Coupled Motions in Î²2AR:GÎ±s Conformational Ensembles - Journal of

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Coupled motions in #AR:G#s conformational ensembles Dimitar V. Pachov, Rasmus Fonseca, Damien Arnol, Julie Bernauer, and Henry van den Bedem J. Chem. Theory Comput., Just Accepted Manuscript • DOI: 10.1021/acs.jctc.5b00995 • Publication Date (Web): 12 Jan 2016 Downloaded from http://pubs.acs.org on January 18, 2016

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Journal of Chemical Theory and Computation

Coupled motions in β2AR:Gαs conformational ensembles Dimitar V. Pachov,†,‡ Rasmus Fonseca,¶,‡ Damien Arnol,¶ Julie Bernauer,¶ and Henry van den Bedem∗,‡ Department of Chemistry, Stanford University, Stanford, CA 94305, USA, Division of Biosciences, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA 94025, USA, and AMIB INRIA - Bioinformatics group, LIX, École Polytechnique, 91128 Palaiseau, France E-mail: [email protected]

Abstract G protein-coupled receptors (GPCRs) act as conduits in the plasma membrane, facilitating cellular responses to physiological events by activating intracellular signal transduction pathways. Extracellular signaling molecules can induce conformational changes in GPCR, which allow it to selectively activate intracellular protein partners such as heterotrimeric protein G. However, a major unsolved problem is how GPCRs and G proteins form complexes, and how their interaction results in G protein activation. Here, we show that an inactive, agonist-free β2 AR:Gαs complex can collectively sample intermediate states of the receptor on an activation pathway. An in silico conformational ensemble around the inactive state manifests significant conformational To whom correspondence should be addressed Department of Chemistry, Stanford University, Stanford, CA 94305, USA ‡ Division of Biosciences, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA 94025, USA ¶ AMIB INRIA - Bioinformatics group, LIX, École Polytechnique, 91128 Palaiseau, France ∗ †

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coupling between structural elements implicated in G protein activation throughout the complex. While Gαs helix α5 has received much attention as a driver for nucleotide exchange, we also observe interactions between helix αN with Intra Cellular Loop 2, which can be transmitted by β1 to facilitate nucleotide exchange by disrupting a salt bridge between the P-loop and Switch I. These interactions are moderated in an active state ensemble. Collectively, our results support an alternative view of G protein activation, in which pre-coupling can allosterically modulate an agonist-free receptor. Subsequent selective agonist recruitment would result in collective activation of the complex. This alternative view can help us understand how distinct extracellular binding partners result in different but interdependent signaling pathways, with broad implications for GPCR drug discovery.

1

Introduction

Macromolecules exhibit significant conformational flexibility, especially when they interact with ligands or protein partners to form assemblies. 1–3 By combining diverse and complementary experimental data, 4,5 physical theory and statistical analyses, 6 integrative computational biology has enabled us to structurally characterize large biomolecular complexes. 7,8 However, a dynamic characterization of large complexes remains a major challenge. An atomically detailed spatiotemporal ensemble of the components and their interactions is central to understanding cellular processes. It can help answer key questions about the formation of complexes, their molecular mechanisms, and their role in important cellular pathways. Accessing the spatiotemporal scales associated with biological activity of complexes with molecular dynamics (MD) simulations demands formidable resources. 9,10 In simulations, temporal scales are often bridged with techniques that lower energy barriers, such as accelerated MD, 11 biased simulations, or transition path sampling. 12 By contrast, hierarchical conformational sampling or protein design algorithms can overcome barriers associated with a complex energy landscape, 13,14 often in excellent agreement with experimental data. 15–18 2


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An important biomolecular complex that has so far eluded a comprehensive dynamic characterization, despite exacycle 9 and supercomputer 10 simulations, is human β2 AR:Gs. β2 Adrenergic Receptor is a class A transmembrane G protein-coupled receptor (GPCR). Extracellular ligands bind to the GPCR, which allows it to transmit messages to cytosolic heterotrimeric stimulatory G proteins (Gs) and other proteins to initiate signaling. 19–22 The dynamics of β2 AR and Gs complexation, signal modulation and biological activity remain poorly understood. For example, the protein Gs signaling pathway depends on β2 AR-induced nucleotide release from the Gs α sub-unit (Gαs). GDP is bound at the interface of the Ras and alphahelical (AH) domain in inactive Gs (Fig. 1). Recent simulations established that domain separation is necessary but not sufficient for nucleotide release. 10 In the absence of an inactive β2 AR:Gαs complex, these researchers performed extensive molecular dynamics simulations starting from homologous proteins and complexes, and a nucleotide-free, activated β2 AR:Gαs complex. Their choice of systems assumes that structural similarity implies comparable activation mechanisms. Like our previous work, 23 these simulations identify conformational changes in helix α5 concomitant with AH domain opening. It suggests that the repositioned helix α5 weakens interaction with GDP, facilitating its release from protein Gs, which fluctuates between open and closed states. However, important questions remain unanswered in this model, notably the strength of coupling between helix α5 and the AH domain, and if additional, receptor-induced mechanisms could play a role. Here, we extend our multi-scale conformational Kino-Geometric Sampling procedure (KGS) 23–25 from single proteins to complexes to examine collective motions in the interaction between β2 AR and the α subunit of the Gs heterotrimer. KGS requires only modest computational resources, which enables it to generate atomically detailed ensembles bridging spatiotemporal scales. In KGS, a protein is represented by an all-atom kinematic linkage with rotatable bonds as degrees of freedom. Non-covalent intra- and intermolecular interactions are encoded as spatial constraints, which reduces the dimensionality of conformation space

