Structural basis of agonist specificity of α1A-adrenergic receptor

Su, Minfei; Wang, Jinan; Xiang, Guoqing; Do, Hung Nguyen; Levitz, Joshua; Miao, Yinglong; Huang, Xin-Yun

doi:10.1038/s41467-023-40524-2

Download PDF

Article
Open access
Published: 10 August 2023

Structural basis of agonist specificity of α_1A-adrenergic receptor

Nature Communications volume 14, Article number: 4819 (2023) Cite this article

3665 Accesses
2 Citations
23 Altmetric
Metrics details

Subjects

Abstract

α₁-adrenergic receptors (α₁-ARs) play critical roles in the cardiovascular and nervous systems where they regulate blood pressure, cognition, and metabolism. However, the lack of specific agonists for all α₁ subtypes has limited our understanding of the physiological roles of different α₁-AR subtypes, and led to the stagnancy in agonist-based drug development for these receptors. Here we report cryo-EM structures of α_1A-AR in complex with heterotrimeric G-proteins and either the endogenous common agonist epinephrine or the α_1A-AR-specific synthetic agonist A61603. These structures provide molecular insights into the mechanisms underlying the discrimination between α_1A-AR and α_1B-AR by A61603. Guided by the structures and corresponding molecular dynamics simulations, we engineer α_1A-AR mutants that are not responsive to A61603, and α_1B-AR mutants that can be potently activated by A61603. Together, these findings advance our understanding of the agonist specificity for α₁-ARs at the molecular level, opening the possibility of rational design of subtype-specific agonists.

Stepwise activation of a metabotropic glutamate receptor

Article 17 April 2024

Promiscuous G-protein activation by the calcium-sensing receptor

Article 17 April 2024

Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning

Article Open access 17 April 2024

Introduction

Epinephrine and norepinephrine are neurotransmitters of the sympathetic nervous system, and hormones secreted by the adrenal medulla^1,2. They function through nine distinct human adrenergic receptor (AR) subtypes: α₁-type (α_1A, α_1B, α_1D), α₂-type (α_2A, α_2B, α_2C,) and β-type (β₁, β₂, and β₃)^3,4. Due to the lack of selective pharmacological agonists for α₁-ARs, the therapeutic potential of α₁-ARs has been largely unexplored^5,6,7,8,9. However, mouse genetic studies with individual gene deletions have demonstrated distinct but overlapping physiological functions of α₁-AR subtypes in the regulation of blood pressure, cardiac hypertrophy, vascular smooth muscle contraction, neurotransmission, learning and memory, and metabolism^{10,11,12,13,14,15,16,17}. For example, the α_1A-AR subtype is a vasopressor expressed in resistance arteries and is required for normal arterial blood pressure regulation¹¹. In addition, α_1B-AR in human coronary endothelial cells mediates vasodilation^18,19.

α₁-ARs couple to the Gq family of G-proteins, leading to the stimulation of phospholipase C-β to cleave phosphatidylinositol-4,5-bisphosphate into inositol-1,4,5-trisphosphate and 2-diacylglycerol²⁰. The former promotes the release of Ca²⁺ from intracellular stores, while the latter activates protein kinase C^21,22. While some pharmacological compounds exist that can target α₁-ARs, there are only two structurally related α_1A-specific agonists, and no α_1B-AR and α_1D-AR selective agonists have been reported¹⁷. A61603 is a high affinity, selective α_1A-AR agonist which shows almost no activity at α_1B-AR and α_1D-AR^7,23. Furthermore, there are no structures available for the active states of α₁-ARs. In this paper, we use cryo-EM to determine the structures of α_1A-AR/Gq signaling complexes with epinephrine or A61603. These structures reveal the molecular basis for the binding specificity of A61603 for α_1A-AR, and the different conformations of epinephrine in interacting with α-ARs versus β-ARs. Gaussian accelerated molecular dynamics (GaMD) simulations and functional studies provide further insights into the mechanisms of specificity, ultimately enabling the validation of key sites that determine the ability of A61603 to specifically activate α_1A-AR but not α_1B-AR^24,25.

Results

Cryo-EM structures of A61603–α_1A-AR–Gq and epinephrine–α_1A-AR–Gq signaling complexes

To understand the agonist specificity of α₁-AR, we solved cryo-EM structures of human α_1A-AR bound to A61603 (a synthetic specific agonist for α_1A-AR) at 2.6 Å or epinephrine (an endogenous agonist for all ARs) at 3.0 Å, both in complex with its cognate signaling Gq heterotrimer (mini-Gα_qGβ₁Gγ₂) (Fig. 1a, b, Supplementary Figs. 1–3, and Supplementary Table 1). Comparisons of these structures should provide insights into subtype-specific agonist binding. Overall, the structures of the α_1A-AR–Gq complex in the presence of epinephrine or A61603 are similar (Fig. 1a, b). However, there are local conformational differences, especially in the ligand-binding pockets (see below). While some of the interacting residues are common to both ligands, epinephrine, and A61603 each make a unique set of interactions in the orthosteric ligand-binding pocket (see below).

**Fig. 1: Cryo-EM structures of the complexes of A61603–α_1A-AR–Gq and epinephrine–α_1A-AR–Gq.**

Since the A61603–α_1A-AR–Gq and epinephrine–α_1A-AR–Gq structures reveal the first active state conformation of α₁-AR family receptors, we first investigated the structural basis of activation. For the α₁-AR family, there is only one recently reported structure: the X-ray crystal structure of the inactive state of α_1B-AR bound with the inverse agonist cyclazosin (PDB: 7B6W)²⁶. We thus compared our active state structures of α_1A-AR with this inactive state structure of α_1B-AR. This comparison revealed characteristic conformational changes associated with class A GPCR activation (Fig. 1c–e, and Supplementary Fig. 4)²⁷. Since the structures of α_1A-AR bound with A61603 and epinephrine were similar, we focused our comparison on the A61603–α_1A-AR–Gq complex. The overall root-mean-square deviation between the structures of the active α_1A-AR and inactive α_1B-AR is 1.6 Å over 198 Cα atoms. The largest structural changes upon activation occur on the cytoplasmic side of α₁-AR (Fig. 1c–e), with an outward rotation of TM6 by ~10 Å (measured at the Cα of A^6.33) (The superscript is the Ballesteros-Weinstein numbering²⁸) (Fig. 1c, e), and an inward ~5 Å movement of TM7 (measured at the Cα of Y^7.53) (Fig. 1c, e). In addition to these TM conformational changes, rearrangements of side chains of certain residues are observed as part of the α₁-AR activation process. Just below the orthosteric ligand-pocket, the rotameric change of W^6.48 (within the CWxP motif) denotes the opening of TM6 in class A GPCRs for G-protein engagement²⁷ (Supplementary Fig. 4a). A layer below the CWxP motif, the rotation of F^5.47 causes the change of L^5.51, accompanied by the translational movement of V^6.45, F^6.44 (part of the PIF motif), and W^6.48 to form new contacts (Supplementary Fig. 4b). Moving closer to the G-protein-interacting site below the PIF motif, the highly conserved NPxxY motif at the cytoplasmic end of TM7 is another key micro-switch of GPCR activation²⁷ (Supplementary Fig. 4c). TM7 rotates around the NPxxY motif. This moves Y^7.53 toward the position that was occupied by TM6 in the inactive structure (Supplementary Fig. 4c). Among the G-protein-interacting residues, the rearrangement of side chains in the highly conserved D(E)/RY motif in TM3 is critical for GPCR activation²⁷ (Supplementary Fig. 4d). Therefore, α₁-ARs undergo conformational changes propagating from the orthosteric ligand-binding site to the G-protein-interacting site during its activation.

