1. Structural Biology and Molecular Biophysics
Download icon

Identification of ligand-specific G protein-coupled receptor states and prediction of downstream efficacy via data-driven modeling

  1. Oliver Fleetwood
  2. Jens Carlsson
  3. Lucie Delemotte  Is a corresponding author
  1. Science for Life Laboratory, Department of Applied Physics, KTH Royal Institute of Technology, Sweden
  2. Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Sweden
Research Article
  • Cited 2
  • Views 2,091
  • Annotations
Cite this article as: eLife 2021;10:e60715 doi: 10.7554/eLife.60715

Abstract

Ligand binding stabilizes different G protein-coupled receptor states via a complex allosteric process that is not completely understood. Here, we have derived free energy landscapes describing activation of the β2 adrenergic receptor bound to ligands with different efficacy profiles using enhanced sampling molecular dynamics simulations. These reveal shifts toward active-like states at the Gprotein-binding site for receptors bound to partial and full agonists, and that the ligands modulate the conformational ensemble of the receptor by tuning protein microswitches. We indeed find an excellent correlation between the conformation of the microswitches close to the ligand binding site and in the transmembrane region and experimentally reported cyclic adenosine monophosphate signaling responses. Dimensionality reduction further reveals the similarity between the unique conformational states induced by different ligands, and examining the output of classifiers highlights two distant hotspots governing agonism on transmembrane helices 5 and 7.

Introduction

G protein-coupled receptors (GPCRs) are membrane proteins which activate cellular signaling in response to extracellular stimuli. This process is controlled by extracellular ligands such as hormones and neurotransmitters, and the binding of these increases the probability of activating intracellular partners. GPCRs are vital in many physiological processes and constitute the most common class of drug targets (Hauser et al., 2017).

Much of the current understanding of GPCR signaling at the molecular level can be attributed to the progress in GPCR structure determination during the last decade (Cherezov et al., 2007; Hanson et al., 2008; Masureel et al., 2018; Rasmussen et al., 2011a; Ring et al., 2013; Wacker et al., 2010). GPCRs interconvert between inactive (R) and active (R*) states, which control G protein binding to a conserved intracellular domain via conformational rearrangements among the seven transmembrane (TM) helices (Figure 1aManglik and Kruse, 2017). In the absence of a bound agonist, this process is called basal activity. Ligands can bind to the orthosteric site in the receptor’s extracellular domain and thereby control conformational rearrangements. Orthosteric ligands are traditionally classified as either agonists, which promote activation, antagonists, which bind to the orthosteric site but do not alter basal activity, or inverse agonists that also reduce basal activity. However, this classical view of ligand efficacy is complicated by the fact that GPCRs can signal via several intracellular partners; for example, G-proteins or β-arrestins. Most agonists will activate several signaling pathways, but agonists with the ability to activate one specific intracellular partner have also been identified, a phenomenon referred to as biased signaling. Based on spectroscopy and structure determination studies, conformational changes in the receptor govern the activation of signaling pathways (Frei et al., 2020; Liu et al., 2012; Masureel et al., 2018), although the underlying molecular mechanisms remain elusive. Characterization of the allosteric process guiding interactions with intracellular partners is a major challenge and can only be fully understood by using a combination of different methodologies.

Structure and microswitches of the β2 adrenergic receptor.

(a) A molecular dynamics (MD) snapshot of the β2 adrenergic receptor in complex with adrenaline in an active-like state (simulation starting from PDB 3P0G). The vignettes show the conformations of residue pairs reflecting important microswitches in the active and inactive structures 3P0G (color) and 2RH1 (white): the TM5 bulge (red), measured as the closest heavy atom distance between S207(5.46) and G315(7.41); the connector region’s conformational change (pink), measured as the difference in root-mean-square deviation (RMSD) between the active and inactive state structure of the residues I121(3.40) and F282(6.44); the Y-Y motif (black), measured as the C-ζ distance between Y219(5.58) and Y326(7.53) of the NPxxY motif; and the Ionic lock displacement (orange), measured as the closest heavy atom distance between E268(6.30) and R131(3.50). (b) Ligands examined in this study: agonists BI-167107 and adrenaline; biased partial agonist salmeterol; antagonists timolol and alprenolol; and the inverse agonist carazolol. Atoms are colored according to their partial charge.

The term microswitch, or molecular switch, describes local structural changes in the receptor that contribute to controlling activation and can, for example, involve side chain rotamers, movement of two domains relative to each other, or a helix twist. Two microswitches implicated in the activation of class A GPCRs are an outward displacement of the transmembrane helix 6 (TM6) and twist of the highly conserved N(7.49)P(7.50)xxY(7.53) motif (superscripts notation according to Ballesteros–Weinstein numbering; Ballesteros and Weinstein, 1995), which take part in the formation of the intracellular binding site. In the orthosteric site, microswitches are typically less conserved and depend on the type of ligand recognized by the receptor (Manglik and Kruse, 2017).

Considering the high dimensionality of a protein with over 300 interacting residues, it is difficult to identify relevant microswitches from the sequence or static experimental structures. Historically, sequence analysis and mutagenesis experiments (Gregorio et al., 2017; Lamichhane et al., 2020, Lamichhane et al., 2015; Manglik et al., 2015; Picard et al., 2019) have been used to characterize motifs important for signaling, but this approach may overlook the role of less conserved residues, water, and ions in ligand recognition and receptor activation (Chen et al., 2020). Molecular dynamics (MD) simulations can generate trajectories from experimental starting structures, capture the dynamics of all microswitches and allow us to derive the free energy landscapes governing the equilibrium between protein states. MD simulations have indeed been used extensively over the last decade to study GPCR activation (Bhattacharya and Vaidehi, 2010; Dror et al., 2011; Hu et al., 2019; Kohlhoff et al., 2014; Li et al., 2013; Miao and McCammon, 2016; Niesen et al., 2011; Shan et al., 2012; Tikhonova et al., 2013). Due to the computational cost of brute-force MD simulations, it is nearly impossible to obtain converged results without enhanced sampling methods, although the use of special purpose hardware (Dror et al., 2011) has pushed the boundaries of what is achievable by conventional MD simulations. Enhanced sampling strategies have emerged as an alternative, where exploration of the conformational landscape is promoted by the introduction of a bias in the simulations (Harpole and Delemotte, 2018). In a post-processing step, the bias can be adjusted for, and it is thus possible to derive theoretically exact results at a fraction of the cost of unbiased simulations. However, the ever-increasing size of simulation data and diverse conformations sampled by MD simulations makes it difficult to identify and determine the importance of microswitches by simple visualization of the conformational ensemble. Data-driven and machine learning approaches can help to condense the data and reduce human bias in the interpretation of the results (Fleetwood et al., 2020c; Hu et al., 2019).

In this study, we focus on the prototypical β2 adrenergic receptor (β2AR), which interacts with Gs proteins to trigger a cyclic adenosine monophosphate (cAMP) response, and arrestins, which control endocytosis and kinase activation (Jean-Charles et al., 2017). Both pathways are physiologically relevant and are modulated by therapeutic drugs. The β2AR is a drug target for bronchoconstriction medication and was the first receptor crystallized in complex with a G protein (Rasmussen et al., 2011a; Rasmussen et al., 2011b). Experimental studies, including crystallography (Masureel et al., 2018; Rasmussen et al., 2011b; Ring et al., 2013), spectroscopy methods (Gregorio et al., 2017; Imai et al., 2020; Kofuku et al., 2012; Lamichhane et al., 2020; Liu et al., 2012), and computational methods (Provasi et al., 2011; Chen et al., 2020; Dror et al., 2011; Kohlhoff et al., 2014; Tikhonova et al., 2013), have investigated the activation mechanism of the β2AR. Agonists bound in experimental structures show a key interaction with S207(5.46) (Chan et al., 2016) and an inward bulge of TM5 in the active state. In the TM domain between the orthosteric site and G protein-binding site, the connector region (Weis and Kobilka, 2018), partially overlapping with the P(5.50)I(3.40)F(6.44) motif, undergoes a rotameric change and thereby influences the hydrated cavity surrounding the conserved D79(2.50) (Imai et al., 2020), which in turn interacts with the conserved NPxxY motif in TM7 and reorients Y326(7.53) to form water-mediated interaction with Y219(5.58) (the Y–Y motif) (Latorraca et al., 2017). The combination of several microswitches leads to conformational changes that promote an outward movement of TM6 and binding of an intracellular binding partner, such as a G protein or arrestin. Understanding how ligands modulate individual microswitches could aid the development of biased agonists.

Enhanced sampling techniques have been used to characterize the activation mechanism of β2AR, from early coarse-grained protocols (Bhattacharya and Vaidehi, 2010; Niesen et al., 2011) to more refined methodologies involving Gaussian accelerated MD (Tikhonova et al., 2013), metadynamics using path collective variables (CVs) derived from adiabatic biased MD simulations (Provasi et al., 2011), or adaptive sampling on cloud-based computing resources (Kohlhoff et al., 2014). Following in these footsteps, we recently introduced a version of the string with swarms of trajectories method designed to capture the activation pathway and the free energy landscapes along various microswitches (Fleetwood et al., 2020b).

In this study, thanks to our cost-effective computational approach, we have derived the activation free energy and characterized the details of the active-like state of the β2AR (Figure 1a) in its ligand-free state and bound to six ligands with different efficacy profiles (Figure 1b), all of which were resolved bound to the β2AR (Figure 1b) and several of which are clinically approved drugs (Woo and Robinson, 2015). The free energy landscapes revealed a stabilization of active-like states for the receptor bound to agonists and a shift toward inactive-like states for the receptor bound to antagonists or inverse agonists. Remarkably, we obtained a strong quantitative correlation between experimentally measured intracellular cAMP responses and the expectation values of the upper and transmembrane microswitches, highlighting the predictive power of our approach. In a second step, we introduce an adaptive sampling protocol developed to quantitatively sample the most stabilized states kinetically accessible from the activated starting structure (which we will refer to as the active-like state). Using dimensionality reduction techniques, we find that all ligands stabilize distinct receptor states and that ligands with similar pharmacological properties cluster together. Several of the microswitches considered to be of significance for GPCR activation, such as the NPxxY motif and the extracellular end of TM5, were automatically identified as important with our protocol. Combined with the activation free energies, our results show how ligands control the population of states. They modulate the conformational equilibrium by tuning important allosteric microswitches, in particular near the G protein-binding site. By inspecting the inter-residue contacts formed for different ligands, we identified an allosteric pathway between the two binding sites and a large heterogeneity of TM7 states. Our results thus build on the earlier use of enhanced sampling methods and demonstrate how such protocols combined with today’s computational capacities and availability of high-resolution structures in various states can provide insights into the structural basis of allosteric communication and ligand efficacy profiles, and potentially find use in the design of novel GPCR drug candidates.