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(Supporting Information). Importantly, the constraints define admissible velocities for degrees of freedom, which govern collective motions and conformational flexibility. 26 We can access admissible velocities for a given conformation by projecting a trial vector of conformational changes onto the constraint manifold. 26 Complexation broadly affects intra-chain conformational flexibility of the components, and can allosterically regulate inter-molecular interactions. KGS has previously uncovered hidden excited states in the RNA stem loop HIV1-TAR, 24,27 and established that opening angles of the AH domain of receptor-free, apo Gαs in a conformational ensemble were in excellent agreement with double electron-electron resonance (DEER) spectroscopy measurements. 23 Protein Gs reportedly can allosterically modulate agonist-free receptors, thereby favoring certain agonists over others. 3,28 However, a crystal structure of an inactive complex which could test this hypothesis has remained elusive. To complement simulations using homologous systems, 10 we directly probe activation mechanisms starting from a carefully constructed inactive binary β2 AR:Gαs complex, as well as a binary complex extracted from the activated crystal structure. 29 We find that in a conformational ensemble centered on the inactive complex, the receptor samples an intermediate substate towards activation. We observe correlated motion between helix α5 and AH domain opening of Gαs, which is stronger in the ensemble around the inactive state than in the activated state. A biased conformational ensemble, which targets the active state from the inactive state, reveals nonlinear coupling between the receptor and Gs. Our conformational pathways are validated with DEER data from a rhodopsin:Gi complex. While our complex is nucleotide-free, these findings are consistent with a model in which Gs pre-couples to an inactive receptor and jointly reach activation. In our models, fluctuations in constraint satisfaction can provide a molecular basis for peptide amide hydrogen-deuterium exchange mass spectrometry (HDXMS) data. The fluctuations suggest that interaction of helix αN with Intra Cellular Loop 2 (ICL2) and the AH domain opening induces tensile strain on the Gαs β1 strand in a direction perpendicular to the β-sheet, which may further facilitate nucleotide release.

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2 2.1

Methods KGS methodology

The KGS methodology is described in detail in previous work. 23–26 Briefly, a protein chain is represented as a kinematic linkage K, with rotatable bonds φ, ψ and χ as degrees of freedom and groups of atoms as rigid bodies. We represent K by a rooted, directed spanning tree, i.e., an acyclic graph G = (V, E) that connects all vertices V such that each one, except the root, has only one incoming, directed edge E. Vertices Vi , i = 1, . . . , |V | represent rigid bodies, and edges Ej , j = 1, . . . , n represent degrees of freedom. A vector q ∈ Sn , q = (q1 , . . . , qn )T completely specifies a conformation for a molecule with n rotational degrees of freedom. Non-covalent bonds, such as hydrogen bonds, are encoded as geometrical constraints, which result in closed cycles (Fig. 2a). Only a rotation around the hydrogen bond is permitted. Degrees of freedom within a cycle are coupled, and have to coordinate their motion to maintain the constraint. Nested cycles further couple degrees of freedom between distal sites in molecules, and modulate their flexibility. Thus, constraints prescribe collective, admissible velocities for the degrees of freedom on a lower-dimensional constraint manifold. For m cycles, the resulting 5m holonomic constraints Φ = Φ(q) define a constraint manifold M = {q ∈ Sn |Φ(q) = 0}, which is in general (nc − 5m)-dimensional, where nc ≤ n is the number of cyclic degrees of freedom. The instantaneous constraint equations Jq˙ = 0 define admissible motions on M, which result in coordinated changes to degrees of freedom that satisfy the geometric constraints, and thus maintain hydrogen bonds. The matrix J is called the Jacobian matrix. We approximate the manifold locally by its tangent space Tq M at q. Admissible velocities in Tq M at q can be obtained by projecting a trial velocity vector ∆q onto M : NNT ∆q ∈ Tq M. The matrix N is a basis for the null space of J. The first rigid body at the N-terminus of each chain was selected as the root.

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2.2

KGS methodology for biomolecular complexes

We adapted KGS to accommodate multi-chain complexes, i.e., molecules interacting with each other through non-covalent bonds. Here, we only consider hydrogen bonds. Maintaining inter-chain hydrogen bonds in complexes requires coordinated motion between intra-chain degrees of freedom and the rigid body motions of the chains (Fig. 2b). To account for such coupled intra- and inter-chain rearrangements, we attached each chain to a virtual root, a ’super-root’, with a free motion joint encoding three position and three rotation coordinates (Fig. 2b,c). Each inter-chain hydrogen bond introduces five constraints–a rotation around the hydrogen bond is permitted. As above, each chain or kinematic linkage Ki , i = 1, . . . , K is represented by a rooted, directed spanning tree Gi = (Vi , Ei ). To adjust the position of chain Ki with respect to all others while maintaining inter-chain constraints, we constructed a ’super-graph’ G. The graph G contains all Gi as subgraphs, which are connected to a virtual ’super-root’ by a free motion joint between the super-root RG and each chain root RGi . If ζji , j = 1, . . . , 6 denote the position and orientation coordinates of the root of Ki , then each inter-chain, non-covalent bond between chains Ki1 and Ki2 contributes the following linear system of constraint equations: P =