Different conformations of epinephrine bound to α-ARs and β-ARs

Epinephrine is a chiral endogenous full agonist common for all ARs. In the complex of epinephrine–α_1A-AR–Gq, the para-hydroxyl group of the catechol ring of epinephrine forms H-bond with the hydroxyl side-chain of S188^5.43 (Fig. 2a–c and Supplementary Fig. 5). The protonated amine of epinephrine forms a salt bridge with residue D106^3.32 (Fig. 2a–c and Supplementary Fig. 5). Previous experiments have shown that this interaction is critical for both affinity and efficacy²⁹. Y316^7.42 and W313^7.39 stabilize this salt bridge through a hydrogen bond between D106^3.32 and Y316^7.42, and π–π interactions between Y316^7.42 and W313^7.39 (Fig. 2b, c). The catechol ring forms π–π interactions with F288^6.51 and F289^6.52 (Fig. 2c). F312^7.38 acts as a lid covering the catechol ring of epinephrine from the extracellular side (Fig. 2b). Furthermore, epinephrine adopted a binding conformation similar to the cryo-EM structure in the GaMD simulations (Supplementary Fig. 5a), with mostly <2 Å root-mean-square derivation (RMSD) (Supplementary Fig. 5b). The ligand maintained stable interactions with residues D106^3.32 and S188^5.43 in α_1A-AR at 3.82 ± 0.33 Å and 3.10 ± 0.33 Å, respectively (Supplementary Fig. 5c, d).

**Fig. 2: Interactions between epinephrine and α_1A-AR.**

The conformation of epinephrine in the complex with α_1A-AR is different from that in the previously reported complexes with β-ARs (Fig. 2d and Supplementary Fig. 6). Currently there are no structures available for epinephrine in complex with any β-ARs and a G-protein. However, there are two structures of epinephrine bound to β₁-AR or β₂-AR with a nanobody 6B9 that stabilizes β-ARs in an active state^30,31. In the complex of epinephrine–β₁-AR–nanobody 6B9 and of epinephrine–β₂-AR–nanobody 6B9^30,31, the β-carbon hydroxyl and the N-methyl group face opposite directions when compared with the conformation in complex with α_1A-AR. The β-carbon hydroxyl faces the intracellular side, and the N-methyl group points toward TM3 when epinephrine was complexed with β-ARs (Fig. 2d, Supplementary Fig. 6). On the other hand, the β-carbon hydroxyl group faces the extracellular side, and the N-methyl group points to Y316^7.42 on TM7, when epinephrine was in the complex with α_1A-AR (Fig. 2b, Supplementary Fig. 6). These two different epinephrine conformations result from the rotation around the chemical bond linking the catechol ring and the β-carbon of epinephrine (Supplementary Fig. 6). In β-ARs, N363^7.38 (in β₁-AR) and N312^7.38 (in β₂-AR) interacted through hydrogen bonds with the β-carbon hydroxyl and the N-methyl group of epinephrine. In α_1A-AR, it is F312^7.38 at the equivalent position, and its side-chain did not form hydrogen bonds with epinephrine (Supplementary Fig. 6). The different conformations of epinephrine, when bound with β-ARs and α-ARs, might explain the higher affinity of epinephrine for β-ARs than α-ARs³². Together these data show that epinephrine adopts different conformations in order to interact with α and β families of ARs.

Specific interaction between A61603 and α_1A-AR

Since A61603 is a selective agonist for α_1A-AR²³, we asked what residues mediated this specific interaction. In the complex of A61603–α_1A-AR–Gq, S188^5.43 forms multiple interactions with A61603 (Fig. 3a–c). The hydroxyl group of S188^5.43 forms H-bonds with the hydroxy attached to the tetrahydronaphthalene group, the amide, and the oxygen atom of the methanesilfonamide group of A61603 (Fig. 3b, c). In addition, the side-chain of D106^3.32 forms a salt bridge with the imidazoline group of A61603 (Fig. 3b, c), and the backbone amide hydrogen of A189^5.44 interacts with the oxygen atom of the methanesilfonamide group of A61603 (Fig. 3b, c). There are also extensive hydrophobic interactions between A61603 and α_1A-AR (Fig. 3c). These include V107^3.33, C110^3.36, I178^45.52, W285^6.48, F288^6.51, F289^6.52, M292^6.55, F312^7.38, G315^7.41 and Y316^7.42 (Fig. 3c). Since this is the first structure of A61603 bound to any proteins, we performed GaMD simulations to validate the ligand pose (Fig. 3d–g and Supplementary Fig. 7). In these simulations, the conformation of A61603 is similar to that observed in the cryo-EM structure (Fig. 3d, e). The imidazoline group of A61603 and residue D106^3.32 in α_1A-AR maintained the stable salt-bridge interaction during the GaMD simulations (Fig. 3f). Residue S188^5.43 in α_1A-AR formed hydrogen bonds with different atoms (e.g., N, O, and O₂) in the methanesulfonamide group of A61603 in the GaMD simulations (Fig. 3g and Supplementary Fig. 7). Together, these data reveal the molecular interactions between A61603 and α_1A-AR.

**Fig. 3: Interactions between A61603 and α_1A-AR.**

Molecular basis for the discrimination between α_1A-AR and α_1B-AR by A61603

To further probe the structural basis for the specificity of A61603 for α_1A-AR, we compared the ligand-binding pockets of all three α₁-AR subtypes (Supplementary Fig. 8). Among the ligand-binding pocket residues, α_1A-AR and α_1B-AR have almost identical composition with the exception of three residues: V185^5.40 in α_1A-AR but A204^5.40 in α_1B-AR, A189^5.44 in α_1A-AR and S208^5.44 in α_1B-AR, and M292^6.55 in α_1A-AR but L314^6.55 in α_1B-AR (Fig. 4a and Supplementary Fig. 8b).

**Fig. 4: Participation of V185, A189, and M292 of α_1A-AR in interacting with A61603.**

In the A61603–α_1A-AR–Gq structure, these three residues form a hydrophobic surface surrounding the methanesulfonamide group of A61603 (Fig. 4b). To understand why A61603 could not bind to α_1B-AR, we modeled a complex of α_1B-AR and A61603 (Fig. 4c). Based on our cryo-EM structure of the active state of α_1A-AR with A61603 and the structure of the inactive state of α_1B-AR, the modeled A61603–α_1B-AR complex showed a steric clash between the methanesulfonamide group of A61603 and L314^6.55 (Fig. 4c). In α_1D-AR, the corresponding residue is also a Leu residue (Supplementary Fig. 8b). Together, these observations suggest a molecular basis for the specific binding of A61603 to α_1A-AR, but not α_1B-AR and α_1D-AR.

Based on insights from our structures and molecular modeling, we attempted to engineer α_1A-AR variants that would lose the response to A61603, and α_1B-AR mutants that would gain the response to A61603. These loss-of-function and gain-of-function studies should provide evidence for the molecular basis of A61603-binding specificity. Based on the above data, we focused on three residues V185^5.40, A189^5.44, and M292^6.55. We generated point mutants of α_1A-AR(V185A), α_1A-AR(A189S), and α_1A-AR(M292L) by mutating these residues to the corresponding residues in α_1B-AR. Since these residues are involved in hydrophobic interactions with A61603 (Fig. 3), we also made a mutant with all three residues mutated α_1A-AR(V185A, A189S, M292L). Since α_1A-AR is coupled to Gq, we used calcium responses as a functional readout (Fig. 5). As a control, wild-type α_1A-AR produced robust dose-dependent Ca²⁺ responses to A61603 with an EC₅₀ of ~1 nM with a similar amplitude at saturating doses compared to saturating epinephrine (Fig. 5a, c). The triple mutant α_1A-AR(V185A, A189S, M292L) reduced the apparent affinity of the A61603 response by more than 1000-fold (Fig. 5b, c). This triple mutant was still functional in response to epinephrine (Fig. 5b). The single-point mutants of V185A and M292L also decreased the α_1A-AR response to A61603, while the A189S mutant showed wild-type-like responses (Fig. 5c).

