Unveiling the Activation Mechanism of Glucagon-Like Peptide-1 Receptor by an Ago-Allosteric Modulator via Molecular Dynamics Simulations
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Unveiling the Activation Mechanism of Glucagon-Like Peptide-1 Receptor by an Ago-Allosteric Modulator via Molecular Dynamics Simulations
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Journal of Chemical Information and Modeling

Cite this: J. Chem. Inf. Model. 2026, XXXX, XXX, XXX-XXX
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https://doi.org/10.1021/acs.jcim.6c00224
Published March 25, 2026

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Abstract

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The glucagon-like peptide-1 receptor (GLP-1R) is a key therapeutic target for metabolic disorders, particularly type 2 diabetes and obesity. Although current treatments are effective, their unavoidable side effects continue to drive the search for novel therapeutic strategies. Ago-allosteric modulators (ago-PAMs), which act as agonists on their own while enhancing the affinity and efficacy of orthosteric agonists, represent a promising avenue to overcome limitations associated with traditional peptide-based therapies. However, the molecular mechanisms by which ago-PAMs modulate GLP-1R activation remain poorly understood. In this work, we selected compound 2, a validated ago-PAM of GLP-1R, as a probe to explore these mechanisms at the atomic level. Using molecular dynamics (MD) simulations, we elucidate how compound 2 stabilizes the active conformation of GLP-1R through allosteric binding and reveal distinct pathways by which it enhances the binding of both peptide and non-peptide orthosteric agonists. Enhanced sampling simulations further provided a comprehensive conformational landscape of GLP-1R activation, identifying two intermediate states that bridge inactive and active conformations. Compound 2 was found to bias the receptor toward active-like ensembles, consistent with its intrinsic agonist activity. Together, our findings provide mechanistic insights into ago-allosteric modulation of GLP-1R, offering useful information for the rational design of small-molecule modulators with improved therapeutic profiles.

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Introduction

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The glucagon-like peptide-1 receptor (GLP-1R), a class B G protein-coupled receptor (GPCR), is primarily expressed in pancreatic β-cells, playing a critical role in maintaining glucose homeostasis and metabolic balance. (1,2) Upon activation by its endogenous peptide ligand GLP-1, GLP-1R stimulates insulin secretion, suppresses glucagon release, and delays gastric emptying. These physiological effects have established GLP-1R as a highly validated therapeutic target for the treatment of type 2 diabetes and obesity. (3,4) Beyond the pancreas, GLP-1R is also found in the central nervous system (CNS) and is implicated in a range of physiological processes, including cardiovascular function, neurotrophic support, anti-inflammatory responses, and renal protection. (5−8) These broader roles have expanded therapeutic interest in GLP-1R agonists for conditions such as chronic kidney disease (CKD), nonalcoholic steatohepatitis (NASH), cardiovascular diseases (CVDs), and Parkinson’s disease (PD). (9−12)
Structurally, GLP-1R shares the common seven-transmembrane helical architecture of GPCRs, but like other class B receptors, it features a large extracellular N-terminal domain (ECD) composed of over 100 amino acid residues. (13,14) Since 2017, high-resolution cryo-electron microscopy (cryo-EM) has provided significant insights into GLP-1R activation mechanisms. These structural studies have revealed multiple ligand-binding modes, distinct ECD conformations, a characteristic outward displacement of transmembrane helix 6 (TM6), and G protein engagement at the intracellular surface, together forming a molecular basis for understanding ligand recognition and receptor activation. (15)
Currently, the therapeutic landscape of GLP-1R is dominated by peptide agonists, including liraglutide, dulaglutide, and semaglutide. (8,16,17) While these agents are clinically effective, they present notable drawbacks such as poor oral bioavailability, gastrointestinal side effects (e.g., nausea and vomiting), and the need for parenteral administration. (18,19) These limitations have spurred efforts to develop orally available small-molecule GLP-1R agonists, which could improve both pharmacokinetic profiles and patient adherence. Several candidates, including danuglipron, lotiglipron, and orforglipron, have shown promise in clinical trials. (20−22) However, none have reached the market to date, partly due to challenges in achieving sufficient selectivity (given the highly conserved orthosteric sites within the glucagon receptor subfamily) and in replicating the extensive receptor-peptide interaction network using small, non-peptide molecules.
An emerging strategy to address these challenges involves the use of positive allosteric modulators (PAMs), compounds that can enhance orthosteric ligand activity by engaging non-overlapping, allosteric sites. (23,24) Among them, several PAMs can also activate the receptor on their own without an orthosteric agonist, which are called ago-allosteric modulators (ago-PAMs) and compound 2 is a prototypical example of this class. (25) Although its clinical development was discontinued due to poor pharmacokinetics, (26) compound 2 effectively activates GLP-1R and enhances orthosteric ligand binding. (27) It therefore remains a valuable probe for dissecting allosteric mechanisms and understanding how intracellular binding sites modulate receptor conformational dynamics. Such insights could support future structure-guided design of improved GLP-1R-targeted therapeutics.
Recently resolved cryo-EM structures of GLP-1R in complex with compound 2, alone and together with orthosteric ligands, provide a foundation for studying ago-allosteric modulation at atomic resolution. (28) In this study, we employed extensive molecular dynamics (MD) simulations, enhanced sampling techniques, and quantum chemical calculations to characterize the effects of compound 2 on GLP-1R activation, conformational stability, and cooperative interactions with both peptide and non-peptide orthosteric agonists. Our findings offer mechanistic insights into compound 2-induced conformational selection and allosteric coupling, advancing the structural basis for the rational design of next-generation GLP-1R modulators with improved drug-like properties.