P

ϕi 1

∂(x,y,z)O,H dϕi1 ∂ϕi1

+

P

ϕi2

∂(x,y,z)O,H dϕi2 ∂ϕi2

+

P

ζ i1

∂(x,y,z)O,H dζ i1 ∂ζ i1

ζ i2

∂(x,y,z)O,H dζ i2 , ∂ζ i2

where ϕi denote the intra-chain degrees of freedom φ, ψ and χ of chain Ki , and (x, y, z)O,H the position of the atoms forming the non-covalent bond. The Jacobian matrix of the complex adopts a block structure, with K blocks of ni columns representing intra-chain degrees of freedom, and 6K columns representing ’global’ coordinates of the free-motion joints. Additionally, the Jacobian has 5mic rows representing mic interchain non-covalent bonds. The instantaneous constraint equation takes the form Jqc = 0, where qc is a vector of all global and intra-chain rotational degrees of freedom. 6


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2.3

Diffusive sampling strategy

We describe the protocol to generate conformational ensembles starting from the final MD equilibrated structures of active and inactive agonist-free and nucleotide-free β2 AR:Gαs complexes. The conformational ensemble for the synthetic protein complex was generated with only minor changes to parameter settings. To ensure rapid and broad diffusion of the sampled ensemble, the Rapidly-exploring Random Tree 30 inspired sampling protocol of previous work was used. 23–25 The sampling pools were initialized with the minimized conformations qinit of active or inactive binary complexes. We generated a pool of 30, 000 samples in an exploration sphere of fixed radius (20Å RMSD) from qinit , which was subdivided into shells Si , i ∈ {1, . . . , 100} of width 0.2Å, as follows. At each sampling step, a shell Sk was selected at random from the subset of shells containing at least one conformation. Next, an entirely random conformation qrandom was generated. The conformation that was RMSD-closest to qrandom in Sk was selected as qseed , and qrandom was discarded. A random perturbation ∆q to qseed was proposed, that was then projected onto the constraint manifold and applied to qseed to obtain a new conformation qnew , i.e. qnew = qseed + NNT ∆q where N is the nullspace matrix of the constraint matrix J. 26 If qnew did not contain clashes, it was added to the pool in the shell corresponding to its RMSD from qinit , else it was discarded. A scale factor of 0.75 was applied to the van der Waals radius for clash detection. The exploration radius and shell width are adjustable parameters.

3 3.1

Results Validation of multi-chain KGS on a small model system

We first tested and validated our procedure on a synthetic protein complex, consisting of five small single-domain proteins (chains) connected by seven inter-chain hydrogen bonds (Fig. 3a). To enhance both diversity and flexibility in the model protein-complex, we included

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two α-helical domains and three loop domains without secondary structure characteristics. We generated 50,000 samples to explore the conformational freedom of the components of the complex. The degrees of freedom corresponding to the super root were set to zero in the random trial vector qtrial before projection onto the constraint manifold to avoid global translations and rotations. Note that these degrees of freedom generally adopt nonzero values after projection, corresponding to compensatory translations and rotations of components to maintain distance constraints. We validated that our super-root method maintained constraints by measuring the rootmean-square-fluctuations (RMSF) of the intra- and inter-component hydrogen-bond distances during sampling (Fig. 3b). The fluctuations were consistently below 3.5%. These small fluctuations result from the linearized, kinematic approximation, but are well within experimentally observed fluctuations of hydrogen bond distances. This suggests that KGS efficiently probes intra- and inter-component conformational variability of the native ensemble of protein complexes.

3.2

Intra-chain conformational variability altered by complexation

Kinematic cycles spanning multiple components of the complex propagate local changes to distant sites, which can highlight structural relationships such as conformational coupling, coordinated motion or allostery. We examined how components of the synthetic protein complex were affected by inter-chain constraints. Free degrees of freedom, like those of chain termini without hydrogen-bonds, provide a baseline level of deformability. Their RMSFs can deviate from a uniform distribution by clashes, which affects four degrees of freedom (Fig 3c, circled blue dots). Their angles are perturbed nearly uni-directionally, resulting in large, one-sided fluctuations of the termini of the grey and yellow loops (Fig. 3a). By contrast, cycle degrees of freedom additionally encode coordinated motion and conformational coupling. Unsurprisingly, average random perturbations of cycle degrees of freedom are reduced compared to free degrees of freedom (Fig 3c, red and blue lines). The cycle degrees of 8


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freedom concentrate conformational variability of the complex to two specific, highly mobile areas (Fig 3c,d, circled). Furthermore, kinematic cycles can completely rigidify degrees of freedom, as in the orange helix and grey loop where the RMSF vanishes (Fig 3c). The green loop has no internal hydrogen bonds, but all of its main-chain degrees of freedom are subject to cycles spanning multiple components, moderating their RMSFs. It creates a rigid core of the complex.