**Fig. 5: Functional studies of mutant α_1A-AR and α_1B-AR.**

In parallel, GaMD simulations were performed on α_1A-AR(V185A), α_1A-AR(A189S), α_1A-AR(M292L), and α_1A-AR(V185A, A189S, M292L) (Supplementary Fig. 9). During the GaMD simulations, RMSDs of A61603 in α_1A-AR(V185A), α_1A-AR(M292L), and α_1A-AR(V185A, A189S, M292L) relative to the starting cryo-EM structure were 4.62 ± 1.68 Å, 3.21 ± 1.05 Å, and 3.99 ± 1.13 Å, respectively (Supplementary Fig. 9a, g, j). Hence, α_1A-AR(V185A), α_1A-AR(M292L), and α_1A-AR(V185A, A189S, M292L) reduced the binding of A61603 to the receptor (Supplementary Fig. 9a, g, j). On the other hand, A61603 in α_1A-AR(A189S) exhibited a smaller RMSD with lower fluctuations in the GaMD simulations (Supplementary Fig. 9d). The distances between the imidazoline group of A61603 and residue D106^3.32 in α_1A-AR(V185A), α_1A-AR(A189S), α_1A-AR(M292L), and α_1A-AR(V185A, A189S, M292L) were 4.25 ± 0.98 Å, 3.43 ± 0.47 Å, 4.13 ± 1.33 Å, and 4.38 ± 1.27 Å, respectively (Supplementary Fig. 9b, e, h, k). Therefore, the interaction between the imidazoline group of A61603 and residue D106^3.32 was disrupted in α_1A-AR(V185A), α_1A-AR(M292L), and α_1A-AR(V185A, A189S, M292L), but still maintained in α_1A-AR(A189S), during the simulations (Supplementary Fig. 9b, e, h, k). This is consistent with our functional data where V185A and M292L produced a large impairment of A61603 responses while A189S maintained WT-like properties. For further information, we performed molecular mechanics/generalized Born surface area (MM/GBSA) binding free energy calculations using GaMD trajectories^33,34,35. Due to the inherent inaccuracy of entropy calculations, we focused on comparing only the enthalpy values (Supplementary Table 2). The A61603-binding free energy was less favorable in the α_1A-AR mutants, i.e., −22.18 ± 0.73 kcal/mol for α_1A-AR(V185A) and −11.52 ± 2.47 kcal/mol for α_1A-AR(V185A, A189S, M292L), compared to −26.73 ± 0.22 kcal/mol for the wild-type α_1A-AR. α_1A-AR(A189S) exhibited a similar binding free energy of A61603 (−26.54 ± 1.89 kcal/mol) as the wild-type α_1A-AR. However, an exception was observed in α_1A-AR(M292L), where the binding free energy was −27.88 ± 1.53 kcal/mol. This likely resulted from the exclusion of the entropy and MM/GBSA free energy calculations of GaMD simulation frames without the energetic reweighting³⁶. In general, the systems with a stronger functional response to A61603-binding exhibited more favorable binding free energy compared to those with weaker or no functional responses. Therefore, the GaMD simulation findings correlated well with the calcium response data.

Conversely, we attempted to convert α_1B-AR from non-responsive to responsive to A61603. This type of exercise is usually difficult given that many residues are typically involved in ligand interactions. However, based on our above data, most of the agonist interacting residues are the same between α_1A-AR and α_1B-AR, except for the three residues (Supplementary Fig. 8), suggesting that it may be possible to replace the three distinct residues in α_1B-AR by those in α_1A-AR. We thus generated the corresponding triple mutant α_1B-AR(A204V, S208A, L314M). Wild-type α_1B-AR did not respond to A61603 at concentrations up to 10 μM, while it was responsive to epinephrine (Fig. 5d). Remarkably, the triple mutant α_1B-AR(A204V, S208A, L314M) responded to A61603 in a dose-dependent manner with an EC₅₀ around 10 nM (Fig. 5e, f). The single-point mutants α_1B-AR(A204V), α_1B-AR(S208A) or α_1B-AR(L314M) alone did not respond to A61603 (Fig. 5f), indicating that more of a large-scale reshaping of the orthosteric site than can be achieved by a single mutation is needed to rescue the binding. In parallel, we have built a homology model of the active α_1B-AR using the cryo-EM structure of A61603-bound α_1A-AR as the template and conducted GaMD simulations on the wild-type α_1B-AR, α_1B-AR(S208A), and α_1B-AR(A204V, S208A,L314M). In wild-type α_1B-AR and α_1B-AR(S208A), A61603 exhibited large RMSDs relative to the starting conformation, with reduced interactions between A61603 and α_1B-AR (Supplementary Fig. 10). In contrast, A61603 in α_1B-AR(A204V, S208A, L314M) exhibited low RMSD at ~2.5 Å, forming stable interactions with residues D125^3.32 and S207^5.43 in the receptor (Supplementary Fig. 10). α_1B-AR(A204V, S208A, L314M) showed the strongest binding affinity (-28.21 ± 2.80 kcal/mol), followed by α_1B-AR(S208A) (-25.81 ± 2.67 kcal/mol) and wild-type α_1B-AR (-11.52 ± 2.47 kcal/mol) (Supplementary Table 2).

We also compared the root-mean-square fluctuations (RMSF) of A61603 in different receptor systems. In this context, larger RMSF values indicate weaker binding of A61603. The calculated RMSF values of A61603 in α_1A-AR (WT), α_1A-AR(V185A), α_1A-AR(A189S), α_1A-AR(M292L), and α_1A-AR(V185A, A189S, M292L) were 1.46 ± 0.10 Å, 2.36 ± 0.52 Å, 1.10 ± 0.088 Å, 1.74 ± 0.39 Å, and 2.58 ± 0.33 Å, respectively (Supplementary Table 2). α_1A-AR(A189S) (with a functional response to A61603) exhibited a similar RMSF value as wild-type α_1A-AR, while the other α_1A-AR mutants (with impaired functional responses to A61603) showed higher A61603 fluctuations. The RMSF values of A61603 in α_1B-AR(A204V, S208A, L314M), α_1B-AR(S208A) and wild-type α_1B-AR were 0.98 ± 0.21 Å, 3.55 ± 1.97 Å, and 4.28 ± 0.30 Å, respectively (Supplementary Table 2). Additionally, we calculated the 2D free energy profiles of A61603 RMSD and the distance between the CG atom of D106^3.32 and N₂ atom of A61603, as well as the distance between the OG atom of S188^5.43 and O2 atom of A61603 in both the α_1A-AR and α_1B-AR systems (Supplementary Fig. 11). In wild-type α_1A-AR and α_1A-AR(A189S), one low-energy state of A61603 with a narrow conformational space was sampled. Conversely, the other mutant α_1A-ARs sampled larger conformational space with at least two distinct low-energy states. Among the α_1B-AR systems, α_1B-AR(A204V, S208A, L314M) sampled only one low-energy state, while α_1B-AR(WT) and α_1B-AR(S208A) sampled at least two low-energy states with much larger conformational space (Supplementary Fig. 12). These findings suggest that the systems with functional responses to A61603 binding are sampling only one stable A61603-binding pose, whereas the systems without functional responses to A61603 could explore larger conformational space, indicating reduced stability of A61603. Together, our studies present molecular insights into the mechanism underlying the discrimination between α_1A-AR and α_1B-AR by A61603. The observed differences in selectivity are consistent with a model that is affinity-based, as opposed to efficacy-based.

