Abstract
Gαq is a ubiquitous molecular switch that activates the effectors phospholipase-C-β3 (PLC-β3) and Rho guanine-nucleotide exchange factors. Gαq is inactivated by regulators of G protein signaling proteins, as well as by PLC-β3. Gαq further interacts with G protein-coupled receptor kinase 2 (GRK2), although the functional role of this interaction is debated. While X-ray structures of Gαq bound to representatives of these partners have revealed details of their interactions, the mechanistic basis for differential Gαq interactions with multiple partners (i.e., Gαq multi-specificity) has not been elucidated at the individual residue resolution. Here, we map the structural determinants of Gαq multi-specificity using structure-based energy calculations. We delineate regions that specifically interact with GTPase Activating Proteins (GAPs) and residues that exclusively contribute to effector interactions, showing that only the Gαq “Switch II” region interacts with all partners. Our analysis further suggests that Gαq-GRK2 interactions are consistent with GRK2 functioning as an effector, rather than a GAP. Our multi-specificity analysis pinpoints Gαq residues that uniquely contribute to interactions with particular partners, enabling precise manipulation of these cascades. As such, we dissect the molecular basis of Gαq function as a central signaling hub, which can be used to target Gαq-mediated signaling in therapeutic interventions.
Similar content being viewed by others
Introduction
Gαq is a ubiquitous protein hub that interacts with multiple protein partners as part of its function as a molecular switch. Gαq belongs to the family of heterotrimeric (αβγ) G proteins that are activated by G protein-coupled receptors (GPCRs), promoting the exchange of GDP for GTP in the active site of the Gα subunit1. Activated Gαq dissociates from the βγ subunits and stimulates two major classes of downstream effectors – phospholipase C-β (PLC-β) isozymes2 and Rho guanine nucleotide exchange factors (RhoGEFs), such as p63RhoGEF3. These Gαq effectors can regulate cellular processes such as smooth muscle contraction, platelet activation, immune responses, and neuronal function4,5,6,7,8,9,10,11,12, and are crucial in pathologies such as cancer, neurological disorders, and cardiovascular diseases13,14,15,16,17.
The ability of proteins such as Gα subunits to interact with multiple partners, often using partially overlapping interfaces, is termed multi-specificity18. Indeed, the interactions of Gαq with diverse partners (i.e., GPCRs, regulators of G protein signaling (RGS) proteins, phospholipase C-β isoforms, G protein-coupled receptor kinases (GRKs), and RhoGEFs) define Gαq as a multi-specific protein19. Gαq, as well as Gα subunits of the homologous Gi sub-family, can be inactivated by RGS proteins. The latter bind Gα-GTP and allosterically accelerate the intrinsic GTPase activity of the Gα subunit, thereby acting as GTPase-activating proteins (GAPs)20,21. Uniquely, the PLC-β effector also shows GAP activity towards Gαq22,23,24,25,26, although the significance of this dual regulation is unclear27,28. In another Gα-specific interaction, proteins containing “GoLoco motifs” can bind specifically to members of the Gi sub-family29,30, functioning as guanine nucleotide dissociation inhibitors (GDIs). Gαq can also interact with GRKs, and in particular, with GRK231,32. The central function of GRK2 is to mediate initial steps in GPCR desensitization33,34, although it was also proposed to sequester activated Gαq from other effectors, and was suggested to function as a GAP towards Gαq31,35,36. Overall, the residue-level determinants of Gαq interactions with its multiple partners remain to be explicitly defined, and in particular using a common and objective framework for analyzing interactions with different partners.
Gαq can interact with its partners via different regions of the Gα subunit. However, the residue-level determinants of Gαq multi-specificity are still not sufficiently understood. Complexes of Gαq with representatives of these partners have been solved, namely Gαq-RGS237, Gαq-RGS838, Gαq-PLC-β339, Gαq-GRK240, and Gαq-p63RhoGEF41. Gαq contains two structural domains, the GTPase domain, which is also found in other G proteins, and the α-helical domain, which is unique to Gα subunits. The GTPase domain contains three flexible regions called “switch regions” (Sw I, II, and III), which undergo conformational changes, depending on whether the Gα subunit binds GTP or GDP42. These regions contain two residues that are critical for the GTPase reaction – a catalytic arginine in Sw I (Gαq-Arg183) and a catalytic glutamine in Sw II (Gαq-Gln209). In addition, Gα GTPase domains also include an “effector-binding site” that was previously defined as the C-terminal half of Sw II, the α3 helix, and the subsequent loop that connects the latter to the β5 strand43. This region was shown to participate in the binding of Gα subunits to effectors, such as the binding of Gαs to adenylyl cyclase43. RGS proteins were shown to substantially interact with the Gα GTPase domain, yet were also shown to interact with the Gα helical domain37,38,44,45,46. More recently, it was suggested that the helical domain is a major determinant of the specific interactions between Gαq and RGS247. PLC-β3 was shown by Waldo et al. to engage three Gαq regions in the GTPase domain, Sw I, Sw II and the effector-binding site, while interactions with the Gαq helical domain were not mentioned39. This study also suggested that a helix-turn-helix motif at the C-terminus of the PLC-β3 C2 domain determines its binding to Gαq as an effector. On the other hand, a loop that connects EF hands 3 and 4 in PLC-β3 was shown to mediate its GAP function24,39. Similar to PLC-β3, p63RhoGEF binds the Gαq effector-binding site via a conserved helix-turn-helix motif19,39,41, but also binds the C-terminal region of Gαq41. Diversely, while GRK2 also binds to the Gαq effector-binding site, this partner lacks a helix-turn-helix motif and, despite sharing an RGS homology (RH) domain with RGS proteins, GRK2 and RGS proteins were suggested to bind to non-overlapping surfaces of Gαq40. Indeed, a precise and quantitative definition of which Gαq residues contribute to the interface with each partner is lacking, as well as a clear-cut delineation of where these interfaces overlap.