3.3

Coupled motions in the β2 AR:Gαs complex

We generated two conformational ensembles, of 30,000 samples each, around an inactive and active state of β2 AR:Gαs with KGS. A starting structure for the active conformational ensemble was prepared from the crystal structure of activated β2 adrenergic receptor–Gs (Fig. 4a) 29 (Supporting Information). A starting structure for the inactive ensemble was prepared from the crystal structures of inactive β2 adrenergic receptor 31 and the GTPγSbound form of Gαs 32 (Fig. 4b). In previous, receptor-free, apo Gαs simulations we found that conformational heterogeneity in the Ras domain was localized to helices α4 and α5 . 23 An inactive β2 AR:Gαs complex was constructed by aligning the stable portions of the inactive and active Gαs Ras domains, which repositioned inactive Gαs to allow helix α5 to dock into the cytoplasmic side of the receptor without much overlap (Fig. 4b). Inactive Gα fits surprisingly well below the receptor, with the kinked helix preventing steric interactions. KGS sampling is currently limited to protein or RNA structures. Therefore, agonists were removed. The nucleotide was removed too. Both starting structures were immersed in a lipid bilayer, solvated and subsequently minimized and equilibrated with MD simulations. The final, equilibrated structures of the active and inactive complexes, with membrane and solvent removed, served as the input models for the KGS sampling algorithms. (Supporting Information).

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3.3.1

Helix α5 and AH domain coupled in inactive state

We first examined coupled motions between helix α5 and the AH-domain in complexed, apo Gαs. We previously observed concurrent motions between α5 and the AH-domain in free apo Gαs. 23 Weak coupling between these structural elements in complexed Gαs was also observed in MD simulations of the active complex. 10 Our conformational distributions of β2 AR:Gαs reveal that the amplitudes of helix α5 and the AH-domain are sharply reduced for complexed apo Gαs compared to free apo Gαs (Table 1). The distributions suggest that the interaction of helix α5 with the receptor modulates the AH-domain more strongly in the inactive state than in the active state. While the relative amplitudes for α5 are still similar between inactive and active for the complexed and free Gαs, the amplitude of the inactive AH-domain is reduced much more sharply in the complex than the active AH-domain (80% vs. 35%). Thus, a limited conformational ensemble accessible to α5 , owing to interaction with the receptor, results in a more limited ensemble for the AH-domain in the inactive state than the active state. Stronger coupling with the AH domain in the inactive state than in the activated state is consistent with a structural role for α5 in nucleotide release. 10 3.3.2

Complexed receptor adopts an intermediate state

Massively parallel, millisecond (ms) time-scale MD simulations of the receptor suggested that activation occurs through many parallel pathways, and the formation of intermediates. 9 Upon activation, helices TM3 and TM6 adopt a substate characterized by separation toward the cytoplasmic site. An R131-E268 ionic lock would provide a barrier to reaching such an intermediate state, but although intermittent formation of the lock was observed in MD simulations of apo β2 AR, 33 it is absent in both crystal structures that form the basis of our Table 1: Amplitudes of AH-domain and α5 of free versus complexed Gαs (Å). Inactive Active α5 AH-domain α5 AH-domain β2 AR:Gαs 0.8 2.7 0.6 3.8 free Gαs 7.9 13.5 8.3 5.8 10


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complexes. Strikingly, in our inactive conformational ensemble we observed a highly bimodal distribution for the TM3-TM6 distance (Fig. 5). The first mode of 8.6Å corresponds to the starting, inactive conformation for our conformational sampling. An additional mode around 9.1Å corresponds to an intermediate state of the receptor. 9 Approximately 10.8% of the conformational ensemble has a TM3-TM6 distance > 9.0Å (Fig. 5), compared to 4.5% for the apo receptor in microsecond MD simulations. 9 The conformational distribution of the TM3-TM6 distance around the active state also exhibits features of bimodality. Surprisingly, and unlike the inactive distribution, neither mode coincides with the starting conformation. While the absence of an agonist likely affects the mode of the distribution in the active state, a mode smaller than the starting value of the TM3-TM6 distance can also suggest that lattice formation in the crystal structure played a role in selecting its conformational substate. 3.3.3

Collective motions in Gαs

Next, we examined the conformational distributions for signs of collective motion. The joint distributions of spatial orientations of the principal axis of inertia for helix α5 (Fig. 5g, yellow) and the AH domain (Fig. 5g, red) reveal coupled motion between helix α5 and AH domain (Fig. 5c-d) for the inactive conformational distribution along the activation pathway. Strongest couplings were observed along the X and Y directions of α5 , and the Zaxis of the AH domain, which corresponds to a domain opening motion along the activation pathway. We also observed coupling between the Z-axis of α5 and the X-axis of the AH domain (Suppl. Fig. 1). This motion corresponds to a rotation around helix α1 , which was also implicated in nucleotide release. 34 By contrast, coupling was moderated around the active state; the Pearson’s correlation coefficient did not exceed 0.5 (Suppl. Fig. 2). For instance, we observed weaker conformational coupling between α5 and the AH domain around the active state (Fig. 5e-f). Conformational distributions of the α5 Z-axis show weak evidence for multiple substates of α5 , but coupling to the AH domain is weak (Suppl.

