The class Frizzled of G protein-coupled receptors (GPCRs), consisting of ten Frizzled (FZD1-10) subtypes and Smoothened (SMO), remains one of the most enigmatic GPCR families. While SMO relies on cholesterol binding to the 7TM core of the receptor to activate downstream signaling, underlying details of receptor activation remain obscure for FZDs. Here, we aimed to investigate the activation mechanisms of class F receptors utilizing a computational biology approach and mutational analysis of receptor function in combination with ligand binding and downstream signaling assays in living cells. Our results indicate that FZDs differ substantially from SMO in receptor activation-associated conformational changes. SMO manifests a preference for a straight TM6 in both ligand binding and functional readouts. Similar to the majority of GPCRs, FZDs present with a kinked TM6 upon activation owing to the presence of residue P6.43. Functional comparison of FZD and FZD P6.43F mutants in different assay formats monitoring ligand binding, G protein activation, DVL2 recruitment and TOPflash activity, however, underlines further the functional diversity among FZDs and not only between FZDs and SMO.
Human, nonsensory G protein-coupled receptors (GPCRs) are classified into the four main classes A, B, C, and F based on their sequence homology1. While high-resolution crystal or CryoEM structures are available for representatives of all these GPCR classes in the inactive state, the active, G protein-coupling or nanobody-stabilized, high-resolution structures are more rare—currently these have been resolved for different receptors of class A and class B, and one representative of class F2. The very first active GPCR structures were of rhodopsin at low pH3 or bound to a peptide derived from the C-terminus of Gα subunit4 in the year 2008, followed by a nanobody-stabilized β2 adrenoceptor and a β2 adrenoceptor-Gs complex from year 20115,6. Studying GPCR activation has led to the common agreement that an outward movement of the transmembrane helix 6 (TM6) is the main hallmark of the active GPCR conformation7,8. This reorganization of the TM6 involves a complex interaction network within the receptor structures and is also tied to the presence of the conserved proline residues P5.50, P6.50, and P7.50 allowing helix dynamics (for Ballesteros-Weinstein numbering, see refs. 9,10). These proline residues are highly conserved within class A GPCRs, but missing for some part in the other GPCR classes10.
The class F or class Frizzled, which consist of ten Frizzled (FZD1–10) subtypes and Smoothened (SMO) remains one of the most enigmatic groups of GPCRs11,12. Class F receptors are intrinsically involved in embryonic development, tissue homeostasis and pathology, most prominently oncogenesis13,14, thus emphasizing the need for a better understanding of the molecular mechanisms of receptor activation. FZDs mediate WNT signaling and SMO mediates hedgehog signaling, but underlying mechanisms of ligand binding, receptor activation and signal specification and initiation remain obscure15. Our recent work on class F structure-function aspects emphasized that two distinct, conformational activation pathways exist in FZDs. On the one hand, interaction with G proteins is accompanied by the opening of a molecular switch (R6.32/W7.55) in the receptor core between TM6 and TM7. On the other hand, mutational disruption of the molecular switch prevents interaction with DVL at the same time as it enhances WNT-5A-induced recruitment of miniG proteins16 indicating that FZDs employ different active conformations for pathway selectivity.
While inactive SMO and FZD structures have provided detailed insight into the overall receptor structure, the first high-resolution structures of active class F receptors were reported in 2019 showing active SMO in complex with either heterotrimeric Gi protein or a nanobody, and currently six active SMO structures are available15,17,18,19. Keeping the preconception of the bent TM6 causal for GPCR activation in mind, it was unexpected that the outward movement of the TM6 in these SMO structures originated from a movement of the whole helix rather than merely bending it (Fig. 1a)17,18. Even though SMO and FZDs bear structural similarities in both transmembrane ligand and intracellular effector binding sites16,20 and interact with and activate heterotrimeric G proteins (for full references see refs. 12,15), SMO signaling is different from FZD signaling. The differences are most distinct when comparing ligand binding and downstream signaling along the FZD/β-catenin or the SMO/GLI pathways21,22. Based on what is known from class A and B GPCRs and SMO regarding the conformational changes occurring upon receptor activation, we employ here computational biology and mutational analysis of receptor function to shed light on details of class F receptor activation mechanisms.
Several lines of evidence point into the direction of ligand-induced and constitutive activity of class F receptors resembling the typical mode of action of class A and B GPCRs including an opening of the TM bundle by a TM6 swing out, which can for example be monitored using conformational FZD biosensors20,23,24 and through the recruitment of miniG proteins as conformational sensors of the active conformation16. While FZDs all have a proline at position 6.43 (P6.43) in TM6, SMO carries a phenylalanine underlining differences in receptor activation mechanisms and cholesterol binding in these closely related GPCRs. We propose that SMO activation relies on cholesterol binding to the 7TM core of the receptor maintaining a straight TM6, whereas activation-associated conformational changes of FZD6 rather manifest in a kinked TM6 and are independent of cholesterol binding in the 7TM core of the receptor. A better understanding of differences in class F receptor activation offers possibilities to develop small molecule compounds targeting both SMO and FZDs with higher selectivity and improved pharmacological profile.
MD simulations of SMO and FZD6 model reveal different TM6 topologies
In order to broadly compare all class F GPCRs, we performed a sequence alignment of class F receptors in human, orangutan, rat, mouse, dog, chicken, frog, and fruit fly based on the sequences obtained from the UniProtKB/Swiss-Prot database (Supplementary Data 1). In the evolutionarily conserved class F, only P5.50 is conserved similarly as reported for class A; location 6.50 is occupied by a cysteine residue and 7.50 by an isoleucine residue (Supplementary Data 1). While TM7 lacks proline residues almost throughout the class F receptors (only in FZD3 amino acid 7.51 is a proline), TM6 harbours a P6.43 in all FZDs but not in SMO (Fig. 1b). This is also in line with the outcome of a previously published large scale sequence alignment of over 750 mammalian and non-mammalian FZDs and SMO (Supplementary Fig. 1) underlining the strict conservation of the P/F6.43 across the animal kingdom16.
This striking difference between FZDs and SMO regarding position 6.43 raised the question how the presence of a P6.43, which is unable to complete the hydrogen bonding network of the α-helix and thus interrupts the preferred helical geometry in TM6, affects activation-associated conformational rearrangements in FZDs compared to SMO. In order to assess, whether P6.43 indeed affects the overall conformation of active FZDs, we utilized a homology model of FZD6 built using active SMO (PDB ID: 6OT0) as a template20. The sequence of SMO is most similar to that of FZD3 and FZD616. Of these, we selected FZD6 for the MD simulations due to the fact that simulating FZD6 allowed us to utilize the SMO agonist SAG1.3 in maintaining the simulated receptors in an active-like conformation in absence of the intracellular effector similar to what we have reported before20. The best representative model, as well as the active SMO, were then used as a starting structure for MD simulations.
In these MD simulations, a distinct kink was observed in the TM6 of FZD6 (angle 158.5° ± 4.5° throughout the MD trajectory run in four independent replicas comprising 1250 ns of simulation per simulation system), whereas the TM6 remained notably straighter in SMO (168.4° ± 4.2°; Fig. 1c–e). To further evaluate, whether this difference in angle was indeed mediated only by P6.43, we set up similar MD simulations with mutated FZD6 (P6.43F) and SMO (F6.43P). The MD data with the point-mutated receptors inevitably indicated that the observed bend of FZD6 was mediated by P6.43: the TM6 of P6.43F FZD6 remained in the straight conformation, whereas the TM6 of F6.43P SMO was bending (Fig. 1d–e). For further details of these MD simulations, see Supplementary Fig. 2 (protein backbone RMSDs) and Supplementary Fig. 3 (χ2 torsion angles of P6.43 to monitor the ring puckering states).
To control further that the observed kink in TM6 is not caused by the lack of the intracellular effector, we set up a control simulation with wild-type FZD6-SAG1.3-miniGi protein complex (ca. 550 ns of simulation divided into three replicas; Supplementary Fig. 4). The utilized miniGi protein construct acts as a conformational sensor for active FZD616,20, and we selected it for these control simulations instead of the full Gi due to its notable smaller size. In these control simulations, the TM6 orientation remained similar to the active-like model of the wild-type FZD6 without miniGi (Supplementary Fig. 4).
F6.43P (in SMO) mutation affects ligand binding and 7TM pocket volume
SMO crystal structures, both active and inactive, have shown that the transmembrane binding site for small molecular ligands extends deeply inside the 7TM core of the receptor17,25. Furthermore, our recent study implementing a NanoBRET binding assay for SMO, suggested the existence of two binding pockets especially in the ΔCRD-SMO (SMO lacking the extracellular cysteine-rich domain) assessed by BODIPY-cyclopamine binding26. In presence of the extracellular CRD, BODIPY-cyclopamine binds mainly to the high-affinity (lower) binding site of SMO, which is also targeted by cholesterol and synthetic antagonists of the SANT series17,25. For FZD6 instead, our previous BODIPY-cyclopamine binding data support only binding to the pocket that is analogous to the low-affinity (upper) binding site of SMO that is also targeted by the SAG series of SMO agonists20.