Results

Ligands control efficacy by reshaping microswitches’ probability distributions

We derived the free energy landscape along the most probable activation pathway of the β2AR bound to different ligands using the string method with swarms of trajectories (Figure 1b). The set of ligands studied consisted of the full agonists BI-167107 and adrenaline, the G protein-biased agonist salmeterol, the antagonists alprenolol and timolol (sometimes classified as a partial inverse agonist; Hanson et al., 2008), and the inverse agonist carazolol. After 305 iterations, corresponding to 4 µs of aggregated simulation time per ligand-receptor complex, the activation pathways had converged (Table 1 and Figure 2—figure supplements 16). Based on the short swarm trajectories, we calculated free energy landscapes along different microswitches identified previously (Fleetwood et al., 2020bFigure 2a,b and Figure 2—figure supplement 7): (1) the TM5 bulge, which is an indicator of contraction in the ligand binding site; (2) the connector ΔRMSD, a rotameric switch involving residues I121(3.40) and F282(6.44) in the TM region; (3) the ionic lock distance reflecting the outward movement of TM6, measured as the closest heavy atom distance between E268(6.30) and R131(3.50); and (4) the Y-Y motif as the C-ζ distance between Y219(5.58) and Y326(7.53), which acts as an indicator of the twist of the NPxxY motif and a slight reorientation of TM5.

Table 1
Total simulation time per string of swarm simulation ensemble*.
LigandSteered molecular dynamics simulation time [µs]#Restrained equilibration trajectories (30 ps each)#Swarm trajectories (10 ps each)Total simulation time [µs]
Carazolol0.2148783528164.17
Alprenolol0.2148783648564.29
Timolol0.2148783638084.28
Salmeterol0.2148783632324.28
Adrenaline0.2148783729364.38
  1. * The previously published apo and BI-167107 initiated systems followed a slightly different initialization protocol with three substrings (Fleetwood et al., 2020b) and have been excluded from the table.

Figure 2 with 11 supplements see all
Ligand-dependent free energy landscapes and expected downstream response.

(a) Free energy landscapes for the different ligands projected along the connector ΔRMSD microswitch. The inactive and active states are marked by R and R* respectively. (b) Free energy projected onto the TM5 bulge CV in the orthosteric site and the ionic lock distance (measuring TM6 displacement in the G protein-binding site). (c)–(f) Correlation between experimental values of downstream cyclic adenosine monophosphate (cAMP) signaling and the expectation value of different microswitches for the receptor bound to different ligands, (c) for the TM5 bulge, (d) for the connector ΔRMSD, (e) for the Y-Y motif, and (f) for the ionic lock distance. The cAMP Emax values for carazolol and BI-167107 (marked with an asterisk), which were not available in the experimental study, are inferred from the linear regression. Red dashed lines highlight the clustering of the agonist-bound structures.

The free energy landscapes projected along the connector ΔRMSD (Figure 2a) reveal two states. In agreement with what could be expected, agonists lower the relative free energy of the active state (R*) of this microswitch, whereas non-agonists favor the inactive state (R) more. A loose coupling between the orthosteric ligand and G protein-binding site was proposed based on correlated motions between the two domains in long timescale MD simulations of the BI-167107-bound receptor (Dror et al., 2011). The free energy landscapes projected along the TM5 bulge in the orthosteric site and the ionic lock distance in the G protein-binding site (Figure 2b) provide a quantitative view of this correlation and reveal that the activation pathway and the precise conformation of the stabilized states along the pathway depends on the ligand (Tikhonova et al., 2013; Kohlhoff et al., 2014). In general, the TM5 bulge assumed an outward conformation when TM6 was in its inward, inactive state. Furthermore, non-agonists favored a conformation with both a fully inactive TM5 bulge and an inactive TM6, whereas agonists favored a more contracted binding site even in the inactive state of TM6. However, despite the relatively loose coupling, it should be noted that agonists were generally observed to shift the energy balance to favor active-intermediate receptor conformations with a TM6 displacement larger than in the inactive state.

It is not straightforward to predict ligand efficacy by visual inspection of a free energy landscape, since it is the Boltzmann integrals over the basins that determine the relative free energy of the active and inactive states (ΔG). To investigate if ligand efficacy could be quantified using our simulation results, we computed expectation values and ΔG of the microswitches and compared them to functional experiments measuring the maximal G protein-mediated cAMP production (Emax) (Figure 2c–f and Figure 2—figure supplement 8van der Westhuizen et al., 2014). Remarkably, the expectation values associated with the upper microswitches were strongly correlated to the previously reported experimental values, in particular the TM5 bulge (Figure 2c; R = −0.95) and the connector ΔRMSD (Figure 2d; R = 0.93). Emax values of BI-167107 and carazolol were not available, and we thus inferred their predicted Emax from the linear correlation obtained for the other ligands: we predicted BI-167107 to have a cAMP Emax value slightly higher than adrenaline and salmeterol, and carazolol to have an Emax similar to the values of the ligand free receptor and inverse agonist timolol (Figure 2c,d). These results are in line with expectations; BI-167107 is indeed a known full agonist and carazolol an inverse agonist (Manglik et al., 2015; Rasmussen et al., 2011a; Ueda et al., 2019). Similar results were obtained using the free energy difference of the active and inactive states, ΔG (Figure 2—figure supplement 8). These results thus suggest that our simulations accurately captured the relative stability of states and should therefore be able to provide insights into how ligands with different efficacy profiles control the conformational ensemble of the receptor. Moving down the microswitch cascade toward the intracellular region, the cAMP response was less well correlated with the expectation values and the ΔG of the Y-Y motif and Ionic lock distance (Figure 2e; R = −0.75 and Figure 2f; R = 0.58, respectively, and Figure 2—figure supplement 8), as expected from the looser coupling between these microswitches and the ligand binding sites.

As a control, we converged the activation string for a simulation set initiated from the inactive state structure, where the starting activation pathway was sampled in the reverse direction (Figure 2—figure supplements 12 and 9a). The TM microswitch expectation values led to a similar prediction of Emax, accurately classifying carazolol as a non-agonist (Figure 2—figure supplement 10). Inspection of the free energy landscapes (Figure 2—figure supplement 9b–d), on the other hand, revealed two differences between the carazolol-bound receptors’ active states obtained starting from different initial strings (Figure 2—figure supplement 9c–d): (1) in the 2RH1-initiated system, the intracellular domain of TM6 assumed an orientation with the ionic lock residues’ side chains pointing away from each other (Figure 2—figure supplement 10d), although the backbone distance between TM6 and TM3 was very similar (Figure 2—figure supplement 9d), and (2) the 2RH1-initiated system sampled a conformation with an inactive TM5 bulge domain and active cytosolic domain (Figure 2—figure supplement 9c), unlike any conformation captured in experimental structures. We hypothesize that the conformation obtained starting from the inactive state could be an artifact of pulling the inverse agonist-bound receptor directly toward its unfavorable active state, without targeting metastable intermediate states along the pathway. Moving forward, we thus favor a protocol in which the receptor was pulled along a pathway identified by unbiased MD simulations, presumably closer to the most favorable converged activation pathway (Dror et al., 2011; Fleetwood et al., 2020b).

Although downstream efficacy is an important metric for drug discovery purposes, alternative methods are required to characterize the molecular basis of receptor activation. Spectroscopy experiments have proven useful for this purpose (Gregorio et al., 2017; Imai et al., 2020; Kofuku et al., 2012; Manglik et al., 2015; Ma et al., 2020; Nygaard et al., 2013; Ueda et al., 2019; Weis and Kobilka, 2018), yet they are often difficult to compare quantitatively to atomistic simulations due to chemical modifications introduced and/or complex interpretation of measured signals. 19F-fluorine NMR and double electron-electron resonance (DEER) spectroscopy experiments have shown that the conformational ensembles of carazolol and the apo receptor have similar TM6 distance distributions (Manglik et al., 2015), in agreement with the similarity in their microswitch expectation values and free energy landscapes (Figure 2b,f and Figure 2—figure supplement 8e). It has also been proposed that the inactive receptor exists in two sub-states (Manglik et al., 2015), one with a formed and one with a broken ionic lock. Our simulations rarely captured a sub-state with the ionic lock formed, which suggest that the state with a broken ionic lock is of lower free energy, although modeling of missing residues in the cytosolic domain of TM6 and TM3 may alter the dynamics of this region (Dror et al., 2009). Nevertheless, the agonists stabilized a state with the side chains of the ionic lock residues pointing away from each other, while the inverse agonist carazolol favored a state with the side chains pointing toward each other, although the TM6 displacement was too large for the residues to fully form an ionic bond (Figure 2—figure supplement 11a). In general, the ligands stabilized active-like states of different ionic lock displacements (Figure 2b). As GPCRs only assume their fully active state in the presence of an intracellular binding partner—a condition not met in the simulations carried out in this work—a loose allosteric coupling between intracellular microswitches and the cellular response is expected.

β2AR crystal structures reveal a number of stabilized water molecules in the inactive state, while this region is dehydrated in the G protein-bound state (Cherezov et al., 2007; Rasmussen et al., 2011b). The disruption of intra-receptor water networks and the formation of a hydrophobic barrier, a prerequisite of activation (Trzaskowski et al. 2012), are likely conserved features of activation (Venkatakrishnan et al., 2019). Hydration in the active state may also contribute to the change in probe environment observed in spectroscopy experiments (Lamichhane et al., 2015). We investigated the hydration near the intracellular binding site by counting the number of water molecules within 0.8 nm of L266(6.28) (Figure 2—figure supplement 11b), and found that BI-167107 stabilized a partially dehydrated active state when TM6 assumed its outward pose. Carazolol and the apo condition, on the other hand, did not induce dehydration with TM6 in its active conformation (Figure 2—figure supplement 11b). This finding shows that, to fully understand agonist control of GPCR activation, one needs to combine the shift in free energy with the conformational differences between the states induced by the ligands.

Data-driven analysis reveals that ligands stabilize unique states

To pinpoint the molecular basis of signaling, we reduced the high dimensional datasets to a more compressed representation using methods from machine learning (Figure 3). We used three different methods to analyze all inter-residue contacts: principal component analysis (PCA), multidimensional scaling (MDS), and t-distributed stochastic neighbor embedding (t-SNE). All these approaches were designed to find a low dimensional embedding of the high dimensional data, but differ in their underpinnings. PCA seeks a linear transformation of the input data into an orthogonal basis of principal components (PCs) and is designed to cover as much of the variance in the data as possible (Figure 3a). MDS projects the high dimensional space into a low dimensional representation using a non-linear transformation which preserves the distance between points (Figure 3b). T-SNE is a visualization technique which seeks to disentangle a high-dimensional data set by transforming it into a low-dimensional embedding where similar points are near each other and dissimilar objects have a high probability of being distant (Figure 3c).

Figure 3 with 4 supplements see all
Dimensionality reduction techniques applied to the active-like simulation ensembles.