Previous mutagenesis studies tested a limited number of Gαq residues, located in the switch regions and the effector-binding site, and showed them to be important for interactions with particular partners. Two residues in Gαq Sw II, three residues in Sw III, and three residues in the effector-binding site were identified as playing roles in PLC-β activation39,48. Shankaranarayanan et al. identified two residues in Gαq Sw III, one residue in the α3 helix, and one residue in the Gαq C-terminal region (Tyr356) as important only for activating p63RhoGEF49. Site-directed mutagenesis also assigned four Gαq residues in the GTPase domain as being important for binding GRK2 – one residue in Sw I, two residues in Sw III and one residue adjacent to Sw III50. This study also showed that mutations in four Gαq helical domain residues impaired GRK2 binding. Finally, Tesmer et al. showed that four Gαq residues in the Gαq effector-binding site were also required for GRK2 binding40. Nevertheless, which Gαq residues interact with all partners and which residues interact specifically with only one partner has yet to be defined.
Here, we used structural comparisons and energy-based calculations to produce a comprehensive map of the residue-level determinants of Gαq multi-specificity. We used structure-based Finite Difference Poisson–Boltzmann (FDPB) and burial-based energy calculations to accurately pinpoint which amino acids contribute to the interaction of Gαq with each of its different partners. We further identified unique Gαq regions that specifically interact with GAPs and disparate Gαq regions that interact with effectors. We also identified Gαq regions that contribute to interactions with multiple partners, and particular Gαq residues that specifically contribute to interactions with only one select partner.
Results
Delineation of structurally-similar domains and sub-structures in Gαq partners
Towards analyzing the multi-specificity determinants of Gαq with its partners at the individual residue level, we first characterized and precisely defined the structural building blocks used by Gαq partners to recognize Gαq using structural alignments – to compare the available experimentally-solved complexes of Gαq with GRK2, RGS2, RGS8, PLC-β3, and p63RhoGEF37,38,39,40,41,51. The structure-based ECOD classification database classifies GRK2 residues 29–185 and RGS2 residues 69–200 as homologous RGS homology domains (RH domains) belonging to the same structural family (i.e., the same ECOD F-group). On the other hand, Lodowski et al. defined the GRK2 RH domain as two discontinuous segments, the first being a nine-helix bundle (residues 30–185), and the second corresponding to an extended helix (residues 513–547)52. Structural alignment of GRK2 and RGS2 showed that the cores of the RH domains in RGS2 and GRK2 are similar. Specifically, GRK2 residues 52–176 and RGS2 residues 81–200 structurally aligned with a root mean square deviation (RMSD) of 2.8 Å (Fig. 1a). In contrast, a helical segment in the N-terminal region of the previously defined GRK2 RH domain (residues 36–52) and the extended helix at the C-terminus of this domain (residues 513–553) have no structural equivalents in RGS2 (Fig. 1a). Aligning the complexes using only the coordinates of Gαq showed that the RH domains of RGS2 and GRK2 indeed interact with distinct regions of Gαq (Fig. 1b). Furthermore, as noted previously52, we observed that the GRK2 and the RGS2/8 RH domains use different regions to interact with Gαq. In GRK2, the region encompassing helices α5 and α6 binds Gαq (Fig. 1c), while in RGS2 and RGS8 these helices are peripheral to the interface and helix α7 is an RGS-unique determent of the interaction (Fig. 1d). Taken together, these results show that only the core RH domain of GRK2 (residues 52–176) is homologous to the RGS domains of RGS2 and RGS8 and is relevant to a comparison of interactions with Gαq. Moreover, because of their disparate binding poses, dissimilar Gαq residues are expected to contribute to the binding of RGS proteins and GRK2.
We next compared the structures of Gαq with PLC-β3, RGS2, and RGS837,38,39, noting that their interfaces with Gαq are indeed structurally dissimilar (Fig. 2a,b). On the other hand, in all three of these interfaces, an asparagine residue (Asn260 in PLC-β3, Asn149 in RGS2 and Asn122 in RGS8) adopts the same orientation (Fig. 2c), interacting with the catalytic glutamine that is essential for GTP hydrolysis, as previously observed39.
Finally, we compared the structures of Gαq with p63RhoGEF and PLC-β339,41. Both of these proteins contain a structurally-similar pleckstrin homology (PH) domain. According to the ECOD database, p63RhoGEF residues 343–490 and PLC-β3 residues 12–146 adopt a PH domain-like fold. While this domain is structurally similar in both proteins, it interacts directly with Gαq in p63RhoGEF, whereas in PLC-β3, this domain is far from the interface with Gαq (Fig. 3a cf. b). On the other hand, a shorter helix-turn-helix motif in both p63RhoGEF and PLC-β3 binds Gαq similarly (Fig. 3c). These structurally-aligned helix-turn-helix motifs include residues 468–490 in p63RhoGEF and residues 852–874 in PLC-β3, binding Gαq at its previously-defined43 effector-binding site (Fig. 3c).