Results and Discussion

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Compound 2 Binding Stabilizes the Active Conformation of GLP-1R

GPCRs are intrinsically dynamic proteins, and it is well established that full stabilization of their active conformation typically requires the coordinated binding of both an orthosteric ligand and an intracellular transducer. (29−31) Interestingly, compound 2, an ago-PAM, has been shown to fully activate GLP-1R despite binding outside the orthosteric site. (25,27) To investigate how compound 2 influences GLP-1R dynamics, we performed 2-μs unbiased MD simulations starting from the active-state cryo-EM structure of the GLP-1R–compound 2 complex (PDB ID: 7DUR (28), Figure 1A) with and without compound 2. It is important to note that these simulations, initiated from an experimentally active-state structure, are designed to evaluate the stability and maintenance of the active conformation in the absence of G protein rather than to capture the full activation transition between inactive and active states. The latter process is explored through enhanced sampling simulations described below.

Figure 1

Figure 1. (A) Overall structure of GLP-1R shown in purple cartoon bound to compound 2 (brown sticks) (PDB ID: 7DUR (28)). (B) TM6 movement during 2-μs production simulations of apo (green) and compound 2-bound (orange) GLP-1R, quantified by the Cα distance between Y2503.53 and K3466.35. Representative snapshots sampled from each trajectory are superimposed onto the starting structure (purple) for comparison. (C) Residue-wise importance profiles derived from RF analysis highlight structural differences between apo and compound 2-bound GLP-1R conformations. (D) Distribution of significant structural features distinguishing apo and compound 2-bound GLP-1R states. Representative active cryo-EM structures of GLP-1R includes 7DUR, 7DUQ, 7E14. (28)

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A hallmark of GPCR activation is the outward displacement of transmembrane helix 6 (TM6), which opens the intracellular G protein-binding site. (30) We therefore monitored TM6 movement, quantified by the Cα-Cα distance between Y2503.53 and K3466.35, across both systems. As shown in Figure 1B, both apo and compound 2-bound receptors stay in an active-like conformation throughout the simulations, maintaining a partially open G protein-binding site (>20 Å). Closer examination of TM6 dynamics revealed that compound 2 induces a subtle but consistent difference: the G protein-binding site remained ∼2 Å wider in the presence of compound 2 than in the apo system. This suggests that even in the absence of G protein, compound 2 can partially stabilize the fully active conformation of GLP-1R.
To assess whether compound 2 exerts allosteric effects on the extracellular domain, particularly the N-terminal region, which plays a crucial role in Class B GPCRs activation, (14,28,32) we analyzed structural features distinguishing the apo and compound 2-bound states. We extracted 5,000 snapshots from trajectories of each system and used both supervised (Random Forest, RF) and unsupervised (Principal Component Analysis, PCA) machine learning methods to identify key discriminative features. In addition to TM6, several residues in the N-terminal domain and extracellular loops, notably R64N-term and D215ECL1, were highlighted with normalized importance scores above 0.80 (Figure 1C and Figure S1). Further distance analyses involving the two residues shown in Figure 1D, including the closest heavy-atom distances for R64N-term-L217ECL1, V100N-term-D215ECL1, and R64N-term-D215ECL1, revealed clear separation of snapshots into two clusters. Notably, compound 2-bound conformations overlapped with experimentally determined active-state structures. These results thus reveal that compound-2 bound snapshots can maintain the active-like distances between the N-term and ECL1 and suggest that compound 2 binding promotes positive allosteric communication between the extracellular loops and the N-terminal domain.
Together, our findings demonstrate that compound 2 binding at the intracellular region can stabilize a fully open G protein-binding site and allosterically promote an activated conformation of the extracellular domain.

Binding Mode and Covalent Interaction of Compound 2 with GLP-1R

Previous structural study (28) has revealed that compound 2 binds at the membrane-facing side of the cytoplasmic end of TM6 of GLP-1R (Figure 2A), forming a covalent bond with residue C3476.36. Key stabilizing interactions include hydrophobic contacts with A3506.39 and K3516.40, which have been shown to contribute to the compound’s potency and efficacy in promoting cAMP accumulation. Additionally, a cholesterol molecule was resolved in TM6, which was proposed to enhance the stability of compound 2 within the allosteric binding pocket.

Figure 2

Figure 2. (A) Compound 2-binding mode at the GLP-1R. (B) Changes in interaction frequencies between compound 2 and nearby residues at the allosteric site. (C) Computed energy profiles for the reaction between Cys3476.36 and compound 2. Insets illustrate the key features of the reactant, transition, and product states for two possible pathways: one with Cys3476.36 in its protonated form and the other in its deprotonated form.