Each point represents a simulation snapshot, colored according to the ligand bound to the receptor. Red dashed lines highlight regions where agonists cluster. The features are computed as the inverse closest heavy atom distances between residues. (a) Principal component analysis (PCA) projection onto the first two principal components (PCs), (b) multi-dimensional scaling (MDS) and (c) t-distributed stochastic neighbor embedding (t-SNE). (d) The similarity between conformations sampled when agonist (BI-167107, adrenaline and salmeterol) and non-agonist ligands are bound, measured as the average distance between configurations in the full feature space.

We evaluated two datasets: the equilibrated active-like state ensemble (Figure 3) and the swarm trajectories from the final iteration of the converged string. For the latter, which represent the converged activation pathways, the dimensionality reduction techniques created embeddings which separated snapshots by their progression along the activation path (Figure 3—figure supplement 1). This is expected because unsupervised dimensionality reduction methods tend to emphasize large scale amplitude motions, such as the displacement of TM6 in the case of GPCR activation (Fleetwood et al., 2020c). These results confirmed that this feature is shared among all activation pathways, regardless of which ligand the receptor was bound to.

Whereas the activation path ensembles contained inactive, intermediate, and active-like states for every ligand–receptor complex, the active-like state ensemble simulations revealed that the conformations sampled in the presence of different ligands differed substantially. Indeed, after eight iterations with an accumulated simulation time of 1.4 μs per ligand (see Materials and methods), the method for finding single equilibrated states generated trajectories that diffused around the most stabilized state kinetically accessible from the starting structure (Figure 3—figure supplement 2). Dividing the dataset into thirds yielded similar results (Figure 3—figure supplement 3), showing that the states were adequately sampled.

The details of the active state ensemble varied among the ligands (Figure 3a–c). Simulations with agonists bound tended to be grouped together for all three dimensionality reduction methods, but each of them generally also led to a distinct conformational ensemble. The simulation snapshots with the agonist adrenaline bound were generally close to the full agonist BI-167107 and the partial agonist salmeterol. For the other ligands, the receptor explored a different conformational space than with agonists, but the ensembles were more diverse. In agreement with the projections, the similarity matrix based on the average distance between snapshots in the full feature space (Figure 3d) showed that agonists and non-agonists stabilized significantly different states.

Using PCA, we note that timolol clusters together with the agonist ligands. Thus, the first two PCs are not sufficient to completely separate the dataset according to the ligands present (Figure 3a), but including more PCs in the projection leads to a satisfactory separation (Figure 3—figure supplement 4). The non-linear methods separated the classes well in two dimensions. As expected, a few points deviated from the other snapshots in the same class due to sampling slightly outside defined free energy basins. We also note that although t-SNE generates an embedding with perfect separation between classes, the micro-clusters depend on parametrization of the method and their exact placement is stochastic (Schubert, 2017).

To summarize, an analysis of the simulations by machine learning shows that ligands share many overall features of activation, but stabilize unique local states, in agreement with previous work (Kohlhoff et al., 2014; Tikhonova et al., 2013; Provasi et al., 2011; Liu et al., 2012; Lamichhane et al., 2020; Frei et al., 2020; Suomivuori et al., 2020). Together with the free energy landscapes, our findings support that ligands control the relative time a receptor spends in active-like states, and induces small conformational state-specific signatures throughout the protein.

Ligands control residues near the G protein-binding site

To capture the important characteristics of receptor activation, we applied PCA on the swarms of trajectories datasets representing the activation ensemble and extracted important features from these (Figure 4c). This analysis identified parts of TM6 and TM7 near the G protein-binding site as particularly important (Figure 4c and Figure 4—figure supplement 1), adding further support for the importance of these microswitches for activation. To characterize molecular differences between the active-like states controlled by the different ligands, we applied supervised learning on our dataset. With this approach, we derived features discriminating between the classes based on inter-residue distances and thereby identified residues which could be important for activation. Importance profiles were computed for discriminating between agonists and non-agonists (Figure 4a) and to distinguish all ligands from each other (Figure 4b) using a symmetrized version of the Kullback–Leibler (KL) divergence (Fleetwood et al., 2020c; Kullback and Leibler, 1951). With this approach, two residues constituting a distance were scored as important if the active-like states formed non-overlapping distance distributions, corresponding to a high KL divergence. As a control, we also evaluated the important features learned by a random forest (RF) classifier, a machine learning classifier constructed by an ensemble of decision trees. The importance profiles of the KL and RF feature extractors were similar, although the RF classifier generated importance profiles with more distinct peaks. Since the datasets included a few simulation frames that fluctuated outside the equilibrated states, the RF classifier probably suppressed some features to enhance prediction accuracy for these frames. KL divergence estimated how much the distributions overlapped along individual features and was therefore less likely to discard features based on these frames. Remarkably, both data-driven methods identified established microswitches as the most important regions for classification: the NPxxY motif and the intracellular part of TM6 in the intracellular binding site (Figure 4a,b and Figure 4—figure supplement 2).

These results underpin experimental evidence that differences in these regions are related to biased signaling (Frei et al., 2020; Lamichhane et al., 2020, Lamichhane et al., 2015; Liu et al., 2012; Suomivuori et al., 2020). The TM5 bulge was particularly important for discriminating between agonists and non-agonists (Figure 4a). This region does not show up as important when differentiating between all ligands (Figure 4b), which means that the TM5 conformations within the two groups of ligands were so similar that this region could not be used to, for example, discriminate agonists from each other. The NPxxY motif, on the other hand, assumed a unique conformation for each ligand (Figure 4b).

Figure 4 with 4 supplements see all
Important residues for distinguishing ligand-dependent activation mechanisms.

(a)–(b) Residues identified to be important for classification of the equilibrated active-like states. Importance was derived by computing the Kullback–Leibler (KL) divergence along all features, followed by averaging per residue. (a) Comparison of agonists and non-agonists. One signaling hotspot is located at the transmembrane 5 (TM5) bulge and another on TM7 close to the NPxxY motif. (b) Important residues to discriminate between all ligands. The main hotspot is located near the NPxxY motif. (c) Important residues for the activation ensemble from the swarms of trajectories method, extracted with PCA. The importance per feature was computed as the product of the PC’s weights and the PC’s projection onto the input feature. The intracellular end of TM6, which undergoes a large conformational change upon activation, is marked as important. Inverse closest heavy atom distances were used as input features in all figures. (d) Conceptual model describing allosteric communication between the hotspots. Ligands exercise direct control of TM5, which is stabilized in different states by agonists and non-agonists. In turn, residues approximately one helical turn below the orthosteric site, including the connector region, couple to the conformations in the orthosteric site. This leads to distinct interaction patterns between TM6 and TM7 in the TM domain. The importance of TM7 is further enhanced by direct ligand interactions. By favoring distinct TM7 states and modulating the probability of stabilizing TM6 in an active conformation, ligands control the G protein-binding site.

Spectroscopy experiments have shown that the agonist BI-167107 stabilizes an intermediate, pre-active state (Manglik et al., 2015). It was hypothesized that receptor activation involves a transition via this state before forming the fully active state together with an intracellular binding partner (Manglik et al., 2015). The experimental response was too weak to discern a corresponding pre-active state for antagonists, but the authors found it likely that such a state is accessible to all ligands. Moreover, spectroscopy experiments found that different agonists induced different states in the cytoplasmic domain (Manglik et al., 2015). Our results provide molecular models for the pre-active ensemble, and identified the region around the NPxxY domain a major source of conformational heterogeneity (Figure 4b and Figure 4—figure supplements 14). In agreement, conformational differences in this domain have been shown to correlate to efficacy and biased signaling in 19F NMR and single-molecule fluorescence spectroscopy experiments (Frei et al., 2020; Lamichhane et al., 2020; Liu et al., 2012).

Taken together, we arrived at a conceptual model to describe how different ligands control the G protein-binding site (Figure 4d). Ligands exercise direct control of TM5, where agonists and non-agonists stabilize different states. In turn, residues approximately one helical turn below the orthosteric site—including the connector region, which was identified as a good predictor of downstream response (Figure 2d)—couple to the conformations in the orthosteric site. This leads to distinct interaction patterns between TM6 and TM7 in the TM domain. The importance of TM7 is further enhanced by direct ligand interactions, generating a variety of ligand-specific NPxxY motifs. By favoring distinct TM7 states and modulating the probability for TM6 to be in an active conformation, ligands hence control the G protein-binding site. The overall pattern is compatible with observations made from an MSM analysis of large-scale computations (Kohlhoff et al., 2014). However, since our simulation protocol achieves conformational sampling at a fraction of the computational cost, it has allowed us to compute the free energy landscape for a larger ligand dataset and to thus find the molecular basis for the effect of binding of various agonists, antagonists, and inverse agonists.

Molecular basis for allosteric transmission from the orthosteric to the intracellular binding site

To further explore the atomistic basis of our conceptual model (Figure 4), we systematically inspected the most important features connecting the two hotspots near the TM5 bulge and the NPxxY motif. As in the previous section, we computed the KL divergence of the inter-residue distance distributions between the ligands. Distances with a high KL divergence that contributed to the formation of ligand-specific active-like states were further investigated. Although the identified residue-pairs did not necessarily reflect the causality of molecular interactions driving the conformational changes, key features of activation were captured by this automated approach.

We first identified features shared between agonists near the orthosteric site. We found that V206(5.46) could form van der Waals interactions with T118(3.37) only in the presence of agonists (Figure 5a and b). This interaction is probably caused by hydrogen bonding between S207(5.46), the TM5 bulge microswitch, and the ligand. In the TM domain, agonists induce a contraction between L284(6.46) and F321(7.48) (Figure 5a and b) compared to non-agonists. Both of these residues face the lipid bilayer and are only weakly interacting in the simulations with agonists bound, but are located in a hotspot region for activation. L284(6.46) is located just above the part of TM6 that kinks upon activation. The identified feature essentially connects the binding site and PIF motif to the NPxxY motif. F282(6.44) of the PIF motif is close to L284(6.46). T118(3.37), which was identified as important near the TM5 bulge (Figure 5b), is only one helix turn above I121(3.40) of the PIF motif in the connector region. Thus, the connector region is likely a driving factor behind the allosteric communication between the ligand and G protein-binding sites, which influences the region surrounding F321(7.48). F321(7.48) is located next to the NPxxY motif, which undergoes a twist upon activation, and is part of the important hotspot on TM7. In this region, our machine-learning analysis also identified that the backbone carbonyl of S319(7.46) formed a hydrogen bond with the side chain of N51(1.50) on TM1 for agonists, whereas this interaction was destabilized for the other ligands (Figure 5d). N51(1.50) is one of the most conserved residues across class A GPCRs (Isberg et al., 2014) and stabilizes a water network together with D79(2.50) and Y326(7.53) in the inactive receptor (Cherezov et al., 2007; Venkatakrishnan et al., 2019). Thus, this agonist-specific interaction, together with the D79(2.50)-N322(7.49) interaction (Fig. S7c and S8c), may promote dehydration of the water-filled cavity around conserved residue D79(2.50) and a twist of the NPxxY motif. Overall, agonists favored contractions between local inter-residue distances compared to non-agonists. By inspecting the most substantially changing large-scale distances, we also identified a contraction of the entire protein for agonist-bound receptors, as reflected by the decrease in distance between S203(5.43) and E338(8.56) on helix 8 (H8) and between S207(5.46) and V307(7.33) across the orthosteric binding site (Figure 5a and c).