During class A GPCR activation, the outward-swing of TM6 is accompanied by reorganization of several amino-acid residues (including so-called microswitches) throughout the receptor structure27. Some of these structural rearrangements take also place at the orthosteric ligand binding cavity underlining the allosteric cooperativity between G protein binding and the extracellular ligand binding site as originally defined in the ternary complex model28. With the intramolecular interaction networks and the deep binding cavity of SMO in mind, we assessed whether also the FZD6 and SMO cavities are affected by the conformational changes in TM6. In order to quantify the consequence of the alterations in the ligand binding cavity, we employed a NanoBiT/BRET-based binding assay using BODIPY-cyclopamine and HiBiT-tagged receptors15,20,29 (Fig. 2a and b). For these experiments, we used wild-type HiBiT-SMO and HiBiT-FZD6, together with their respective mutants: F6.43P and P6.43F. BODIPY-cyclopamine binding to HiBiT-SMO resulted in a monophasic and saturable concentration-dependent bioluminescence resonance energy transfer (BRET) signal (pKd ± SD = 6.87 ± 0.13). Most importantly, we observed a dramatic decrease (P = 0.0105) in the BODIPY-cyclopamine binding affinity to HiBiT-SMO F6.43P (pKd ± SD = 5.44 ± 1.02). Interestingly, there were no differences between BODIPY-cyclopamine association to HiBiT-FZD6 and HiBIT-FZD6 P6.43F (pKd ± SD = 6.45 ± 0.23 vs. 6.28 ± 0.20, P = 0.5945). In order to confirm that the reduction of BODIPY-cyclopamine in HiBiT-SMO F6.43P monitors the lower binding site in SMO, we also compared wild-type and F6.43P mutant ΔCRD-SMO, which exposes also the upper binding site as previously shown26. In support of our assumption, the binding data with BODIPY-cyclopamine argue that the upper binding site is accessible to BODIPY-cyclopamine in ΔCRD HiBit-SMO wild-type and F6.43P (Supplementary Fig. 5). Of note, the NanoBiT/BRET-based binding assay monitors only the signal originated from the receptors expressed on the cell surface, as LgBiT is not cell-permeable.
As demonstrated by the decreased BODIPY-cyclopamine binding when comparing wild-type SMO to the SMO F6.43P, the lower binding pocket of SMO seems to be disrupted by the F6.43P mutation. Furthermore, the unaffected BODIPY-cyclopamine binding in FZD6 P6.43F compared to the wild-type receptor indicated that straightening out TM6 in FZD6 neither affected the upper binding site nor enabled the lower binding site to bind BODIPY-cyclopamine. In order to obtain deeper insights into this, we monitored the binding pocket volumes of SMO (wild-type and F6.43P) and FZD6 (wild-type and P6.43F) throughout the MD trajectories (Fig. 2c and d). In both wild-type receptors, the binding cavity volume was higher than that of the corresponding mutant. In case of the mutant SMO F6.43P, the bent TM6 clearly reduced the volume of the lower pocket, whereas the upper pocket even slightly increased in volume (Fig. 2c; right panels). In FZD6, the upper pocket remained similar despite the point mutation, but also here the volume below it was reduced; in FZD6 the reduction appeared to be driven by a tight inter- and intrahelical interaction network involving TMs 3, 6, and 7 (Fig. 2c, left panels; see also the interaction network chapter below).
The effect of the F6.43P mutation on the observed BODIPY-cyclopamine binding to SMO could thus be explained by the notable reduction in the volume of the lower binding cavity. Similarly, the almost similar BODIPY-cyclopamine binding to FZD6 wild-type and P6.43F mutant can be explained by the relatively minor changes at the upper binding cavity. Notably, the shapes and volumes of the 7TM binding cavities of the wild-type FZD6 and SMO are different—even though they both can accommodate SAG1.320. Also, apart from the upper pocket, the 7TM cavities of FZD6 and SMO are in slightly different locations; in FZD6 the additional cavity is lined by TMs 1, 2, 3, and 7, whereas it is close to TMs 3, 5 and 6 in SMO (Fig. 2c). However, in the FZD6-miniGi control simulations, the additional volume close to TM2 was missing and the TM2 conformation resembled that of FZD6 P6.43F (Fig. 2c and Supplementary Fig. 4); this suggests that the additional space at the lower part of the 7TM pocket of wild-type FZD6 (run without the miniGi) could be a simulation artefact. Altogether, these observations underline that the lower ligand binding pocket does not exist in FZD6 as it does in SMO and explains why BODIPY-cyclopamine interacts only with the upper 7TM pocket in FZD620.
Aromatic π-π interaction network extends the molecular switch
We have previously identified a conserved molecular switch between R6.32 (K6.32 in FZD4 and FZD9) and W7.55, which stabilizes the inactive state in class F receptors (Supplementary Fig. 7)16. Upon activation, the hydrogen bond between the positively charged side chain of R6.32 and the backbone oxygen atom of W7.55 breaks, presumably allowing the outward movement of TM6. When studying the binding cavities of the active-like FZD6 and SMO here, we observed that there is an extended network of aromatic π interactions between the previously described molecular switch and P/F6.43 in both FZD6 and SMO (Fig. 2c, Supplementary Fig. 4, Supplementary Fig. 8 and Supplementary Fig. 9). In FZD6, the network includes residues Y6.40, W3.43, F6.36, and W7.55, while in SMO there are A6.40 and F3.43 instead, rendering the network less extensive. The shorter network in SMO leaves space within the 7TM core of the receptor close to TM5 and TM6 forming the lower ligand (cholesterol) binding pocket17,19,26.
Apart from the MD data, the aromatic interaction network is present in the crystal/cryoEM structures of active SMO (PDB IDs: 6OT0, 6O3C, 6XBJ, 6XBK, 6XBL, and 6XBM; Supplementary Fig. 7); however the molecular switch is open only in three structures (6OT0, 6O3C, and 6XBL). In the structures with a closed molecular switch, helix 8 is not resolved, rendering the precise location of the switch W7.55 less certain. In the inactive SMO structures (PDB IDs 4QIM, 4O9R, 4N4W, 4JKV, and 5L7I), the molecular switch is closed and F6.36 is facing towards W7.55 breaking the contact with F3.43. The only FZD structures currently available are FZD4 and FZD5 (both in the ligand-free apo form; PDB IDs: 6BD4 and 6WW2, respectively;30,31), where the same interactions are observed—F6.36 is facing towards W7.55 and is not interacting with W3.43. Additionally, Y6.40 and Y2.51 in the apo FZD4 and FZD5 structures are within a hydrogen bonding distance (≤4 Å). In SMO, a similar hydrogen bond is not possible, as there are A6.40 and F2.51 instead. In order to extrapolate these findings also to FZD6, we built an inactive FZD6 model based on SMO and FZD4 crystal structures. In the inactive FZD6 model, the Y6.40–Y2.51 hydrogen bond is observed, while the aromatic network between W3.43 and F6.36 is broken. Notably, Y2.51 and Y6.40 are conserved throughout all ten FZDs but not in SMO (Supplementary Fig. 7, Supplementary Data 1).
SMO F6.43P mutation abolishes constitutive Gi protein activity
Based on the indications that the F6.43P replacement in SMO (and P6.43F in FZD6) correlates with reduced volume of the lower 7TM cavity, we aimed to test experimentally whether such a change in receptor topology has any functional consequences. Both SMO and FZD6 interact with common heterotrimeric G proteins and act as GPCRs leading to the activation of pertussis-sensitive Gi/o signalling16,18,20,27,32,33,34,35. Therefore, we examined the effect of the SMO F6.43P and FZD6 P6.43F mutations on the constitutive activity of SMO and FZD6 towards heterotrimeric Gi proteins using a BRET-based Gi1 sensor (Fig. 3a and b, for cell-surface expression of the receptor constructs, see Supplementary Fig. 6e). Additionally, we assessed constitutive activity of Gs-coupled FZD7 with a BRET-based Gs sensor (Fig. 3c)16,20,36,37. The contribution of endogenous, autocrine acting WNTs to receptor activation was excluded by pharmacological inhibition of porcupine using 10 nM C59 preincubation overnight.
As observed previously, wild-type SMO exerts significant constitutive activity in HEK293 cells as a consequence of low endogenous levels of its inhibitor PATCH135,38,39,40,41. In order to relate the constitutive activity of FZD6 and SMO towards the Gi1 BRET sensor to that of a bona fide constitutively active GPCRs from class A, we included the histamine H3 receptor in the panel42. Interestingly, the levels of constitutive Gi1 protein input of H3 receptor, FZD6 and SMO are in a similar range (not normalized for receptor surface expression). The dramatic abolishment of the activity towards Gi1 of SMO upon F6.43P mutation suggests that the lower 7TM ligand binding site of SMO is required for maintaining this activity of SMO towards the Gi pathway. This conclusion is also in line with the active SMO structures17,19, where the lower 7TM cavity is occupied by a cholesterol when the receptor is in the active conformation. The constitutive activity of FZD6, however, is not as much affected by the P6.43F mutation as SMO activity is by F6.43P mutation; this indicates that the lower 7TM pocket of FZD6 does not play a role in FZD6-mediated Gi activation. However, it seems that the bent TM6 of FZD6 is slightly preferable over the straight TM6 (as seen in FZD6 P6.43F mutant) in maintaining the constitutive activity of these receptors towards Gi. The constitutive activity towards heterotrimeric Gs of FZD7 that is in the same range and even stronger than the activity of isoprenaline-stimulated adrenergic β2 receptors (see also ref. 37), is not affected by the P6.43F mutation. Notably, the cell-surface expression of the SNAP-tagged FZD6 is not affected by the point mutation indicating that the effect observed here is due to the change in the ability of FZD6 to activate Gi1 upon point mutation and not the inability of the mutant to efficiently be presented at the cell surface (Supplementary Fig. 6e). Even though SMO F6.43P is slightly less expressed than wild-type SMO, the change in the Gi1 activity is clearly of different magnitude than that of the surface expression (Fig. 3b and Supplementary Fig. 6e). Furthermore, the trend in SMO F6.43P surface expression is similar to that of FZD7 P6.43F, whereas the G protein activity signals of these SNAP-tagged receptors are notably different. This supports further that the dramatic effect of the F6.43P mutation on the Gi1 activity of SMO is due to the direct change in the receptor conformation and not cell-surface expression.