Discussion

α₁-ARs are one of the three major subfamilies of ARs. Due to the lack of specific agonists, the physiological functions of α₁-ARs are still not completely revealed, and their therapeutic potentials in treating cardiovascular, neurological, neuropsychiatric, and inflammatory disorders are not fully exploited. Here we have investigated the structural basis of activation and agonist interactions with α₁-AR. Our data shows that M292^6.55 and V185^5.40 of α_1A-AR define the specificity of A61603 for α_1A-AR. Hydrophobic M292^6.55 in α_1A-AR corresponds to hydrophobic L^6.55 in α_1B-AR and α_1D-AR, while aromatic Y^6.55 in α₂-AR and polar N^6.55 in β-ARs (Supplementary Fig. 8b). Hydrophobic V185^5.40 in α_1A-AR corresponds to A^5.40 in α_1B-AR, α_1D-AR, β₁-AR and β₂-AR, I^5.40 in α_2B-AR and α_2C-AR, and V^5.40 in α_2A-AR and β₃-AR (Supplementary Fig. 8b). Given that these residues form one hydrophobic surface (Fig. 4), this hydrophobic pocket should be explored for the design of AR subtype-specific ligands.

Comparisons of the two agonists (epinephrine and A61603) revealed that they share some conserved interactions while also displaying certain unique interactions (Supplementary Fig. 13). Epinephrine is a monoamine that contains an amino group linked to an aromatic ring by a two-carbon chain (Fig. 2a). In α_1A-AR, β₁-AR and β₂-AR, the aromatic ring forms π–π interactions to F^6.51 and F^6.52 (Fig. 2b, c). The positively charged amino group forms a salt bridge to D^3.32 (Fig. 2c). Although A61603 is not a monoamine, it uses the imidazoline group to form the same salt bridge with the conserved D^3.32 (Fig. 3c and Supplementary Fig. 13). Its polycyclic aromatic ring consists of a benzene fused to cyclohexane (Fig. 3a). The benzene of A61603, as the catechol ring of epinephrine, forms π–π interactions to the same F^6.51 and F^6.52 (Fig. 3b, c, and Supplementary Fig. 13). Furthermore, the para-hydroxyl group of epinephrine and the hydroxyl group of A61603 interact with the same S^5.43 (Figs. 2, 3 and Supplementary Fig. 13). As discussed above, the molecular basis for the receptor specificity lies in the hydrophobic interaction of the methenesulfonamide group of A61603 with the hydrophobic surface formed by M^6.55 and V^5.40, whereas the meta-hydroxyl group of epinephrine occupies this space (Figs. 2 and 3). The differences in selectivity are consistent with a model that is affinity-based, as opposed to efficacy-based. Together our results provide insights into the design and development of selective agonists targeting individual α₁-AR subtypes, and polypharmacological agonists tailored for multiple related receptors.

Methods

Expression and purification of α_1A-AR, G_q, and scFv16

The human α_1A-AR construct used in the cryo-EM study was optimized by truncation of N-terminal residues 1-14, third intracellular loop (ICL3) residues 228-255, and C-terminal residues 351-466. A hemagglutinin (HA) signal peptide, a FLAG tag, and a T4 lysozyme were fused to its N-terminus, and a PreScission protease cleavage site, an eGFP, and an 8xHis tag were added to its C-terminus. This construct was subcloned into pFastBac1 vector for expression in Spodoptera frugiperda Sf9 insect cells. Sf9 cells were grown to 2 to 3 million cells per ml in ESF 921 protein-free medium (Expression Systems) before 50 ml of baculoviruses were added for infection. 72 h later, cells were harvested by centrifugation, flash frozen in liquid nitrogen, and stored at −80 °C until use. For α_1A-AR purification, thawed cell pellets were lysed with 1% n-Dodecyl-β-d-Maltopyranoside (DDM, Anatrace) in a buffer containing 25 mM HEPES (pH 7.0), 350 mM NaCl, 100 μM A61603 (Cayman Chemicals) or epinephrine (MP Biomedicals), supplemented with protease inhibitors (0.5 μM PMSF, 2 μg/ml leupeptin, 0.8 μM aprotinin and 2 μM Pepstatin A, Goldbio) at 4 °C. Insoluble fractions were removed by ultracentrifugation at 142,000 × g for 1 h at 4 °C. The supernatant was then incubated with house-made GFP nanobody beads for at least 2 h, and washed with a buffer containing 25 mM HEPES (pH 7.0), 350 mM NaCl, 0.05% DDM, and 100 μM A61603 or epinephrine before PreScission protease was added to elute α_1A-AR from GFP nanobody beads overnight at 4 °C. Eluted α_1A-AR was further purified by size-exclusion chromatography using a Superdex 200 Increase 10/300 column (GE Healthcare) equilibrated with a buffer containing 25 mM HEPES (pH 7.0), 150 mM NaCl, 0.02% Lauryl Maltose Neopentyl Glycol (LMNG, Anatrace), and 100 μM A61603 or epinephrine. Peak fractions were pooled and concentrated for complex assembly.

The expression constructs of heterotrimeric mini-Gαq_iN–G_β1–G_γ2 and scFv16 were kindly provided by Dr. Bryan L Roth (University of North Caroline at Chapel Hill)³⁷. For the expression of heterotrimeric mini-Gαq_iN–G_β1–G_γ2, a single virus that encodes all three subunits, including mini-Gαq_iN, G_β1, and G_γ2, with a 6xHis tag fused to the N-terminus of G_β1, was used to infect Sf9 cells. 48 h post infection, cells were harvested by centrifugation, flash frozen in liquid nitrogen, and stored at −80 °C. Cell pellets were thawed in a lysis buffer containing 20 mM HEPES (pH 7.5), 100 mM NaCl, 2 mM β-mercaptoethanol, 1 mM MgCl₂, 0.1 mM GDP, 0.2% (v/v) Triton X-100, supplemented with protease inhibitors (0.5 μM PMSF, 2 μg/ml leupeptin, 0.8 μM aprotinin and 2 μM Pepstatin A) at 4 °C and insoluble fractions were removed by centrifugation at 142,000 × g for 1 h. Supernatant was collected and incubated with HisPur Ni-NTA Resin (Thermo Fisher Scientific) for 2 h at 4 °C. Resin was then washed with 20 mM imidazole in the lysis buffer, and eluted with 250 mM imidazole in the lysis buffer. Eluted heterotrimeric G-protein was further purified by size-exclusion chromatography using a Superdex 200 Increase 10/300 column (GE Healthcare) equilibrated with a buffer containing 20 mM HEPES (pH 7.5), 100 mM NaCl, 0.02% LMNG, 0.1 mM TCEP, 1 mM MgCl₂, and 10 μM GDP. Peak fractions were pooled and concentrated for complex assembly.

scFv16 was expressed and purified from Sf9 cells as a secreted protein³⁷. 72 h after infection, Sf9 cell culture media expressing scFv16 was collected and PH balanced to pH 8.0 by addition of Tris buffer. 10 mM calcium chloride was then added to quench the chelating agents with stirring at room temperature for 1 h. The precipitate was removed by centrifugation at 8000 × g for 30 min. The supernatant was incubated with HisPur Ni-NTA Resin with stirring for 4 h at 4 °C. The Ni-NTA resin was then collected with centrifugation at 800 g and washed with 20 mM imidazole in a buffer containing 20 mM HEPES (pH 7.5) and 100 mM NaCl before 200 mM imidazole in the same buffer was used to elute the protein. The C-terminal 8xHis tag was removed by the treatment of PreScission protease overnight at 4 °C. The protein was then further purified by size-exclusion chromatography using a Superdex 200 Increase 10/300 column (GE Healthcare) equilibrated with a buffer containing 20 mM HEPES (pH 7.5) and 100 mM NaCl. Peak fractions were pooled and concentrated for complex assembly.