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However, while static structures offer valuable insights into ligands recognition, GPCRs are inherently dynamic proteins. In this case, it remains unclear how compound 2 interacts with its environment over time and how these interactions fluctuate during the receptor activation. To address this, we performed ligand-protein interaction analysis of compound 2-GLP-1R complex, tracking how its binding interactions evolved across the simulation ensembles. Consistent with functional data, (28,33) our simulations confirmed that A3506.39 and K3516.40 play dominant roles in stabilizing compound 2 via persistent hydrophobic and hydrogen bonding interactions (Figure 2B). We also observed stable hydrophobic contacts with I3456.34 and L3496.38. However, previous mutagenesis studies suggest that L3496.38 may not be essential for downstream signaling, (34,35) pointing to a supportive but non-essential role for this contact. Interestingly, although the cholesterol is clearly resolved in the initial cryo-EM structure near the compound 2 binding site, our simulations revealed only transient association, with direct contact maintained in ∼20% of the trajectory. This dynamic behavior suggests that, while cholesterol may preferentially localize to this membrane-facing interface, it does not function as a rigid structural cofactor required to maintain the integrity of the allosteric pocket. Instead, cholesterol is more likely to influence the local membrane microenvironment and receptor conformational dynamics in a non-obligatory manner, rather than serving as a fixed architectural component.
We also performed quantum chemical calculations to evaluate the energy profile for the formation of the covalent bond between compound 2 and C3476.36. Both protonated and deprotonated methanethiol was considered as a simplified cysteine model. (36−38) When using the deprotonated methanethiol, the activation barrier for the substitution reaction was 3.2 kcal/mol. In contrast, the protonated methanethiol gave a barrier of 37.2 kcal/mol at the same level of theory (Figure 2C), making the reaction essentially inaccessible. This indicates that the highly solvent-exposed C3476.36 in the tail region of TM6 is most likely deprotonated under physiological conditions. The calculated energies of the product complexes were 30.3 and 14.2 kcal/mol lower than the sum of the separated reactants in the deprotonated and protonated methanethiol systems, respectively (Table S1). These results suggest that product formation is thermodynamically favorable regardless of cysteine protonation state. Notably, compared to many typical acrylamide or aromatic 2-chloroacetamide warheads, (37,39) compound 2 exhibits much higher reactivity toward deprotonated cysteine. Formation of the stable covalent complex is expected to enhance persistence of intracellular engagement and thereby influence TM6 dynamics, potentially via an induced-fit stabilization mechanism.
Experimental evidence further supports the importance of covalent engagement for compound 2 pharmacology. Non-reactive analogs fail to produce measurable cAMP responses, and mutation of C3476.36 significantly diminishes compound 2 potency without affecting GLP-1 activity. (28,33) These observations indicate that covalency is critical for the pharmacological efficacy of compound 2, but not intrinsically required for GLP-1R activation itself. In this context, covalent attachment likely enhances intracellular residence time and stabilization of TM6 displacement. However, our simulations reflect sustained intracellular engagement (Figure 1B) and may, in principle, be reproduced by high-affinity reversible modulators that stabilize similar conformational states.

Compound 2 Enhances the Binding of Orthosteric Ligands via Allosteric Synergy

As an ago-allosteric modulator, compound 2 not only activates GLP-1R in the absence of orthosteric ligands, but also enhances the binding affinity of both peptide and small-molecule agonists at the orthosteric site. (25,28) To explore the mechanism underlying this modulatory effect, we selected two representative agonists for subsequent MD simulations: the endogenous peptide agonist GLP-1 (residues S7-R36, Figure 3A) and the synthetic small-molecule agonist LY3502970 (Figure 3C). Both exhibit enhanced binding in the presence of compound 2, as previously confirmed experimentally. (25,28) Accordingly, we performed 2-μs MD simulations for four systems, respectively, including GLP-1-GLP-1R, Compound 2-GLP-1-GLP-1R, LY3502970-GLP-1R, and Compound 2-LY3502970-GLP-1R. Initial coordinates were derived from the respective cryo-EM structures. (25,28)

Figure 3

Figure 3. Compound 2 enhances the binding of GLP-1 or LY3502970 at the orthosteric site. (A) Overall structure of the compound 2-GLP-1-GLP-1R complex, highlighting the GLP-1 binding mode. (B) Changes in interaction frequencies between GLP-1 and nearby residues with and without compound 2. Solid bars represent the apo system, while dashed bars indicate the compound 2-bound system. (C) Overall structure of the compound 2-LY3502970-GLP-1R complex, highlighting the LY3502970 binding mode. (D) Changes in interaction frequencies between LY3502970 and nearby residues with and without compound 2. Solid bars represent the apo system, while dashed bars indicate the compound 2-bound system.

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To evaluate the impact of compound 2 on GLP-1 binding, we first compared the RMSDs of GLP-1 in the two GLP-1-containing systems. Although initial fluctuations were observed in both systems, compound 2 contributed to a more stable positioning of GLP-1 within the binding pocket. (Figure S2). Further interaction analysis revealed enhanced contacts between GLP-1 residues (H7, E9, T13, S17, A24, L32, and V33) and surrounding receptor residues, including R121N-term, K1972.67, Y2052.75, Q211ECL1, R3105.40, and T3917.46 (Figure 3A and B). Notably, the increased frequency of hydrophobic and hydrogen bond interactions, specifically between T13-K1972.67, S17-Y2052.75, L32-R121N-term, and V33-R121N-term, likely underpins the enhanced binding affinity observed in the presence of compound 2. These findings align with experimental mutagenesis data showing that substitutions at R121N-term, K1972.67, and Y2052.75 significantly reduce GLP-1 potency and affinity, . (35,40−43) Additional stabilizing contacts were observed between H7-R3105.40, E9-T3917.46, A24-Y2052.75, W31-Q211ECL1, and L32-Y2052.75, further supporting the role of these residues in maintaining GLP-1 in an active-bound conformation.
In contrast, LY3502970 binding exhibited a distinct modulation pattern (Figure 3C and D). Across both LY3502970-containing systems, a stable hydrogen bond with Y2203.32, a known activation-critical contact, was consistently observed. (42−44) However, compound 2 primarily affected hydrophobic interactions, enhancing contacts involving E1381.33, L1421.37, K2022.72, and Y2052.75. Among these, K2022.72 and Y2052.75 have been experimentally validated as key modulators of receptor activation through mutagenesis studies. (42,43)
Our findings highlight K1972.67 and Y2052.75 as central residues mediating the allosteric enhancement of both peptide and small-molecule agonist binding in the presence of compound 2. The distinct contact profiles between the GLP-1 and LY3502970 systems underscore the ligand-specific modes of receptor engagement, implying that compound 2 supports receptor activation through differentiated, conformationally selective allosteric pathways.

