Structural design principles that underlie the multi-specific interactions of Gαq with dissimilar partners

Navot, Shir; Kosloff, Mickey

doi:10.1038/s41598-019-43395-0

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Published: 03 May 2019

Structural design principles that underlie the multi-specific interactions of Gα_q with dissimilar partners

Scientific Reports volume 9, Article number: 6898 (2019) Cite this article

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Abstract

Gα_q is a ubiquitous molecular switch that activates the effectors phospholipase-C-β3 (PLC-β3) and Rho guanine-nucleotide exchange factors. Gα_q is inactivated by regulators of G protein signaling proteins, as well as by PLC-β3. Gα_q further interacts with G protein-coupled receptor kinase 2 (GRK2), although the functional role of this interaction is debated. While X-ray structures of Gα_q bound to representatives of these partners have revealed details of their interactions, the mechanistic basis for differential Gα_q interactions with multiple partners (i.e., Gα_q multi-specificity) has not been elucidated at the individual residue resolution. Here, we map the structural determinants of Gα_q 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 Gα_q “Switch II” region interacts with all partners. Our analysis further suggests that Gα_q-GRK2 interactions are consistent with GRK2 functioning as an effector, rather than a GAP. Our multi-specificity analysis pinpoints Gα_q residues that uniquely contribute to interactions with particular partners, enabling precise manipulation of these cascades. As such, we dissect the molecular basis of Gα_q function as a central signaling hub, which can be used to target Gα_q-mediated signaling in therapeutic interventions.

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Introduction

Gα_q is a ubiquitous protein hub that interacts with multiple protein partners as part of its function as a molecular switch. Gα_q 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 Gα_q 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 Gα_q effectors can regulate cellular processes such as smooth muscle contraction, platelet activation, immune responses, and neuronal function^{4,5,6,7,8,9,10,11,12}, and are crucial in pathologies such as cancer, neurological disorders, and cardiovascular diseases^{13,14,15,16,17}.

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 Gα_q 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 Gα_q as a multi-specific protein¹⁹. Gα_q, as well as Gα subunits of the homologous G_i 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 Gα_q^{22,23,24,25,26}, although the significance of this dual regulation is unclear^27,28. In another Gα-specific interaction, proteins containing “GoLoco motifs” can bind specifically to members of the G_i sub-family^29,30, functioning as guanine nucleotide dissociation inhibitors (GDIs). Gα_q can also interact with GRKs, and in particular, with GRK2^31,32. The central function of GRK2 is to mediate initial steps in GPCR desensitization^33,34, although it was also proposed to sequester activated Gα_q from other effectors, and was suggested to function as a GAP towards Gα_q^31,35,36. Overall, the residue-level determinants of Gα_q 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.

Gα_q can interact with its partners via different regions of the Gα subunit. However, the residue-level determinants of Gα_q multi-specificity are still not sufficiently understood. Complexes of Gα_q with representatives of these partners have been solved, namely Gα_q-RGS2³⁷, Gα_q-RGS8³⁸, Gα_q-PLC-β3³⁹, Gα_q-GRK2⁴⁰, and Gα_q-p63RhoGEF⁴¹. Gα_q 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 (Gα_q-Arg183) and a catalytic glutamine in Sw II (Gα_q-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 Gα_s 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 domain^{37,38,44,45,46}. More recently, it was suggested that the helical domain is a major determinant of the specific interactions between Gα_q and RGS2⁴⁷. PLC-β3 was shown by Waldo et al. to engage three Gα_q regions in the GTPase domain, Sw I, Sw II and the effector-binding site, while interactions with the Gα_q 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 Gα_q 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 function^24,39. Similar to PLC-β3, p63RhoGEF binds the Gα_q effector-binding site via a conserved helix-turn-helix motif^19,39,41, but also binds the C-terminal region of Gα_q⁴¹. Diversely, while GRK2 also binds to the Gα_q 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 Gα_q⁴⁰. Indeed, a precise and quantitative definition of which Gα_q 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 Gα_q 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 Gα_q Sw II, three residues in Sw III, and three residues in the effector-binding site were identified as playing roles in PLC-β activation^39,48. Shankaranarayanan et al. identified two residues in Gα_q Sw III, one residue in the α3 helix, and one residue in the Gα_q C-terminal region (Tyr356) as important only for activating p63RhoGEF⁴⁹. Site-directed mutagenesis also assigned four Gα_q 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 Gα_q helical domain residues impaired GRK2 binding. Finally, Tesmer et al. showed that four Gα_q residues in the Gα_q effector-binding site were also required for GRK2 binding⁴⁰. Nevertheless, which Gα_q 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 Gα_q 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 Gα_q with each of its different partners. We further identified unique Gα_q regions that specifically interact with GAPs and disparate Gα_q regions that interact with effectors. We also identified Gα_q regions that contribute to interactions with multiple partners, and particular Gα_q residues that specifically contribute to interactions with only one select partner.