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Fig. 2). Coupling between the receptor TM3-TM6 distance and either the α5 helix or the AH domain was not observed in our unbiased conformational distributions, possibly owing to limited amplitudes of the motions–inter-domain conformational coupling may reveal itself only for receptor motions beyond the intermediate state. Taken together, our observations suggest that complexed β2 AR and Gαs can jointly reach an intermediate receptor state from the inactive state, while the AH domain experiences relatively small fluctuations (Table 1). Previous studies have suggested that an inactive apo receptor samples intermediate states, which are then stabilized by heterotrimeric protein Gs recruitment prior to activation. 9,35 Our results suggest that early pre-coupling of protein Gs does not preclude the receptor from adopting an intermediate state. Observations from HDXMS experiments on protein Gs in complex with agonist-bound β2 AR by Sunahara and coworkers were consistent with such a pre-coupling scenario. 36 A conceivable activation mechanism would involve an inactive β2 AR:Gs complex signaling that it is poised for activation by sampling an intermediate state. Concurrent agonist recruitment at the intermediate state would collectively activate the β2 AR:Gs complex.

3.4

A molecular basis for HDXMS measurements suggests a mechanism for nucleotide release

The active and inactive conformational distributions can provide a molecular basis for peptide amide hydrogen-deuterium exchange mass spectrometry (HDXMS) experiments, which measure changes in the stability of backbone amide hydrogens. Higher exchange rates indicate that hydrogen bonds are less stable and prone to dissociate. While our constraint equations are exact, finite sampling step sizes result in fluctuations of the hydrogen bond distances in the conformational distributions. Remarkably, strained constraints are distributed unevenly throughout the complex, and these deformation zones coincide with areas implicated in the mechanism of β2 AR activation of Gαs (Fig. 6). The hydrogen bonds at the interface of Gαs and the receptor are strained. We observe 12


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highest strains for hydrogen bond D331R –Y391G between the receptor and helix α5 (Fig. 6a,b right panels). The RMSF of the bond length are elevated in the active and inactive distributions, suggesting that the adjacent NPxxY region of the receptor interacts with α5 as the region deforms upon activation. 9,37 The RMSF fluctuations of hydrogen bond D79R – N322R , which connects the NPxxY-region to TM2, are also elevated. D79 is known to stabilize NPxxY in the activated receptor, and play a role in agonist binding. 9 Additionally, hydrogen bond D192–K305 between ECL2 and ECL3 is strained in both ensembles, while hydrogen bond W99–T199 between ECL1 and ECL2 is strained in the active ensemble only. Most striking is the strain observed on the amide hydrogen bonds flanking β1 of Gαs. HDXMS experiments on Gαs established that exchange rates increase for β1 amide hydrogens upon complexation with agonist-bound β2 AR. 36 These observations, which signify that the bonds become unstable, agree remarkably well with the strained bonds observed in our conformational distributions. While HDXMS data cannot distinguish exchange rates between the active and inactive ensembles, our results suggest that hydrogen bonds are strained in both states, and observed even in the absence of an agonist (Fig. 6a,b left panels). The strain is highly localized; we do not observe strain outward from β1 beyond the immediately adjacent strands β3 and β4 . While the interaction of ICL2 with αN and β1 through F139R –H41G is critical for G protein coupling, 38 these residues are separated by 7Å in our complex. Rather, hydrogen bond Q142R –K38G couples motion of ICL2 with αN and β1 . ICL2 adopts an α-helical shape upon activation of the receptor, which can stress the inter-domain hydrogen bond, and result in compensating motions of β1 . Interaction of helix αN with the membrane as Gαs rides up to dock with the receptor would further stress β1 through the short αN -β1 loop. Superposition of the inactive and active conformation of the Gαs Ras β sheet reveals a minor rotational motion of β1 , in agreement with observed strains and HDXMS observations (Fig. 6c). A concomitant conformational change of the adjacent phosphate binding (P-) loop would disrupt the E50G –R201G salt bridge, which stabilizes SW I. A homologous E43–R178 P-loop–SW I salt bridge in protein Gαi1 was implicated in

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a receptor-mediated allosteric pathway of GDP release. 39 Simulations revealed that GDP was preferentially ejected along a pathway on its phosphate side, requiring dissociation of E43–R178. 34

KGS activation pathway agrees with DEER data. Next, we examined if a biased conformational ensemble connecting the inactive and active crystal structure conformations of the AH domain represents an actual structural pathway. We randomly selected CA atoms (’markers’) in the AH domain to calculate a distance gradient from the inactive to the active conformation. The algorithm was instructed to take small steps along the gradient projected onto the constraint manifold, and recalculate the gradient at each step (Supporting Information). While our approach drives the AH domain only, the projection of the gradient couples the motion to all cycle degrees of freedom throughout the complex, including the Ras domain and receptor. Additionally, we let the pathway slowly emerge out of the inactive conformational distribution. Perturbations ∆q′ to the current conformation were designed as a weighted sum of the gradient ∇D and a random trial vector ∆qrandom according to ∆q′ = λ∇D + (1 − λ)∆qrandom , where λ ∈ [0, 1] increases linearly over the sampling. Figure 7a shows that the distance to the active AH domain quickly converges, starting from the inactive conformation. In addition to the large domain motion, internally the AH domain conformationally adjust along the pathway. A larger number of markers distributed throughout the AH domain results in a closer conformational match to the target conformation at the expense of slower convergence. Figure 7b shows the progression of two reaction coordinates, the opening angle of the AH domain and a pseudo-torsion angle between the Ras and AH domains, along the activation pathway for five random selections of markers. The pseudo-torsion angle describes an out-of-plane rotation of the AH domain with respect to the Ras domain (Supporting Information). We observe that all pathways are in a narrow band, independent of the choice or 14


