α5 in an orientation as the 14-mer-peptide derived from the C-terminus of α5, co-crystallized with the β2AR (PDB ID: 6E67)16. Notably, Y391α5 and H387α5, that interact with the receptor in the crystal structure of β2AR-Gsempty (PDB ID: 3SN6, inset A4) are still buried (inset A1), while E392α5 and R389α5, that bind to the receptor in β2AR-GsGDP (inset A2) and in the β2AR-α5-peptide complex6 (inset A3), are solvent exposed and ready to interact. To provide sufficient space for structural adaptations that occur upon complex formation, in the initial model, the side chains of R389α5 and E392α5 were put at 23.5 and 15.0 Å away from the side chains of D1303.49 and R1313.50, respectively. A2) In the β2AR-GsGDP complex, α5 adopts a similar orientation and binding mode as in the β2AR-α5-peptide complex. A3) Crystal structure of the β2AR-α5-peptide complex (PDB ID: 6E67)16, without showing the stabilizing modifications. The minor deviations observed when comparing this complex with the β2AR-GsGDP complex (inset A2), were expected, because of the stabilizing modifications of the β2AR-α5-peptide complex. A4) In β2AR-Gsempty (PDB ID 3SN6) α5 binds differently to the receptor when compared to the previous complexes. The inset shows that Y391α5 and H387α5, that are initially buried in GsGDP (inset A1), engage the receptor in the crystal structure of β2AR-Gsempty, while E392α5 and R389α5, that engage the receptor in the other two complexes, point away from the interface. B) G protein labels for subdomains as typically used in G-protein nomenclature (CGN13). C) Different orientations of α5 (the remainder of the G protein is hidden) relative to the receptor in β2AR-GsGDP when compared to β2AR-Gsempty. Top view of α5 from the cytoplasmic side reveals that α5 is rotated by ca. 40° when the two complexes are compared. The side view highlights the different binding angles of α5 relative to the membrane normal.
Extended Data Fig. 2 Time-traces of selected parameters describing structural changes at the nucleotide binding pocket in the β2AR-GsGDP simulations.
As a reference, the same parameters are also shown for the GssGDP simulations. The panels on the left show the time-trace of run 2 of the β2AR-GsGDP simulations (solid blue curve, example trajectory in Fig. 2) and all runs of the GsGDP simulations (black curves, mostly invariant), whereas the panel on the right shows the distributions for all runs of the β2AR-GsGDP and GsGDP simulations. A) R201SwitchI-E50Ploop residue-residue distance. All β2AR-GsGDP simulations show increased fluctuations of this interaction compared with GsGDP simulations. B) Solvent-Accessible-Surface-Area, SASA, of GDP in the Gs nucleotide binding pocket. All β2AR-GsGDP simulations show increased SASA compared with GsGDP simulations. C) Distance between the center of mass (COM) of the helices αF-α1, showing a clear increase only in run2 of the β2AR-GsGDP simulations. A smoothing window of 20 ns is applied (solid lines) with the raw data as gray backdrop.
Extended Data Fig. 3 Principal-Component-Analysis (PCA) of the pooled β2AR-GsGDP and GsGDP simulation data reveals large scale conformational differences between individual PCA states.
A) PCA of the β2AR-GsGDP and of the GsGDP simulations (Methods, Table S1), clustered onto four main states, A, B, C, D, represented as circles overlaid on top of the 2D surface. In orange (bottom right) is the state that only appears in the GsGDP simulations, which displays the least αN-α5 opening (cf C, D). In purple (top right) is the state named β2AR-GsGDP intermediate, in which we observe opening of the AHD. B) PCA of β2AR-GsGDP, GsGDP, and β2AR-GsGDP spawns simulations (Methods, Table S1) in which two additional states (4 and 5) are visited. Please note that in B the x and y axes are swapped to visually conserve state A in the upper right corner of the 2D surface. C) Overlay of representative structures of the four states of panel A (same color-code as the circles) revealing differences in αN-α5 and in the AHD opening. D) Opening of the α5/αN-β1 scaffold upon formation of the β2AR-GsGDP intermediate. Also highlighted is GDP (in VdW representation) at the base of the α5 helix. E) Color-coded time-traces of all trajectory data, mapping when each state is visited in each trajectory. The arrows indicate the timepoints from which the structures in F) origin. F1-F5) Left to right: representative structures for states D, A (three times) and 5, respectively, captured from the trajectories and frames indicated by the arrows of E). The opening of the RHD-AHD exposing the GsGDP binding pocket can be appreciated in F2-F5. However, whereas both β2AR-GsGDP intermediates A (F2-F4) and 5 (F5) display large RHD-AHD openings, the way in which they open differs. G) Per-state, color-coded point-clouds of the terminal NH atoms of R1313.50 of the ionic-lock. H) Per-state, color-coded helical propensity of the ICL2, showing that it is most folded in the β2AR-GsGDP intermediate.