Results

Delineation of structurally-similar domains and sub-structures in Gα_q partners

Towards analyzing the multi-specificity determinants of Gα_q with its partners at the individual residue level, we first characterized and precisely defined the structural building blocks used by Gα_q partners to recognize Gα_q using structural alignments – to compare the available experimentally-solved complexes of Gα_q with GRK2, RGS2, RGS8, PLC-β3, and p63RhoGEF^{37,38,39,40,41,51}. 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 Gα_q showed that the RH domains of RGS2 and GRK2 indeed interact with distinct regions of Gα_q (Fig. 1b). Furthermore, as noted previously⁵², we observed that the GRK2 and the RGS2/8 RH domains use different regions to interact with Gα_q. In GRK2, the region encompassing helices α5 and α6 binds Gα_q (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 Gα_q. Moreover, because of their disparate binding poses, dissimilar Gα_q residues are expected to contribute to the binding of RGS proteins and GRK2.

We next compared the structures of Gα_q with PLC-β3, RGS2, and RGS8^37,38,39, noting that their interfaces with Gα_q 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 Gα_q with p63RhoGEF and PLC-β3^39,41. 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 Gα_q in p63RhoGEF, whereas in PLC-β3, this domain is far from the interface with Gα_q (Fig. 3a cf. b). On the other hand, a shorter helix-turn-helix motif in both p63RhoGEF and PLC-β3 binds Gα_q similarly (Fig. 3c). These structurally-aligned helix-turn-helix motifs include residues 468–490 in p63RhoGEF and residues 852–874 in PLC-β3, binding Gα_q at its previously-defined⁴³ effector-binding site (Fig. 3c).

Our analyses suggest that because of the structural dissimilarities between Gα_q partners and the dissimilarities in their binding poses in relation to Gα_q, an alternative approach to precisely delineate the multi-specificity determinants of Gα_q should be used. This approach involves assessing which residues are common and which are unique to such interactions by focusing on the Gα_q side of the interface and by analyzing which Gα_q residues contribute to each interaction using a quantitative energy-based approach.

Residue-level mapping of Gα_q interactions with individual partners

To map the individual residues that contribute to the interactions of Gα_q with each of its partners, we characterized the five complexes detailed above using an energy-based computational methodology developed previously by our lab^{45,46,47,53,54,55}. The FDPB method was used to calculate the net electrostatic and polar contributions (ΔΔG_elec) of each residue within 15 Å of the Gα_q-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 ΔΔG_elec ≥ 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 (ΔΔG_np) 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 ΔΔG_np ≥ 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 Gα_q 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 Gα_q do so via electrostatic contributions (Fig. 4b). The electrostatic dominance in interactions of RGS domains with Gα subunits was also observed in interactions of Gα_o and Gα_i with various RGS domains⁴⁶. The number of Gα_q 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 Gα_q residues, namely 36 and 31 residues, respectively. In contrast, in the complex with GRK2, only 16 Gα_q residues contribute to the interaction. While about a quarter of the Gα_q residues contributing to interactions with RGS domains are located in the Gα_q helical domain, only three Gα_q helical domain residues contribute to interactions with PLC-β3, and no contributions with p63RhoGEF and GRK2 originate from the Gα_q 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 Gα_q; four of these residues are identical and contribute similarly to interactions with Gα_q 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 Gα_q Gln209 residue. Our calculations predict that this residue contributes to interaction with Gα_q via side-chain electrostatic and non-polar interactions in all three structures.