Figure 5 with 1 supplement see all
Molecular basis for agonists’ control of receptor activation.

(a) Molecular basis for agonists’ control of receptor activation. S203(5.43) and S207(5.46) (red sticks) are part of the transmembrane 5 (TM5) bulge, which forms direct contacts with the ligand. V206(5.46) forms van der Waals interactions with T118(3.37) (red), which is located above I121(3.40) of the PIF motif in the connector region (pink sticks). TM6 and TM7, highlighted as L284(6.46) (orange sticks) close to F282(6.44) of the PIF motif and F321(7.48) (orange sticks) above the NPxxY motif, move closer together in the presence of agonists. Half a helix turn above F321(7.48), S319(7.46) forms a backbone interaction with the side chain of N51(1.50) for agonist-bound receptors, whereas water molecules interact with these residues for non-agonists. Together with TM7-ligand contacts in the orthosteric site, these interaction pathways stabilize the second important hotspot on TM7 close to the NPxxY motif (Y326(7.53) shown in black). (b) The T118(3.37)-V206(5.46) distance near the orthosteric site against the L284(6.46)-F321(7.48) distance in the TM region. Agonists contract both of these regions. (c) The distance across the orthosteric site between S207(5.461) and V307(7.33) (dark red in [a]) against the S203(5.43)-E338(8.56) distance across the TM domain. Agonists stabilize more compact receptor conformations. (d) The N51(1.50)-S319(7.46) distance against the L275(6.37)-Y326(7.53) distance. Agonists share the common feature of stabilizing the N51(1.50)-S319(7.46) backbone interaction, but form different NPxxY orientations, shown as the distance from Y326(7.53) to L275(6.37). (e) The three agonists stabilize slightly different TM6 and TM7 orientations, here illustrated by the distance between L275(6.37) and Y326(7.53). Adrenaline (purple) induces an active-like NPxxY motif, whereas BI-167107 (dark blue) stabilizes an inactive-like motif. The salmeterol-bound receptor (slate gray) adopts a distinct Y326(7.53) orientation.

Near the NPxxY motif, we found that the agonists stabilized different TM6 and TM7 orientations (Figure 5d and e). Adrenaline favored the most active-like NPxxY motif, which was also maintained throughout its activation path (Fig. S7d), with Y326(7.53) closer to L275(6.37) (Figure 5d). Salmeterol stabilized a distinct NPxxY conformation, which was also observed in the activation path ensemble (Figure 5—figure supplement 1), in which Y326(7.53) underwent a rotation, bringing the tyrosine’s side chain further into the interface between TM6 and TM7. This pose is reminiscent of conformations suggested by 19F NMR studies on the β1-adrenergic receptor (Frei et al., 2020). BI-167107 stabilized an inactive-like NPxxY motif with Y326(7.53) pointing away from L275(6.37) (Figure 5d).

In general, our data-driven approach automatically identified highly conserved residues involved in receptor activation (Figure 4). A noteworthy example is the D79(2.50) cavity, which is partially formed by strongly conserved residues S319(7.46), N51(1.50), and D79(2.50) and N322(7.49) (Isberg et al., 2014), and mutation of these may lead to non-functional receptors (Chung et al., 1988). Another example is the identification of V206(5.46), S207(5.46), and the PIF motif as a key region for allosteric communication. V206(5.46) and S207(5.46) were shown to interact with a recently discovered negative allosteric modulator that binds in an extrahelical site adjacent to the PIF motif (Liu et al., 2020). Our results do not only illustrate the usefulness of MD combined with data-driven analysis; they allow us to identify potential allosteric sites that can be targeted by ligands, and reveal that, against our expectations, signaling hotspots near the NPxxY motif, far away from the orthosteric site, experience the largest ligand-induced conformational heterogeneity.

Discussion

Following the progress in GPCR research, it has become evident that a simple two-state model of activation is an oversimplification with considerable limitations. To explain biased and partial agonism, there is a need for a more comprehensive model. Many ligands have been characterized as full, partial, or biased agonists (van der Westhuizen et al., 2014). However, a systematic characterization of the molecular mechanisms which transmit this allosteric communication across the cell membrane remains elusive. Researchers have successfully managed to discriminate between arrestin and G protein bias using spectroscopic probes (Lamichhane et al., 2020; Liu et al., 2012), but the conformational changes and dynamics of many microswitches cannot be captured in a single measurement. MD simulations have the potential to provide additional insights thanks to the atomistic-level description they enable (Lamim Ribeiro and Filizola, 2019). Whereas the free energy landscapes clearly show that ligands influence several microswitches, a direct comparison between free energy profiles may be misleading if the other orthogonal microswitches are ignored. For example, the local minima in the connector ΔRMSD landscapes are located at similar positions for all ligands, but our analysis clearly shows that the overall conformational ensembles differ (Figure 2a and Figure 2—figure supplement 7b). Such projections onto single variables can thus obscure major differences in other microswitches. To address this limitation, we have used data-driven analysis methods, which are better suited for handling high-dimensional data than mere visual inspection, and found that there were indeed significant conformational differences in the states stabilized by the different ligands. Remarkably, this protocol automatically identified both receptor-specific and conserved motifs considered to be of significance for GPCR activation, such as the TM5 bulge and the NPxxY motif, as important. The different dimensionality reduction techniques found similar partitioning of the data, which strongly indicates that the results are not due to fortuitous parameter tuning or method choice. One of the remaining enigmas in GPCR research is to understand how the same overall activation mechanism can be conserved in spite of the fact that very different ligands are recognized by the family. Our machine learning-inspired data analysis protocol provides an unbiased approach to identify key features of activation for different receptor types.

The derivation of free energy landscapes and the corresponding microswitch expectation values provide a tool for estimating the stability of activation states, and thus also the relative efficacy of different ligands. Given the high correlations between microswitch expectation values and experimental data, we anticipate that microswitches located in the orthosteric and connector regions can be used for future predictions of ligand efficacy. Additionally, an advantage of the methods used in this study is that results were derived from several simulation replicas, which reduces the statistical error related to the stochastic nature of MD simulations on short time scales. Since we allow the strings to diffuse around the converged equilibrium pathway for many iterations, the statistical error in the microswitch expectation values and energy landscapes is small, although it may be somewhat underestimated since swarm trajectories launched from the same point are correlated to each other. The systematic error is likely bigger for reasons related to the choice of force field (Guvench and MacKerell, 2008). An important limitation is that the string method can only identify one out of multiple activation pathways. Our control simulation starting from the inactive crystal structure, which converged to a different active state, indeed demonstrated this issue and highlights the importance of starting from a well-chosen input pathway.

Understanding ligand control of receptor activation is of great interest in drug development. The conformational selection of intracellular binding partners enables the construction of molecules or nanobodies that have a high binding affinity only in combination with a specific ligand (Sencanski et al., 2019), which has been utilized in structure determination (Masureel et al., 2018; Rasmussen et al., 2011a; Ring et al., 2013; Staus et al., 2016). Similarly, a well-designed nanobody or allosteric modulator could enhance or block the binding of a specific compound (Staus et al., 2016). However, in this study, the ligands were bound to the receptor throughout the simulations and no intracellular binding partner was considered. GPCRs only assume their fully active state in the presence of an intracellular binding partner (Gregorio et al., 2017; Manglik et al., 2015; Nygaard et al., 2013). The β2AR undergoes a basal activity, where it fluctuates between active-like and inactive states (Gregorio et al., 2017; Lamichhane et al., 2015), which can be inferred from the relatively low free energy difference between the basins in the energy landscapes. While extracellular and TM microswitches only require an agonist to maintain their active state conformations, intracellular microswitches interact with an intracellular binding partner directly and are more affected by its absence, which is likely why correlations between microswitch expectation value and Emax are worse for the intracellular microswitches.

Other important aspects of receptor–ligand interactions, such as identifying the binding pose of novel compounds or estimating a ligand’s binding free energy, cannot be estimated with this approach. There are other complementary techniques such as free energy perturbation methods (Cournia et al., 2017Matricon et al., 2021; Matricon et al., 2017), which can be used to rigorously estimate binding affinity. Combined with enhanced sampling MD, we move toward having a complete toolkit for in silico drug design for development of high-affinity GPCR ligands with a specific efficacy.

Despite the increasing number of β2AR structures, the receptor has not been solved in a form bound to arrestin. However, conformations observed in our simulations share properties with other arrestin-bound states observed for other receptors. For example, the supervised learning methods identified the C-terminal and H8 below the NPxxY motif as relatively important (Figure 4a, Figure 4—figure supplements 14). This region is known to be stabilized in ligand-dependent states for the angiotensin II type 1 receptor (Suomivuori et al., 2020; Wingler et al., 2019). While the reorganization of H8 may be a secondary effect due to modulation of the NPxxY motif, this region could be important for arrestin recruitment (Lally et al., 2017; Staus et al., 2018). Salmeterol’s distinct NPxxY state only formed in combination with a lost interaction between salmeterol and S207(5.46) and S203(5.43), which is remarkable considering that the two binding sites are believed to be only loosely coupled (Dror et al., 2011; Fleetwood et al., 2020b). A similar phenomenon has been reported in a recent structure of β1AR (Lee et al., 2020), where the corresponding serines in the orthosteric site experienced weakened interactions to the biased agonist formoterol. The fact that Y(7.53) forms contacts with arrestin for β1AR (Lee et al., 2020) suggests that our derived β2AR conformation may have biased signaling properties.

Our results show that the activation pathways as well as the stabilized states are significantly altered upon ligand binding, and that ligands with shared efficacy profiles generate similar, albeit not identical, ensembles of states. It therefore cannot be taken for granted that two ligands which lead to a similar downstream response necessarily stabilize the same receptor conformations. As we considered several compounds in this study, similarities and differences between different compound classes emerged. The results in this study provide a good starting point for further analysis and allowed us to catch a glimpse of the complexity underlying receptor signaling. A thorough quantification of biased and partial agonism will require studies of ligands that stimulate various signaling pathways to different extents.