Extended Data Fig. 4 Interface between ICL2 and Gs, for the PCA states A (β2AR-GsGDP intermediate), B, and C (connecting states).
A) Residue-residue contact frequencies (interaction cutoff 4 Å) shown as circular flareplots. Residues are represented as dots in a circle and residue-residue contacts as black lines connecting them. The opacity of the line indicates the contact frequency. Moreover, each residue’s secondary structure is displayed with a letter (Helix, Beta-sheet, Coil) next to each dot. Finally, the sum of interactions (interface strength) for each residue is indicated in the outer gray ring, making it easier to locate where the interactions are concentrated, for example in the central region of α5 in state A, the β2AR-GsGDP intermediate. B) Representative structures of states A, B, and C with the interface strength (sum of contact-frequencies per residue) overlaid as a heatmap. ICL2 engages the most with the middle part of α5 and β6 in state A (the β2AR-GsGDP intermediate, on the left). C) Detailed view of the residue-residue interactions at the ICL2 α5/β6 interface in state A (the β2AR-GsGDP intermediate).
Extended Data Fig. 5 Per-state violin-plots for the residue-residue distance-distributions of L394α5 with the TM6 lysine ladder K2676.29, K2706.32, and K2736.35.
K273 (bottom right), which lies deepest in the G protein binding pocket of the receptor, has the most contact for state A, the β2AR-GsGDP intermediate, whereas there is none in β2AR-Gsempty. All violins have been normalized to the same width. The number of samples behind each violin are 3499, 859, 2876 and 992 for State A, State B, State C and State β2AR-Gsempty, respectively.
Extended Data Fig. 6 Time-traces of selected parameters related to signal transfer (Fig. 5) for simulation runs of the β2AR-GsGDP (run 2, blue), the β2AR-GsGDPspawns (run 5, green) and all runs of the GsGDP simulations (black).
A) Residue-residue distances involved in the suggested path 1 (Fig. 5), where a shift of F212β2 toward α1 leads to breakage of αF-α1, switchI-GDP and TCAT (β6-α5 loop)-αF interactions (residues are tagged with their CGN-labels6). B) Residue-residue distances involved in the suggested path 2, where ICL2 interacts with the middle of α5 and β6 (first three rows), leading to the displacement of αG (second three panels). Residues are tagged with the Ballesteros-Weinstein labels91,92. C) Solvent-Accessible-Surface-Area, SASA (in nm2) of GDP for all runs of all simulations. The panels on the left show the SASA time-traces for all trajectories, while the panels on the right show the per-trajectory SASA distributions. Both in the time-traces and in the distributions, we see increased SASA when β2AR is present. Moreover, we see sharp increases when the simulation enters the β2AR-GsGDP intermediate state A (solid blue line, ca. 2 µs) and an even larger increase when entering the β2AR-GsGDP intermediate state 5 (solid green line, ca 10 µs) (see Extended Data Fig. 3E for state time traces). Traces have been smoothed using a moving window of 100 ns, with the raw data shown as a gray backdrop for the blue and green (the backdrop of the black lines has been left out for clarity).
Extended Data Fig. 7 Experimental validation of the β2AR–GsGDP coupling interface by functional and binding studies.