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number of markers. In Fig. 7b the same reaction coordinates obtained from conformational substates determined from DEER spectroscopy measurements upon activation of a closely related rhodopsin protein Gαi complex (Supporting Information), are superimposed onto the pathways as green squares. The majority of these substates agree very well with our pathways, which pass through or are close to these substates. Clearly, the domain opening and pseudo-torsion angles corresponding to the DEER measurements for Gαi, 100◦ and 80◦ , remain larger than those obtained from the active crystal structure of Gαs, which are 70◦ and 40◦ . Figure 7c shows the Gαs conformational substate corresponding to the most extreme AH domain opening measured by DEER on Gαi. This substate is reached early on, at 22.5% into the sampling trajectory, and is strikingly similar to a substate obtained from three-dimensional reconstructions of a nucleotide-free T4L-β2 AR-Gs complex from electron microscopy (EM) data. 41 However, in contrast to the interpretation of the EM data, our AH domain is turned approximately ninety degrees in the plane perpendicular to the long axis of the receptor. The axis of helix A points upwards towards the membrane at an angle of approximately 40◦ . The biased ensemble allowed us to further examine coupling between the receptor, helix α5 and the AH domain. The TM3-TM6 distance in the receptor is nonlinearly coupled to the motion of helix α5 (Fig. 8a). Surprisingly, in our biased ensemble, the TM3-TM6 distance of the apo receptor first increases to that associated with a transition state, but falls back to the inactive distance as helix α5 rotates further. We simultaneously observed marked, near linear coupling between helix α5 and the AH domain (Fig. 8b). Helix α5 adopts a conformation associated with activation as the AH domain progresses along the activation pathway. Strikingly, inspecting the relationship between the TM3-TM6 distance and the AH domain opening directly (Fig. 8c), we observe that the non-linear coupling is transmitted through helix α5 to the AH domain opening. The non-linearity of coupling between the receptor TM3-TM6 distance and Gαs domain opening is likely an artifact of the absence of an agonist. Recruitment of an agonist near

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the maximum of the TM3-TM6 distance (Fig. 8a, bottom) may promote docking of helix α5 into the receptor in its fully active conformation, which, in turn, could establish proportional coupling of the TM3-TM6 distance to the AH domain opening (Fig. 8b, bottom). Our biased ensemble also suggests an additional mechanism for the strained hydrogen bonds around β1 observed with HDXMS. Helix αF is displaced significantly throughout the trajectory, keeping its orientation observed in inactive, nucleotide-free Gαs, but rotating away from the Ras domain. This motion stretches the adjacent loop SW I, which, in turn, places tensile strain on the N-terminus of the outermost β2 -strand in a direction roughly perpendicular to the β-sheet. Indeed, the N-terminus of β2 is melted to accommodate the AH domain opening of the active crystal structure. 29 We also note that hydrogen-deuterium exchange levels for I207 and F208 are elevated in receptor-free and receptor-bound Gαs, consistent with a strained SW I and N-terminal β2 . The length of SW I in inactive, nucleotide-free Gαs is simply too short to accommodate the dislocation of helix αF suggested by the active crystal structure. While the neighboring β3 strand was not detected by mass spectrometry, it likely propagates strain to β1 resulting in elevated exchange.

4

Discussion

Spatiotemporal characterizations of biological molecules and their interactions are central to understanding cellular processes. Transient biomolecular processes or excited states are difficult to characterize from any single experimental technique or simulations alone. Their characterization requires a conformational ensemble model that is structurally and kinetically consistent with experimental observations from diverse sources. 5,36,41 A major obstacle to achieving that goal is our general inability to adequately sample a complex energy landscape in a high-dimensional space to identify conformational substates. We established an efficient conformational sampling procedure for large biomolecular complexes based on KGS by linking each molecular chain to a super-root with a free-motion joint. KGS probes molec-

