Benchmarking Docking Protocols for GPCR Allosteric Modulators

Identification of allosteric sites and allosteric inhibitors in the glucose-dependent insulinotropic polypeptide receptor via molecular simulations.

This study integrates long-timescale all-atom molecular dynamics (MD) simulations with multidimensional drug discovery strategies to identify potential allosteric sites of the GIPR and screen for allosteric modulators. Based on the constructed GIPR-GIP complex, we performed conformational sampling and combined dynamic pocket detection algorithms, MDpocket and FTMove, to identify six characteristic cryptic pockets within the dynamic trajectories. Subsequently, using representative conformations as templates, a structure-based virtual screening of 1.6 million compounds from the ChemDiv database was conducted, yielding 30 candidate compounds. Surface plasmon resonance (SPR) experiments showed binding responses. The cAMP accumulation assay demonstrated that compound C30 could dose-dependently antagonize GIP-induced receptor activation, displaying negative allosteric modulator (NAM) activity. MD simulations revealed that C30 primarily restricts the outward movement of the transmembrane helix TM6. This study provides potential lead compounds for the design of small-molecule allosteric drugs targeting class B1 GPCRs.

Residue Interaction Network Reveals Allosteric Pathways Linking Orthosteric and Intracellular Sites in Class A GPCRs

Understanding how allosteric modulators influence protein dynamics is essential for guiding drug design. This work analyses a total of 45 μs of classical molecular dynamics simulations for four class A G-protein-coupled receptors (GPCRs), namely the Complement C5a receptor (C5AR1), the Purinergic Receptor P2Y (P2RY1), and the Cannabinoid Receptors 1 and 2 (CNR1/CNR2). Protein dynamics is essential to detect the shallow extrahelical binding sites, such as the one found in P2RY1. Current methods for computing Allosteric Communication Networks (ACNs) produce complex outputs requiring expert interpretation. To address this, we focus on the shortest paths of information transfer between the orthosteric and G-protein binding sites in Class A GPCRs. Our retrospective analysis reveals state- and bias ligand-dependent residue interactions along these communication pathways. Furthermore, focusing on the predicted binding site of allosteric modulator EC21a at cannabinoid receptors, the ACN framework was used to prioritize two residues for mutational analysis that may contribute to allosteric communication.

Peptide ligand discovery of G protein-coupled receptors

Molecular Dynamics and Free Energy Calculations Predict Binding Mode and Affinity Determinants of Specialized Pro-Resolving Mediators at GPR101

GPR101 is an orphan G protein-coupled receptor (GPCR) with unusually high constitutive activity and has recently emerged as a target for specialized pro-resolving mediators (SPMs), endogenous lipids that actively terminate inflammation and promote tissue repair. Given the therapeutic relevance of pro-resolution signaling in chronic pain and inflammatory disorders, understanding how SPMs engage GPR101 is of great significance. Although cryo-EM structures suggest an occluded orthosteric cavity, SPMs such as RvD5n‑3 DPA are potent agonists, creating uncertainty about their binding modes. Long-timescale molecular dynamics (MD) simulations, MM-GBSA per-residue energy decomposition, residue-interaction network analysis, and alchemical relative binding free-energy (RBFE) calculations were used to predict interactions between SPMs with GPR101. MD trajectories revealed a stable RvD5n‑3 DPA pose beneath an extracellular loop, stabilized by M184, W186, and Y415. Transmembrane distance metrics across five independent 1 μs MD simulation trajectories showed persistent stabilization of an active-like state even without modeled G-protein coupling. RBFE analyses quantified the scaffold-dependent effects of C17 alcohol stereochemistry, oxidation, methylation, and 3-oxa substitution. A double mutant cycle calculation identified a coupling between C17 alcohol and residue M184. Novel dual-modified analogs were computationally predicted to retain high affinity while improving metabolic stability. Benchmarking demonstrated that membrane-free thermodynamic integration (AMBER) yielded accurate, low-variance results with shorter wall time than membrane-inclusive replica exchange (NAMD). These results provide the first atomistic model of SPM binding to GPR101 and establish an RBFE-guided framework for designing next-generation pro-resolving mediator analogs with enhanced pro-resolving effects and stability.

Ligand Drift Rate Descriptor in Receptor Pocket: Molecular Docking Sampling for Differentiating Partial Agonists in Nuclear Receptors

The recent discovery that inverse agonists bind to a secondary orthosteric site in PPARγ reveals unexpected structural complexity in this nuclear receptor. To differentiate between full and partial agonists, we hypothesize that partial agonists exhibit dynamic positional behavior, moving among alternative binding sites at equilibrium when saturated, while full agonists bind directly and consistently to the primary orthosteric site. Using a two-state model derived from the Boltzmann distribution and molecular docking (MODO) sampling, we developed a computational metric called the Ligand Drift Rate (LDR) Descriptor to differentiate full agonists (Emax ≥ 90%) from partial agonists (Emax ≤ 45%) of PPARγ by simulating ligand positional instability within the primary orthosteric binding pocket. We combined molecular docking and hydrogen bond fluctuation analysis across 28 PPARγ-ligand complexes (18 full agonists and 10 partial agonists). MODO sampling was performed with several algorithms, each tested on five carefully selected receptor-binding pockets from 356 crystal complexes. Hydrogen bonds between ligands and key residues (His323, His449, Tyr473) were analyzed at various hydrogen-bond displacement (HBD) thresholds (6–12 Å). The LDR was calculated as the percentage of the top 20-scoring poses that lacked hydrogen bonds. We found that adding constraints on pose diversity and allowing receptor flexibility can improve LDR’s ability to distinguish full from partial agonists. Finally, MODO-sampling-based LDR calculations were conducted on 1079 ChEMBL ligands (393 full agonists and 686 partial agonists). In this validation set, LDR successfully differentiated partial from full agonists (t-test, p = 0.00043) using the genetic algorithm with the 3B1M-KRC binding pocket and a 10 Å hydrogen-bond threshold. In QSAR applications for drug discovery, we have developed a dynamics-aware molecular descriptor, LDR, that can be a key factor in quantifying partial agonism arising from small-molecule ligand drift away from the primary orthosteric site.

Collaborative multi-agent intelligence uncovers subtype-selective allosteric sites at GPCR-lipid interaction interface

Closely related G protein-coupled receptor (GPCR) subtypes often share highly conserved orthosteric pockets, making subtype-selective ligand development challenging. Here, we developed a five-agent workflow to systematically identify divergent protein-membrane-interface sites across class A GPCRs and exploit them for selective allosteric ligand discovery. By combining dMaSIF-derived surface fingerprints with Ballesteros-Weinstein (BW) position alignment, we compared structurally equivalent membrane-facing regions and identified the three most divergent hotspots for each of 163 receptor pairs. These regions showed substantial spatial overlap with experimentally characterized allosteric sites. Paired target-off-target screening of one million lead-like compounds, followed by detail-mode redocking and multi-seed consistency filtering, yielded 352 receptor-pair-specific candidates corresponding to 344 unique compounds across 104 receptor pairs. These candidates, together with their divergent sites and predicted selectivity profiles, were integrated into a searchable database. Our findings establish a scalable strategy for translating GPCR membrane-interface divergence into precise allosteric sites and testable subtype-selective ligand candidates.