Protein complex assembly and purification

To assemble the α_1A-AR–G_q–scFv16 complex, α_1A-AR, and heterotrimeric G_q were mixed at a 1:1.5 molar ratio and incubated for 30 min at room temperature followed by the treatment of 0.4 U Apyrase (New England Biolabs) for another 30 min at room temperature. A 1.5 molar access of scFv16 was then added to the mixture and incubated overnight at 4 °C. The next day, the mixture was centrifuged at 16,000 × g for 10 min to remove any precipitants. The supernatant was then loaded onto a Superdex 200 Increase 10/300 column equilibrated with 10 mM HEPES pH 7.0, 100 mM NaCl, 0.1 mM TCEP, 0.02% LMNG, and 30 μM A61603 or epinephrine. The elution fractions from a single peak containing pure α_1A-AR–G_q–scFv16 complex concentrated to 2.5 mg/ml and used directly for making cryo-EM grids.

Cryo-EM data collection

3.5 µL of α_1A-AR–G_q–scFv16 complex at a concentration of 2.5 mg/ml was applied to glow-discharged 400 mesh gold Quantifoil R1.2/1.3 holey carbon grids (Quantifoil Micro Tools) and vitrified using a Vitrobot Mark IV (Thermo Fisher Scientific/FEI) at 22 °C and 100% humidity. Micrographs were collected on a 300 kV Titan Krios electron microscope (Thermo Fisher Scientific/FEI) with a Gatan K3 direct electron detector (Gatan, Inc.) in super-resolution mode at a nominal ×81,000 and ×64,000 magnification for the A61603 and epinephrine-bound complexes, respectively. For the A61603-bound complex, 7,773 movies in the defocus range of −1.0 to −1.8 μm were recorded with a total accumulated dose of 50 e⁻/Å². For the epinephrine-bound complex, 10,486 movies in the defocus range of −1.0 to −1.8 μm were recorded with a total accumulated dose of 52 e⁻/Å².

Image processing, 3D reconstructions, modeling, and refinement

Super-resolution movies were aligned, two-times Fourier cropped, and dose-weighted using MotionCor2 implemented in Relion 4.0-beta^{38,39,40,41,42,43,44}. The effects of contrast-transfer function were estimated with CTFFIND v4.1.8⁴⁵. For the A61603 dataset, Relion 2D template-based auto-picking was used to pick particles and the resulting particle stacks were two-times Fourier cropped and processed through 2D classification in CryoSparc v3.3.1 to remove false positives, receptor alone, or G-proteins alone particles⁴⁶ (Supplementary Fig. 1). A stack of 1,497,830 intact complex particles were then subjected to 3D classification in Relion. For the epinephrine dataset, both Relion 2D and 3D template-based auto-picking was used to pick particles, and the resulting particle stacks from both 2D and 3D template-based auto-picking were separately two-times Fourier cropped and processed through 2D classification in CryoSparc v3.3.1 to remove false positives, receptor alone, or G-proteins alone particles⁴⁶ (Supplementary Fig. 2). After Relion 3D classification, the intact complex particles stacks from 2D and 3D template-based auto-picking were selected and combined, and duplicate particles were removed. The resulting stack of 880,531 intact complex particles were then subjected to another round of 3D classification in Relion. Stacks of 773,300 (A61603) and 782,191 (epinephrine) particles that went to high resolution were selected after 3D classification and further polished by Bayesian polishing in Relion. 3D variability analysis in cryoSPARC revealed the twisting of the α_1A-AR transmembrane region around a vertical axis, perpendicular to the membrane in both A61603 and epinephrine datasets. Subsets of 360,489 (A61603) and 219,834 (epinephrine) particles obtained by 3D variability analysis that yielded higher resolution in the transmembrane region were selected and non-uniform refinement of these particles in CryoSparc yielded maps with 2.6 Å-resolution (A61603) and 3.0 Å-resolution (epinephrine) using the 0.143 Fourier Shell Correlation criterion. These consensus stacks were also subjected to focused refinement of α_1A-AR and Gα_q region in CryoSparc. The focused refinement map showed significant improvement in the α_1A-AR region compared to the consensus map. Models were built starting from AlphaFold predicted structure of inactive α_1A-AR (AF-P35348-F1) and scFv16 bound Gq heterotrimer (PDB code 6WHA) in Coot v0.9.1, which combined with consensus and focused refinement maps, were used to generate composite maps in Phenix dev-4694^47,48. The resulting maps were super-sampled in Coot to 0.856 Å per pixel (A61603) and 0.8608 Å (epinephrine) with 320-voxel boxes. The A61603 and epinephrine bound α_1A-AR–G_q–scFv16 models were real-space refined against the composite maps in Phenix, and a work/free half-map pair was used to ensure against over-fitting (Supplementary Figs. 1 and 2). The binding poses of A61603 and epinephrine were initially determined by density maps as well as their geometry restraints and were further supported by MD simulations⁴⁹. All model statistics were validated using MolProbity⁵⁰.

Gaussian accelerated molecular dynamics (GaMD)

GaMD is an enhanced sampling method that works by adding a harmonic boost potential to reduce the system energy barriers^24,25. When the system potential $V(\mathop{r}\limits^{ \rightharpoonup })$ is lower than a reference energy E, the modified potential ${V}^{*}(\mathop{r}\limits^{ \rightharpoonup })$ of the system is calculated as:

$${V}^{*}\left(\mathop{r}\limits^{ \rightharpoonup }\right)=V\left(\mathop{r}\limits^{ \rightharpoonup }\right)+\Delta V\left(\mathop{r}\limits^{ \rightharpoonup }\right)$$

$$\Delta V\left(\mathop{r}\limits^{ \rightharpoonup }\right)=\left\{\begin{array}{c}\frac{1}{2}k{\left(E-V\left(\mathop{r}\limits^{ \rightharpoonup }\right)\right)}^{2},\,V\left(\mathop{r}\limits^{ \rightharpoonup }\right) \, < \, E\\ 0,\, V\left(\mathop{r}\limits^{ \rightharpoonup }\right)\ge E,\hfill \end{array}\right.$$

(1)

where k is the harmonic force constant. The two adjustable parameters E and k are automatically determined on three enhanced sampling principles. First, for any two arbitrary potential values ${v}_{1}(\mathop{r}\limits^{ \rightharpoonup })$ and ${v}_{2}(\mathop{r}\limits^{ \rightharpoonup })$ found on the original energy surface, if ${V}_{1}(\mathop{r}\limits^{ \rightharpoonup }) \, < \, {V}_{2}(\mathop{r}\limits^{ \rightharpoonup })$, $\Delta V$ should be a monotonic function that does not change the relative order of the biased potential values; i.e., ${V}_{1}^{*}(\mathop{r}\limits^{ \rightharpoonup }) \, < \, {V}_{2}^{*}(\mathop{r}\limits^{ \rightharpoonup })$. Second, if ${V}_{1}(\mathop{r}\limits^{ \rightharpoonup }) \, < \, {V}_{2}(\mathop{r}\limits^{ \rightharpoonup })$, the potential difference observed on the smoothened energy surface should be smaller than that of the original; i.e., ${V}_{2}^{*}(\mathop{r}\limits^{ \rightharpoonup }){-V}_{1}^{*}(\mathop{r}\limits^{ \rightharpoonup }) \, < \, {V}_{2}(\mathop{r}\limits^{ \rightharpoonup }){-V}_{1}(\mathop{r}\limits^{ \rightharpoonup })$. By combining the first two criteria and plugging in the formula of ${V}^{*}(\mathop{r}\limits^{ \rightharpoonup })$ and $\Delta V$, we obtain

$${V}_{\max }\le E\le {V}_{\min }+\frac{1}{k},$$

(2)