In order to compare the effect of the F6.43P and P6.43F mutations in SMO and FZD6, respectively, on agonist-induced receptor activation, we assessed the ability of SAG1.3 to elicit miniGi protein (mGsi) recruitment in accordance with previous findings16,20. Since miniG proteins serve as conformational sensors of the active state of GPCRs, this readout monitors both ligand binding and conformational changes in the receptor. In accordance with the data on the constitutive activity of these receptors, the mutations of residue 6.43 extinguished the SAG1.3-induced mGsi recruitment to SMO, while that to FZD6 remained unaffected (Supplementary Fig. 10). Note that the SAG1.3-concentration response curves follow the bell shape, which is characteristic for this ligand20,43.
SMO F6.43P represents a distinct receptor conformation from the active-state SMO
SMO mediates GLI signaling in a G protein-independent manner relying on sequestration of the catalytic subunits of cyclic AMP-dependent protein kinase (cPKA) to the phosphorylated C-terminus of SMO44. In order to assess the consequences of the previously described topological changes of SMO for this additional signaling pathway, we measured recruitment of cPKA (fluorescently tagged cPKA-YFP) to wild-type and F6.43P SMO (with C-terminal Nluc tag) in direct BRET titration experiments (Fig. 4a). Intriguingly, SMO F6.43P was still able to interact with the cPKA, though to a notably lesser extent than wild-type SMO (Fig. 4b).
Since the SMO F6.43P mutant was still able to recruit cPKA albeit to a lesser degree, we set up to monitor whether the F6.43P mutation changes the SMO conformation as dramatically as our MD data suggested. As a direct readout of SMO conformation in living cells, we quantified the recruitment of a recently described conformational sensor, the YFP-tagged nanobody NbSmo2 (NbSmo2-YFP; Fig. 4c), which exclusively interacts with the active-state SMO and competes with NbSmo8 that was used for stabilizing the active conformation of SMO for CryoEM17,44. While wild-type SMO recruited NbSmo2, the SMO F6.43P-NbSmo2-YFP BRET titration curve resembled a linear fit, similar to that of the negative control CD86-Nluc (Fig. 4d). This indicates that the F6.43P mutation in SMO indeed stabilizes a receptor conformation that is substantially different from that of the active-state SMO, and thus incapable to interact with the active-state specific nanobody. Notably, this finding also suggests that the BRET signal observed in the cPKA recruitment assay with the F6.43P SMO reflects either a specific receptor conformation (i.e., different from that of the active-state SMO detected by NbSmo2-YFP binding) to recruit cPKA or that cPKA interaction depends only on phosphorylation sites on intracellular epitopes of the receptor, which are not, or only slightly, affected by structural rearrangements resulting from the F6.43P mutation.
P6.43F mutation interferes with DVL-dependent mechanisms
Unlike SMO, FZDs interact with the phosphoprotein dishevelled (DVL) acting at the crossroads of WNT/β-catenin and WNT/planar cell polarity signaling45. Thus, assessing the interaction of DVL with FZDs provides yet another readout to relate FZD conformation with signal initiation. In WNT/β-catenin signaling, the recruitment of DVL1-3 to for example FZD4, FZD5 and FZD7 reduces degradation of the transcriptional regulator β-catenin, ultimately leading to its nuclear translocation and regulation of so-called WNT target genes14. Of note, the β-catenin signaling cascade initiated by DVL recruitment to FZDs functionally resembles the GLI signaling cascade initiated by the cPKA recruitment to SMO44. In addition, FZD6, which is predominantly associated with β-catenin-independent WNT/PCP-like signaling, also recruits DVL16,46. Thus, we assessed the effect of altered receptor topology on DVL2 recruitment to the plasma membrane by FZD4, FZD5, FZD6, and FZD7 and their corresponding P6.43F mutants using a bystander BRET assay (Fig. 5a). DVL2 recruitment to FZD5 and FZD6 was significantly reduced by the P6.43F mutation suggesting that the bent TM6 conformation plays a role in the receptor-DVL-complex formation (Fig. 5b). The DVL2 recruitment to FZD7 and FZD4 and their corresponding P6.43F mutants, however, were similar (Fig. 5b, Supplementary Fig. 6e and Supplementary Fig. 11). As DVL2 recruitment to the membrane is correlated with the receptor surface expression of the recruiting FZD, the presented data are normalized to the surface expression of the individual FZD constructs as shown in Supplementary Fig. 6e.
Since FZD6 showed the most pronounced difference in the recruitment of full-length DVL2 comparing wild-type and the P6.43F receptors, we investigated this receptor paralogue further with an additional assay employing the DVL2 DEP domain as a conformational sensor. The DEP domain interacts with FZDs and in particular with FZD646,47. Here, we established a bystander BRET assay assessing Nluc-DEP recruitment to Venus-KRas in dependence of SNAP-FZD6 surface expression (Fig. 5c). Since DEP recruitment is directly proportional to receptor construct surface expression, we plotted the bystander BRET over the SNAP-surface signal originating from the SNAP-FZD6 or SNAP-FZD6 P6.43F constructs labelled with membrane impermeable SNAP substrate. The bystander BRET over a broad receptor construct surface expression range emphasized that FZD6 P6.43F presents with altered ability to recruit Nluc-DEP indicating that the overall conformation of FZD6 P6.43F does not accommodate the DEP domain the same way as the wild-type receptor. Assessing SMO function using DVL recruitment assays is not suitable because SMO does not recruit DVL2 (Supplementary Fig. 11).
In order to assess how mutation of residue 6.43 affects agonist-induced and receptor-mediated signaling, we employed the TCF/LEF reporter TOPflash assay reporting WNT/β-catenin signaling in ΔFZD1–10 HEK293 cells48. Agonist stimulation of FZD4, FZD5, and FZD7 but not FZD6 or SMO-transfected cells (WNT-3A for FZDs or SAG1.3 for SMO) resulted in an increased TOPflash signal (Fig. 5d and e), which is in agreement with the understanding of class F receptor pathway selectivity49. Mutation P6.43F reduced the ability of FZD4 and FZD5 to evoke β-catenin-dependent signaling in response to WNT-3A, whereas FZD7-mediated effects were not affected. The decrease needs to be seen in the light of the surface expression data for the receptor constructs though (Supplementary Fig. 6e), since FZD5—but not FZD4—P6.43F shows lower surface expression compared to the wild-type. As our previous findings indicate that WNT/β-catenin signaling evoked by WNT-3A in HEK293 cells is independent of heterotrimeric G proteins50, these findings most likely reflect the ability of these receptors to communicate through DVL. Indeed, this statement is supported by the bystander BRET data with FZD6 (Fig. 5c) pinpointing the changed DVL DEP recruitment in FZD6 P6.43F compared to FZD6.
Our data provide a structural and functional distinction between SMO and the ten FZDs clarifying what was previously surmised, namely that SMO activation depends on interaction with a cholesterol molecule accommodated by a straight TM6. FZDs on the other hand act independently of cholesterol binding to the 7TM core, and FZD activation—depending on the pathway and FZD paralogue—involves a proline-based kink in TM6. These findings provide therefore deeper insight into the molecular details of the activation of SMO and most importantly of FZDs of which no active, effector-bound high-resolution structures exist so far.
While our data presented here suggest that FZDs and SMO are activated differently, it should be noted that also other GPCRs, which are structurally even more distinct, are able to activate heterotrimeric G proteins. Most notably, the degree to which TM6 kinks differs dramatically, for example when comparing class A to class B GPCRs (Fig. 1a), underlining that an enormous structural variability in the receptor topology can accommodate heterotrimeric G proteins and even the same G protein subtypes. Thus, it is not as controversial as it would first seem that also FZDs and SMO could be adopting different conformations while interacting with and activating the same G proteins. Even though the atomistic details of the active FZD6 and SMO conformations are quite different (for more detailed discussion see below), the existence of the previously reported molecular switch mechanism between TM6 and TM7—and the extension of it as we present here—emphasizes that class F receptors follow some superfamily-wide concepts of receptor activation despite their structural differences. However, our findings underline as well that the paralogues of the FZD family are different, not only in their pathway selectivity or their selectivity to heterotrimeric G proteins but also in their dependence on the P6.43-mediated kink.