A) Kinetic traces capturing the initial onset of Gs activation at β2AR WT and mutants upon stimulation (t = 0 s) with 10 µM isoproterenol (ISO) by quantifying Gβγ association with a membrane-associated reporter GRK3-C-tail following after dissociating from Gα (Gβγ-release-BRET). Data represent mean ± SEM from (n) independent experiments: WT (n = 14), R63Q/N69M (n = 7), Y141V/Q142L (n = 7), K273Q (n = 7), K270Q/K273Q (n = 7), K267Q/K270Q/K273Q (n = 7), G276A/G280A (n = 7) and G276V/G280V (n = 6). B) Surface expression levels of β2AR WT and mutants for the Gs activation assay (Gβγ-release- BRET). Data represent mean ± SEM from (n) independent experiments: WT (n = 5), R63Q/N69M (n = 4), Y141V/Q142L (n = 5), K273Q (n = 5), K270Q/K273Q (n = 3); K267Q/K270Q/K273Q (n = 5), G276A/G280A (n = 5) and G276V/G280V (n = 5). C) Surface expression levels of β2AR WT and mutants for the cAMP accumulation assay. Data represent mean ± SEM from 7 independent experiments. D) Concentration response curves of β2AR WT and mutants and derived pEC50 values for Gs-mediated cAMP accumulation. Data represent mean ± SEM from 7 independent experiments. Statistical significance (P) was determined by one-way ANOVA, followed by Dunnett’s multiple comparisons test. P values: WT high (P = 0.5183), R63Q/N69M (P = 0.0051), Y141V/Q142L (P = < 0.0001), K273Q (P = 0.801), K270Q/K273Q (P = 0.0008), K267Q/K270Q/K273Q (P = < 0.0001), G276A/G280A (P = 0.9956) and G276V/G280V (P = 0.202). ns; not significant, P < 0.05 = *, P < 0.01 = **, P < 0.001 = ***, P < 0.0001 = ****. E) A representative graph of an SDS-PAGE gel showing purified β2AR WT, K273Q, K267Q/K270Q/K273Q (3 M) fractions used for the Glo assay (for gel source data, see Supplementary Fig 1). F) Saturation binding experiments of [3H]-DHA to purified β2AR WT, K273Q and K267Q/K270Q/K273Q showing similar affinities (KD). Data indicate similar sub-micromolar affinities (Ki) of purified β2AR WT, K273Q and K267Q/K270Q/K273Q. Data represent mean ± SEM from 3 independent experiments. H) [3H]-DHA binding affinities (KD) and estimated isoproterenol affinities (pKi) for WT and mutant receptors expressed in HEK293 membranes. KD and pKi values indicate similar estimated binding affinities of isoproterenol for WT and mutants. KD values were determined from similar [3H]-DHA saturation as in F). pKi values were determined from similar competition curves as in G) to first determine isoproterenol IC50 followed by application of the Cheng-Prussoff equation to factor in radioligand affinity and concentration (see Methods). Data from [3H]-DHA saturation binding experiments (KD) represent mean ± SEM from 3 independent experiments. Data from [3H]-DHA competition binding experiments (pKi) represent mean ± SEM from 5 independent experiments.
Extended Data Fig. 8 Time-traces for selected parameters of a composite trajectory leading to GDP release.
The composite trajectory consists of run 2 of the β2AR-GsGDP simulations followed by a 4.5 µs biased simulation, followed by 2.5 µs unbiased simulation (Methods, Table S1, movie 5). A) Increasing Solvent-Accessible-Surface-Area (SASA, in nm2) of GDP in the GsGDP nucleotide binding pocket. The SASA value of GDP in solution (ca. 6 nm2), was estimated for the same frames after eliminating all non-GDP atoms and re-computing SASA. B) Decreasing number of GDP contacts (interaction cutoff is 4 Å). C) αN-α5 opening as tracked by the H41αN-H387α5 interaction. D) αF-α1 distance, as tracked by the distance between their respective center of masses (COMs). E) Distance traveled by the COM of GDP during the composite trajectory. In panels A)-E) a smoothing window of 20 frames is applied (solid lines) with the raw data as gray backdrop. F) First (in gray) and last (in color) frames of the composite trajectory, showing how far away from the nucleotide binding pocket the GDP is dislodged. Also shown are the displacements of α1 and αG.
Extended Data Fig. 9 WT β2AR and mutants of GsGDP coupling interface in the nano-luciferase BRET assay for Gα subunit and Gβγ dissociation.