Conclusion

In this study, we derived the activation free energy of the β2AR bound to ligands with different efficacy profiles using enhanced sampling MD simulations. We found a strong correlation between cellular response and the computed expectation values of the upper and TM microswitches, which suggests that our approach holds predictive power. Not only did the results show how ligands control the population of states, they also modulate the conformational ensemble of states by tuning important allosteric microswitches in the vicinity of the G protein-binding site. By inspecting the contacts formed for agonists and non-agonist ligands, we identified an allosteric pathway between the two binding sites and a large heterogeneity of TM7 states. Our results show how enhanced sampling MD simulations of GPCRs bound to ligands with various activation profiles, in combination with a data-driven analysis, provide the means for generating a comprehensive view of the complex signaling landscape of GPCRs. We anticipate that our protocol can be used together with other computational methods to understand GPCR signaling at the molecular level and provide insights that make it possible to design ligands with specific efficacy profiles.

Materials and methods

Simulation system configuration

Request a detailed protocol

We initiated simulation systems from a nanobody-bound active-state BI-167017-bound structure (PDB ID: 3P0G) (Rasmussen et al., 2011a) and an inactive carazolol-bound structure (PDB ID: 2RH1) (Cherezov et al., 2007) in CHARMM-GUI (Lee et al., 2016) with the CHARMM36m force field (Huang et al., 2017). Since the two structures are missing certain residues and have different thermostabilizing mutations, we used GPCRDB’s (Isberg et al., 2014) improved version of 2RH1, removed residues not present in 3P0G, and mutated E27(1.26) to Q, a residue frequently found in the human population (Dallongeville et al., 2003). As a result, the two simulation systems were identical. Following the protocol of a previous study (Fleetwood et al., 2020b), we reversed the N187E in the crystallized structures, protonated E122(3.41), and protonated the two histidines H172(4.64) and H178(4.70) at their epsilon positions. The receptor was embedded in a POPC membrane bilayer (Klauda et al., 2010) of 180 molecules, then solvated in a 0.15M concentration of neutralizing sodium and chloride ions with 79 TIP3P water molecules (Jorgensen et al., 1983) per lipid molecule. We performed the MD simulations with GROMACS 2018.6 (Abraham et al., 2015) patched with PLUMED (Tribello et al., 2014). Ligands present in PDB structures 2RH1 (Cherezov et al., 2007), 3NYA (Wacker et al., 2010), 3D4S (Hanson et al., 2008), 6MXT (Masureel et al., 2018), and 4LDO (Ring et al., 2013) were inserted into the 3P0G structure after alignment of residues interacting with the ligand. Input files required to run the simulations in this study are available online (Fleetwood, 2019a).

String method with swarms of trajectories

Request a detailed protocol

We used an optimized version of the string method with swarms of trajectories to enhance sampling and to estimate the free energy along various microswitches (Fleetwood, 2020a; Pan et al., 2008; Lev et al., 2017). This method finds the most probable transition pathway between two end states in a high-dimensional space spanned by a set of CVs (in this context synonymous with reaction coordinates). Given the initial guess of points distributed along a string in CV space, in this case the transition path from the previously published apo simulation, the 3P0G-initiated systems were equilibrated by running 200 ns steered MD simulations along the string with force constant per CV of 3366 kJ/mol*nm2 scaled by its estimated importance (more details in the following section). This was then followed by a 7 ns initial restrained equilibration at every point. Next, a swarm of 10-ps-long trajectories were launched from the output coordinates of every restrained simulation. The average drift of the swarm was computed as the mean displacement of the short swarm trajectories in CV space, which is proportional to the gradient of the free energy landscape. Every swarm consisted of 16–32 trajectories and the exact number was determined adaptively to converge the drift vector. The points were displaced according to their drift and re-aligned along the string to maximize the number of transitions between neighboring points. The string was updated iteratively with 30 ps of restrained equilibration per point, followed by a batch of swarms and string reparametrization. Gradually, the string relaxed into the most probable transition path connecting energetically stable intermediates between the two endpoints. We ran the simulations for 305 iterations, requiring an aggregated simulation time of 4.3 µs per ligand.

As a control, we also initiated a string from the carazolol-bound inactive state structure 2RH1. Steered MD from the inactive to the active state resulted in a slight unfolding of the intracellular part of TM6. Instead, we followed a slightly different protocol (Lev et al., 2017) and initiated the pathway by applying 200 ns targeted MD with a stronger 100 MJ/mol*nm2 force constant on all protein-heavy atoms. From this pathway, the string with swarms of trajectories was launched using the same CVs and algorithm as described above.

Convergence is generally reached when the string diffuses around an equilibrium position. Due to MD simulations’ stochasticity, two strings from subsequent iterations may therefore differ even when the system has reached equilibrium. To evaluate the convergence we averaged the strings over 60 iterations, and stopped sampling every simulation after 305 iterations, at which point the average strings for all ligand–receptor complexes had converged.

Kinetically trapped active-like state sampling

Request a detailed protocol

In order to quantitatively sample the most stabilized state kinetically accessible from the starting structure without applying an artificial force on the system, we developed an adaptive sampling protocol. A single swarm with twenty-four 7.5-ns-long trajectories was launched from the same initial active configuration as described in the previous paragraph. The swarm’s center point, c, in CV space was taken as the mean of the trajectories’ endpoint coordinates, xi. Next, we computed the average distance, d, from the endpoints to the center and assigned every replica, i, a weight, wi(x)=exp(-(|xi-c| / d) 2). New trajectories were iteratively seeded by extending niwi/∑jwj copies of each replica, keeping the total number of trajectories fixed to 24. With this approach, only replicas close to the center were extended and the ensemble of trajectories eventually diffused around a single equilibrated state.

We evaluated convergence by monitoring the distance between the center point of subsequent iterations until it converged to a constant value, which occurred within eight iterations for all systems. To demonstrate the robustness of the results, we split the walkers into three sub-groups for cross validation analysis.

Collective variable selection

Request a detailed protocol

We derived the CVs for the string method with swarms of trajectories in a data-driven manner from the swarm coordinates of the apo simulation’s final iteration using demystifying (Fleetwood et al., 2020c), a software which utilizes machine learning tools and dimensionality reduction methods to identify important features from MD simulation trajectories. As features, we chose inverse inter-residue C-α distances and filtered them to only include those which sampled values in the interval 6–8 Å. We then used the features to train a restricted Boltzmann machine (RBM) (Smolensky, 1986). An RBM is a single-layer neural network with a number of hidden components (two in this manuscript), equivalent to a fully connected bipartite graph. Upon training, the network is tuned to fit a certain statistical model, which maximizes the joint probability between the components in the input layer and the hidden layer (Pedregosa et al., 2011). The input features were ranked by their importance using layer-wise relevance propagation, an algorithm originally developed to identify important pixels in image classification problems (Montavon et al., 2018). Since we used stochastic solvers, the results were averaged over the results from 50 independent RBMs. Only CVs with an estimated importance above 0.33 were included in the final set (Table 2). Every CV was first scaled unitless in order to keep all values between 0 and 1, then rescaled according to its importance, so that the restraining force and the drift in the swarms of trajectories method would better emphasize the conformational changes along important degrees of freedom. Finally, we derived a new pathway in the resulting CV space by interpolating between the restrained points of the converged apo simulation. All string simulations used these CVs and this new pathway as a starting point to launch swarms except for the previously published apo and BI-167107 systems.

Table 2
String simulations: collective variables.
ResiduesImportance
F223(5.62)-A271(6.33)1.0
Q224(5.63)-K227(5.66)0.97
F223(5.62)-L272(6.34)0.76
I325(7.52)-R328(7.55)0.73
F223(5.62)-K227(5.66)0.72
A226(5.65)-K267(6.29)0.69
V54(1.53)-C327(7.54)0.67
L324(7.51)-R328(7.55)0.67
A134(3.53)-Y1410.66
I135(3.54)-L272(6.34)0.6
V222(5.61)-A271(6.33)0.59
A226(5.65)-E268(6.30)0.59
Q26(1.25)-D29(1.28)0.57
I135(3.54)-E225(5.64)0.57
R131(3.50)-L275(6.37)0.57
A134(3.53)-A271(6.33)0.56
C285(6.47)-V317(7.43)0.56
A76(2.47)-P323(7.50)0.56
Q26(1.25)-E30(1.29)0.55
A226(5.65)-A271(6.33)0.54
I121(3.40)-F208(5.47)0.53
E338(8.56)-R3430.53
I334(8.52)-R3440.52
G50(1.49)-L324(7.51)0.49
T25-D29(1.28)0.47
I135(3.54)-A271(6.33)0.46
T25-E30(1.29)0.46
W286(6.48)-G315(7.41)0.45
C285(6.47)-N318(7.45)0.44
Q27(1.26)-E30(1.29)0.44
R63-D331(8.49)0.43
Q197(5.37)-V297(6.59)0.43
A134(3.53)-E268(6.30)0.42
P288(6.50)-L311(7.37)0.42
T281(6.43)-N318(7.45)0.42
C341(8.59)-R3440.42
I135(3.54)-P1380.4
R328(7.55)-R333(8.51)0.36
L311(7.37)-G315(7.41)0.36
I135(3.54)-E268(6.30)0.35
C285(6.47)-I314(7.40)0.34

Free energy estimation

Request a detailed protocol

Free energy landscapes were estimated during post-processing by discretizing a grid along a chosen set of variables and constructing a regularized transition matrix from the swarm trajectories’ transitions between bins. We then derived the resulting free energy landscape from the stationary probability distribution of the transition matrix using Boltzmann inversion (Fleetwood et al., 2020b; Flood et al., 2019; Lev et al., 2017). To estimate the convergence of the free energy landscapes, we applied a Bayesian Markov chain Monte Carlo method (Harrigan et al., 2017) to sample 1000 different transition matrices from the dataset, each with a corresponding probability distribution and free energy landscape. From these we could estimate standard statistical properties such as the mean value and sample standard error. Swarm trajectories from a well-sampled equilibrium ensemble with multiple transitions between states will generate a narrower distribution of free energy values than trajectories drawn from a non-equilibrium process or a poorly sampled system.

Microswitch expectation values and equilibrium between states

Request a detailed protocol

To quantify the effect of a ligand on different microswitches, we computed the expectation value and the relative difference in free energy between the active and inactive state (ΔG) of individual microswitches. ΔG was obtained by integrating the free energy landscape over the active and inactive basins, respectively. We defined approximate state boundaries by visual inspection of the free energy landscapes and the crystal structures.

We then evaluated the correlation of these two values with experimental measurements of cellular response to ligand binding (van der Westhuizen et al., 2014) using linear regression in the python software package SciPy (Enthought Inc, Austin, TX; Millman and Aivazis, 2011). Finally, we applied the derived linear relationship to predict the efficacy of the two ligands not part of the experimental dataset, BI-167107 and carazolol, based on their microswitch expectation values.