Our analyses suggest that because of the structural dissimilarities between Gαq partners and the dissimilarities in their binding poses in relation to Gαq, an alternative approach to precisely delineate the multi-specificity determinants of Gαq should be used. This approach involves assessing which residues are common and which are unique to such interactions by focusing on the Gαq side of the interface and by analyzing which Gαq residues contribute to each interaction using a quantitative energy-based approach.
Residue-level mapping of Gαq interactions with individual partners
To map the individual residues that contribute to the interactions of Gαq with each of its partners, we characterized the five complexes detailed above using an energy-based computational methodology developed previously by our lab45,46,47,53,54,55. The FDPB method was used to calculate the net electrostatic and polar contributions (ΔΔGelec) of each residue within 15 Å of the Gαq-partner interface in each complex. For each residue, we separately calculated the electrostatic contributions from the side chain and/or those originating from the main chain of each residue. Residues that substantially contribute to the interaction were defined as those contributing ΔΔGelec ≥ 1 kcal/mol to the interactions (i.e. twice the numerical error of the electrostatic calculations)56. Note that this approach calculates the net difference between the interaction of a residue with its protein partner in relation to its interaction with the water and ions in the solvent, and thereby identifies only residues that are calculated to substantially contribute to binding. Non-polar energy contributions (ΔΔGnp) were calculated as a surface-area proportional term by multiplying the per-residue surface area buried upon complex formation by a surface tension constant of 0.05 kcal/mol/Å2. Residues with substantial non-polar contributions were defined as those contributing ΔΔGnp ≥ 0.5 kcal/mol to the interactions (namely, more than 10 Å2 of each protein surface is buried upon complex formation). To reduce false positives and negatives, we applied a consensus approach across comparable biological replicates in multiple PDB structures or across multiple dimers in an asymmetric unit (see Methods and Supplementary Figs S1 and S2), which substantially improved the accuracy of our predictions. Residues thus calculated to contribute substantially to intermolecular interactions were mapped to the structure of each individual protein (Fig. 4).
Our results show that in all of the complexes analyzed, the majority of Gαq residues contribute to interactions with the cognate partners via non-polar interactions (Fig. 4a,c,e,g, Supplementary Fig. S3, Supplementary Tables 1–4). A similar majority of non-polar contributing residues was also observed in PLC-β3 (Fig. 4d), p63RhoGEF (Fig. 4f), and GRK2 (Fig. 4h). In contrast, the majority of RGS residues that contribute to interactions with Gαq do so via electrostatic contributions (Fig. 4b). The electrostatic dominance in interactions of RGS domains with Gα subunits was also observed in interactions of Gαo and Gαi with various RGS domains46. The number of Gαq residues that contribute to interactions with RGS2 and RGS8 are 27 and 25, respectively. Complexes with PLC-β3 and p63RhoGEF involve a larger number of Gαq residues, namely 36 and 31 residues, respectively. In contrast, in the complex with GRK2, only 16 Gαq residues contribute to the interaction. While about a quarter of the Gαq residues contributing to interactions with RGS domains are located in the Gαq helical domain, only three Gαq helical domain residues contribute to interactions with PLC-β3, and no contributions with p63RhoGEF and GRK2 originate from the Gαq helical domain.
On the opposing face of these interfaces, the structurally similar helix-turn-helix motifs in PLC-β3 and p63RhoGEF (Fig. 3) contain 12 residues that contribute to interactions with Gαq; four of these residues are identical and contribute similarly to interactions with Gαq in p63RhoGEF and in PLC-β3 (Supplementary Fig. S4). As mentioned above (Fig. 2c), Asn260 in PLC-β3 and the corresponding Asn149/122 in RGS2/8 adopt essentially the same orientation and interact similarly with the catalytic Gαq Gln209 residue. Our calculations predict that this residue contributes to interaction with Gαq via side-chain electrostatic and non-polar interactions in all three structures.
Comparison of the multi-specific interactions of Gαq with its different partners
To precisely define the shared and unique determinants responsible for interactions of Gαq with its partners, we compared which Gαq residues contribute to the interaction with each partner (Fig. 5). We thus identified a single residue in the Gαq P-loop that contributes to interactions with RGS proteins, and one or two residues in the Gαq β1 strand, which immediately precedes the P-loop, that contribute to interactions with PLC-β3 and p63RhoGEF. As mentioned above, the Gαq helical domain makes no contributions to interactions with p63RhoGEF or GRK2. Rather, this domain mostly interacts with RGS domains, and in a limited fashion with PLC-β3 (Fig. 5b,c cf. d,e). Residues in the Gαq Sw II contribute to interactions with all partners, while Sw I and III residues only contribute to interactions with RGS domains and PLC-β3 (Fig. 5). Between nine and 12 Gαq residues in the region immediately following Sw III (residues 248–265, termed here the α3 motif, Fig. 5a) only contribute to interactions with PLC-β3, p63RhoGEF and GRK2 (Fig. 5c–e). Several Gαq residues closer to the C-terminus (residues 319–321 and 353–357) only contribute to the interaction with p63RhoGEF (Fig. 5d).
The majority of Gαq residues contribute to interactions with only one partner (Fig. 6a,b). Most residues in Sw II and some residues in Sw III contribute to interactions with both effectors and GAPs (Fig. 6c), while the helical domain and Sw I contribute to interactions only with GAP proteins (Fig. 6c,d). Moreover, the Gαq helical domain contains five residues that contribute to interactions with RGS proteins alone (Fig. 6b,d). Residues in the Gαq effector-binding site do not contribute to interactions with RGS proteins, and true to the name of this site, contribute only to interactions with effectors (Fig. 6d). Overall, there are only two Gαq residues, located in Sw II, that contribute to interactions with all four partners (Fig. 6a,d).