In most of the class A GPCRs, the inactive receptor conformation is stabilized by the allosteric Na+ ion coordinated by D2.50 complexing amino-acid residues from TMs 1, 2, 3, 6, and 751. Unlike other class A GPCRs, the actions of visual rhodopsin do not involve Na+, but there its function in stabilizing the inactive receptor structure is replaced by a hydrogen bond interaction between W6.48 and Y7.48 52. Similarly, class F crystal structures do not reveal the presence of the allosteric Na+ ion, and the amino acid at location 2.50 is a cysteine throughout the receptor class (Supplementary Data 1). This suggests that the allosteric Na+ binding site does not exist in class F receptors.
As observed from the inactive FZD4 and FZD5 structures (PDB IDs: 6BD4 and 6WW2, respectively) and the inactive FZD6 model (Supplementary Fig. 7), Y6.40 of FZD4/5/6 is at hydrogen bonding distance from Y2.51. These interaction counterparts are conserved throughout the FZDs (Supplementary Data 1), emphasizing an important role in the function of these receptors – possibly similar to the stabilizing effect of W6.48–Y7.48 on the inactive receptor conformation in visual rhodopsin. Interestingly, when removing SAG1.3 from the FZD6 wild-type simulation system, Y6.40 starts to move slowly towards Y2.51 (Supplementary Fig. 9); however, the 500 ns simulation time is naturally not enough for observing the full change towards the inactive receptor conformation. In SMO, an equivalent hydrogen bond is not possible due to the lack of both interaction counterparts (F2.51 and A6.40 in SMO). Also, SMO is notably rich in small hydrophobic residues pointing towards the lower 7TM binding site and no apparent polar interactions seem to be present in that region of the inactive SMO crystal structures. This again is most likely one of the key factors in the capability of SMO to accommodate cholesterol at the lower 7TM pocket17,19. It is possible, that lack of cholesterol solely stabilizes inactive SMO while FZDs rely more on intramolecular interactions similar to visual rhodopsin.
The recently solved structures of SMO-cholesterol complexes (PDB IDs: 6O3C, 6XBJ, 6XBK, and 6XBL) show that the 7TM binding site of SMO accommodates cholesterol at the bottom (i.e., the lower pocket), middle and upper parts of the binding site17,19. Also, those data strongly suggest that the cholesterol molecule enters the lower 7TM pocket directly from the membrane from between the TMs 5 and 6. The simulation data that we present here agree with these findings, since the 7TM binding pocket extends outside the 7TM core through TMs 5 and 6 in SMO wild-type simulations (Supplementary Fig. 12). In FZD6 instead, the binding pocket remains in the middle of the transmembrane helices at this receptor region. This difference seems to be due to the bulkier residues at the locations 5.59, 6.37, and 6.40 in FZD6 when compared to SMO (L/G5.59, S/G6.37, and Y/A6.40 in FZD6 and SMO, respectively; Supplementary Fig. 12). This is also in line with the point mutation study of Deshpande et al.17, where G5.59F and A6.40F point mutations rendered SMO partially or fully insensitive to stimulation.
As presented in Supplementary Fig. 8, the aromatic network remains stable in the majority of the simulations; however, some exceptions are observed. In replica 3 of FZD6 wild-type simulation, W3.43 withdraws from the network by flipping towards TM2 (Supplementary Fig. 8, upper-left panel). Subsequently, the remaining part of the network reorganizes bringing F6.40 closer to F6.36. Similar reorientation of the network amino acids is seen in replica 2 of SMO F6.43P simulation (Supplementary Fig. 8). Another visible fluctuation is present in FZD6 wild-type simulation replica 2, where W7.55 moves further from F6.36 (Supplementary Fig. 8). However, the range of the aromatic π–π interaction can be up to 7.5 Å and thus some attraction between the network counterparts is most likely maintained despite these changes. In general, it is hard to judge, whether these reorientations reflect reality, as the simulations are run in absence of the intracellular G protein; it is possible that some of the inconsistencies we observe are merely simulation artefacts. Notably, the aromatic network fluctuations discussed above remained absent in the FZD6 -SAG1.3-miniGi simulations (Supplementary Fig. 4).
In conclusion, obtaining active FZD conformation involves different structural rearrangements compared to SMO. FZD6 not only lacks the lower 7TM ligand binding pocket, which could accommodate cholesterol, but also the ‘gate’ between TMs 5 and 6, through which cholesterol could enter the receptor from the inner leaflet of the lipid bilayer. Instead, FZD6 (and FZDs in general) harbors a conserved proline residue in TM6, which allows the helix to bend similar to what is observed in other GPCR classes. The kinked conformation of TM6 is stabilized by the extensive aromatic network involving residues from TMs 3, 6, and 7, and the inactive FZD6 conformation is stabilized in analogy to the intramolecular stabilization in visual rhodopsin. Generally, it appears that FZDs can accept the mutation of P6.43 since receptor functionality is only partially impaired or not affected by the mutation. This could indicate that additional residues can contribute to TM6 dynamics such as the completely conserved G6.34 aiding the receptor to present with an open conformation to accommodate e. g. the heterotrimeric G protein. Previous sequence alignment and analysis of class F receptor mutations occurring naturally in humans indicated that the natural variability of residue 6.43 is comparatively highemphasizing that FZDs with a mutated P6.43 are not primarily associated with disease16. However, SMO requires the straight TM6 conformation in G protein activation and prefers it for cPKA recruitment. All this is in line with the active SMO structures, which have shown that cholesterol plays a notable role in SMO activation; however, no active FZD structure is yet available. Our analysis predicts though, that future structural high-resolution insight through CyroEM or crystal structures of activated FZDs will reveal a TM6 kink emphasizing the role of P6.43 in FZDs.
Even though the SMO data we present here are rather distinct—that is that removing the cholesterol binding site reduces the SMO activity in all tested readouts—the magnitude of the reduction is not identical in the different assays. Both G protein activation and NbSmo2 recruitment recognizing the active conformation of SMO (against the G protein pathway), are totally abolished by the F6.43P mutation indicating that the lack of cholesterol keeps SMO F6.43P mutant in a conformation, which is completely Gi inactive (Fig. 3b and Fig. 4d). Interestingly, SMO F6.43P mutant maintains also some of its ability to recruit cPKA (Fig. 4b), suggesting that the SMO conformation required for that interaction is also slightly different than the receptor conformation required when interacting with G proteins.
The interpretation of the data concerning FZDs is more complex, and unfortunately, no unifying FZD-family-wide conclusion is apparent. In case of FZD6, the bent TM6 is preferred in Gi activity, however, also the straight conformation is able to activate Gi to some level (Fig. 3b). This might be because FZD6 does not rely on the lower 7TM pocket and cholesterol binding in forming the active conformation, and thus the functional outcome of the changed TM6 topology is not as dramatic as in SMO (where the lack of the lower 7TM pocket means directly the lack of cholesterol, which in turn means the inaccessibility of the Gi-active receptor conformation). The Gs activity of FZD7 is not affected by the P6.43F mutation suggesting even higher flexibility in the G protein-active conformation (Fig. 3c), however we cannot specify whether Gs is more tolerant to the straight TM6 topology than Gi or whether the overall FZD7 topology is less affected by the point mutation than that of FZD6.
In the DVL2 recruitment instead, FZD6 P6.43F mutation strongly reduces the signal, and the trend is apparent also with FZD5 (Fig. 5b, Supplementary Fig. 11). As FZD6 is mainly associated with WNT/planar cell polarity-like signaling and not WNT/β-catenin signaling, it seems possible that the receptor conformation required for DVL2 recruitment to FZD6 differs from that of the three other monitored FZDs. However, the TOPflash data (Fig. 5d) show that the ligand-induced WNT/β-catenin signaling mediated by FZD4 and FZD5 is also reduced by the P6.43F mutation indicating that the TM6 topology has a role in the active conformations of these FZD paralogues towards WNT/β-catenin signaling pathway as well. This could be due to the disturbance the P6.43F mutation causes to the complex formation with other involved proteins, such as LRP5/6.
It should be underlined that the presented assays comparing the ability of wild-type and mutant receptor to recruit transducers are affected by the cell-surface expression of the respective receptor construct. Thus, it is essential to take the effect of mutations on receptor trafficking into account when validating the receptor’s constitutive activity. For this purpose, we provide surface expression data for all receptor constucts used in this study (Supplementary Fig. S6) and provide acceptor titration experiments, wherever possible and meaningful. Comparing the effects of the receptor mutants on cell-surface expression and function, however, we are confident that the observed functional alterations are indeed caused by the mutants directly and cannot be explained by differences in cell-surface expression.
Altogether, our study provides new insights onto the molecular details of class F activation mechanisms and highlights differences between SMO and FZDs. In the context of our recent findings that SMO-targeting small molecule ligands can be repurposed for FZDs20, the appreciation of mechanistic details and differences among class F receptors indeed offers new opportunities for developing compounds that target these receptors with improved selectivity and pharmacological profile.