Dynamic Allosteric Networks Reveal Ligand-Specific Signaling in GLP-1R

Extensive research has focused on ligand recognition and the interactions between GPCRs and intracellular transducers, providing critical insights into receptor activation and downstream signaling mechanisms. (45−49) However, the allosteric communication occurring within the transmembrane domain remains largely unexplored, a “black box” in many cases. To better understand the molecular basis of the allosteric synergy between compound 2 and orthosteric agonists, we constructed dynamic residue interaction networks from conventional MD simulations of four systems at the active state: GLP-1-GLP-1R, compound 2-GLP-1-GLP-1R, LY3502970-GLP-1R, and compound 2-LY3502970-GLP-1R. These network models enabled us to trace optimal pathways of signal propagation from the intracellular to extracellular domains. Using the NetworkView plugin in VMD (see Methods), we treated individual residues as nodes and persistent interactions as edges, weighted by interaction frequency. C3476.36 was designated as the source node, while the sink nodes were R36 of GLP-1 or the centroid of LY3502970, respectively, in order to compute the shortest allosteric communication pathways.
As shown in Figure 4, in the GLP-1-GLP-1R system, the dominant signal route travels through residues in TM5 before reaching the extracellular loop 2 (ECL2), where R299 and S301 serve as key relay points, ultimately linking to GLP-1 at S14. This path highlights a classical receptor-peptide allosteric coupling mechanism via both the helical core and extracellular domains. (48,50) Interestingly, compound 2 binding induces a significant shift in the pathway within the compound 2-GLP-1-GLP-1R system. The signal is now primarily transmitted through TM6, directly linking to GLP-1 via residues such as E3646.53, which is known to be essential for ligand binding and receptor activation. (41,42,50) This compound 2-specific transduction route emphasizes a distinct conformational landscape and presents a potentially valuable framework for the development of future ago-allosteric modulators targeting GLP-1R.

Figure 4

Figure 4. Allosteric signal pathways from the intracellular to extracellular domains in GLP-1R. Optimal communication pathways were computed from C3476.36 (source node) to R36 of GLP-1 in the GLP-1-GLP-1R and compound 2-GLP-1-GLP-1R systems, and from C3476.36 to the centroid of LY3502970 in the LY3502970-GLP-1R and compound 2-LY3502970-GLP-1R systems. Residues involved in the allosteric pathways are shown as spheres and colored according to their community assignments. Connecting edges represent inter-residue communication, with line widths proportional to edge betweenness.

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In the LY3502970-GLP-1R system, TM5 also plays a central role in allosteric communication. However, unlike the peptide-bound system, extracellular loops are not involved in the signal propagation, reflecting a more compact and localized binding characteristic of the small-molecule agonist. Strikingly, in the compound 2-LY3502970-GLP-1R system, the allosteric network is further reorganized. TM5 remains key for signaling transmission, but enhanced coupling is observed between TM3, TM4, and TM5, suggesting a more integrated communication interface that strengthens ligand-receptor interactions. Notably, F2303.33 and W2844.60 emerge as key bridging residues between TM5 and LY3502970, underscoring a compound 2-mediated enhancement of the non-peptide agonist’s binding.
These results reveal that compound 2 exerts ligand-specific modulation of the GLP-1R allosteric network. While TM6 becomes more prominent in peptide-mediated signaling, TM3 and TM4 are more involved in stabilizing the receptor conformation for non-peptide ligands. These findings support a versatile role of compound 2 in fine-tuning receptor pharmacology through distinct, ligand-dependent allosteric pathways.

Activation Mechanism of GLP-1R Revealed by Enhanced Sampling Simulations

Advances in structural biology have led to the resolution of numerous GPCR structures, offering key insights into ligand recognition, receptor activation, and downstream signaling mechanisms. (49,51,52) However, these static snapshots fall short of fully capturing the dynamic process of receptor activation, particularly the transient intermediate states that bridge inactive and active conformations. Experimental approaches such as cryo-EM and X-ray crystallography often struggle to resolve these short-lived intermediates, prompting increasing reliance on MD simulations to probe GPCR conformational dynamics. (52−54)
To elucidate the conformational landscape underlying GLP-1R activation and to characterize potential intermediate states, our first attempt was to extend the 2-μs unbiased MD simulations of both apo and compound 2-bound GLP-1R systems to 5 μs. As shown in Figure S3, although compound 2 cannot stabilize the receptor in a fully open state after 2-μs simulations, which aligns with previous studies showing that GPCRs, in the absence of intracellular effectors, fail to stabilize a fully active conformation, (31) both systems still remained partially open throughout, with TM6 outward displacement exceeding 20 Å. Neither transitioned to a canonical inactive state, which would feature a more compact intracellular region and a TM3-TM6 distance of ∼10 Å. These findings highlight the limitations of conventional MD simulations in overcoming high energy barriers during large-scale conformational transitions.
To address this, we employed the string method with swarms of trajectories (see Methods), an enhanced sampling technique designed to map the most probable activation pathway between two known endpoint conformations. This approach has proven successful in capturing functionally relevant transitions in membrane proteins including GPCRs, ion channels, and transporters. (55−57) We applied this method to GLP-1R in the absence and presence of compound 2, GLP-1, and LY3502979, respectively, running 400 iterations of string refinement (2.24 μs in total), followed by analysis of the final 200 iterations to construct free energy landscapes. We confirmed the robustness of these landscapes by calculating the associated uncertainties, finding them to be below 2 kT across the relevant conformational space (Figure S8).
Figure 5A presents the resulting free energy profiles projected onto two key reaction coordinates: the outward movement of TM6 (measured by the Cα distance between Y2503.35 and K3466.35) and the swing of the N-terminal α-helix (measured by the Cα distance between W33N-term and F3857.40). All systems explored a similar conformational space, showing a clear positive correlation between the two features. This indicates that intracellular rearrangement and extracellular helix motion occur in a coordinated manner during activation, in contrast to most Class A GPCRs. Previous studies have shown that, although conformational changes in the extracellular and intracellular domains of Class A GPCRs can influence each other during activation, they can still occur independently, suggesting a more complex activation mechanism. (58,59)