Supervised and unsupervised feature extraction and learning

Request a detailed protocol

We analyzed the trajectories from the last iteration of the adaptive state sampling protocol and the swarms of trajectories method with various dimensionality reduction methods. To identify conformational differences induced by the ligands, we performed supervised and unsupervised feature extraction with the software demystifying (Fleetwood et al., 2020c), and projected important features onto snake plot templates downloaded from GPCRDB (Isberg et al., 2014). As features, we used scaled inverse closest-heavy atom distances. Furthermore, we performed unsupervised dimensionality reduction in Scikit-learn (Pedregosa et al., 2011) with PCA (Tipping and Bishop, 1999), MDS (Borg and Groenen, 2005) with a Euclidean distance metric and t-SNE (van der Maaten and Hinton, 2008 ), and projected the simulation snapshots onto the reduced feature spaces. We constructed a similarity metric by taking the average Eucledian distance between all simulation snapshots in two classes, and normalized the class similarities between 0 and 1, with higher values representing similar classes.

Moreover, we computed how important individual residues were for discriminating between agonists and non-agonists and to distinguish all ligands from each other, using a symmetrized version of the KL divergence (Fleetwood et al., 2020c; Kullback and Leibler, 1951). With this approach, two residues constituting a distance were scored as important if the active-like states formed non-overlapping distance distributions, corresponding to a high KL divergence. As a control, we evaluated the important features learned by a RF classifier (Ho, 1995), a machine learning model constructed by an ensemble of randomly instantiated decision trees. The importance of inter-residue distances was computed during training by normalizing the RF's mean decrease impurity (Breiman et al., 1984; Pedregosa et al., 2011), which measures how frequently a distance is used to split the decision trees.

Unsupervised feature extraction was performed with PCA (Tipping and Bishop, 1999), which transformed the input dataset of distances, F, into to a set of orthogonal variables called PCs. The PCs are equivalent to the eigenvectors of FTF, and their eigenvalues measure how much of the variance in F they cover. Thus, by multiplying the PCs with their egeinvalues, and projecting them back onto the input features, we obtained an estimate of importance corresponding to how much the individual distances contributed to the variance in F.

Software code to reproduce the results in this manuscript is available online (Fleetwood, 2020a; Fleetwood, 2019a; Fleetwood, 2019b).

Data availability

The data necessary to reproduce the findings presented in this paper can be found on OSF (DOI 10.17605/OSF.IO/6XPYV). The code used to run and analyze simulations has been deposited on GitHub (https://github.com/delemottelab/demystifying, https://github.com/delemottelab/gpcr-string-method-2019 and https://github.com/delemottelab/state-sampling; copies archived at https://archive.softwareheritage.org/swh:1:rev:e8527b52d5fbe0570cd391921ecda5aefceb797a/, https://archive.softwareheritage.org/swh:1:rev:bc3b7ce2e74e5ac95644d57a1b24f717a7ec74a4/, https://archive.softwareheritage.org/swh:1:rev:f0f56430ce581f0338771c126da212ecc2f218a0/).

References

  1. Book
    1. Borg H
    2. Groenen P
    (2005)
    Modern Multidimensional Scaling
    Springer .
  2. Book
    1. Breiman L
    2. Friedman JH
    3. Olshen RA
    4. Stone CJ
    (1984)
    Classification and Regression Trees
    Wadsworth & Brooks/Cole Advanced Books & Software.
  3. Software
    1. Fleetwood O
    (2019b) Delemottelab/demystifying: Updated Benchmarks, Zenodo
    Delemottelab/demystifying: Updated Benchmarks, Zenodo.
  4. Software
    1. Fleetwood O
    (2020a) Delemottelab/state-Sampling Initial-Release, Zenodo
    Delemottelab/state-Sampling Initial-Release, Zenodo.
  5. Conference
    1. Ho TK
    (1995)
    Random decision forests
    Proceedings of 3rd International Conference on Document Analysis and Recognition. pp. 278–282.
  6. Software
    1. Schubert E
    (2017)
    interpretation - Clustering on the output of t-SNE
    interpretation - Clustering on the output of t-SNE.
  7. Conference
    1. Smolensky P
    (1986)
    Information processing in dynamical systems: foundations of harmony theory
    Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations. pp. 194–281.
    1. Tipping ME
    2. Bishop CM
    (1999) Probabilistic principal component analysis
    Journal of the Royal Statistical Society: Series B 61:611–622.
    https://doi.org/10.1111/1467-9868.00196
    1. van der Maaten L
    2. Hinton G
    (2008)
    Visualizing data using t-SNE
    Journal of Machine Learning Research : JMLR 9:2579–2605.
  8. Book
    1. Woo TM
    2. Robinson MV
    (2015)
    Pharmacotherapeutics for Advanced Practice Nurse Prescribers
    F. A. Davis Company.

Decision letter

  1. Toby W Allen
    Reviewing Editor; RMIT University, Australia
  2. José D Faraldo-Gómez
    Senior Editor; National Heart, Lung and Blood Institute, National Institutes of Health, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This manuscript reports advanced molecular dynamics simulations to describe

β2 adrenergic receptor modulation, expanding significantly on existing knowledge. The study has made use of an atomistic string method to measure the effects of agonists, antagonists and inverse agonists and to understand how ligands affect GPCR activity. The authors have presented sufficient analysis to demonstrate statistical significance of the data and have made connections to experimental measurements that are well described in the revised manuscript. Overall, this is a high-level computational study of biological significance that will be impressive to many eLife readers.

Decision letter after peer review:

Thank you for submitting your article "Identification of ligand-specific G protein-coupled receptor states and prediction of efficacy via data-driven modeling" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by José Faraldo-Gómez as the Senior Editor. The reviewers have opted to remain anonymous.

Based on the reviews received, which are enclosed below, as well as a follow-up discussion amongst reviewers and editors, we regret to inform you that we cannot publish your manuscript in its current form. However, the reviewers have agreed to re-evaluate a revised version of the manuscript that convincingly addresses the concerns raised. While we encourage you to submit a revision, please note that a positive outcome is not guaranteed.

As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

The reviewers have appreciated many aspects of the string method solution for the allosteric modulation of the β2 adrenergic receptor, but were concerned about the reliability of the simulations, their connections to experiments and the absence of discussion on the relationship to past GPCR activation studies. Concern is expressed about the use of Emax as the sole proxy for activated conformation, as well as a lack of connection of free energy calculations to experiments. A critical point to address is the absence of a description of the statistical reliability and error estimates from the string method solutions, as well as the dependence of the converged solutions on the starting conformation. This necessitates at least one demonstration that reliable results emerge with a very different initial structure. A significantly revised manuscript would need to adequately address the above concerns to the satisfaction of the reviewers.

Essential revisions:

The full reviews have been included below. Essential revisions include:

* Proof of independence of string solutions on starting structure.

* Proof of statistical reliability and error estimates.

* Improved connections to experiments that do not rely solely on Emax values.

* Improved relationship to past studies.

Reviewer #1:

This manuscript reports advanced MD simulation to describe the allosteric modulation of the β2 adrenergic receptor, expanding on the authors' recent simulations (Biochemistry 2020, 59:880-891) that explored communication of ligand binding to the G protein-binding site via "microswitches" within the protein. This study uses the same string method to examine the effects of full and biased agonists, antagonists and inverse agonists using previously solved Xray structures. The study also calls on a series of data-based analysis methods to seek answers to the question of how different ligands communicate their changes and affect GPCR activity. Observation of stabilization of active states for agonists and inactive states for other ligands is an important achievement for MD simulation, even if reproducing known experimental results. Also, characterizing how the distribution of kinetically stabilized (active-like) states are controlled by ligands, and correlations between microswitch expectation values and cAMP response experiments are important. However, the manuscript is highly technical and jargon-based such that many readers will not follow the analysis. The statistical reliability and dependence of the converged string solutions on starting conformation do not appear to have been tested. Finally, relationship between this and the previous manuscript (Fleetwood et al., 2020), as well as how results distinguish themselves from knowledge in the field could be made more clear.

String methods are notorious for depending on starting structure as well as the number and choice of collective variables, and may converge slowly on the optimal path, if at all. The authors do not show plots of convergence (how each PMF changes over #iterations), and do not appear to undertake tests for reproducibility. i.e. Starting from very different initial structures to show that the same final pathways emerge, leading to consistent free energy profiles. For example, starting again with an initial structure for an inverse agonist instead of one for an agonist and show the same results are computed. I note that in the Discussion it is said that several simulation replicas were used to reduce error. But to what does this apply (apparently not to the swarms)? Also, collective variables (CVs) are those defined in the previous study (Fleetwood et al., 2020) but what if the set of CVs is changed or the number of CVs is reduced/increased? While I do not suggest redoing with a different set, how does the reader judge the robustness of the CV-reliant results?

The method for free energy calculation uses a transition matrix on a CV grid with stationary solution. In the subsection “Free energy estimation”, the authors cite Pan, Sezer and Roux, 2008 (and Fleetwood et al., 2020), but it is not clear that Pan, Sezer and Roux, 2008, did this, and in fact I think the first to do this from a swarms of trajectories solution was Lev et al., 2017 (see also Flood, et al., 2019), not cited here.

Having computed free energy projections along collective variables, receptor stability in relation to efficacy instead relies on estimation values for variables and not free energies, and the justifications for this could be improved. The authors write that the width and depth of the basins determine the most probable state projected into that space. This sentence is not clear, but its meaning is important, because ultimately the relative values of free energies are discussed. If the 2D maps are valid projections of the full configurational space (albeit with sampling guided/biased by the string), then Boltzmann integrals over each basin should yield a valid equilibrium constant. I presume the concern is that projections along different pairs of CVs can lead to different apparent free energies, because different CVs map out different proportions of phase space, and potentially envelop multiple states of the system, but does this mean a Boltzmann integral over a site is not a true thermodynamic quantity? In the Discussion, the authors return to add that examination of one projection can overlook what is happening in other coordinates/switches, but more discussion/justification is needed. Related, the authors state that they have "accurately captured the relative stability of states", but this is confusing given the relative stability of states from the maps appears to have been discredited.

Instead of using free energies, the authors correlate "expectation values" of their CVs to experiments in the bottom of Figure 2. I assume a plot against free energy was abandoned as it was not working as planned? Ideally one would want to see free energies plotted against experimental efficacy, because estimation values of variables such as TM5…, may correlate to efficacy, but not uniquely map to shifting state equilibrium.

Many analysis tasks were completed by existing packages for which the meanings are not obvious. e.g. Demystifying, Scikit-learn… Materials and methods, even if accepted, deserve a sentence to explain and motivate. e.g. CVs were short inverse inter-residue distances used to train a restructured Boltzmann machine (the principles of this machine could be explained simply). The subsection “Data driven analysis reveals that ligands stabilize unique states” is highly technical and not well explained. While many will know about PCA, less will be known about MDS and T-SNE. The ability to identify signaling hotspots in Figure 4 is impressive, leading to a model for allosteric communication. But the machine learning approaches used come across as black box and require better physical interpretation.