To gain a wider perspective on the Gα family in terms of multi-specific interactions with different partners, we compared the interactions of Gαq analyzed above with the interactions of Gαi with RGS proteins and GoLoco motifs (Fig. 7). Between 25 and 28 Gαi residues contribute to interactions with different RGS proteins (Fig. 7b), while 35–41 Gαi residues contribute to interactions with the GoLoco motifs in RGS14 and LGN (Fig. 7c). Similar to Gαq, the majority of Gαi residues contributing to interactions with RGS proteins rely on electrostatic interactions (Fig. 7b), with Gαi regions interacting with RGS proteins being similar to Gαq regions that engage RGS proteins (Figs 5b cf. 7b). In contrast, the majority of Gαi interactions with the GoLoco motifs involve non-polar interactions (Fig. 7c). Unlike the interactions of RGS domains with either Gαi or Gαq, we found six to eight residues in the Gαi P-loop that contribute to interactions with GoLoco motifs. A third of Gαi residues contributing to interactions with the RGS14 GoLoco motif are located in the helical domain, while a sixth of the residues contributing to the interaction of Gαi with the GoLoco motif of LGN are located in the helical domain (Supplementary Fig. S5). Moreover, the majority of the contributing residues in the Gαi helical domain are involved only in interactions with GoLoco motifs (Fig. 7d). On the other hand, the Gαi Sw I and Sw II regions contain numerous residues that contribute to interactions with either the GoLoco motifs or with RGS proteins, while Gαi Sw III makes only limited contributions to interactions with either the GoLoco motifs or the RGS proteins (Fig. 7b,c). Lastly, the Gαi α3 motif contains eight residues that contribute only to interactions with the GoLoco motifs but not with RGS proteins.
Discussion
Our energy-based computational methodology provides a quantitative framework to compare the multi-specific interactions of Gαq with RGS2/8, PLC-β3, p63RhoGEF, and GRK2 at the individual residue level. Our results revealed that only residues from one Gαq region, Sw II, interact with all of its partners, GAPs and effectors alike – suggesting that Sw II is a necessary and central motif for the recognition of the activated state of Gαq by its partners, rather than a major specificity determinant towards a specific partner. The Gαq effector-binding site is also rather promiscuous, interacting with all partners except for RGS proteins, while the Gαq helical domain, Sw I and parts of Sw III only interact with RGS proteins and PLC-β3. Our multi-specificity analysis identified numerous residues across the surface of Gαq that interact with only one partner, such as the Gαq C-terminal region that exclusively interacts with p63RhoGEF and several helical domain residues that only interact with RGS domains. This analysis enables precise manipulation of individual interactions. Our results also show that most of the Gαq effector-binding site interacts only with effectors, as opposed to the Gαq regions that uniquely interact with GAPs. In particular, the Gαq effector-binding site contains eight residues that contributed to interactions with all three effectors – PLC-β3, p63RhoGEF and GRK2; four Gαq positions contribute to interactions with two effectors. The effector-binding site also contains two residues that contributed specifically to interactions only with PLC-β3, three residues that contributed to interactions only with p63RhoGEF, and one residue that contributed to interactions only with GRK2. Therefore, the Gαq effector-binding site also includes specificity determinants for particular Gαq effectors.
Our calculations also delineate which interactions with particular Gαq regions can underlie GAP activity. The Gαq Sw I region contains residues that contribute to interactions only with PLC-β3 and RGS proteins, suggesting a functional role for Sw I interactions with proteins possessing GAP activity towards Gαq. This hypothesis is sustained by a previous study57 that used FTIR spectroscopy to show that RGS4 interactions with the intrinsic arginine finger of Gαi, which is located in Sw I, are important for GAP activity. We also found that the Gαq helical domain contains seven residues that contribute to interactions with RGS proteins. We note that, in addition to RGS proteins, the Gαq helical domain also contributes to interactions with PLC-β3 – interactions that were not discussed or investigated in previous studies. This suggests that interactions with the Gαq helical domain play a common role in mediating the function of these different GAPs. On the other hand, we observed no interactions between residues in the Gαq effector-binding site and RGS proteins. Relevantly, the extended loop in PLC-β3, which connects EF hands 3 and 4 and was shown to be a critical component for GAP activity24,39, interacts mostly with the Gαq Sw I and the N-terminus of Sw II. Taken together, this suggests that the Gαq effector-binding site interacts solely with effectors or with regions responsible for effector activation and does not play a role in interactions with GAPs. On the other hand, no residues in Gαq Sw I or in the Gαq helical domain contribute to interactions with GRK2. Although GRK2 contains an RH domain that is similar to RGS domains in RGS proteins, all GRK2 interactions with Gαq are with Sw II and the effector-binding site. Therefore, while Carman et al. suggested that GRK2 has weak GAP activity towards Gαq31, our results did not identify any interactions that might underlie a GAP activity of GRK2. Overall, our results suggest that the Gαq effector-binding site interacts solely with downstream effectors, while the Gαq helical domain and Sw I region interact uniquely with GAPs.