Where ${V}_{\min }$ and ${V}_{\max }$ are the system minimum and maximum potential energies. To ensure that Eq. 2 is valid, k has to satisfy: $k\le 1/({V}_{\max }-{V}_{\min })$. Let us define: $k={k}_{0} \, \cdot \, 1/({V}_{\max }-{V}_{\min })$, then $0{ \, < \, k}_{0}\le 1$. Third, the standard deviation (SD) of $\Delta V$ needs to be small enough (i.e., narrow distribution) to ensure accurate reweighting using cumulant expansion to the second order: ${\sigma }_{\Delta V}=k(E-{V}_{{avg}}){\sigma }_{V}\le {\sigma }_{0}$, where ${V}_{{avg}}$ and ${\sigma }_{V}$ are the average and SD of $\Delta V$ with ${\sigma }_{0}$ as a user-specified upper limit (e.g., ${10k}_{B}T$) for accurate reweighting. When E is set to the lower bound $E={V}_{\max }$ according to Eq. 2, ${k}_{0}$ can be calculated as

$${k}_{0}={{\min }}\left(1.0,\,{k}_{0}^{{\prime} }\right)=\min \left(1.0,\frac{{\sigma }_{0}}{{\sigma }_{V}} \, \cdot \, \frac{{V}_{\max }-{V}_{\min }}{{V}_{\max }-{V}_{{{{{\rm{avg}}}}}}}\right),$$

(3)

Alternatively, when the threshold energy E is set to its upper bound $E={V}_{\min }+1/k$, ${k}_{0}$ is set to:

$${k}_{0}={k}_{0}^{\prime\prime}\equiv \left(1-\frac{{\sigma }_{0}}{{\sigma }_{V}}\right) \, \cdot \, \frac{{V}_{\max }-{V}_{\min }}{{V}_{{{{{\rm{avg}}}}}}-{V}_{\min }},$$

(4)

If ${k}_{0}^{\prime\prime}$ is calculated between 0 and 1. Otherwise, ${k}_{0}$ is calculated using Eq. 3.

System setup and simulation analysis

The epinephrine–α_1A-AR–G_q and A61603–α_1A-AR–G_q cryo-EM structures were used for setting up simulation systems to explore the stability of the complex. The missing resides in the Helix 8 of α_1A-AR (KKAFQNVLR, residue number 334-342) were added by SWISS-MODEL⁵¹. The initial models of the A61603-bound mutants of α_1A-AR(V185A), α_1A-AR(A189S), α_1A-AR(M292L), and α_1A-AR(V185A, A189S, M292L) were built by VMD based on the A61603–α_1A-AR–G_q cryo-EM structure. All six simulation systems were prepared with the CHARMM-GUI web server for using the membrane protein input generator⁵². The receptor was inserted into a palmitoyl-oleoyl-phosphatidyl-choline (POPC) bilayer. All chain termini were capped with neutral patches (acetyl and methylamide). All the disulfide bonds in the complexes that were resolved in the cryo-EM structures were maintained in the simulations. The systems were solvated in 0.15 M NaCl solution at temperature 310 K. The AMBER ff14SB and AMBER LIPID 21 parameter sets were used for the receptor and lipids^53,54. The GAFF2 parameters with RESP charges were used for epinephrine and A61603⁵⁵. For each of the complex systems, initial energy minimization, thermalization, and 20 ns cMD equilibration were performed before GaMD simulations. A cutoff distance of 9 Å was used for the van der Waals and short-range electrostatic interactions and the long-range electrostatic interactions were computed with the particle-mesh Ewald summation method⁵⁶. A 2-fs integration time step was used for all MD simulations. The SHAKE algorithm was applied to all hydrogen-containing bonds. With all other atoms fixed, the lipid tails were energy minimized for 5000 steps using the conjugate gradient algorithm and melted with a constant number, volume, and temperature (NVT) run for 0.5 ns at 310 K. The six systems were further equilibrated using a constant number, pressure, and temperature (NPT) run at 1 atm and 310 K for 1 ns with 1.0 kcal/(mol Å²) harmonic position restraints applied to the protein and ligand atoms. Then, conventional MD simulations were performed on each system for 20 ns at 1 atm pressure and 310 K with a constant ratio constraint applied on the lipid bilayer in the X-Y plane. The GaMD module implemented in the GPU version of AMBER22 was then applied to perform the simulations^24,57. GaMD simulations included an 8-ns short cMD simulation used to collect the potential statistics for calculating GaMD acceleration parameters, and a 48-ns equilibration after adding the boost potential. Finally, three independent 500-ns GaMD simulations with randomized initial atomic velocities were performed for the epinephrine–α_1A-AR–G_q, A61603–α_1A-AR–G_q, and A61603-bound mutant α_1A-AR–G_q complexes. The average and SD of the system potential energies were calculated every 800,000 steps (1.6 ns). All GaMD simulations were run at the “dual-boost” level by setting the reference energy to the lower bound. One boost potential was applied to the dihedral energetic term and the other to the total potential energetic term. The upper limit of the boost potential SD, σ₀ was set to 6.0 kcal/mol for both the dihedral and the total potential energetic terms.

CPPTRAJ was used to analyze the GaMD simulations⁵⁸. Root-mean-square fluctuations (RMSFs) were calculated for the agonist A61603, averaged over three independent GaMD simulations. Interactions between epinephrine and the receptor were measured by the distance between the CG atom of D106 and N1 atom of epinephrine, and the OG atom of S188 and O₂ atom of epinephrine. Interactions between A61603 and the receptor were measured by the distance between the CG atom of D106 and the N2 atom of A61603, the N atom of A189 and the O2 atom of A61603, the OG atom S188 and the N atom of A61603, the OG atom of S188 and the O atom of A61603, and the OG atom of S188 and the O2 atom of A61603. Time courses of these reaction coordinates obtained from the GaMD simulation were plotted in Fig. 3, Supplementary Figs. 5, 7, 9, and 10.

Calcium imaging

HEK293T cells were purchased from ATCC (CRL-11268; CRL-1573), authenticated by Bio-Synthesis, Inc. and routinely tested negative for mycoplasma. Cells were maintained in DMEM supplemented with 10% fetal bovine serum (FBS) in 5% CO₂ at 37^oC in humidified incubator. HEK293T cells were seeded on poly-L-lysine coated glass coverslips (18 mm) one day before transfection. GCaMP 6 f (0.2 μg per well) and α_1A-AR plasmid (0.5 µg per well) were used to transfect cells using Lipofectamine 2000 in 12-well plates. Cells were protected with 10 μM Prazosin 6–8 hours after transfection. Calcium imaging experiments were performed 24 hours after transfection. Cells were placed on an inverted Nikon Eclipse Ti2-E microscope equipped with an Andor Zyla 5.5 sCMOS camera, Lumencor SOLA-SE II light engine, and CFI Nikon Plan Apo Lambda ×20 with 0.75 NA. GCaMP6f was excited using 488 nm SOLA LED light. Cells were continuously perfused with extracellular (EX) solution (containing (in mM): 135 NaCl, 5.4 KCl, 10 HEPES, 2 CaCl₂, 1 MgCl₂, pH = 7.4) utilizing a gravity-driven perfusion system at room temperature. Drugs (A61603 or epinephrine) were dissolved in EX solution. Calcium imaging was analyzed using ImageJ 1.52a and NIS-Elements Advance Research 5.2.6. Each single cell was manually selected as region of interest and the fluorescence intensity was normalized to the baseline before agonist application. An averaged trace (60–200 cells per recording) was used to quantify maximum fluorescence amplitude after drug application. Dose–response curve was done using nonlinear fit in GraphPad Prism.