Figure 5

Figure 5. (A) Two-dimensional free energy landscapes of apo and compound 2-, GLP-1-, and LY3502970-bound GLP-1R systems, projected along the outward movement of TM6 (measured by the Cα distance between Y2503.35 and K3466.35) and the swing of the N-terminal α-helix (measured by the Cα distance between W33N-term and F3857.40). (B) Four representative conformations, inactive (cyan), intermediate 1 (I1, green), intermediate 2 (I2, pink), and active (orange), extracted from the string simulation trajectory of apo GLP-1R and superimposed on the inactive experimental structure (gray), corresponding to local energy minima in the free energy landscape.

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In the apo system, four distinct energy basins were identified: the canonical inactive and active states, as well as two intermediate conformations (I1 and I2). These major basins are separated by small free-energy differences, typically within 0–0.5 kT (approximately 0–0.3 kcal/mol). Such small barriers indicate that the receptor can readily sample multiple conformational states, allowing frequent transitions between inactive, intermediate and active-like conformations. This observation is consistent with the inherent flexibility of apo GPCRs and their preference for inactive-like conformations in the absence of an agonist. Upon ligand binding, whether by the orthosteric agonists GLP-1 and LY3502970 or the allosteric modulator compound 2, no major changes were observed in the overall shape of the free-energy landscape. However, the population distribution shifts toward the active-like basin. In these ligand-bound systems, the active basin remains within 0.5 kT of the global minimum, whereas the inactive and intermediate basins are shifted upward to approximately 1–2 kT (0.6–1.2 kcal/mol). Although these energetic differences are modest, they correspond to statistically meaningful population biases that favor active-like conformations. It is important to note that the free energies derived from these landscapes represent relative statistical populations rather than intrinsic thermodynamic barriers. (55) Additionally, no new intermediates were observed in the ligand-bound system, supporting a mechanism of conformational selection.
To further visualize the activation process, representative structures corresponding to each energy basin in the apo landscape were extracted (Figure 5B). These include the inactive (cyan), intermediate I1 (green), intermediate I2 (pink), and active (orange) states, aligned to the experimental inactive structure (gray). The inactive model closely matches the reference, aside from loop fluctuations in the extracellular domain. In I1, TM6 undergoes modest displacement, but a significant shift in the N-terminal α-helix of the ECD suggests that activation initiates at the extracellular domain. Progressing to I2, the helix continues its outward swing, and the intracellular pocket partially opens, potentially allowing for G protein engagement. Full activation involves further stabilization of the transducer-binding site and an anticlockwise rotation of the N-terminal α-helix of the ECD into a vertical orientation.
These simulations provide a detailed map of the conformational transition pathway of GLP-1R, revealing two intermediate states that mediate the shift from inactive to active conformations. The free energy landscape confirms that compound 2 promotes activation by stabilizing preexisting active-like states rather than inducing new intermediates. We propose a stepwise activation model in which the process is initiated by structural changes in the extracellular domain, followed by partial intracellular opening, effector binding, and final allosteric reinforcement of the fully active state via the remodeling of N-terminal α-helix of the ECD.
Although compound 2 was discontinued from clinical development due to pharmacokinetic limitations, the mechanistic insights presented here are not restricted to this specific molecule. Importantly, several key observations are expected to extend to other intracellular ago-PAMs or reversible modulators. First, stabilization of the outward TM6 displacement through intracellular engagement represents a general mechanism for promoting G protein-compatible conformations. Second, a coordinated coupling between intracellular rearrangement and extracellular helix motion highlights the importance of long-range allosteric communications in GLP-1R activation. Third, the redistribution of conformational populations toward active-like states supports a conformational selection model that should be broadly applicable to reversible modulators capable of stabilizing similar intracellular pockets. These findings therefore provide a transferable mechanistic framework for the rational design of next-generation intracellular ago-PAMs with improved pharmacokinetic and safety profiles.