Our analysis of Gαq multi-specificity also pinpointed specific determinants responsible for particular partner interactions. Several residues in the Gαq helical domain consist of a unique specificity determinant towards RGS proteins, while only one residue in this domain is specific to PLC-β3. We found eight residues in the Gαq C-terminal region (319–321 and 353–357) that contribute solely to interactions with p63RhoGEF. The role of residues in this Gαq region in mediating interactions with p63RhoGEF is supported by a previous study that mutated Tyr356 in the Gαq C-terminal region and showed that p63RhoGEF binding was impaired49. However, our results suggest the Gαq C-terminal region that contributes to specific interactions with p63RhoGEF is more extensive than previously suggested. Furthermore, the Gαq N-terminal region preceding the P-loop also contains two residues that contribute to interactions only with p63RhoGEF, while one residue in this region contributes only to the interaction with PLC-β3. Most of these contributing residues were not investigated previously and represent new Gαq specificity determinants towards these partners.
Finally, we compared the interactions of Gαi and Gαq with multiple partners and showed that interactions with RGS proteins involving the switch regions were nearly identical, while RGS interactions with the helical domain differed substantially. Our analysis showed that RGS proteins contributed essentially the same to interactions with the Sw I and Sw III regions in both Gαq and Gαi, suggesting these regions do not play a role in determining RGS domain specificity towards Gαq and Gαi. This contrasts with a previous study that suggested, based on visual inspection of the crystal structure, that the Gα Sw I and Sw III regions contain key residues responsible for the selectivity of RGS domains for Gαq and Gαi38. Relevantly, both GoLoco motifs and RGS domains interacted with Sw I in the Gα subunits. Taken together, these commonalities suggest that proteins whose function involves the guanine nucleotide, possessing either GAP or GDI activity, bind Sw I as part of their function. On the other hand, the extensive interactions of GoLoco motifs with the Gαi helical domain, combined with their interactions with the effector-binding site, are unique to the Gαi sub-family and stand out from the interactions of Gαq with its partners. More general conclusions regarding the multi-specificity determinants of the entire Gα family will require applying the approach used here to additional members.
In summary, the energy-based computational analysis described here presents a precise comparison of Gαq interactions with multiple partners using a common quantitative framework. This framework allows extension of such analyses to other Gα subunits involved in interactions with different partners and to additional multi-specific proteins. Our analysis suggests that multiple Gαq residues contribute to the discrimination between different protein partners, and provides a structural basis for precisely mutating Gαq residues in order to manipulate and unravel its interactions in vivo and in cells. From a wider perspective, our results provide residue-level insight into protein-protein interactions that drive cellular signaling processes and lay the basis for specifically targeting Gαq-mediated signaling in therapeutic interventions.
Methods
Protein structures
The following representative 3D structures were used in our analysis and visualization of Gα subunits with different classes of partners (PDB IDs are provided for each structure): Gαq–RGS2 (4EKD)37, Gαq–RGS8 (5DO9)38, Gαq–PLC-β3 (3OHM, 4QJ3, 4QJ4, 4QJ5)39,51, Gαq–p63RhoGEF (2RGN)41, Gαq–GRK2 (2BCJ)40, Gαi1–RGS4 (1AGR)58, Gαi1–RGS16 (2IK8)44, Gαi1–RGS1 (2GTP)44, Gαi1–RGS14-GoLoco (2OM2, 3ONW, 3QI2)59,60,61, Gαi1–LGN-GoLoco4 (4G5Q)62, Gαi3–LGN-GoLoco3 (4G5S)62, and Gαi3–LGN-GoLoco4 (4G5R, 4G5O)62. Missing short segments in PDB entries 3OHM, 4QJ3, 4QJ4 and 4QJ5 (PLC-β3 residues 90–98 and 472–574), entry 2RGN (p63RhoGEF residues 367–373 and 396–403), entry 2BCJ (GRK2 residues 476–491 and 668–689), entry 3QI2 (RGS14-GL residues 511–512), and entry 4G5S (Gαi3 chain A residue 117 and chain B residues 204–205) were modeled using Loopy63 and partial or missing side chains were modeled using Scap63. Hydrogen atoms were added using CHARMM, and the structures were subjected to conjugate gradient minimization with a harmonic restraint force of 50 kcal/mol/Å2 applied to the heavy atoms. Structure alignments were performed using the Combinatorial Extension (CE) method, as implemented in the RCSB protein comparison tool (https://www.rcsb.org/pdb/workbench/workbench.do). 3D structural visualization was carried out with the PyMol molecular graphics program (https://www.pymol.org/).
Energy calculations to map residue-level specificity determinates
We followed the methodology described previously45,46,47,53,54,55,64 to analyze the per-residue contributions of Gαq residues to interactions with their partners (PLC-β3, p63RhoGEF, RGS2/8, GRK2) in the crystal structures mentioned above. The Finite Difference Poisson–Boltzmann (FDPB) method, as implemented in DelPhi65, was used to calculate the net electrostatic and polar contributions (ΔΔGelec) of each residue found within 15 Å of the dimer interface. For each residue, electrostatic contributions from the side chain and/or originating from the main chain were calculated separately. Residues contributing ΔΔGelec ≥ 1 kcal/mol to the interactions (twice the numerical error of the electrostatic calculations) were deemed as substantially contributing to the interaction45,56. Non-polar energy contributions (ΔΔGnp) were calculated as a surface-area proportional term by multiplying the per-residue surface area buried upon complex formation, calculated using surfv66, by a surface tension constant of 0.05 kcal/mol/Å2 56. Residues contributing ΔΔGnp ≥ 0.5 kcal/mol to the interactions (namely, those that bury more than 10 Å2 of each protein surface upon complex formation) were defined as making substantial non-polar contributions64. To reduce false positives and negatives, we applied a consensus approach across comparable biological replicates in multiple PDB structures (3OHM, 4QJ3, 4QJ4, 4QJ5 – see Supplementary Fig. S1; 2OM2, 3ONW, 3QI2; 4G5R, 4G5O) or across multiple dimers in an asymmetric unit (5DO9 – see Supplementary Fig. S2; 2RGN; 4G5Q; 4G5S; 4G5R, 4G5O), substantially improving prediction accuracy.