Reviewer #2:

This manuscript describes the use of enhanced sampling molecular dynamics to calculate free energy landscapes for the β2 adrenergic receptor. A particular focus is the conformational dynamics and thermodynamics of microswitches that change conformation upon receptor activation. A variety of ligands are investigated to identify shared and divergent features of receptor conformational modulation. Unbiased methods are shown to identify known conformational switches as key regulators of receptor activation.

Overall the manuscript is interesting and clearly written. Figures are very clear and well presented. The subject matter has been very extensively studied in the GPCR family as a whole and in the β2 receptor in particular, and the results presented here largely align with existing understanding of GPCR activation and conformational regulation by ligands. A major concern is the question of whether the results presented here truly enhance our understanding of GPCR activation, or simply confirm things that are already known. Many groups have published detailed analysis of similar questions to those presented here, including the Dror, Nagarajan, and McCammon groups, among others. There is very little discussion of this prior work, which makes it difficult to see how the present manuscript fits within the broader context of GPCR activation molecular dynamics analysis.

A significant technical concern is the reliance on cAMP Emax values as the sole experimental validation. A prospective experimental test of hypotheses generated here would significantly enhance the manuscript. Barring this, a more extensive comparison of computational results with experimental data is essential in my view. The cAMP pathway is highly amplified, which may confound analysis linking Emax values directly to conformational equilibria. Manglik et al., 2015, presents an actual biophysical analysis of conformational equilibria (albeit with fewer ligands) and comparison to this would be helpful. Do relative energy predictions match these experimental observations?

A smaller issue is that the utility of the results presented here appears to be a bit overstated. For example, it is claimed (Introduction) that the results provide insight into how ligands with specific efficacy profiles can be designed. To support this statement, the authors should present examples of compounds they have designed based on their results, together with experimental data confirming these molecules have the intended efficacy profiles.

Reviewer #3:

This is an interesting paper, and I like to attempt to connect analysis of allostery to function. However, I'm extremely concerned about statistical uncertainty - it's not really discussed, and it would be easy to chalk all of the results up to limited sampling. It will be important for the authors to demonstrate this isn't the case.

Basically, my concern is that there's essentially no mention of statistical uncertainty or convergence anywhere in the document. One of the major claims is that the different agonists each populate a distinct substate - this is an incredibly important and interesting observation if true, but could also be explained by saying each simulation wandered in its own space and didn't have time to explore anywhere else. If it were run again, it might wander into a totally different place. I don't have a great feel for how rapidly swarms explore, but I do know the total sampling time of 1.4 µs per ligand sounds awfully small. There are examples in the literature showing that conventional simulations several times this length are not converged as far the configurations in the ligand binding pocket go (for example, Leioatts et al., Biophysical Journal, 2015, or some of the work from Dror's group).

I have a couple of ideas for how the authors could convince me this isn't just a sampling artifact. The best would probably be to pick one ligand and redo the whole calculation 4 more times, start to finish - looking at the variation between those replicates would be a decent estimate of the uncertainty. Ideally, they'd do this for all of the ligands, but I recognize that's not a reasonable request, which is why I suggest doing it for 1 ligand.

A weaker test would be to break the individual ligand calculations into blocks, somehow - I'm not totally sure how to do it - and show that the blocks are self-similar. If they're all wandering through the whole of the blob in Figure 3, you're more likely to be ok. If each block populates a discrete chunk of the blob (and the overall swarm data doesn't revisit), then there's a big problem.

I'd want to see error estimates on the free energies in Figure 2, plus a redo of the dimension reduction in Figure 3 to see if the ligands still separate more than the replicates of 1 ligand.

On a more minor note, the use of p-values in the subsection “Ligands control efficacy by reshaping microswitches’ probability distributions”, isn't really correct. The point the authors are trying to make is that the computed value doesn't predict the experiment (for good reason), and the correlation coefficients make that point for you.

https://doi.org/10.7554/eLife.60715.sa1

Author response

Reviewer #1:

This manuscript reports advanced MD simulation to describe the allosteric modulation of the β2 adrenergic receptor, expanding on the authors' recent simulations (Biochemistry 2020, 59:880-891) that explored communication of ligand binding to the G-protein binding site via "microswitches" within the protein. This study uses the same string method to examine the effects of full and biased agonists, antagonists and inverse agonists using previously solved Xray structures. The study also calls on a series of data-based analysis methods to seek answers to the question of how different ligands communicate their changes and affect GPCR activity. Observation of stabilization of active states for agonists and inactive states for other ligands is an important achievement for MD simulation, even if reproducing known experimental results. Also, characterizing how the distribution of kinetically stabilized (active-like) states are controlled by ligands, and correlations between microswitch expectation values and cAMP response experiments are important. However, the manuscript is highly technical and jargon-based such that many readers will not follow the analysis. The statistical reliability and dependence of the converged string solutions on starting conformation do not appear to have been tested. Finally, relationship between this and the previous manuscript (Fleetwood et al., 2020), as well as how results distinguish themselves from knowledge in the field could be made more clear.

We thank the reviewer for this assessment. Regarding the high technical level, we have tried to make simplifications and clarifications throughout the revised manuscript. For example, we now give more background on what the software and machine learning tools do. We have also tried to better define the connection between this manuscript and the previous work. We hope the changes appear satisfactory.

String methods are notorious for depending on starting structure as well as the number and choice of collective variables, and may converge slowly on the optimal path, if at all. The authors do not show plots of convergence (how each PMF changes over #iterations), and do not appear to undertake tests for reproducibility. i.e. Starting from very different initial structures to show that the same final pathways emerge, leading to consistent free energy profiles. For example, starting again with an initial structure for an inverse agonist instead of one for an agonist and show the same results are computed. I note that in the Discussion it is said that several simulation replicas were used to reduce error. But to what does this apply (apparently not to the swarms)? Also, collective variables (CVs) are those defined in the previous study (Fleetwood et al., 2020), but what if the set of CVs is changed or the number of CVs is reduced/increased? While I do not suggest redoing with a different set, how does the reader judge the robustness of the CV-reliant results?

In addition to the arguments listed in the cover letter, we point out that the previously published apo and BI-167107-bound string simulations were performed with a different set of Collective Variables (CVs) and initiated from different pathways (Fleetwood et al., 2020) than the new ligand-receptor simulations presented in this manuscript. Despite the differences in simulation set-up, it is evident from Figure 2 that the free energy landscapes for the agonists adrenaline and salmeterol are more similar to BI-167107 than to the other ligands. The same holds for the apo free energy landscape, which is strikingly similar to the inverse agonists and antagonists. If the starting conditions and CVs alone defined the outcome, this would not be the case.

Regarding the convergence of the PMF, we previously performed cross validation over the string iterations for the TM5 bulge microswitch expectation values (Supplementary Table 2 in the original manuscript). The microswitch expectation values were consistent between the first and second half of the last 224 string iterations, which indicates that these values have converged and were sampled from an equilibrium process. Additionally, samples drawn from an unconverged distribution, would lead to a large standard deviation and/or statistical outliers in the free energy landscapes. Thus our statistical error analysis and convergence estimation provide much strong evidence of convergence.

The method for free energy calculation uses a transition matrix on a CV grid with stationary solution. In the subsection “Free energy estimation”, the authors cite Pan, Sezer and Roux, 2008 (and Fleetwood et al., 2020), but it is not clear that Pan, Sezer and Roux, 2008, did this, and in fact I think the first to do this from a swarms of trajectories solution was Lev et al., 2017 (see also Flood, et al., 2019), not cited here.

The reviewer is indeed correct here. We have updated the citations accordingly in the manuscript.

Having computed free energy projections along collective variables, receptor stability in relation to efficacy instead relies on estimation values for variables and not free energies, and the justifications for this could be improved. The authors write that the width and depth of the basins determine the most probable state projected into that space. This sentence is not clear, but its meaning is important, because ultimately the relative values of free energies are discussed. If the 2D maps are valid projections of the full configurational space (albeit with sampling guided/biased by the string), then Boltzmann integrals over each basin should yield a valid equilibrium constant. I presume the concern is that projections along different pairs of CVs can lead to different apparent free energies, because different CVs map out different proportions of phase space, and potentially envelop multiple states of the system, but does this mean a Boltzmann integral over a site is not a true thermodynamic quantity? In the Discussion, the authors return to add that examination of one projection can overlook what is happening in other coordinates/switches, but more discussion/justification is needed. Related, the authors state that they have "accurately captured the relative stability of states", but this is confusing given the relative stability of states from the maps appears to have been discredited.

We thank the reviewer for this astute set of comments, which we have enjoyed considering and addressing.

To illustrate our point, we highlight Figure 2 and Author response image 1, in which we can certainly say qualitatively that agonists stabilize the active basin. However, due to the issues with projections pointed out by the reviewer and the fact that these energy landscapes are not ideal two-state systems, an integration over state boundaries requires us to, somewhat arbitrarily, define the state boundaries. This will in turn influence any quantitative comparison to experimental values. The expectation values (Figure 2C-F), on the other hand, require no state boundaries, justifying our choice of this methodology.

Author response image 1

That being said, we followed the reviewer’s advice and computed the relative stability of states for the different microswitches by taking the difference in free energy between the active and inactive regions of the free energy landscape (ΔG ∝-lnK, where K is the equilibrium constant). See the results in Figure 2—figure supplement 8 of the revised manuscript. State boundaries that were chosen are depicted as vertical dashed lines in Figure 2—figure supplement 7.The correlation between ΔG and cAMP signaling is strong for transmembrane microswitches, which also had a strong correlation between cAMP and their expectation values. Therefore, we see little practical difference between the two methods, but would recommend using the expectation value because it does not require defining arbitrary state boundaries.

Instead of using free energies, the authors correlate "expectation values" of their CVs to experiments in the bottom of Figure 2. I assume a plot against free energy was abandoned as it was not working as planned? Ideally one would want to see free energies plotted against experimental efficacy, because estimation values of variables such as TM5…, may correlate to efficacy, but not uniquely map to shifting state equilibrium.

Please see our comments in the previous paragraph. We chose expectation values since it does not require us to set state boundaries. This approach provides a single numerical value which, unlike having an entire probability distribution, is easier to correlate to an experimental numerical value. With that in mind, by merely taking the average and ignoring the characteristics of the free energy landscapes, such as the shape and locations of the basins, leaves out potential useful information. For this reason, we included the microswitch free energy landscapes as a basis of discussion together with the correlation plots in Figure 2.

Many analysis tasks were completed by existing packages for which the meanings are not obvious. e.g. Demystifying, Scikit-learn… Materials and methods, even if accepted, deserve a sentence to explain and motivate. e.g. CVs were short inverse inter-residue distances used to train a Restructured Boltzmann Machine (the principles of this machine could be explained simply). The subsection “Data driven analysis reveals that ligands stabilize unique states” is highly technical and not well explained. While many will know about PCA, less will be known about MDS and T-SNE. The ability to identify signaling hotspots in Figure 4 is impressive, leading to a model for allosteric communication. But the machine learning approaches used come across as black box and require better physical interpretation.