A) Concentration response curves (CRC) of wild-type (WT) β2AR and the mutants in the Nano-luciferase BRET assay for Gα subunit and Gβγ dissociation. Single mutant K273Q6.35, the double mutant K270Q6.32/K273Q6.35, the triple mutant K267Q6.29/K270Q6.32/K273Q6.35 and the wild-type were titrated with isoproterenol (ISO) and used to determine the EC50. The CRC curves were normalized on the WT response. 6 experiments in triplicates per concentration were performed for WT, double and triple lysine mutants and 7 experiments for the single lysine K273Q6.35. Error bars indicate the standard error of the mean. B) Mean maximal delta BRET values of wild-type and mutant β2AR in the Nano-luciferase BRET assay for Gα subunit and Gβγ dissociation. Error bars indicate the standard error of the mean, number of experiments (N) indicated at the bottom of the bars, N is 6 for K267Q6.29/K270Q6.32/K273Q6.35. Statistical significance was calculated using t-test with Welch´s correction, two-tailed p values < 0.05 = * (p value = 0.0167), < 0.001 = *** (p value = 0.0002), < 0.0001 = **** C) EC50 values of agonist-induced Gs dissociation using Nano-luciferase BRET assay. Number of independent experiments (N) performed in triplicates indicated in columns; error bars indicate the SEM – Standard Error of the Mean. P values were calculated using unpaired t-test with Welch´s correction. Two-tailed P value < 0.05 = * (p value of 0.0486 for wt vs. K273Q6.35, p value of 0.0119 for wt vs. K267Q6.29/K273Q6.35. EC50 values of the triple mutant could not be determined due to low delta BRET% values.
Extended Data Fig. 10
Time traces of the GDP-R201SwitchI and E50Ploop-R201SwitchI residue-residue distance (heavy atoms) for several setups shown for the first 2 µs of the each run. A smoothing window of 50 ns is applied (solid lines) with the raw data as gray backdrop. A, B) β2AR-GsGDP simulations, C, D) Same starting point as A, but with the ICL3 region modelled in. E, F) Same starting point as A, but with the TM6 mutants K267Q6.29/K270Q6.32/K273Q6.35. G, H) Receptor-free simulations. See Table S1 and Methods for an overview of setups and simulations.
Supplementary information
Supplementary Information
Supplementary Tables 1–8.
Supplementary Video 1
Example trajectory (run 2) of the β2AR–GsGDP simulations leading to the β2AR–GsGDP intermediate.
Supplementary Video 2
Side-by-side comparison of example trajectory (run 2) of the β2AR–GsGDP simulations and example trajectory (run 3) of the GsGDP simulations.
Supplementary Video 3
Side-by-side comparison of example trajectory (run 2) of the β2AR–GsGDP simulations and example trajectory (run 3) of the GsGDP simulations, focusing on the Gs nucleotide-binding pocket.
Supplementary Video 4
File. Side-by-side comparison of example trajectory (run 2) of the β2AR–GsGDP simulations and example trajectory (run 5) of the β2AR–GsGDP spawns, both leading to β2AR–GsGDP intermediates.
Supplementary Video 5
Composite trajectory leading to GDP release.
Source data
Source Data Fig. 2
Residue–residue distances shown in panels A and C.
Source Data Fig. 3
Residue contact frequencies, activation rates (K) derived from kinetic traces, mean dBret in % of wild-type and mutants, GLO-assay luminescence (LU).
Source Data Extended Data Fig./Table 2
Time-traces for R201-E50 residue–residue distance, GDP SASA, Distance α1-αF COM.
Source Data Extended Data Fig./Table 3
Time-traces for the Principal Components (PCs) for the two pooled datasets. Time-traces of the cluster-labels.
Source Data Extended Data Fig./Table 4
Per-state residue contact frequencies.
Source Data Extended Data Fig./Table 5
Per-state residue–residue distances of L394α5 and the lysine ladder on TM6.
Source Data Extended Data Fig./Table 6
Time-traces for selected residue–residue distances and GDP SASA.
Source Data Extended Data Fig./Table 7
Source Data for all panels in Extended Data Fig. 7, including uncropped gels as embedded figure.
Source Data Extended Data Fig./Table 8
Time-traces for selected geometric parameters and GDP SASA for the composite trajectory showing GDP ejection from the GDP-binding pocket.
Source Data Extended Data Fig./Table 9
Source data for the Gs-CASE-BRET assays.
Source Data Extended Data Fig./Table 10
Time-traces for GDP–R201 and E50-GDP residue–residue distances for different setups.
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Batebi, H., Pérez-Hernández, G., Rahman, S.N. et al. Mechanistic insights into G-protein coupling with an agonist-bound G-protein-coupled receptor. Nat Struct Mol Biol (2024). https://doi.org/10.1038/s41594-024-01334-2
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DOI: https://doi.org/10.1038/s41594-024-01334-2