Data Availability
The datasets generated during or analyzed during the current study are available from the corresponding author on reasonable request.
References
Gilman, A. G. G proteins: transducers of receptor-generated signals. Annu Rev Biochem 56, 615–649, https://doi.org/10.1146/annurev.bi.56.070187.003151 (1987).
Rhee, S. G. Regulation of phosphoinositide-specific phospholipase C. Annu Rev Biochem 70, 281–312, https://doi.org/10.1146/annurev.biochem.70.1.281 (2001).
Aittaleb, M., Boguth, C. A. & Tesmer, J. J. Structure and function of heterotrimeric G protein-regulated Rho guanine nucleotide exchange factors. Mol Pharmacol 77, 111–125, https://doi.org/10.1124/mol.109.061234 (2010).
Johnson, G. J., Leis, L. A. & Dunlop, P. C. Specificity of G alpha q and G alpha 11 gene expression in platelets and erythrocytes. Expressions of cellular differentiation and species differences. Biochem J 318(Pt 3), 1023–1031 (1996).
Offermanns, S., Toombs, C. F., Hu, Y. H. & Simon, M. I. Defective platelet activation in G alpha q-deficient mice. Nature 389, 183–186, https://doi.org/10.1038/38284 (1997).
Keys, J. R., Greene, E. A., Koch, W. J. & Eckhart, A. D. Gq-coupled receptor agonists mediate cardiac hypertrophy via the vasculature. Hypertension 40, 660–666 (2002).
Wettschureck, N. & Offermanns, S. Mammalian G proteins and their cell type specific functions. Physiol Rev 85, 1159–1204, https://doi.org/10.1152/physrev.00003.2005 (2005).
Mizuno, N. & Itoh, H. Functions and regulatory mechanisms of Gq-signaling pathways. Neurosignals 17, 42–54, https://doi.org/10.1159/000186689 (2009).
Momotani, K. et al. p63RhoGEF couples G alpha q/11-mediated signaling to Ca2+ sensitization of vascular smooth muscle contractility. Circ Res 109, 993–1002, https://doi.org/10.1161/CIRCRESAHA.111.248898 (2011).
Svensson, L., Stanley, P., Willenbrock, F. & Hogg, N. The G alpha q/11 proteins contribute to T lymphocyte migration by promoting turnover of integrin LFA-1 through recycling. PLoS One 7, e38517, https://doi.org/10.1371/journal.pone.0038517 (2012).
Wang, Y., Li, Y. & Shi, G. The regulating function of heterotrimeric G proteins in the immune system. Arch Immunol Ther Exp (Warsz) 61, 309–319, https://doi.org/10.1007/s00005-013-0230-5 (2013).
Sanchez-Fernandez, G. et al. Gαq signalling: the new and the old. Cellular signalling 26, 833–848, https://doi.org/10.1016/j.cellsig.2014.01.010 (2014).
Offermanns, S. et al. Impaired motor coordination and persistent multiple climbing fiber innervation of cerebellar Purkinje cells in mice lacking G alpha q. Proceedings of the National Academy of Sciences of the United States of America 94, 14089–14094 (1997).
Adams, J. W. et al. Enhanced Gαq signaling: a common pathway mediates cardiac hypertrophy and apoptotic heart failure. Proceedings of the National Academy of Sciences of the United States of America 95, 10140–10145 (1998).
Jalili, T., Takeishi, Y. & Walsh, R. A. Signal transduction during cardiac hypertrophy: the role of G alpha q, PLC beta I, and PKC. Cardiovasc Res 44, 5–9 (1999).
Van Raamsdonk, C. D. et al. Mutations in GNA11 in uveal melanoma. N Engl J Med 363, 2191–2199, https://doi.org/10.1056/NEJMoa1000584 (2010).
O’Hayre, M. et al. The emerging mutational landscape of G proteins and G-protein-coupled receptors in cancer. Nat Rev Cancer 13, 412–424, https://doi.org/10.1038/nrc3521 (2013).
Schreiber, G. & Keating, A. E. Protein binding specificity versus promiscuity. Curr Opin Struct Biol 21, 50–61, https://doi.org/10.1016/j.sbi.2010.10.002 (2011).
Litosch, I. D. G alpha q signaling. Life Sci 152, 99–106, https://doi.org/10.1016/j.lfs.2016.03.037 (2016).
Hepler, J. R., Berman, D. M., Gilman, A. G. & Kozasa, T. RGS4 and GAIP are GTPase-activating proteins for Gq alpha and block activation of phospholipase C beta by gamma-thio-GTP-Gq alpha. Proceedings of the National Academy of Sciences of the United States of America 94, 428–432 (1997).
Ross, E. M. & Wilkie, T. M. GTPase-activating proteins for heterotrimeric G proteins: regulators of G protein signaling (RGS) and RGS-like proteins. Annu Rev Biochem 69, 795–827, https://doi.org/10.1146/annurev.biochem.69.1.795 (2000).
Berstein, G. et al. Phospholipase C-beta 1 is a GTPase-activating protein for Gq/11, its physiologic regulator. Cell 70, 411–418 (1992).