We thank the reviewer for pointing this out. It is easy to take one’s own methodological choice for granted! We have made revisions to clarify our rationale and better describe the tools used. For example, we describe what the software demystifying does when we mention it, and try to explain how we find important microswitches by computing the KL divergence in simpler terms.

Reviewer #2:

This manuscript describes the use of enhanced sampling molecular dynamics to calculate free energy landscapes for the β2 adrenergic receptor. A particular focus is the conformational dynamics and thermodynamics of microswitches that change conformation upon receptor activation. A variety of ligands are investigated to identify shared and divergent features of receptor conformational modulation. Unbiased methods are shown to identify known conformational switches as key regulators of receptor activation.

Overall the manuscript is interesting and clearly written. Figures are very clear and well presented. The subject matter has been very extensively studied in the GPCR family as a whole and in the β2 receptor in particular, and the results presented here largely align with existing understanding of GPCR activation and conformational regulation by ligands. A major concern is the question of whether the results presented here truly enhance our understanding of GPCR activation, or simply confirm things that are already known. Many groups have published detailed analysis of similar questions to those presented here, including the Dror, Nagarajan, and McCammon groups, among others. There is very little discussion of this prior work, which makes it difficult to see how the present manuscript fits within the broader context of GPCR activation molecular dynamics analysis.

We appreciate that the reviewer found the manuscript well presented. Following the reviewer’s comments, we have extended the discussion of prior work in the manuscript, as also described in the cover letter. Please also find arguments about the novelty of our work in the same place.

A significant technical concern is the reliance on cAMP Emax values as the sole experimental validation. A prospective experimental test of hypotheses generated here would significantly enhance the manuscript. Barring this, a more extensive comparison of computational results with experimental data is essential in my view. The cAMP pathway is highly amplified, which may confound analysis linking Emax values directly to conformational equilibria. Manglik et al., 2015, presents an actual biophysical analysis of conformational equilibria (albeit with fewer ligands) and comparison to this would be helpful. Do relative energy predictions match these experimental observations?

We believe that a comparison to cAMP measurements is relevant and interesting, since it represents a downstream cellular response and a strong correlation suggests our approach to have predictive powers. A comparison to Emax values instead of e.g. EC50 values makes sense, since our simulations have a ligand present at all times, which corresponds to experimental conditions with a very high ligand concentration. We agree that a prospective study would be ideal, but in the absence of experimental validation, we have followed the reviewer’s advice and have improved the comparison to experimental data, and in particular Manglik et al., 2015, as this was suggested by the reviewer. For a more detailed description of what has been done in this regard, refer to our comments under “Essential revisions” above.

A smaller issue is that the utility of the results presented here appears to be a bit overstated. For example, it is claimed (Introduction) that the results provide insight into how ligands with specific efficacy profiles can be designed. To support this statement, the authors should present examples of compounds they have designed based on their results, together with experimental data confirming these molecules have the intended efficacy profiles.

In the revised manuscript we are more precise in what our results allow us to do while avoiding speculative claims. We anticipate to use these methods in prospective studies going forward, but it is indeed not part of this study.

Reviewer #3:

This is an interesting paper, and I like to attempt to connect analysis of allostery to function. However, I'm extremely concerned about statistical uncertainty - it's not really discussed, and it would be easy to chalk all of the results up to limited sampling. It will be important for the authors to demonstrate this isn't the case.

We are glad that the reviewer found our paper interesting and agree that the statistical validation was not clearly presented and also lacking specific tests.

Basically, my concern is that there's essentially no mention of statistical uncertainty or convergence anywhere in the document. One of the major claims is that the different agonists each populate a distinct substate - this is an incredibly important and interesting observation if true, but could also be explained by saying each simulation wandered in its own space and didn't have time to explore anywhere else. If it were run again, it might wander into a totally different place. I don't have a great feel for how rapidly swarms explore, but I do know the total sampling time of 1.4 microseconds per ligand sounds awfully small. There are examples in the literature showing that conventional simulations several times this length are not converged as far the configurations in the ligand binding pocket go (for example, Leioatts et al., Biophysical Journal, 2015, or some of the work from Dror's group).

The reviewer is right, we had omitted important information, relying on information present in our previous paper. We have revised the manuscript such that this manuscript now stands on its own. Specifically, our assessment of statistical reliability can be found in the cover letter.

Note that the single equilibrated states are by definition converged from a starting structure using an adaptive sampling approach with multiple walkers. Sampling a large region of conformational space would require much more than 1.4 microsecond, but a small region kinetically accessible from a starting structure may be well-sampled within this time-frame. Actually, an important methodological aspect of this manuscript is that we could identify ligand-induced pharmacological properties by adaptive sampling of a small portion of conformational space.

I have a couple of ideas for how the authors could convince me this isn't just a sampling artifact. The best would probably be to pick one ligand and redo the whole calculation 4 more times, start to finish - looking at the variation between those replicates would be a decent estimate of the uncertainty. Ideally, they'd do this for all of the ligands, but I recognize that's not a reasonable request, which is why I suggest doing it for 1 ligand.

A weaker test would be to break the individual ligand calculations into blocks, somehow - I'm not totally sure how to do it - and show that the blocks are self-similar. If they're all wandering through the whole of the blob in Figure 3, you're more likely to be ok. If each block populates a discrete chunk of the blob (and the overall swarm data doesn't revisit), then there's a big problem.

We thank the reviewer for the specific suggestions! We indeed followed the reviewer’s second suggestion and performed stratified cross validation with 3 subsets of the walkers. More details can be found under Essential Revisions. We argue that this statistical test is as legitimate as rerunning the simulation for one ligand for two reasons. First, we can perform this test for all ligands, not just one, without wasting computational resources. Second, the method is based on finding a center point in CV space by tracking a number of walkers launched from the same starting structure. Considering the high number of walkers used and that our results pass a cross validation examination, we may conclude that the position of the center point is insensitive to the random seed of the individual MD trajectories. Thus, we expect a new set of walkers launched from the same starting structure to converge to the already sampled state.

I'd want to see error estimates on the free energies in Figure 2, plus a redo of the dimension reduction in Figure 3 to see if the ligands still separate more than the replicates of 1 ligand.

See our comments above.

On a more minor note, the use of p-values in the subsection “Ligands control efficacy by reshaping microswitches’ probability distributions”, isn't really correct. The point the authors are trying to make is that the computed value doesn't predict the experiment (for good reason), and the correlation coefficients make that point for you.

We have removed the use of the p-value and only refer to the correlation between cAMP response and microswitch expectation values.

https://doi.org/10.7554/eLife.60715.sa2

Article and author information

Author details

  1. Oliver Fleetwood

    Science for Life Laboratory, Department of Applied Physics, KTH Royal Institute of Technology, Stockholm, Sweden
    Contribution
    Conceptualization, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4277-2661
  2. Jens Carlsson

    Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
    Contribution
    Conceptualization, Supervision, Funding acquisition, Validation, Investigation, Visualization, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4623-2977
  3. Lucie Delemotte

    Science for Life Laboratory, Department of Applied Physics, KTH Royal Institute of Technology, Stockholm, Sweden
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Writing - review and editing
    For correspondence
    lucie.delemotte@scilifelab.se
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0828-3899

Funding

Göran Gustafssons Stiftelse för Naturvetenskaplig och Medicinsk Forskning

  • Jens Carlsson
  • Lucie Delemotte

Science for Life Laboratory

  • Jens Carlsson
  • Lucie Delemotte

Vetenskapsrådet (2017-4676)

  • Jens Carlsson

Swedish strategic research program eSSENCE

  • Jens Carlsson

Knut och Alice Wallenbergs Stiftelse

  • Jens Carlsson
  • Lucie Delemotte

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by grants from the Gustafsson Foundation, Knut and Alice Wallenberg foundation (2019.0130), and Science for Life Laboratory to JC and LD. The work was also supported by grants from the Swedish Research Council (2017–04676) and the Swedish strategic research program eSSENCE to JC. The simulations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at PDC Centre for High Performance Computing (PDC-HPC).

Senior Editor

  1. José D Faraldo-Gómez, National Heart, Lung and Blood Institute, National Institutes of Health, United States

Reviewing Editor

  1. Toby W Allen, RMIT University, Australia

Publication history

  1. Received: July 3, 2020
  2. Accepted: January 27, 2021
  3. Accepted Manuscript published: January 28, 2021 (version 1)
  4. Version of Record published: February 16, 2021 (version 2)

Copyright

© 2021, Fleetwood et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,091
    Page views
  • 317
    Downloads
  • 2
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Microbiology and Infectious Disease
    2. Structural Biology and Molecular Biophysics
    Gukui Chen et al.
    Research Article Updated

    Cyclic-di-guanosine monophosphate (c-di-GMP) is an important effector associated with acute-chronic infection transition in Pseudomonas aeruginosa. Previously, we reported a signaling network SiaABCD, which regulates biofilm formation by modulating c-di-GMP level. However, the mechanism for SiaD activation by SiaC remains elusive. Here we determine the crystal structure of SiaC-SiaD-GpCpp complex and revealed a unique mirror symmetric conformation: two SiaD form a dimer with long stalk domains, while four SiaC bind to the conserved motifs on the stalks of SiaD and stabilize the conformation for further enzymatic catalysis. Furthermore, SiaD alone exhibits an inactive pentamer conformation in solution, demonstrating that SiaC activates SiaD through a dynamic mechanism of promoting the formation of active SiaD dimers. Mutagenesis assay confirmed that the stalks of SiaD are necessary for its activation. Together, we reveal a novel mechanism for DGC activation, which clarifies the regulatory networks of c-di-GMP signaling.

    1. Structural Biology and Molecular Biophysics
    Fang Tian et al.
    Research Article Updated

    SARS-CoV-2 has been spreading around the world for the past year. Recently, several variants such as B.1.1.7 (alpha), B.1.351 (beta), and P.1 (gamma), which share a key mutation N501Y on the receptor-binding domain (RBD), appear to be more infectious to humans. To understand the underlying mechanism, we used a cell surface-binding assay, a kinetics study, a single-molecule technique, and a computational method to investigate the interaction between these RBD (mutations) and ACE2. Remarkably, RBD with the N501Y mutation exhibited a considerably stronger interaction, with a faster association rate and a slower dissociation rate. Atomic force microscopy (AFM)-based single-molecule force microscopy (SMFS) consistently quantified the interaction strength of RBD with the mutation as having increased binding probability and requiring increased unbinding force. Molecular dynamics simulations of RBD–ACE2 complexes indicated that the N501Y mutation introduced additional π-π and π-cation interactions that could explain the changes observed by force microscopy. Taken together, these results suggest that the reinforced RBD–ACE2 interaction that results from the N501Y mutation in the RBD should play an essential role in the higher rate of transmission of SARS-CoV-2 variants, and that future mutations in the RBD of the virus should be under surveillance.