Quantification and statistical analysis

In Fig. 5, the Ca²⁺ assays were repeated three times, and the data are represented as mean ± SD of the three independent experiments. The analysis was done using the log(agonist) vs. response function of Prism 9 (GraphPad) as indicated in the figure legends. Cryo-EM data collection and refinement statistics are listed in Supplementary Table 1.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support this study are available from the corresponding authors upon request. The cryo-EM reconstructions of the A61603–α_1A-AR–G_q–scFv16 complex and the epinephrine–α_1A-AR–G_q–scFv16 complex have been deposited in the Election Microscopy Data Bank (EMDB) under accession codes EMD-41267 (the A61603–α_1A-AR–G_q–scFv16 complex) and EMD-41268 (the epinephrine–α_1A-AR–G_q–scFv16 complex), respectively. The corresponding atomic models have been deposited in the Protein Data Bank (PDB) under ID codes 8THK (the A61603–α_1A-AR–G_q–scFv16 complex) and 8THL (the epinephrine–α_1A-AR–G_q–scFv16 complex), respectively. Publicly available PDB entries used in this study are available under the accession codes 7B6W and 6WHA. The source data underlying Fig. 5c and 5f are provided as a Source Data file. Trajectories for molecular dynamics simulations can be found in the Figshare repository [https://doi.org/10.6084/m9.figshare.23664450.v1] and [https://doi.org/10.6084/m9.figshare.23664270.v1]. Source data are provided with this paper.

References

von Euler, U. S. Adrenergic neurotransmitter functions. Science 173, 202–206 (1971).
Article ADS Google Scholar
Wu, Y., Zeng, L. & Zhao, S. Ligands of adrenergic receptors: a structural point of view. Biomolecules 11, 936 (2021).
Article CAS PubMed PubMed Central Google Scholar
Philipp, M. & Hein, L. Adrenergic receptor knockout mice: distinct functions of 9 receptor subtypes. Pharmacol. Ther. 101, 65–74 (2004).
Article CAS PubMed Google Scholar
Ahles, A. & Engelhardt, S. Polymorphic variants of adrenoceptors: pharmacology, physiology, and role in disease. Pharmacol. Rev. 66, 598–637 (2014).
Article PubMed Google Scholar
Graham, R. M., Perez, D. M., Hwa, J. & Piascik, M. T. alpha 1-adrenergic receptor subtypes. Molecular structure, function, and signaling. Circ Res. 78, 737–749 (1996).
Article CAS PubMed Google Scholar
Proudman, R. G. W., Pupo, A. S. & Baker, J. G. The affinity and selectivity of alpha-adrenoceptor antagonists, antidepressants, and antipsychotics for the human alpha1A, alpha1B, and alpha1D-adrenoceptors. Pharmacol. Res. Perspect. 8, e00602 (2020).
Article CAS PubMed PubMed Central Google Scholar
Proudman, R. G. W. & Baker, J. G. The selectivity of alpha-adrenoceptor agonists for the human alpha1A, alpha1B, and alpha1D-adrenoceptors. Pharmacol. Res. Perspect. 9, e00799 (2021).
Article CAS PubMed PubMed Central Google Scholar
Papay, R. S. & Perez, D. M. alpha(1)-adrenergic receptors increase glucose oxidation under normal and ischemic conditions in adult mouse cardiomyocytes. J. Recept. Signal. Transduct. Res. 41, 138–144 (2021).
Article CAS PubMed Google Scholar
Perez, D. M. alpha(1)-adrenergic receptors: insights into potential therapeutic opportunities for COVID-19, heart failure, and Alzheimer’s disease. Int J Mol Sci 24, 4188 (2023).
Article CAS PubMed PubMed Central Google Scholar
Cavalli, A. et al. Decreased blood pressure response in mice deficient of the alpha1b-adrenergic receptor. Proc. Natl. Acad. Sci. USA 94, 11589–11594 (1997).
Article CAS PubMed PubMed Central ADS Google Scholar
Rokosh, D. G. & Simpson, P. C. Knockout of the alpha 1A/C-adrenergic receptor subtype: the alpha 1A/C is expressed in resistance arteries and is required to maintain arterial blood pressure. Proc. Natl. Acad. Sci. USA 99, 9474–9479 (2002).
Article CAS PubMed PubMed Central ADS Google Scholar
Tanoue, A. et al. The alpha(1D)-adrenergic receptor directly regulates arterial blood pressure via vasoconstriction. J. Clin. Invest. 109, 765–775 (2002).
Article CAS PubMed PubMed Central Google Scholar
O’Connell, T. D. et al. The alpha(1A/C)- and alpha(1B)-adrenergic receptors are required for physiological cardiac hypertrophy in the double-knockout mouse. J. Clin. Invest. 111, 1783–1791 (2003).
Article PubMed PubMed Central Google Scholar
Sanbe, A. et al. Alpha1-adrenoceptors are required for normal male sexual function. Br. J. Pharmacol. 152, 332–340 (2007).
Article CAS PubMed PubMed Central Google Scholar
Jensen, B. C., O’Connell, T. D. & Simpson, P. C. Alpha-1-adrenergic receptors: targets for agonist drugs to treat heart failure. J Mol. Cell Cardiol. 51, 518–528 (2011).
Article CAS PubMed Google Scholar
Zhang, J., Simpson, P. C. & Jensen, B. C. Cardiac alpha1A-adrenergic receptors: emerging protective roles in cardiovascular diseases. Am. J. Physiol. Heart Circ. Physiol. 320, H725–H733 (2021).
Article CAS PubMed Google Scholar
Perez, D. M. Current developments on the role of alpha1-adrenergic receptors in cognition, cardioprotection, and metabolism. Front. Cell. Dev. Biol. 9, 652152 (2021).
Article PubMed PubMed Central Google Scholar
Jensen, B. C., Swigart, P. M., Montgomery, M. D. & Simpson, P. C. Functional alpha-1B adrenergic receptors on human epicardial coronary artery endothelial cells. Naunyn Schmiedebergs Arch. Pharmacol. 382, 475–482 (2010).
Article CAS PubMed PubMed Central Google Scholar
Jones, C. J., DeFily, D. V., Patterson, J. L. & Chilian, W. M. Endothelium-dependent relaxation competes with alpha 1- and alpha 2-adrenergic constriction in the canine epicardial coronary microcirculation. Circulation 87, 1264–1274 (1993).
Article CAS PubMed Google Scholar
Wu, D., Katz, A., Lee, C. H. & Simon, M. I. Activation of phospholipase C by alpha 1-adrenergic receptors is mediated by the alpha subunits of Gq family. J. Biol. Chem. 267, 25798–25802 (1992).
Article CAS PubMed Google Scholar
Exton, J. H. Regulation of phosphoinositide phospholipases by hormones, neurotransmitters, and other agonists linked to G proteins. Annu. Rev. Pharmacol. Toxicol. 36, 481–509 (1996).
Article CAS PubMed Google Scholar
Sprang, S. R., Chen, Z. & Du, X. Structural basis of effector regulation and signal termination in heterotrimeric Galpha proteins. Adv. Protein Chem. 74, 1–65 (2007).
Article CAS PubMed Google Scholar
Knepper, S. M. et al. A-61603, a potent alpha 1-adrenergic receptor agonist, selective for the alpha 1A receptor subtype. J. Pharmacol. Exp. Ther. 274, 97–103 (1995).
CAS PubMed Google Scholar
Miao, Y., Feher, V. A. & McCammon, J. A. Gaussian accelerated molecular dynamics: unconstrained enhanced sampling and free energy calculation. J. Chem. Theory Comput. 11, 3584–3595 (2015).
Article CAS PubMed PubMed Central Google Scholar
Wang, J. N. et al. Gaussian accelerated molecular dynamics: principles and applications. Wires Comput. Mol. Sci. 11, e1521 (2021).
Article CAS Google Scholar
Deluigi, M. et al. Crystal structure of the alpha(1B)-adrenergic receptor reveals molecular determinants of selective ligand recognition. Nat. Commun. 13, 382 (2022).
Article CAS PubMed PubMed Central ADS Google Scholar
Zhou, Q. et al. Common activation mechanism of class A GPCRs. Elife 8, e50279 (2019).
Article PubMed PubMed Central Google Scholar
Ballesteros, J. A. & Weinstein, H. Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors. Methods Neurosci. 25, 366–428 (1995).
Article CAS Google Scholar
Strader, C. D. et al. Identification of residues required for ligand binding to the beta-adrenergic receptor. Proc. Natl Acad. Sci. USA 84, 4384–4388 (1987).
Article CAS PubMed PubMed Central ADS Google Scholar
Xu, X. et al. Binding pathway determines norepinephrine selectivity for the human beta1AR over beta2AR. Cell Res. 31, 569–579 (2021).
Article CAS PubMed Google Scholar
Ring, A. M. et al. Adrenaline-activated structure of beta2-adrenoceptor stabilized by an engineered nanobody. Nature 502, 575–579 (2013).
Article CAS PubMed PubMed Central ADS Google Scholar
Dalal, R. & Grujic, D. Epinephrine. In StatPearls (2022).
Kollman, P. A. et al. Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem. Res. 33, 889–897 (2000).
Article CAS PubMed Google Scholar
Genheden, S. & Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov. 10, 449–461 (2015).
Article CAS PubMed PubMed Central Google Scholar
Wang, E. et al. End-point binding free energy calculation with MM/PBSA and MM/GBSA: strategies and applications in drug design. Chem. Rev. 119, 9478–9508 (2019).
Article CAS PubMed Google Scholar
Han, Y. et al. Elucidation of a dynamic interplay between a beta-2 adrenergic receptor, its agonist, and stimulatory G protein. Proc. Natl. Acad. Sci. USA 120, e2215916120 (2023).
Article CAS PubMed PubMed Central Google Scholar
Kim, K. et al. Structure of a hallucinogen-activated Gq-coupled 5-HT(2A) serotonin receptor. Cell 182, 1574–1588 e1519 (2020).
Article CAS PubMed PubMed Central Google Scholar
Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).
Article CAS PubMed PubMed Central Google Scholar
Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3. Elife 7, e42166 (2018).
Article PubMed PubMed Central Google Scholar
Zivanov, J., Nakane, T. & Scheres, S. H. W. A Bayesian approach to beam-induced motion correction in cryo-EM single-particle analysis. IUCrJ 6, 5–17 (2019).
Article CAS PubMed PubMed Central Google Scholar
Su, M. et al. Structures of beta1-adrenergic receptor in complex with Gs and ligands of different efficacies. Nat. Commun. 13, 4095 (2022).
Article CAS PubMed PubMed Central ADS Google Scholar
Su, M. et al. Structural basis of the activation of heterotrimeric Gs-protein by isoproterenol-bound beta1-adrenergic receptor. Mol. Cell 80, 59–71.e54 (2020).
Article CAS PubMed PubMed Central Google Scholar
Alegre, K. O. et al. Structural basis and mechanism of activation of two different families of G proteins by the same GPCR. Nat. Struct. Mol. Biol. 28, 936–944 (2021).
Article CAS PubMed PubMed Central Google Scholar
Liu, S. et al. Differential activation mechanisms of lipid GPCRs by lysophosphatidic acid and sphingosine 1-phosphate. Nat. Commun. 13, 731 (2022).
Article CAS PubMed PubMed Central ADS Google Scholar
Rohou, A. & Grigorieff, N. CTFFIND4: Fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015).
Article PubMed PubMed Central Google Scholar
Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).
Article CAS PubMed Google Scholar
Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D Biol. Crystallogr. 60, 2126–2132 (2004).
Article PubMed ADS Google Scholar
Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D Biol. Crystallogr. 66, 213–221 (2010).
Article CAS PubMed PubMed Central ADS Google Scholar
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).
Article CAS PubMed Google Scholar
Chen, V. B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D Biol. Crystallogr. 66, 12–21 (2010).
Article CAS PubMed ADS Google Scholar
Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296–W303 (2018).
Article CAS PubMed PubMed Central Google Scholar
Lee, J. et al. CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J. Chem. Theory Comput. 12, 405–413 (2016).
Article CAS PubMed Google Scholar
Maier, J. A. et al. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 11, 3696–3713 (2015).
Article CAS PubMed PubMed Central Google Scholar
Dickson, C. J., Walker, R. C. & Gould, I. R. Lipid21: complex lipid membrane simulations with AMBER. J. Chem. Theory Comput. 18, 1726–1736 (2022).
Article CAS PubMed PubMed Central Google Scholar
Wang, J., 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).
Article CAS PubMed Google Scholar
Darden, T., York, D. & Pedersen, L. Particle mesh Ewald: an N⋅ log (N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089 (1993).
Article CAS ADS Google Scholar
Case, D. A. et al. Amber 2020. https://ambermd.org/doc12/Amber20.pdf (2020).
Roe, D. R. & Cheatham, T. E. PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput. 9, 3084–3095 (2013).
Article CAS PubMed Google Scholar