Biddlecome, G. H., Berstein, G. & Ross, E. M. Regulation of phospholipase C-beta1 by Gq and m1 muscarinic cholinergic receptor. Steady-state balance of receptor-mediated activation and GTPase-activating protein-promoted deactivation. J Biol Chem 271, 7999–8007 (1996).
Lyon, A. M. & Tesmer, J. J. Structural insights into phospholipase C-beta function. Mol Pharmacol 84, 488–500, https://doi.org/10.1124/mol.113.087403 (2013).
Cook, B. et al. Phospholipase C and termination of G-protein-mediated signalling in vivo. Nat Cell Biol 2, 296–301, https://doi.org/10.1038/35010571 (2000).
Lyon, A. M., Dutta, S., Boguth, C. A., Skiniotis, G. & Tesmer, J. J. Full-length Galpha(q)-phospholipase C-beta3 structure reveals interfaces of the C-terminal coiled-coil domain. Nat Struct Mol Biol 20, 355–362, https://doi.org/10.1038/nsmb.2497 (2013).
Ross, E. M. Coordinating speed and amplitude in G-protein signaling. Curr Biol 18, R777–R783, https://doi.org/10.1016/j.cub.2008.07.035 (2008).
Litosch, I. Novel mechanisms for feedback regulation of phospholipase C-beta activity. IUBMB Life 54, 253–260, https://doi.org/10.1080/15216540215673 (2002).
Kimple, R. J., Kimple, M. E., Betts, L., Sondek, J. & Siderovski, D. P. Structural determinants for GoLoco-induced inhibition of nucleotide release by G alpha subunits. Nature 416, 878–881, https://doi.org/10.1038/416878a (2002).
Siderovski, D. P. & Willard, F. S. The GAPs, GEFs, and GDIs of heterotrimeric G-protein alpha subunits. Int J Biol Sci 1, 51–66 (2005).
Carman, C. V. et al. Selective regulation of G alpha q/11 by an RGS domain in the G protein-coupled receptor kinase, GRK2. J Biol Chem 274, 34483–34492 (1999).
Ribas, C. et al. The G protein-coupled receptor kinase (GRK) interactome: role of GRKs in GPCR regulation and signaling. Biochim Biophys Acta 1768, 913–922, https://doi.org/10.1016/j.bbamem.2006.09.019 (2007).
Carman, C. V. & Benovic, J. L. G-protein-coupled receptors: turn-ons and turn-offs. Curr Opin Neurobiol 8, 335–344 (1998).
Evron, T., Daigle, T. L. & Caron, M. G. GRK2: multiple roles beyond G protein-coupled receptor desensitization. Trends Pharmacol Sci 33, 154–164, https://doi.org/10.1016/j.tips.2011.12.003 (2012).
Sallese, M., Mariggio, S., D’Urbano, E., Iacovelli, L. & De Blasi, A. Selective regulation of Gq signaling by G protein-coupled receptor kinase 2: direct interaction of kinase N terminus with activated galphaq. Mol Pharmacol 57, 826–831 (2000).
Kozasa, T. Regulation of G protein-mediated signal transduction by RGS proteins. Life Sci 68, 2309–2317 (2001).
Nance, M. R. et al. Structural and functional analysis of the regulator of G protein signaling 2-G alpha q complex. Structure 21, 438–448, https://doi.org/10.1016/j.str.2012.12.016 (2013).
Taylor, V. G., Bommarito, P. A. & Tesmer, J. J. Structure of the Regulator of G Protein Signaling 8 (RGS8)-G alpha q Complex: Molecular basis for G alpha selectivity. J Biol Chem 291, 5138–5145, https://doi.org/10.1074/jbc.M115.712075 (2016).
Waldo, G. L. et al. Kinetic scaffolding mediated by a phospholipase C-β and Gq signaling complex. Science 330, 974–980, https://doi.org/10.1126/science.1193438 (2010).
Tesmer, V. M., Kawano, T., Shankaranarayanan, A., Kozasa, T. & Tesmer, J. J. Snapshot of activated G proteins at the membrane: the Gαq-GRK2-Gβγ complex. Science 310, 1686–1690 (2005).
Lutz, S. et al. Structure of Gαq-p63RhoGEF-RhoA complex reveals a pathway for the activation of RhoA by GPCRs. Science 318, 1923–1927, https://doi.org/10.1126/science.1147554 (2007).
Sprang, S. R. G protein mechanisms: insights from structural analysis. Annu Rev Biochem 66, 639–678 (1997).
Sprang, S. R., Chen, Z. & Du, X. Structural basis of effector regulation and signal termination in heterotrimeric G alpha proteins. Adv Protein Chem 74, 1–65, https://doi.org/10.1016/S0065-3233(07)74001-9 (2007).
Soundararajan, M. et al. Structural diversity in the RGS domain and its interaction with heterotrimeric G protein alpha-subunits. Proceedings of the National Academy of Sciences of the United States of America 105, 6457–6462 (2008).
Kosloff, M., Travis, A. M., Bosch, D. E., Siderovski, D. P. & Arshavsky, V. Y. Integrating energy calculations with functional assays to decipher the specificity of G protein-RGS protein interactions. Nat Struct Mol Biol 18, 846–853, https://doi.org/10.1038/nsmb.2068 (2011).
Asli, A., Sadiya, I., Avital-Shacham, M. & Kosloff, M. “Disruptor” residues in the regulator of G protein signaling (RGS) R12 subfamily attenuate the inactivation of G alpha subunits. Sci Signal 11, https://doi.org/10.1126/scisignal.aan3677 (2018).