Twenty homology models of active-like FZD6 (UniProt ID: O60353) were built with MODELLER 9.2253 using active 24(S),25-epoxycholesterol- and Gi-bound SMO structure (PDB ID: 6OT018) as a template20. The sequence identity between the template (ΔCRD-SMO) and modelled ΔCRD-FZD6 is approximately 29% and sequence similarity approximately 48%. To select a starting model for MD simulations, SAG1.3 was docked to these models with Glide software54,55,56 in the Schrödinger Maestro 2018-4 molecular modeling platform—the model producing SAG1.3 docking pose best resembling the pose of SAG1.5 in complex with SMO (PDB ID: 4QIN25) was selected as a starting point for the further MD simulations.
For homology modelling of the inactive conformation of FZD6 we used the inactive FZD4 (PDB ID: 6BD430, receptor core and ICL areas) and SMO (PDB IDs: 4QIN25 and 5L7D57, ECLs and extended TM6 helix) crystal structures as the templates24. The sequences of these proteins were aligned with ClustalX258 and 20 models were built with MODELLER 9.22. The model with the highest DOPE score was selected to be used in the structural comparisons.
MiniGi was modelled with a similar procedure as the inactive FZD6 using the miniGs protein of PDB ID: 5G53 as a template. The most stabile miniGi construct, miniGs/i1-43, by Nehmé et al.59 was selected as the modelled miniGi sequence.
Molecular dynamics (MD) simulations
MD simulations were performed on the models of the active-like FZD6, and the active SMO (PDB ID: 6OT0) using GROMACS 2019.260. As we were interested in the active receptor conformations, the systems were “stimulated” by the presence of SAG1.3. To obtain the SMO-SAG1.3 starting complex for the simulation, the 24(S),25-epoxycholesterol was removed and SAG1.3 heavy atoms copied to the structure from the SAG21k-SMO complex (PDB ID: 6O3C;17). SAG1.3 was parametrized with AmberTools18 package using GAFF2 force field and AM1-BCC charges61,62,63.
The FZD6-SAG1.3-miniGi complex was built using guidance from SMO-Gi crystal structure (PDB ID: 6OT0) and FZD6-Gi docking poses (constructed with HADDOCK 2.264,65). The full-length Gi from the crystal structure was docked to FZD6 active-like model (see above) with default parameters and the pose that resembled most the SMO-Gi complex was selected as a reference complex. Then, the miniGi model was superimposed to the full-length Gi of the reference complex, and the FZD6 model was copied from the reference complex to the miniGi structure.
The receptors were oriented using the OPM database66 and embedded in the POPC lipid bilayer (150 lipids/leaflet) by CHARMM-GUI server67 with TIP3p water molecules and 0.15 M NaCl. The system was minimized for approximately 3000 steps and subsequently equilibrated with gradually decreasing position restraints on protein and lipid components. In the last 50 ns of the equilibration run, the harmonic force constants of 50 kJ mol−1 nm−2 were applied on the protein and ligand atoms only.
The FZD6 P6.43F and SMO F6.43P mutants were constructed from the equilibrated structures by point mutation wizard of PyMol (The PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLC). After the point mutation, the receptors were placed back to the equilibrated systems and the receptor topology files were updated. Then, the systems were subjected to an additional energy minimization of ~1000 steps.
The independent isobaric and isothermic (NPT) ensemble production simulations for each system were initiated from random velocities using the CHARMM36m force field68 and a 2 fs time step. The temperature at 310 K was maintained with Nose-Hoover thermostat69 and the pressure at 1 bar with Parrinello-Rahman barostat70. Potential-shift-Verlet was used for electrostatic and van der Waals interactions with 12 Å cutoff. The bonds between hydrogen and other atoms were constrained by the LINCS algorithm71. Original receptor-SAG1.3 systems were simulated for 1250 ns divided into 4 independent replicas. Replicas 1 (500 ns) and 2 (250 ns) were started from frame t = 0 ns (i.e., directly after the equilibration), whereas replicas 3 and 4 (250 ns/each) were started from frame t = 250 ns of replica 2. FZD6-SAG1.3-miniGi system was simulated as three independent replicas all started from t = 0 ns comprising ca. 550 ns of simulation in total.
The MD simulation data were generally analyzed using VMD 1.9.3 (visualization and measurement of RMSDs, distances and angles) and visualized in PyMol. Exception to this was the aromatic network distances, which were measured with the CPPTRAJ via AmberTools1862. The binding site volumes were monitored with MDpocket of the Fpocket suite72,73. For MDpocket, multi-PDB files including one frame/10 ns of each trajectory (126 poses/system in total) were used as an input. The grid points (at isovalue of 3) contributing to a continuous pocket inside the 7TM core were selected to be monitored in the pocket volume analysis. MD simulations will be made available at the open access server, GPCRmd: the MD database for GPCRs (www.gpcrmd.org). Snapshots of the receptor models extracted from the MD trajectories are provided as Supplementary Data 2–21.
Cell culture and transient transfection
Human embryonic kidney cells (HEK293A) wild-type (female origin; Thermo Fisher Scientific, R70507), ΔSMO HEK293A20 or ΔFZD1-10 HEK293T cells74 were used for transient expression and grown in DMEM supplemented with 10% fetal calf serum, 0.1 mg/ml streptomycin, and 100 units/ml penicillin at 37 °C with 5% CO2. All transfections were performed using Lipofectamine 2000 in a 2 µl Lipofectamine 2000/µg total DNA ratio. Absence of mycoplasma contamination was routinely confirmed by PCR using 5′-ggc gaa tgg gtg agt aac acg-3′ and 5′-cgg ata acg ctt gcg act atg-3′ primers detecting 16 S ribosomal RNA of mycoplasma in the media after 2–3 days of cell exposure.
Cloning of receptor constructs and mutagenesis
To generate HiBiT-SMO and ΔCRD HiBiT-SMO, Nluc sequence in Nluc-SMO or ΔCRD Nluc-SMO (coding mouse SMO, ref. 26) were replaced with HiBiT sequence (nucleotides sequence: 5′-GTG AGC GGC TGG CGG CTG TTC AAG AAG ATT AGC-3′; amino acids sequence: VSGWRLFKKIS). To generate FLAG-SNAP-SMO, mouse SMO sequence from SMO-Rluc8 was inserted into an empty FLAG-SNAP-tagged pcDNA3.1 vector between BamHI and HindIII sites. SMO-Rluc8, HiBiT-FZD6, SNAP-FZD4, SNAP-FZD5, SNAP-FZD6, SNAP-FZD7, FZD4-Nluc, FZD6-Nluc, β2AR, Venus-KRas, and Nluc-DVL2 were generated and validated in our previous studies16,29. SMO-Nluc and the Gs BRET sensor were generated using prolonged overlap extension PCR techniques. The plasmid encoding CD86-Nluc was provided by Dr. Ulrike Zabel (University of Wuerzburg, Wuerzburg, Germany). Plasmids encoding cPKA-YFP and NbSmo2-YFP were a kind gift from Benjamin Myers (University of Utah, Salt Lake City, USA)44. Plasmid encoding Venus-mGsi was from Nevin Lambert (Augusta University, Georgia, USA75). Wild-type human H3R DNA vector was purchased from cDNA.org. The desired mutations were generated using GeneArt site-directed mutagenesis kit (Thermo Fisher Scientific). All constructs were validated by sequencing (Eurofins GATC, Konstanz, Germany). Utilized primers are listed in Supplementary Table 1.
ΔSMO HEK293A cells20 were transiently transfected in suspension using Lipofectamine®2000 (Thermo Fisher Scientific). 4 × 105 cells were transfected in 1 ml with 50–200 ng of HiBiT-tagged receptor plasmid DNA and 800–950 ng of pcDNA plasmid DNA. The cells (100 µl) were seeded onto a poly-D-lysine-coated black 96-well cell culture plate with solid flat bottom (Greiner Bio-One). Twenty-four hours post-transfection, the cells were washed once with 200 µl of HBSS (HyClone). Next, the cells were incubated with different concentrations of BODIPY-cyclopamine (80 µl) in HBSS for 90 min at 37 °C with CO2. Subsequently, 80 ul of mix of furimazine (1:100 dilution; Promega) and LgBiT (1:200 dilution; Promega) were added, and the cells were incubated for another 10 min prior to the BRET measurements. The BRET ratio was determined as the ratio of light emitted by BODIPY (energy acceptor) and light emitted by HiBit-tagged receptors (energy donors). The BRET acceptor (bandpass filter 535–30 nm) and BRET donor (bandpass filter 475–30 nm) emission signals were measured using a CLARIOstar microplate reader (BMG). BODIPY fluorescence was measured prior to reading BRET (excitation: 470–15 nm, emission: 515–20 nm). The data were presented as net BRET (average raw BRET ratio of all BODIPY-cyclopamine unlabelled control wells were subtracted from the raw BRET ratios of all BODIPY-cyclopamine labelled wells). Cell-surface expression of HiBiT-tagged receptors was assessed by measuring luminescence of vehicle-treated wells (no BRET acceptor) in the NanoBiT/BRET-binding assay. The data were analyzed using GraphPad Prism 6.