Conclusions

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GLP-1R has become a highly attractive drug target due to its proven therapeutic efficacy in metabolic disorders, (1−4) piquing intense interest from both academia and industry. While previous structural and molecular dynamics studies have substantially advanced our understanding of ligand binding, receptor activation, and allosteric modulation, (15,60,61) how intracellular modulators reshape long-range allosteric communication in a ligand-dependent manner remains incompletely understood. In this work, we employed compound 2, an ago-allosteric modulator that functions both as a direct agonist and as an enhancer of orthosteric ligand efficacy, as a chemical probe to address this gap. By integrating long-timescale MD simulations, enhanced sampling, and dynamic network analysis, we 1) delineated ligand-specific rewiring of allosteric communication pathways across the receptor and 2) reconstructed the inactive-to-active conformational transition, revealing intermediate states not captured in static structural snapshots. This dynamic perspective extends current structural insights toward a pathway-resolved mechanistic framework for intracellular ago-allosteric modulation.
Starting from an active-state cryo-EM structure of GLP-1R bound to compound 2, (28) our MD simulations revealed that compound 2 not only stabilizes a fully open G protein-binding site but also promotes active-like conformations in the N-terminal region and extracellular loops. In agreement with structural data, our analysis confirmed its covalent attachment to C3476.36 and stabilization via surrounding hydrophobic interactions. We further highlighted the critical roles of residues A3506.39 and K3516.40 in ligand binding, while cholesterol interactions were found to be transient (∼20% occupancy), suggesting a dynamic modulatory role. To elucidate how the covalent bond forms between compound 2 and C3476.36, we considered two pathways: one in which cysteine attacks in its protonated form, and another in its deprotonated form. Our quantum chemical calculations support the deprotonated pathway, which requires only a 3.2 kcal/mol barrier to yield the final stable product complex.
As a positive allosteric modulator, compound 2 has been shown to enhance the binding of both the peptidic agonist GLP-1 and the small-molecule agonist LY3502970. However, the underlying mechanisms remained unclear. Our simulations indicate that compound 2 modulates GLP-1 and LY3502970 binding through distinct residue interactions, consistent with their differing binding modes. Notably, K1972.67 and Y2052.75 were found to play significant roles in stabilizing both agonists, consistent with previous mutagenesis studies. (41−43) Dynamic network analysis further elucidated how compound 2 reshapes signaling pathways. In the absence of the allosteric modulator, TM5 plays a central role in passing activation signals from orthosteric agonists. Upon compound 2 binding, the communication network is reorganized. TM6 becomes dominant in the GLP-1-bound system, whereas TM3-TM4 coupling is strengthened in the LY3502970-bound system. These findings suggest that compound 2 engages distinct allosteric mechanisms depending on the nature of the orthosteric ligand, offering valuable insight for both peptide-based and small-molecule drug design.
Given the importance of protein flexibility in function, we employed enhanced sampling techniques to construct the full conformational landscape of GLP-1R activation. This approach captured two intermediate states that bridge the inactive-to-active transition. Our results suggest that both orthosteric agonists GLP-1 and LY3502970 or the ago-PAM compound 2 exert their agonistic effect through a conformational selection mechanism, shifting the population toward active-like ensembles. Based on structural transitions observed throughout the activation process, we propose a stepwise model (Figure 6): agonist binding (whether an orthosteric agonist or ago-allosteric modulator) initiates extracellular domain rearrangement, followed by partial intracellular opening that permits effector engagement. This in turn stabilizes N-terminal α-helix of the ECD remodeling, driving the receptor to a fully active conformation.

Figure 6

Figure 6. Schematic illustration of ligand-mediated GLP-1R activation. In the absence of ligand, GLP-1R dynamically transitions between inactive and intermediate-like states. Binding of agonists to either the orthosteric or allosteric sites induces conformational rearrangements in both the extracellular and intracellular domains, promoting engagement of downstream transducers and driving the receptor toward its fully active state. SM: small molecule; PAM: positive allosteric modulator.

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Taken together, our findings advance our mechanistic understanding of ago-allosteric modulation at GLP-1R and provide a structural framework to guide future rational design of more efficacious and druggable allosteric modulators.

Methods

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Simulation System Setup

Four experimental structures were used to prepare the glucagon-like peptide 1 receptor (GLP-1R) systems: three in the active state and one in the inactive state. The active-state structures included: GLP-1R bound to compound 2 alone (PDB ID: 7DUR (28)), GLP-1R bound to both compound 2 and GLP-1 (PDB ID: 7DUQ (28)), and GLP-1R bound to compound 2 and the non-peptide ligand LY3502970 (PDB ID: 7E14 (28)). The inactive-state structure was unliganded (PDB ID: 6LN2 (62)).
G proteins and nanobodies were removed from all structures, and missing residues were modeled using Modeller 10.2. (63) Specifically, the following segments were repaired: A57-L60, S129-S135, M340-K342, and F369-L379 in the compound 2-GLP-1R complex; K130-S135 and L339-T343 in the compound 2-GLP-1-GLP-1R complex; S129-R134, N338-T343, and F369-R376 in the compound 2-LY3502970-GLP-1R complex; and S129-R134 and S258-S261 in the inactive unliganded structure. Ten mutations in the 6LN2 structure (S193C, I196F, M233C, S271A, S225A, G318I, K346A, C347F, I317C, and G361C) were reverted to the wild-type residues.
All titratable residues were assigned their dominant protonation states at pH 7.0. Each system was embedded in a lipid bilayer composed of 160 POPC lipids and 40 cholesterol molecules, and solvated in a TIP3P water box containing 0.15 M NaCl using CHARMM-GUI. (64) The CHARMM36m force field (65) was applied. Systems were energy minimized, then gradually thermalized and equilibrated under decreasing positional restraints from 2000 to 0 kJ/mol/Å2, following CHARMM-GUI default protocols.
The LINCS algorithm (66) was used to constrain all bonds involving hydrogen atoms, while long-range electrostatic interactions were treated using the Particle Mesh Ewald (PME) method. (67) Production simulations were performed in the NPT ensemble at 310 K using a 2 fs time step without restraints. All simulations were conducted using GROMACS 2025.1, and simulation details for each system are summarized in Table S2.