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ular mechanisms along collective, admissible velocities for the degrees of freedom in a lower dimensional subspace, which are imposed by inter- and intra-molecular constraints. Agonist-free β2 AR:Gαs jointly sampled an intermediate state, which supports an alternative view for an activation mechanism. Previous MD simulations suggested that agonist recruitment by an inactive receptor shifted the equilibrium of the conformational ensemble towards an intermediate state on the cytoplasmic site. 35 G protein may then bind to the intermediate state, promoting full activation of the receptor. Our conformational ensembles support a mirrored view, in which Gs pre-couples to an agonist-free receptor. β2 AR:Gs can jointly access a receptor intermediate state. In the intermediate state, the AH domain partly opens and helices TM3-TM6 of the receptor separate, opening the NPxxY-region. Agonist recruitment would then promote full activation of the β2 AR receptor, which facilitates tighter coupling of α5 deeper in the core of the receptor and elicits nucleotide release. 42 This view is supported by several lines of experimental evidence. For instance, NMR chemical shifts interpreted with long time-scale MD simulations suggest that conformational coupling between the agonist and cytoplasmic binding sites is weak. 37 Structural changes in the NPxxY region are associated with agonist affinity though hydrogen bonding of N322 to D79. 43,44 Mutating the polar residue D79 to alanine resulted a ten-fold reduction in agonist affinity. 43 Gαs-protein binding was also observed to induce changes in the binding site of the receptor, favoring certain agonists over others. 3,28 This mode of action would have important consequences for (intracellular) biased signaling. 45 In-vivo, pre-coupling presumably involves heterotrimeric, GDP-bound Gs protein transiently interacting with an agonist-free receptor. However, to date, a crystal structure of GDP-bound Gαs is lacking. Simulating the full, pre-coupled complex would be a formidable task, even with our reduced representation. Here, we have hypothesized a nucleotide-free β2 AR:Gαs complex to study collective motions between structural elements of the receptor and Gαs. GDP does not prevent AH domain opening, 10 and for our relatively small amplitude fluctuations, the collective motions and receptor response are likely very similar.

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However, it remains unclear to what extent Gs allosterically modulates β2 AR. A similar intermediate TM3-TM6 state was observed in Gs-free β2 AR; 9 the lack of an intra-cellular partner apparently does not prevent the receptor from adopting an intermediate state. The receptor may have evolved generic paths to activation to accommodate a large and diverse set of intracellular partners. Specificity to particular agonists modulated by Gs would be borne out by subtle changes at the binding site. Notwithstanding evidence implicating helix α5 in nucleotide exchange for Gαi1, 39,46 additional mechanisms resulting from β2 AR and Gαs interaction appear to be at play. Strained hydrogen bonds in β1 in inactive or intermediate, agonist-free β2 AR:Gαs suggest that ICL2 interacts with β1 prior to complete docking of α5 and full activation of the receptor. As helix α5 is retracted into the core of the Ras domain in an inactive complex, helix αN could interact more strongly with the ICL2 and/or the membrane at this stage, amplifying the β1 motion. Ensuing conformational changes in the P-loop weaken interaction with the nucleotide, disrupting the E50G –R201G salt bridge. Thus, nucleotide release may be a result of multiple, near simultaneous interactions between Gαs, the receptor, and the membrane. Our findings do not contradict those of the Gαi1 study, 46 in which a 5G linker between α5 and the receptor quenched nucleotide exchange. Instead, the linker may have weakened the ICL2 interactions by increasing the receptor-Gs distance. We also observed strain in the conformational distribution around the active state, which suggests an additional role for the β1 sheet. Understanding how molecular constraints can affect conformational transitions between substates is important. It is now generally accepted that molecular mechanisms involve many parallel pathways. 47 Altered constraints instituted by binding events, temperature fluctuations or other perturbations can shift equilibrium to favor a different set of pathways, which can be evaluated efficiently with KGS. Nevertheless, our approach has important limitations. In this study, we made a strong assumption of an immutable set of hydrogen bonds, which are encoded as geometrical con-

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straints. While admissible velocities on the constraint manifold are first-order approximations of equilibrium fluctuations, conformational transitions often cross energy barriers that involve rearrangement of non-covalent interactions. Direct observations from our conformational ensembles should be interpreted with care, and weighed against experimental evidence. However, KGS sampling can efficiently seed computationally costly but rigorous methods such as TPS or string methods, which could elucidate energy landscapes connecting conformational substates. Unfortunately, molecular mechanisms of proteins and their ensembles cannot be observed directly. Instead, experimental biophysics gives us sparse data that is averaged over an ensemble over specific time windows. KGS can efficiently generate conformational ensembles for biomolecular complexes that exceed spatiotemporal limitations of MD simulation algorithms. Interpreting KGS conformational ensembles against experimental data can yield important insights into mechanisms and function of biomolecular ensembles.

Acknowledgement We thank Brian Kobilka and Bill Weis for numerous stimulating discussions and careful reading of the manuscript. This work was partially supported by a SLAC National Accelerator Laboratory LDRD (Laboratory Directed Research and Development) grant SLAC-LDRD0014-13-2 to HvdB.

Supporting Information Available Coupling between all inertia axes of α5 and the AH domain are provided as supporting information. This information is available free of charge via the Internet at http://pubs.acs.org/.