Download references

Acknowledgements

We thank members of our research groups for helpful discussion and comments on the manuscript, the Laboratory for BioMolecular Structure (L.B.M.S.) staff, and the New York Structural Biology Center staff for the help with the cryo-EM data acquisition. This work was supported by NIH grants GM138676 (X.Y.H.) and GM132572 (Y.M.), as well as AHA grant 23CDA1049353 (https://doi.org/10.58275/AHA.23CDA1049353.pc.gr.168015) (M.S.). This work used supercomputing resources with allocation award TG-MCB180049 through the Extreme Science and Engineering Discovery Environment (XSEDE) and project M2874 through the National Energy Research Scientific Computing Center (NERSC), and the Research Computing Cluster and BigJay Cluster resource funded through NSF Grant MRI-2117449 at the University of Kansas. L.B.M.S. is supported by the DOE Office of Biological and Environmental Research (KP1607011). Some of this work was performed at the Simons Electron Microscopy Center and National Resource for Automated Molecular Microscopy located at the New York Structural Biology Center, supported by grants from the Simons Foundation (SF349247), NYSTAR, the Agouron Institute (F00316), and the NIH (GM103310, OD019994).

Author information

These authors contributed equally: Minfei Su, Jinan Wang.

Authors and Affiliations

Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, NY, 10065, USA
Minfei Su & Xin-Yun Huang
Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, 66047, USA
Jinan Wang, Hung Nguyen Do & Yinglong Miao
Department of Biochemistry, Weill Cornell Medical College of Cornell University, New York, NY, 10065, USA
Guoqing Xiang & Joshua Levitz
Department of Psychiatry, Weill Cornell Medical College of Cornell University, New York, NY, 10065, USA
Guoqing Xiang & Joshua Levitz

Authors

Minfei Su
View author publications
You can also search for this author in PubMed Google Scholar
Jinan Wang
View author publications
You can also search for this author in PubMed Google Scholar
Guoqing Xiang
View author publications
You can also search for this author in PubMed Google Scholar
Hung Nguyen Do
View author publications
You can also search for this author in PubMed Google Scholar
Joshua Levitz
View author publications
You can also search for this author in PubMed Google Scholar
Yinglong Miao
View author publications
You can also search for this author in PubMed Google Scholar
Xin-Yun Huang
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

M.S. expressed and purified α_1A-AR, mini-GαqiN–Gβ1–Gγ2, and the protein complexes, made cryo-EM grids, performed cryo-EM screening, data collection, EM density map determination, and model building. J.W. and H.N.D. performed GaMD simulations under the supervision of Y.M. G.X. performed the calcium imaging under the supervision of J.L. X.Y.H. supervised the project, interpreted data, and wrote the manuscript. All authors contributed to the final version of the manuscript.

Corresponding authors

Correspondence to Yinglong Miao or Xin-Yun Huang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review information

Nature Communications thanks Marcel Bermudez and Arthur Christopoulos for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Peer Review File

Reporting Summary

Source data

Source Data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Su, M., Wang, J., Xiang, G. et al. Structural basis of agonist specificity of α_1A-adrenergic receptor. Nat Commun 14, 4819 (2023). https://doi.org/10.1038/s41467-023-40524-2

Download citation

Received: 08 February 2023
Accepted: 27 July 2023
Published: 10 August 2023
DOI: https://doi.org/10.1038/s41467-023-40524-2

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.