Enhanced Sampling Simulation

Collective Variable Selection

Collective variables (CVs) are designed to distinguish between functionally distinct conformational states and to characterize the overall activation process by capturing the system’s slow degrees of freedom during enhanced sampling simulations. In this study, we used the Demystifying Toolbox (68) to identify suitable CVs for driving the conformational transition of GLP-1R. Using simulation trajectories as input, the toolbox applies a combination of dimensionality reduction and machine learning techniques to extract significant features and normalize their importance scores between 0 and 1.
To train the model, 10,000 snapshots were extracted from 1 μs equilibrated trajectories of both the active and inactive GLP-1R states. A Random Forest (RF) model with 200 estimators was employed, using inverse inter-residue Cα distances as input features. From the resulting feature importance scores, the top 20 Cα distances with normalized importance values above 0.80 were selected as the final CVs for subsequent enhanced sampling simulations (Table S3).

String Method with Swarms of Trajectories

The string method with swarms of trajectories is a powerful approach for identifying the most probable transition pathway between two stable states in high-dimensional space by iteratively refining an initial path. (55,69) To obtain a comprehensive conformational landscape of GLP-1R activation, we first employed steered molecular dynamics (SMD) simulations with an external bias applied to the 20 selected collective variables (CVs, Table S3) to generate an initial transition pathway comprising 18 configurational points for both the apo and compound 2-bound GLP-1R systems.
Using this initial path as a starting point, we applied the string method with swarms of trajectories (55) to refine the pathway until convergence was achieved, revealing energetically stable intermediates along the activation process. For each system, several hundred iterations were performed to ensure convergence of the string connecting the active and inactive states. Each iteration of the string method included three key steps: 1) A 30-ps restrained equilibrium simulation was performed at each configurational point using a harmonic force constant of 10,000 kJ·mol–1·nm–2; 2) This was followed by 32 independent 10-ps unrestrained simulations to compute the average drift in CV space; 3) The string was then re-parameterized to redistribute the points and prevent clustering. Convergence of the string pathways is illustrated in Figures S4–S7.

Free Energy Landscape Calculations

Using the Markov State Model (MSM) framework implemented in the Deeptime Python library, (70) two-dimensional free energy landscapes were constructed by projecting the data onto key importance features representing structural changes in the extracellular domain and the G protein-binding site of GLP-1R. In this study, a maximum likelihood MSM was employed to estimate the transition matrix from the swarm trajectories generated during the string method simulations. For dimensionality reduction, time-lagged independent component analysis (tICA) was applied, followed by discretization using k-means clustering. To ensure accurate representation of the converged transition pathway, trajectory data from the final 200 iterations of each system’s string refinement were used for free energy landscape calculations. We further checked the MSM quality by estimating the uncertainties via a bootstrapping procedure. (71) New MSMs were constructed and validated based on the corresponding new transition probability matrix, in which 100 bootstrapping samples were generated for the standard deviation calculations.

Quantum Chemical Calculations

The quantum chemical calculations using the methanethiol models were performed by Gaussian 16 Rev. C02. (72) The initial species of protonated/deprotonated methanethiol and compound 2 were optimized separately with the level of ωB97X/6-31G(d,p)/smd. (73) Then the two reactants were scanned along the reaction coordinates to get reactant complex, followed by transition state optimization and intrinsic reactant coordinate (IRC) calculations with the same level of theory. The thermos energy corrections and vibrational analysis for all the stationary geometries were also calculated at the same level of theory as in the optimizations. The single point energies of all the optimized geometries were calculated using ωB97XD/6-311+G(2d,p)/smd.

Simulation Analysis

Demystifying

To identify the structural features that distinguish active-like conformations of GLP-1R in the absence and presence of compound 2, we employed the Demystifying Toolbox, (68) a framework that integrates multiple dimensionality reduction and machine learning techniques for feature selection across different conformational states. Specifically, one supervised (Random Forest, RF) and one unsupervised (Principal Component Analysis, PCA) machine learning method were applied to models trained on 10,000 snapshots extracted from equilibrated trajectories of both the apo and compound-bound GLP-1R systems. For each snapshot, the closest heavy-atom distances were calculated and used as input features for model training. To ensure robustness, the final results were averaged over three independent runs, each with 10-fold cross-validation.

GetContacts

GetContacts (https://getcontacts.github.io/atlas/) is a powerful tool designed to facilitate comparative structural analysis by rapidly computing and comparing interatomic interactions across different structures or simulations of the same or related proteins. To investigate the allosteric modulation induced by compound 2 on the binding affinity of GLP-1 and LY3502970, we extracted 5,000 snapshots from each system (GLP-1-GLP-1R, compound 2-GLP-1-GLP-1R, LY3502970-GLP-1R, and compound 2-LY3502970-GLP-1R) for ligand–receptor interaction analysis. We then compared the interaction frequencies to identify differences specifically induced by compound 2.