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4.1

System preparation

The initial coordinates for active apo β2 AR:Gαs were extracted from the crystal structure of β2 AR-Gs complex with PDB ID 3SN6. 29 The GTPγS-bound form of Gαs was obtained from PDB ID 1AZT, 32 and the nucleotide was removed. Initial coordinates for the inactive form of the β2 AR receptor were extracted from PDB ID 2RH1. 31 Methionine residues 96 and 98 of inactive β2 AR were mutated to threonines to match the active state sequence. The crystal structure of active Gαs had four residue gaps: 1-8, 60-87, 203-204, 256-262. Residues 60-87 were built by Xpleo, 203-204 were added in Coot, 48 254-265 were copied from the GTPγS-bound form of Gαs after alignment, and the sequence was truncated to include residues from 9 to 391. The crystal structure of the GTPγS-bound form of Gαs had three residue gaps: 1-34, 70-86, 391-402. Residues 70-86 (Linker I) were added by Xpleo 49 and subsequently refined in Coot. Finally, the structure was truncated to include residues 35 to 391 (357 residues). The crystal structure of the active state of β2 AR included residues 30-341 and had two gaps: 176-178 and 240-264. Residues 176-178 were copied from the active structure of β2 AR with PDB ID 3P0G after alignment, and intracellular loop 3 residues 240-264 were modeled by Xpleo. 49 The crystal structure of the inactive state of β2 AR had a single residue gap 231262, which was built by Xpleo, and the sequence was truncated to include residues 30-341. The inactive structure of the receptor was aligned to the active by using heavy backbone atoms and omitting residues 228-265. The active apo β2 AR:Gαs complex was assembled by combining the modified structures of active states of β2 AR and Gαs. In previous, receptor-free, apo Gαs simulations we found that conformational heterogeneity in the Ras domain was localized to helices α4 and α5 . An inactive β2 AR:Gαs complex was assembled by aligning the stable portions of Gαs:GTPγS and active Gαs Ras domains, which repositioned Gαs:GTPγS to allow helix α5 to dock into the cytoplasmic side of the receptor without much overlap (Fig. 4). However, the modeled 20


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intracellular loop 3 of the receptor and the Gαs Ras domain were colliding. It was moved out of interaction by a sequence of small rotations around anchor bonds formed by specific atoms: residue 262 N-C by 10-15 degrees, 234-C and 235-N by 10-20 degrees, and 230-C and 231-C by -20 degrees.

4.2

Equilibrating MD simulations

We performed short MD simulations to relax the structural models of the complexes. Minimization, heating and equilibration was carried out with the NAMD software package. 50 VMD was used for visualization and analysis. 51 Our active and inactive β2 AR:Gαs complexes were immersed in a lipid membrane and solvated in water by using the web-based CHARMM-GUI server 52 (http://www.charmm-gui.org/?doc=input/membrane) after alignment to pre-oriented protein coordinates with respect to the membrane normal provided by the OPM 53 (http://opm.phar.umich.edu) entry for the active complex of β2 adrenergic receptor, 3SN6. Briefly, the structures were parametrized by the CHARMM27 all-atom force field [33] including the CMAP correction [34], the receptor immersed in a homogeneous palmitoyl-oleoyl-phosphatidylcholine (POPC) lipid layer and the whole system solvated in an hexagonal unit box. The active complex consisted of 44,293 TIP3 water molecules, 391 lipids and was electrostatically neutralized by 131 Na and 126 Cl ions (concentration of roughly 0.15 M) for a total of 196,851 atoms in a periodic boundary cell with dimensions of 131.35Å,131.35Å,134.65Å. Similarly, the inactive complex was formed by 30,698 TIP3 water molecules, 222 lipids and was electrostatically neutralized by 100 Na and 95 Cl ions (concentration of roughly 0.15 M) for a total of 132,998 atoms in a periodic boundary cell with dimensions of 101.12Å,101.13Å,161.18Å. The systems were minimized with a series of steepest descent and conjugate gradient algorithms by gradually reducing constraints on the protein atoms. Hydrogen atoms were constrained with SHAKE. The time step was set to 1fs for the initial phase of the NPT equilibration. After reaching 300K, the density of the system was equilibrated during a 10ns NPT (T = 300K, P = 1.01325 bar) run. The temperature was 21



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controlled with the Langevin dynamics method while keeping the pressure constant using the combined Langevin piston Nose-Hoover method. Long-range electrostatic interactions were treated with PME, with a grid spacing of 1Å. Non-bonded cutoff was switched off from 12Å to 14Å during heating and from 10Å to 12Å during the subsequent NPT equilibration simulation. The integration time step during this stage was increased to 2fs. The final MD structures of active and inactive agonist-free and nucleotide-free complexes served as the input models for the KGS sampling algorithms.

4.3

Biased ensemble and gradient descent on the constraint manifold

While in our study the inactive and active states have an identical number of atoms, often that is not the case. We therefore randomly selected CA atoms in the AH domain to calculate a distance gradient between corresponding atoms in the inactive and active conformation. The number of CA atoms was varied from one for every 20 residues on average to one every other residue, and was kept fixed for a sampling trajectory. (The marker frequency varies from 0.05 to 0.5). We linearly weighted the gradient ∇D by adding a random trial vector ∆qrandom to non-zero gradient entries according to ∆q′ = λ∇D+(1−λ)∆qrandom , where λ ∈ [0, 1] increases linearly over the sample size of 50,000. Global coordinates were not included in the gradient calculation, and hence, like degrees of freedom in the receptor, adopted nonzero values only after projection of the gradient onto the constraint manifold. To eliminate motion of the center of mass, the receptor was RMSD-aligned to its starting conformation every 25 steps. Residues 176-178, and 228-265 were excluded from the RMSD calculation. To compare our conformations to DEER measurements, in addition to a domain opening angle 23 we defined a dihedral angle between the Gαs Ras and AH domain (S286-L291-P192-V159). This material is available free of charge via the Internet at http://pubs.acs.org/.

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Coupled Motions in Î²2AR:GÎ±s Conformational Ensembles - Journal of

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