Comparison of the multi-specific interactions of Gα_q with its different partners

To precisely define the shared and unique determinants responsible for interactions of Gα_q with its partners, we compared which Gα_q residues contribute to the interaction with each partner (Fig. 5). We thus identified a single residue in the Gα_q P-loop that contributes to interactions with RGS proteins, and one or two residues in the Gα_q β1 strand, which immediately precedes the P-loop, that contribute to interactions with PLC-β3 and p63RhoGEF. As mentioned above, the Gα_q 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 Gα_q 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 Gα_q 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 Gα_q residues closer to the C-terminus (residues 319–321 and 353–357) only contribute to the interaction with p63RhoGEF (Fig. 5d).

The majority of Gα_q 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 Gα_q helical domain contains five residues that contribute to interactions with RGS proteins alone (Fig. 6b,d). Residues in the Gα_q 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 Gα_q 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 Gα_q analyzed above with the interactions of Gα_i with RGS proteins and GoLoco motifs (Fig. 7). Between 25 and 28 Gα_i residues contribute to interactions with different RGS proteins (Fig. 7b), while 35–41 Gα_i residues contribute to interactions with the GoLoco motifs in RGS14 and LGN (Fig. 7c). Similar to Gα_q, the majority of Gα_i residues contributing to interactions with RGS proteins rely on electrostatic interactions (Fig. 7b), with Gα_i regions interacting with RGS proteins being similar to Gα_q regions that engage RGS proteins (Figs 5b cf. 7b). In contrast, the majority of Gα_i interactions with the GoLoco motifs involve non-polar interactions (Fig. 7c). Unlike the interactions of RGS domains with either Gα_i or Gα_q, we found six to eight residues in the Gα_i P-loop that contribute to interactions with GoLoco motifs. A third of Gα_i 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 Gα_i with the GoLoco motif of LGN are located in the helical domain (Supplementary Fig. S5). Moreover, the majority of the contributing residues in the Gα_i helical domain are involved only in interactions with GoLoco motifs (Fig. 7d). On the other hand, the Gα_i Sw I and Sw II regions contain numerous residues that contribute to interactions with either the GoLoco motifs or with RGS proteins, while Gα_i Sw III makes only limited contributions to interactions with either the GoLoco motifs or the RGS proteins (Fig. 7b,c). Lastly, the Gα_i 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 Gα_q with RGS2/8, PLC-β3, p63RhoGEF, and GRK2 at the individual residue level. Our results revealed that only residues from one Gα_q 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 Gα_q by its partners, rather than a major specificity determinant towards a specific partner. The Gα_q effector-binding site is also rather promiscuous, interacting with all partners except for RGS proteins, while the Gα_q 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 Gα_q that interact with only one partner, such as the Gα_q 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 Gα_q effector-binding site interacts only with effectors, as opposed to the Gα_q regions that uniquely interact with GAPs. In particular, the Gα_q effector-binding site contains eight residues that contributed to interactions with all three effectors – PLC-β3, p63RhoGEF and GRK2; four Gα_q 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 Gα_q effector-binding site also includes specificity determinants for particular Gα_q effectors.