BRET-based measurement of Gi/s activity
HEK293A cells were transfected in suspension with plasmid encoding the BRET-based Gi1 (Gβ1-2A-cpVenus-Gγ2-IRES-Nluc-Gαi1) or Gs (Gβ1-2A-cpVenus-Gγ1-IRES-Nluc-Gαs(long isoform)) sensor along with wild-type and mutant receptors or pcDNA control using Lipofectamine 2000 and seeded onto poly-D-lysine-precoated white wall, white bottomed 96-well plates. Porcupine inhibitor C59 was added 24 h after transfection to a final concentration of 10 nM. Forty-eight hours after transfection, all wells were rinsed with 150 µl HBSS and incubated with 10 µM coelenterazine-h (in HBSS) for 5 min. Subsequently, luminescence in the Nluc (450-40) and cpVenus (535–30) channel were recorded using a CLARIOstar plate reader (BMG, Ortenberg, Germany). Thereafter, cpVenus fluorescence was quantified (excitation 497-15, emission 540–20) to control for the expression levels of the G protein sensor.
BRET-based mini Gsi recruitment assay
ΔSMO HEK293A cells were transiently transfected in suspension using Lipofectamine®2000 (Thermo Fisher Scientific). 4 × 105 cells were transfected in 1 ml with 100 ng of C-terminally Nluc-tagged receptor plasmid DNA and 900 ng of Venus-mGsi plasmid DNA. The cells (50–100 µl) were seeded onto a poly-D-lysine-coated black 96-well cell culture plate with solid flat bottom (Greiner Bio-One). Twenty-four to forty-eight hours post-transfection, the cells were washed once with 200 µl of HBSS (HyClone). Next, 90 μl of Nluc substrate furimazine was added (1:1000 dilution; Promega) and the cells were incubated for 10 min. Subsequently, the cells were stimulated with ligands (10 μl). The BRET ratio was determined as the ratio of light emitted by Venus (energy acceptor) and light emitted by Nluc-tagged receptors (energy donors). The % ΔBRET ratio for each well is defined as: (raw BRET ratio stimulated – raw BRET ratio basal)/raw BRET ratio basal × 100%. The vehicle corrected % ΔBRET ratio for each well is defined as: % ΔBRET ratio − average % ΔBRET ratio vehicle. BRET acceptor (bandpass filter 535–30 nm) and BRET donor (bandpass filter 475–30 nm) emission signals were measured using a CLARIOstar microplate reader (BMG). The data presented in this study come from the ligand-induced BRET measurements obtained 5 min (FZD6) or 13 min (SMO) after the ligand addition. The data were analyzed using GraphPad Prism 6.
BRET-based cPKA recruitment assay
ΔSMO HEK293A cells20 were transfected in suspension with 100 ng Receptor-Nluc, increasing amounts of cPKA-YFP44 (between 0 and 900 ng) and pcDNA to add up to a total of 1 µg plasmid DNA per ml cell suspension. Transfected cells were seeded onto PDL-precoated black-wall, black-bottomed 96-well plates (30,000 cells/well) and incubated for 48 h. Subsequently, cells were washed with 100 µl HBSS/well and incubated with 60 µl HBSS. YFP fluorescence intensity was recorded upon external excitation (λEx = 497/15; λEm = 540/20 nm) to assess cPKA-YFP expression levels in the individual transfection samples. Next, 20 µl of 40 µM coelenterazine-H was added to all wells, incubated for 2–3 minutes, and BRET between Nluc (λEm = 450/40 nm) and YFP (λEm = 535/30 nm) was recorded in two consecutive reads (integration time 0.3 seconds). All cPKA recruitment experiments were conducted using a CLARIOstar plate reader.
BRET-based NbSmo2 recruitment assay
ΔSMO HEK293A cells20 were transfected in suspension with 100 ng Receptor-Nluc, increasing amounts of NbSmo2-YFP44 (between 0 and 900 ng) and pcDNA to add up to a total of 1 µg plasmid DNA per ml cell suspension. Transfected cells were seeded onto PDL-precoated black-wall, black-bottomed 96-well plates (30,000 cells/well) and incubated for 48 h. Subsequently, cells were washed with 100 µl HBSS/well and incubated with 60 µl HBSS. YFP fluorescence intensity was recorded upon external excitation (λEx = 497/15; λEm = 540/20 nm) to assess NbSmo2-YFP expression levels in the individual transfection samples. Next, 20 µl of 40 µM coelenterazine-H was added to all wells, incubated for 2–3 minutes, and BRET between Nluc (λEm = 450/40 nm) and YFP (λEm = 535/30 nm) was recorded in two consecutive reads (integration time 0.3 seconds). All NbSmo2 recruitment experiments were conducted using a CLARIOstar plate reader.
DVL2 bystander BRET assay
ΔFZD1-10 HEK293 T cells74 were transiently transfected in suspension using Lipofectamine 2000 (Thermo Fisher Scientific). 4 × 105 cells ml−1 were transfected with 780 ng of Venus-KRas plasmid DNA, 200 ng of the SNAP-tagged receptor plasmid DNA and 20 ng of Nluc-DVL2 plasmid DNA. The cells (100 µl) were seeded onto a PDL-coated black 96-well cell culture plate with solid flat bottom (Greiner Bio-One). Twenty-four hours post-transfection, cells were washed once with HBSS (HyClone) and maintained in the same buffer. Subsequently coelenterazine-h (5 µM) was added and after 10 min incubation BRET signal was determined as the ratio of light emitted by Venus-tagged biosensors (energy acceptor) and light emitted by Nluc-tagged biosensors (energy donor). The BRET acceptor (535–30 nm) and BRET donor (475–30 nm) emission signals were measured using a CLARIOstar microplate reader (BMG). Data were analyzed using GraphPad Prism 6.
DEP recruitment assay
ΔFZD1–10 HEK293 T cells were transiently transfected in suspension using Lipofectamine 2000 (Thermo Fisher Scientific). 4 × 105 cells ml−1 were transfected with 500 ng of Venus-KRas plasmid DNA, 0–480 ng of the SNAP-tagged receptor plasmid DNA, 20 ng of Nluc-DEP plasmid DNA and pcDNA to a final amount of 1 µg of DNA. The cells (100 µl) were seeded onto a PDL-coated black 96-well cell culture plate with solid flat bottom (Greiner Bio-One). Twenty-four hours post-transfection, cells were washed once with HBSS (HyClone) and maintained in the same buffer. Subsequently coelenterazine-h (5 µM) was added and after 10 min incubation BRET signal was determined as the ratio of light emitted by Venus-tagged biosensors (energy acceptor) and light emitted by Nluc-tagged biosensors (energy donor). The BRET acceptor (535–30 nm) and BRET donor (475–30 nm) emission signals were measured using a CLARIOstar microplate reader (BMG). Cells were washed once with HBSS and incubated with 50 µl of 1 µM SNAP-surface Alexa Fluor 647 (New England Biolabs, #S9136S) in complete DMEM medium for 30 min at 37 °C and 5% CO2. Subsequently, cells were washed three times with HBSS and the fluorescence (excitation 625–30 nm, emission 680–30 nm) was read with a CLARIOstar microplate reader (BMG). Data were analyzed using GraphPad Prism 6.
6 × 105 cells ml−1 ΔFZD1-10 HEK293 T cells were seeded onto PDL-coated white 96-well cell culture plate with solid flat bottom (Greiner Bio-One). Next day, cells were transfected with 20 ng of SNAP-tagged receptor, 20 ng M50 Super 8× TOPFlash (Addgene, 12456), 2 ng pRL-TK Luc (Promega, E2241) and 58 ng pcDNA plasmid DNA to a final amount of 100 ng of plasmid DNA per well. Four hours after transfection, medium was changed to starvation medium (DMEM without FBS) containing either vehicle or 1000 ng/ml recombinant WNT-3A for FZD transfected cells or 100 nM SAG1.3 for SMO-transfected cells. Twenty-four hours after stimulation, cells were lysed gently shaking with 20 µl 1× Passive Lysis Buffer (Promega, E1910) for 15 min. Subsequently, 20 µl of LAR II (Promega, E1910) were added to all wells after which luminescence (580-80 nm) was read and then 20 µl of Stop & Glo (Promega, E1910) were added to all wells after which luminescence (480-80 nm) was read again with a CLARIOstar microplate reader (BMG). Data were analyzed using GraphPad Prism 6.
SNAP-surface Alexa Fluor 647 staining
For quantification of cell-surface expression of N-terminally SNAP-tagged receptors, ΔFZD1-10 HEK293T cells at the density of 4 × 105 cells ml−1 were transfected in suspension using Lipofectamine 2000 with 200 ng of the indicated SNAP-tagged receptor plasmid DNA and 800 ng of the pcDNA plasmid DNA. The cells (100 µl) were seeded onto a PDL-coated black 96-well cell culture plate with solid flat bottom (Greiner Bio-One). 24 h later the cells were washed once with HBSS (HyClone) and incubated with 50 µl of 1 µM SNAP-surface Alexa Fluor 647 (New England Biolabs, #S9136S) in a complete DMEM medium for 30 min at 37 °C. Subsequently, the cells were washed three times in HBSS and the fluorescence (excitation 625–30 nm, emission 680–30 nm) was read with a CLARIOstar microplate reader (BMG). Data were analyzed using GraphPad Prism 6.