Dynamic Network Analysis

Dynamic network models have been widely used to understand allosteric modulation in protein function. (74,75) To further explore how compound 2 binding at the cytoplasmic end of TM6 enhances the affinity of orthosteric ligands, we performed dynamic network analysis using the NetworkView plugin in VMD (76,77) on four GLP-1R systems: GLP-1-GLP-1R, compound 2-GLP-1-GLP-1R, LY3502970-GLP-1R, and compound 2-LY3502970-GLP-1R. The same 5,000 snapshots used in the GetContacts analysis were employed to construct dynamic network models. In the network representation, each residue was represented as a single node centered on its Cα atom. Edges were defined between residue pairs whose heavy atoms remained within 4.5 Å for at least 75% of the simulation data, thereby ensuring persistent structural proximity. Pairwise dynamical cross-correlations (Cij) were calculated from Cα positional fluctuations over the input data. For residue pairs connected by persistent contacts, edge distances were defined as dij = −log(|Cij|), such that strongly correlated motions correspond to shorter effective communication distances. We then computed the shortest communication paths between the source node (C347, near the compound 2 binding site) and sink nodes (either the centroid of LY3502970 or R36 of GLP-1) using the Floyd–Warshall algorithm. (78) Community structure within the weighted network was analyzed using the Girvan-Newman algorithm. (79) These paths were analyzed to identify differences in allosteric communication between the compound 2 binding site and the orthosteric pocket.

Data Availability

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All data supporting the findings of this study are provided within the article and its supplementary information file. The source code used to perform the string method with swarms of trajectories is openly available at https://github.com/delemottelab/string-method-swarms-trajectories.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.6c00224.

  • Additional details on the simulation analysis of structural changes, comparison of ligand stability, transmembrane helix 6 displacement, sampling convergence estimation, energies from quantum chemical calculations, total simulation time for each system, and collective variables for enhanced sampling simulations in this study (PDF)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Authors
  • Authors
  • Author Contributions

    Y.C. conceptualized and designed the project. Y.C. wrote the manuscript with input from all coauthors. Y.C. performed the molecular dynamics simulations and analyzed the data under the supervision of Y.M. and L.D. J.L. designed and performed quantum chemical calculations. All coauthors discussed the results and revised the manuscript.

  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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This work was supported by the Knut and Alice Wallenberg Foundation (2019.0130), the Science for Life Laboratory, and the Swedish Research Council (VR 2019-02433 and 2022-04305). The National Academic Infrastructure for Supercomputing in Sweden (NAISS) and in the Swedish Research Council through grant agreement no. 2022-06725 funded MD simulations.

References

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  • Abstract

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    Figure 1

    Figure 1. (A) Overall structure of GLP-1R shown in purple cartoon bound to compound 2 (brown sticks) (PDB ID: 7DUR (28)). (B) TM6 movement during 2-μs production simulations of apo (green) and compound 2-bound (orange) GLP-1R, quantified by the Cα distance between Y2503.53 and K3466.35. Representative snapshots sampled from each trajectory are superimposed onto the starting structure (purple) for comparison. (C) Residue-wise importance profiles derived from RF analysis highlight structural differences between apo and compound 2-bound GLP-1R conformations. (D) Distribution of significant structural features distinguishing apo and compound 2-bound GLP-1R states. Representative active cryo-EM structures of GLP-1R includes 7DUR, 7DUQ, 7E14. (28)

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    Figure 2

    Figure 2. (A) Compound 2-binding mode at the GLP-1R. (B) Changes in interaction frequencies between compound 2 and nearby residues at the allosteric site. (C) Computed energy profiles for the reaction between Cys3476.36 and compound 2. Insets illustrate the key features of the reactant, transition, and product states for two possible pathways: one with Cys3476.36 in its protonated form and the other in its deprotonated form.

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    Figure 3

    Figure 3. Compound 2 enhances the binding of GLP-1 or LY3502970 at the orthosteric site. (A) Overall structure of the compound 2-GLP-1-GLP-1R complex, highlighting the GLP-1 binding mode. (B) Changes in interaction frequencies between GLP-1 and nearby residues with and without compound 2. Solid bars represent the apo system, while dashed bars indicate the compound 2-bound system. (C) Overall structure of the compound 2-LY3502970-GLP-1R complex, highlighting the LY3502970 binding mode. (D) Changes in interaction frequencies between LY3502970 and nearby residues with and without compound 2. Solid bars represent the apo system, while dashed bars indicate the compound 2-bound system.

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    Figure 4

    Figure 4. Allosteric signal pathways from the intracellular to extracellular domains in GLP-1R. Optimal communication pathways were computed from C3476.36 (source node) to R36 of GLP-1 in the GLP-1-GLP-1R and compound 2-GLP-1-GLP-1R systems, and from C3476.36 to the centroid of LY3502970 in the LY3502970-GLP-1R and compound 2-LY3502970-GLP-1R systems. Residues involved in the allosteric pathways are shown as spheres and colored according to their community assignments. Connecting edges represent inter-residue communication, with line widths proportional to edge betweenness.

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    Figure 5

    Figure 5. (A) Two-dimensional free energy landscapes of apo and compound 2-, GLP-1-, and LY3502970-bound GLP-1R systems, projected along the outward movement of TM6 (measured by the Cα distance between Y2503.35 and K3466.35) and the swing of the N-terminal α-helix (measured by the Cα distance between W33N-term and F3857.40). (B) Four representative conformations, inactive (cyan), intermediate 1 (I1, green), intermediate 2 (I2, pink), and active (orange), extracted from the string simulation trajectory of apo GLP-1R and superimposed on the inactive experimental structure (gray), corresponding to local energy minima in the free energy landscape.

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    Figure 6

    Figure 6. Schematic illustration of ligand-mediated GLP-1R activation. In the absence of ligand, GLP-1R dynamically transitions between inactive and intermediate-like states. Binding of agonists to either the orthosteric or allosteric sites induces conformational rearrangements in both the extracellular and intracellular domains, promoting engagement of downstream transducers and driving the receptor toward its fully active state. SM: small molecule; PAM: positive allosteric modulator.

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