Our calculations also delineate which interactions with particular Gα_q regions can underlie GAP activity. The Gα_q 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 Gα_q. This hypothesis is sustained by a previous study⁵⁷ that used FTIR spectroscopy to show that RGS4 interactions with the intrinsic arginine finger of Gα_i, which is located in Sw I, are important for GAP activity. We also found that the Gα_q helical domain contains seven residues that contribute to interactions with RGS proteins. We note that, in addition to RGS proteins, the Gα_q 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 Gα_q 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 Gα_q 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 activity^24,39, interacts mostly with the Gα_q Sw I and the N-terminus of Sw II. Taken together, this suggests that the Gα_q 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 Gα_q Sw I or in the Gα_q 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 Gα_q are with Sw II and the effector-binding site. Therefore, while Carman et al. suggested that GRK2 has weak GAP activity towards Gα_q³¹, 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 Gα_q helical domain and Sw I region interact uniquely with GAPs.

Our analysis of Gα_q multi-specificity also pinpointed specific determinants responsible for particular partner interactions. Several residues in the Gα_q 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 Gα_q C-terminal region (319–321 and 353–357) that contribute solely to interactions with p63RhoGEF. The role of residues in this Gα_q region in mediating interactions with p63RhoGEF is supported by a previous study that mutated Tyr356 in the Gα_q C-terminal region and showed that p63RhoGEF binding was impaired⁴⁹. However, our results suggest the Gα_q C-terminal region that contributes to specific interactions with p63RhoGEF is more extensive than previously suggested. Furthermore, the Gα_q 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 Gα_q specificity determinants towards these partners.

Finally, we compared the interactions of Gα_i and Gα_q 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 Gα_q and Gα_i, suggesting these regions do not play a role in determining RGS domain specificity towards Gα_q and Gα_i. 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 Gα_q and Gα_i³⁸. 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 Gα_i helical domain, combined with their interactions with the effector-binding site, are unique to the Gα_i sub-family and stand out from the interactions of Gα_q 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 Gα_q 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 Gα_q residues contribute to the discrimination between different protein partners, and provides a structural basis for precisely mutating Gα_q 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 Gα_q-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): Gα_q–RGS2 (4EKD)³⁷, Gα_q–RGS8 (5DO9)³⁸, Gα_q–PLC-β3 (3OHM, 4QJ3, 4QJ4, 4QJ5)^39,51, Gα_q–p63RhoGEF (2RGN)⁴¹, Gα_q–GRK2 (2BCJ)⁴⁰, Gα_i1–RGS4 (1AGR)⁵⁸, Gα_i1–RGS16 (2IK8)⁴⁴, Gα_i1–RGS1 (2GTP)⁴⁴, Gα_i1–RGS14-GoLoco (2OM2, 3ONW, 3QI2)^59,60,61, Gα_i1–LGN-GoLoco4 (4G5Q)⁶², Gα_i3–LGN-GoLoco3 (4G5S)⁶², and Gα_i3–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 (Gα_i3 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 previously^{45,46,47,53,54,55,64} to analyze the per-residue contributions of Gα_q 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 (ΔΔG_elec) 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 ΔΔG_elec ≥ 1 kcal/mol to the interactions (twice the numerical error of the electrostatic calculations) were deemed as substantially contributing to the interaction^45,56. Non-polar energy contributions (ΔΔG_np) 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/Å²⁵⁶. Residues contributing ΔΔG_np ≥ 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.

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Acknowledgements

This work was supported by the Israel Science Foundation (grants 1454/13, 1959/13 and 2155/15).

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The Department of Human Biology, Faculty of Natural Science, University of Haifa, Haifa, 3498838, Israel
Shir Navot & Mickey Kosloff

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Shir Navot
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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.

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Navot, S., Kosloff, M. Structural design principles that underlie the multi-specific interactions of Gα_q with dissimilar partners. Sci Rep 9, 6898 (2019). https://doi.org/10.1038/s41598-019-43395-0

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Received: 26 February 2019
Accepted: 23 April 2019
Published: 03 May 2019
DOI: https://doi.org/10.1038/s41598-019-43395-0

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