ELISA surface expression
For quantification of cell-surface expression of C-terminally Nluc-tagged receptors, ΔFZD1–10 HEK293T or ΔSMO HEK293A cells at the density of 4 × 105 cells ml−1 were transfected in suspension using Lipofectamine 2000 with either 500 ng of the indicated FLAG-tagged FZD6-Nluc receptor plasmid DNA and 500 ng of the pcDNA plasmid DNA or 1 µg of FLAG-SNAP-tagged SMO-Nluc receptor plasmid DNA (Supplementary Fig. 6f). The cells (100 µl) were seeded onto a PDL-coated transparent 96-well cell culture plate with solid flat bottom. Twenty-four hours later the medium was dispensed from the wells and washed once with 200 µl of ice-cold wash buffer (0.5% BSA in PBS), after which cells were incubated on ice with 25 µl of primary antibody solution (1% BSA in PBS with anti-FLAG 1:500 (Sigma–Aldrich F1804)) for 1 h. Subsequently, cells were washed as above four times and then incubated on ice with 50 µl of secondary antibody solution (1% BSA in PBS with HRP-conjugated anti-mouse 1:3000 (Invitrogen 31430)) for 1 h, after which cells were washed as above four times. Lastly, 50 µl of TMB (3,3′,5,5′-Tetramethylbenzidine, Sigma T0440) was added to each well and incubated for 20 min after which 50 µl of 2 M HCl was added and incubated for 20 min. Absorbance (450 nm) was read with a POLARstar Omega microplate reader (BMG). Data were analyzed using GraphPad Prism 6.
Data handling and analysis
Plate readers were operated using Reader Control 5.21 R4 and Mars 3.20 R2 software for the BMG CLARIOstar plate reader and Omega 1.10 and MARS 1.11 software for the BMG POLARstar Omega plate reader. Excel 2007 or 2013 was used for saving raw data and for data transfer to GraphPad Prism 6, Raw BRET was defined as acceptor emission intensity over donor emission intensity. For the NanoBiT/BRET-binding data presented, the saturation binding curves were fitted using a three- or four-parameter nonlinear regression model. The binding curves represent mean ± standard error of the mean (SEM) from three independent experiments, each performed in two technical replicates. Affinity values are presented as a best-fit Kd ± standard deviation (SD). NanoBiT/BRET-binding models were selected based on an extra sum-of square F-test (P < 0.05). Raw BRET ratios of the G protein sensor experiments were normalized to the pcDNA control to account for interday BRET variability. BRET-based mGsi recruitment data were fitted to a bell-shaped model.
To analyze the cPKA- and NbSmo2-YFP recruitment data, YFP fluorescence intensities (upon external excitation) and raw BRET ratios of each transfection ratio (four replica) were averaged, pooled from all individual experiments and plotted as mean ± SD. Each dataset was then fitted to a one-phase association or linear model and the preferred fit was selected based on an Extra sum-of-squares F-test (P < 0.05). Extra sum-of-squares F-test was further applied to compare the BRET plateaus of SMO-Nluc wild-type and SMO-Nluc F6.43P in the cPKA-YFP recruitment assay (P < 0.05). For the Nluc-DVL2 recruitment data, the net BRET values (bystander BRET) for each receptor subtype were normalized to the corresponding values of the normalized surface expression (Supplementary Fig. 6e). Where applicable, data were analyzed for differences with one-way ANOVA with Fisher’s least significant difference post-hoc analysis or paired t-test. Significance levels are given as *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. Please refer to the figure legends for more details on the displayed data.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data supporting the findings of this manuscript are available from the corresponding author upon reasonable request. Full molecular dynamics simulations are available at the open access database GPCRmd (www.gpcrmd.org; simulation system IDs 239–244). Snapshots of the receptor models presented in this study (extracted from the MD trajectories) are provided as Supplementary Data. Source data is provided with this article. Source data are provided with this paper.
Foord, S. M. et al. International Union of Pharmacology. XLVI. G protein-coupled receptor list. Pharmacol. Rev. 57, 279–288 (2005).
Congreve, M., de Graaf, C., Swain, N. A. & Tate, C. G. Impact of GPCR structures on drug discovery. Cell 181, 81–91 (2020).
Park, J. H., Scheerer, P., Hofmann, K. P., Choe, H. W. & Ernst, O. P. Crystal structure of the ligand-free G-protein-coupled receptor opsin. Nature 454, 183–187 (2008).
Scheerer, P. et al. Crystal structure of opsin in its G-protein-interacting conformation. Nature 455, 497–502 (2008).
Rasmussen, S. G. et al. Structure of a nanobody-stabilized active state of the beta(2) adrenoceptor. Nature 469, 175–180 (2011).
Rasmussen, S. G. et al. Crystal structure of the beta2 adrenergic receptor-Gs protein complex. Nature 477, 549–555 (2011).
Weis, W. I. & Kobilka, B. K. The molecular basis of G protein-coupled receptor activation. Annu. Rev. Biochem. 87, 897–919 (2018).
Latorraca, N. R., Venkatakrishnan, A. J. & Dror, R. O. GPCR dynamics: structures in motion. Chem. Rev. 117, 139–155 (2017).
Ballesteros, J. A. & Weinstein, H. in Methods in Neurosciences Vol. 25 (ed. Stuart C. Sealfon) 366–428 (Academic Press, 1995).
Venkatakrishnan, A. J. et al. Structured and disordered facets of the GPCR fold. Curr. Opin. Struct. Biol. 27, 129–137 (2014).
Schulte, G. International Union of Basic and Clinical Pharmacology. LXXX. The class Frizzled receptors. Pharmacol. Rev. 62, 632–667 (2010).
Schulte, G. & Wright, S. C. Frizzleds as GPCRs—-more conventional than we thought! Trends Pharmacol. Sci. 39, 828–842 (2018).
Wu, F., Zhang, Y., Sun, B., McMahon, A. P. & Wang, Y. Hedgehog signaling: from basic biology to cancer therapy. Cell Chem. Biol. 24, 252–280 (2017).
Logan, C. Y. & Nusse, R. The Wnt signaling pathway in development and disease. Annu. Rev. Cell Dev. Biol. 20, 781–810 (2004).
Kozielewicz, P., Turku, A. & Schulte, G. Molecular pharmacology of class F receptor activation. Mol. Pharmacol. 97, 62–71 (2020).
Wright, S. C. et al. A conserved molecular switch in Class F receptors regulates receptor activation and pathway selection. Nat. Commun. 10, 667 (2019).
Deshpande, I. et al. Smoothened stimulation by membrane sterols drives Hedgehog pathway activity. Nature 571, 284–288 (2019).
Qi, X. et al. Cryo-EM structure of oxysterol-bound human Smoothened coupled to a heterotrimeric Gi. Nature 571, 279–283 (2019).
Qi, X., Friedberg, L., De Bose-Boyd, R., Long, T. & Li, X. Sterols in an intramolecular channel of Smoothened mediate Hedgehog signaling. Nat. Chem. Biol. https://doi.org/10.1038/s41589-020-0646-2 (2020).
Kozielewicz, P. et al. Structural insight into small molecule action on Frizzleds. Nat. Commun. 11, 414 (2020).
Kong, J. H., Siebold, C. & Rohatgi, R. Biochemical mechanisms of vertebrate hedgehog signaling. Development https://doi.org/10.1242/dev.166892 (2019).
Steinhart, Z. & Angers, S. Wnt signaling in development and tissue homeostasis. Development https://doi.org/10.1242/dev.146589 (2018).
Wright, S. C. et al. FZD5 is a Galphaq-coupled receptor that exhibits the functional hallmarks of prototypical GPCRs. Sci. Signal. https://doi.org/10.1126/scisignal.aar5536 (2018).
Schihada, H., Kowalski-Jahn, M., Turku, A. & Schulte, G. Deconvolution of WNT-induced Frizzled conformational dynamics with fluorescent biosensors. Biosens. Bioelectron. 177, 112948 (2021).
Wang, C. et al. Structural basis for Smoothened receptor modulation and chemoresistance to anticancer drugs. Nat. Commun. 5, 4355 (2014).
Kozielewicz, P., Bowin, C. F., Turku, A. & Schulte, G. A nanoBRET-based binding assay for smoothened allows real-time analysis of ligand binding and distinction of two binding sites for BODIPY-cyclopamine. Mol. Pharmacol. 97, 23–34 (2020).
Fleetwood, O., Matricon, P., Carlsson, J. & Delemotte, L. Energy landscapes reveal agonist control of G protein-coupled receptor activation via microswitches. Biochemistry 59, 880–891 (2020).
De Lean, A., Stadel, J. M. & Lefkowitz, R. J. A ternary complex model explains the agonist-specific binding properties of the adenylate cyclase-coupled beta-adrenergic receptor. J. Biol. Chem. 255, 7108–7117 (1980).
Wesslowski, J. et al. eGFP-tagged Wnt-3a enables functional analysis of Wnt trafficking and signaling and kinetic assessment of Wnt binding to full-length Frizzled. J. Biol. Chem. 295, 8759–8774 (2020).
Yang, S. et al. Crystal structure of the Frizzled 4 receptor in a ligand-free state. Nature 560, 666–670 (2018).