Kasom, M., Gharra, S., Sadiya, I., Avital-Shacham, M. & Kosloff, M. Interplay between negative and positive design elements in G alpha helical domains of G proteins determines interaction specificity towards RGS2. Biochem J, https://doi.org/10.1042/BCJ20180285 (2018).
Venkatakrishnan, G. & Exton, J. H. Identification of determinants in the alpha-subunit of Gq required for phospholipase C activation. J Biol Chem 271, 5066–5072 (1996).
Shankaranarayanan, A. et al. G alpha q allosterically activates and relieves autoinhibition of p63RhoGEF. Cellular signalling 22, 1114–1123, https://doi.org/10.1016/j.cellsig.2010.03.006 (2010).
Day, P. W. et al. Characterization of the GRK2 binding site of G alpha q. J Biol Chem 279, 53643–53652, https://doi.org/10.1074/jbc.M401438200 (2004).
Lyon, A. M., Begley, J. A., Manett, T. D. & Tesmer, J. J. Molecular mechanisms of phospholipase C beta3 autoinhibition. Structure 22, 1844–1854, https://doi.org/10.1016/j.str.2014.10.008 (2014).
Lodowski, D. T., Pitcher, J. A., Capel, W. D., Lefkowitz, R. J. & Tesmer, J. J. Keeping G proteins at bay: a complex between G protein-coupled receptor kinase 2 and G beta gamma. Science 300, 1256–1262, https://doi.org/10.1126/science.1082348 (2003).
Zur, Y. et al. Engineering a monomeric variant of macrophage colony-stimulating factor (M-CSF) that antagonizes the c-FMS receptor. Biochem J 474, 2601–2617, https://doi.org/10.1042/BCJ20170276 (2017).
Rabinovich, E. et al. Identifying Residues that Determine SCF Molecular-Level Interactions through a Combination of Experimental and In silico Analyses. Journal of molecular biology 429, 97–114, https://doi.org/10.1016/j.jmb.2016.11.018 (2017).
Salem-Mansour, D., Asli, A., Avital-Shacham, M. & Kosloff, M. Structural motifs in the RGS RZ subfamily combine to attenuate interactions with Galpha subunits. Biochem Biophys Res Commun, https://doi.org/10.1016/j.bbrc.2018.08.033 (2018).
Sheinerman, F. B., Al-Lazikani, B. & Honig, B. Sequence, structure and energetic determinants of phosphopeptide selectivity of SH2 domains. J Mol Biol 334, 823–841 (2003).
Mann, D. et al. Mechanism of the intrinsic arginine finger in heterotrimeric G proteins. Proceedings of the National Academy of Sciences of the United States of America 113, E8041–E8050, https://doi.org/10.1073/pnas.1612394113 (2016).
Tesmer, J. J., Berman, D. M., Gilman, A. G. & Sprang, S. R. Structure of RGS4 bound to AlF4–activated G(i alpha1): stabilization of the transition state for GTP hydrolysis. Cell 89, 251–261 (1997).
Sammond, D. W. et al. Structure-based protocol for identifying mutations that enhance protein-protein binding affinities. J Mol Biol 371, 1392–1404, https://doi.org/10.1016/j.jmb.2007.05.096 (2007).
Bosch, D. E. et al. Structural determinants of affinity enhancement between GoLoco motifs and G-protein alpha subunit mutants. J Biol Chem 286, 3351–3358, https://doi.org/10.1074/jbc.M110.190496 (2011).
Bosch, D. E. et al. A P-loop mutation in Galpha subunits prevents transition to the active state: implications for G-protein signaling in fungal pathogenesis. PLoS Pathog 8, e1002553, https://doi.org/10.1371/journal.ppat.1002553 (2012).
Jia, M. et al. Crystal structures of the scaffolding protein LGN reveal the general mechanism by which GoLoco binding motifs inhibit the release of GDP from Galphai. J Biol Chem 287, 36766–36776, https://doi.org/10.1074/jbc.M112.391607 (2012).
Xiang, Z., Soto, C. S. & Honig, B. Evaluating conformational free energies: the colony energy and its application to the problem of loop prediction. Proceedings of the National Academy of Sciences of the United States of America 99, 7432–7437, https://doi.org/10.1073/pnas.102179699 (2002).
Bakhman, A., Rabinovich, E., Shlamkovitch, T., Papo, N. & Kosloff, M. Residue-level determinants of Angiopoietin-2 interactions with its receptor Tie2. Proteins: Structure, Function and Bioinformatics 87, 185–197, https://doi.org/10.1002/prot.25638 (2019).
Honig, B. & Nicholls, A. Classical electrostatics in biology and chemistry. Science 268, 1144–1149 (1995).
Nicholls, A., Sharp, K. A. & Honig, B. Protein folding and association: insights from the interfacial and thermodynamic properties of hydrocarbons. Proteins 11, 281–296, https://doi.org/10.1002/prot.340110407 (1991).
Acknowledgements
This work was supported by the Israel Science Foundation (grants 1454/13, 1959/13 and 2155/15).
Author information
Authors and Affiliations
Contributions
S.N. designed the research, conducted data analysis, analyzed the results and wrote the manuscript. M.K. designed and supervised the research, analyzed the results and wrote the manuscript.
Corresponding author
Ethics declarations
Competing Interests
The authors declare no competing interests.
Additional information
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Navot, S., Kosloff, M. Structural design principles that underlie the multi-specific interactions of Gαq with dissimilar partners. Sci Rep 9, 6898 (2019). https://doi.org/10.1038/s41598-019-43395-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-019-43395-0
- Springer Nature Limited