Tsutsumi, N. et al. Structure of human Frizzled5 by fiducial-assisted cryo-EM supports a heterodimeric mechanism of canonical Wnt signaling. elife https://doi.org/10.7554/eLife.58464 (2020).
Kilander, M. B., Dahlstrom, J. & Schulte, G. Assessment of Frizzled 6 membrane mobility by FRAP supports G protein coupling and reveals WNT-Frizzled selectivity. Cell Signal. 26, 1943–1949 (2014).
Kilander, M. B. et al. Disheveled regulates precoupling of heterotrimeric G proteins to Frizzled 6. FASEB J. 28, 2293–2305 (2014).
Okashah, N. et al. Agonist-induced formation of unproductive receptor-G12 complexes. Proc. Natl Acad. Sci. USA 117, 21723–21730 (2020).
Riobo, N. A., Saucy, B., Dilizio, C. & Manning, D. R. Activation of heterotrimeric G proteins by Smoothened. Proc. Natl Acad. Sci. USA 103, 12607–12612 (2006).
von Maltzahn, J., Bentzinger, C. F. & Rudnicki, M. A. Wnt7a-Fzd7 signalling directly activates the Akt/mTOR anabolic growth pathway in skeletal muscle. Nat. Cell Biol. 14, 186–191 (2011).
Schihada, H., Shekhani, R. & Schulte, G. Quantitative assessment of constitutive G protein-coupled receptor activity with BRET-based G protein biosensors. bioRxiv https://doi.org/10.1101/2021.02.05.429900 (2021).
DeCamp, D. L., Thompson, T. M., de Sauvage, F. J. & Lerner, M. R. Smoothened activates Galphai-mediated signaling in frog melanophores. J. Biol. Chem. 275, 26322–26327 (2000).
Masdeu, C. et al. Identification and characterization of Hedgehog modulator properties after functional coupling of Smoothened to G15. Biochem. Biophys. Res. Commun. 349, 471–479 (2006).
Shen, F., Cheng, L., Douglas, A. E., Riobo, N. A. & Manning, D. R. Smoothened is a fully competent activator of the heterotrimeric G protein G(i). Mol. Pharmacol. 83, 691–697 (2013).
Myers, B. R., Neahring, L., Zhang, Y., Roberts, K. J. & Beachy, P. A. Rapid, direct activity assays for Smoothened reveal Hedgehog pathway regulation by membrane cholesterol and extracellular sodium. Proc. Natl Acad. Sci. USA 114, E11141–E11150 (2017).
Wieland, K. et al. Constitutive activity of histamine h(3) receptors stably expressed in SK-N-MC cells: display of agonism and inverse agonism by H(3) antagonists. J. Pharmacol. Exp. Ther. 299, 908–914 (2001).
Chen, J. K., Taipale, J., Young, K. E., Maiti, T. & Beachy, P. A. Small molecule modulation of Smoothened activity. Proc. Natl Acad. Sci. USA 99, 14071–14076 (2002).
Arveseth, C. D. et al. Smoothened transduces Hedgehog signals via activity-dependent sequestration of PKA catalytic subunits. bioRxiv https://doi.org/10.1101/2020.07.01.183079 (2020).
Mlodzik, M. The dishevelled protein family: still rather a mystery after over 20 years of molecular studies. Curr. Top. Dev. Biol. 117, 75–91 (2016).
Valnohova, J., Kowalski-Jahn, M., Sunahara, R. K. & Schulte, G. Functional dissection of the N-terminal extracellular domains of Frizzled 6 reveals their roles for receptor localization and Dishevelled recruitment. J. Biol. Chem. 293, 17875–17887 (2018).
Gammons, M. V., Renko, M., Johnson, C. M., Rutherford, T. J. & Bienz, M. Wnt signalosome assembly by DEP domain swapping of dishevelled. Mol. Cell 64, 92–104 (2016).
Korinek, V. et al. Constitutive transcriptional activation by a beta-catenin-Tcf complex in APC−/− colon carcinoma. Science 275, 1784–1787 (1997).
Golan, T., Yaniv, A., Bafico, A., Liu, G. & Gazit, A. The human Frizzled 6 (HFz6) acts as a negative regulator of the canonical Wnt. beta-catenin signaling cascade. J. Biol. Chem. 279, 14879–14888 (2004).
Bowin, C. F., Inoue, A. & Schulte, G. WNT-3A-induced beta-catenin signaling does not require signaling through heterotrimeric G proteins. J. Biol. Chem. 294, 11677–11684 (2019).
Katritch, V. et al. Allosteric sodium in class A GPCR signaling. Trends Biochem. Sci. 39, 233–244 (2014).
Pope, A. L. et al. A Conserved proline hinge mediates helix dynamics and activation of rhodopsin. Structure https://doi.org/10.1016/j.str.2020.05.004 (2020).
Sali, A., Potterton, L., Yuan, F., van Vlijmen, H. & Karplus, M. Evaluation of comparative protein modeling by MODELLER. Proteins 23, 318–326 (1995).
Friesner, R. A. et al. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem. 49, 6177–6196 (2006).
Friesner, R. A. et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47, 1739–1749 (2004).
Halgren, T. A. et al. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem. 47, 1750–1759 (2004).
Byrne, E. F. X. et al. Structural basis of Smoothened regulation by its extracellular domains. Nature 535, 517–522 (2016).
Larkin, M. A. et al. Clustal W and clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007).
Nehme, R. et al. Mini-G proteins: novel tools for studying GPCRs in their active conformation. PLoS ONE 12, e0175642 (2017).
Berendsen, H. J. C., Vanderspoel, D. & Vandrunen, R. Gromacs—a message-passing parallel molecular-dynamics implementation. Comput. Phys. Commun. 91, 43–56 (1995).
Wang, J. M., Wolf, R. M., Caldwell, J. W., Kollman, P. A. & Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 25, 1157–1174 (2004).
Case, D. A. et al. AMBER (University of California, San Francisco, 2018).
Jakalian, A., Jack, D. B. & Bayly, C. I. Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J. Comput. Chem. 23, 1623–1641 (2002).
Dominguez, C., Boelens, R. & Bonvin, A. M. HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J. Am. Chem. Soc. 125, 1731–1737 (2003).
van Zundert, G. C. P. et al. The HADDOCK2.2 Web Server: user-friendly integrative modeling of biomolecular complexes. J. Mol. Biol. 428, 720–725 (2016).
Lomize, M. A., Pogozheva, I. D., Joo, H., Mosberg, H. I. & Lomize, A. L. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res. 40, D370–D376 (2012).
Jo, S., Lim, J. B., Klauda, J. B. & Im, W. CHARMM-GUI membrane builder for mixed bilayers and its application to yeast membranes. Biophys. J. 97, 50–58 (2009).
Huang, J. et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods 14, 71–73 (2017).
Nose, S. & Klein, M. L. Constant pressure molecular-dynamics for molecular-systems. Mol. Phys. 50, 1055–1076 (1983).
Parrinello, M. & Rahman, A. Polymorphic transitions in single-crystals—a new molecular-dynamics method. J. Appl-. Phys. 52, 7182–7190 (1981).
Hess, B., Bekker, H., Berendsen, H. J. C. & Fraaije, J. G. E. M. LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 18, 1463–1472 (1997).
Schmidtke, P., Le Guilloux, V., Maupetit, J. & Tuffery, P. fpocket: online tools for protein ensemble pocket detection and tracking. Nucleic Acids Res. 38, W582–W589 (2010).
Le Guilloux, V., Schmidtke, P. & Tuffery, P. Fpocket: an open source platform for ligand pocket detection. Bmc Bioinformatics https://doi.org/10.1186/1471-2105-10-168 (2009).
Eubelen, M. et al. A molecular mechanism for Wnt ligand-specific signaling. Science https://doi.org/10.1126/science.aat1178 (2018).
Wan, Q. et al. Mini G protein probes for active G protein-coupled receptors (GPCRs) in live cells. J. Biol. Chem. 293, 7466–7473 (2018).
We thank Anna Krook for access to the CLARIOstar plate reader and Benoit Vanhollebeke for the ΔFZD1-10 HEK293T cells. The work was supported by grants from Karolinska Institutet, the Swedish Research Council (2017-04676; 2019-01190), the Swedish Cancer Society (CAN2017/561, 20 1102 PjF, 20 0264 P), the Novo Nordisk Foundation (NNF17OC0026940, NNF20OC0063168), Wenner-Gren Foundations (UPD2018-0064; UPD2019-0193), The Lars Hierta Memorial Foundation (FO2019-0086, FO2020-0304), The Alex and Eva Wallström Foundation for scientific research and education (2020-00228), The Swedish Society of Medical Research (SSMF; P19-0055), and the German Research Foundation (DFG; 427840891). Computational resources were provided by the Swedish National Infrastructure for Computing—National Supercomputer Centre (NSC) in Linköping and KTH Royal Institute of Technology (PDC) in Stockholm (SNIC 2020/5-500).
Open access funding provided by Karolinska Institute.
The authors declare no competing interests.
Peer review information Nature Communications thanks Alissa Hummer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Turku, A., Schihada, H., Kozielewicz, P. et al. Residue 6.43 defines receptor function in class F GPCRs. Nat Commun 12, 3919 (2021). https://doi.org/10.1038/s41467-021-24004-z