Aug 2026· Journal of Chemical Information and Modeling· 0 citations· 57 references
Abstract
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.
This study provides potential lead compounds for the design of small-molecule allosteric drugs targeting class B1 GPCRs and performs conformational sampling and combined dynamic pocket detection algorithms, MDpocket and FTMove, to identify six characteristic cryptic pockets within the dynamic trajectories.
Zhi Dong, Long Cheng, Qingxin Shi et al.· International Journal of Bio...· 0 citations
G protein-coupled receptors (GPCRs) represent the therapeutic targets for an estimated 30–40% of marketed drugs. By translating the majority of the GPCR structures from the Protein Data Bank into structure graphs and analyzing over 2 million structural contacts, this study maps the landscape of GPCR classification and function. A minimal subset of just 30 specific contacts is sufficient to define the signatures of distinct receptor classes, subfamilies, types, subtypes, and functional states. These structural signatures successfully assigned classifications to the orphan GPCRs. Upon activation, class A GPCRs undergo the most radical network reorganization of all classes, retaining 0% of their state-specific contacts when transitioning from the inactive to the active state, a stark contrast to the 32–50% retention observed in classes B1, C, and F. Despite this complete contact turnover across the 7TM bundle, class A activation remains anchored by a highly conserved core of ten universal network nodes. Analysis of representative shortest communication pathways (metapaths) demonstrates that class A GPCRs rely on these highly conserved nodes to bridge structural communication between the orthosteric ligand-binding pocket and the intracellular G-protein-binding site, regardless of the functional state. Furthermore, these metapaths intersect directly with known allosteric binding sites for small allosteric modulators. G protein binding structurally reorganizes the receptor network, funneling a multitude of potential communication pathways into a few preferential routes. These pathways culminate at the highly conserved arginine residue of the E/DRY motif that acts as a key mediator of G-protein recognition, while structural divergences at the receptor-G protein interface dictate the distinctive pathways of specific G-protein signaling. The wide analysis was able to capture key aspects of the structural communication within the GPCR superfamily with implications in drug discovery.
Sara Gentile, Angelo Felline, Sara Mazzali et al.· JACS Au· 0 citations
Allosteric modulation of G protein–coupled receptors (GPCRs) offers major advantages in receptor selectivity and signaling control; yet systematic approaches to identify allosteric modulators, define their binding sites, and map the underlying allosteric networks remain limited. Current molecular dynamics (MD) and machine learning (ML)-based methods often rely on correlation-driven or black-box models that provide limited mechanistic insight. We developed an interpretable probabilistic framework that extracts residue-level dependencies from MD ensembles using Bayesian network modeling (BNM). By representing each residue through its local interaction energy, BNM identifies both local and long-range energetic couplings and maps the allosteric communication pathways linking the AngII binding site to the G-protein interface in the angiotensin II type 1 receptor (AT1R). To functionally prioritize these pathways, we integrated BNM with comprehensive mutational analysis, combining whole-receptor alanine mutagenesis data with exhaustive in silico deep mutational scanning to validate BNM-predicted hotspots. This approach recovered state-dependent allosteric communities, revealed residues in noncanonical regions that regulate Gαq coupling and identified positions whose functional importance emerged only with specific, predicted substitutions, as well as highlighted a cryptic intracellular pocket enriched in communication hubs. Guided by these network-derived residues and pocket geometries, structure-based virtual screening identified a small, fragment-like molecule negative allosteric modulator (NAM) named Q2 that attenuates AngII-mediated Gαq signaling. Mutational mapping supports Q2 binding adjacent to the G-protein interface, consistent with its mechanism of action. Together, these results establish a generalizable and interpretable framework for uncovering GPCR allosteric communication networks and discovering modulators that exploit these networks.
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.
D. Hasselstrøm, Majd Awad, T. Hansen et al.· ACS Omega· 0 citations
GaMD ensemble docking improved early AM enrichment across all four targets under at least one program, and the Boltz-2 deep-learning program showed minimal sensitivity to GaMD templates and underperformed conventional docking, suggesting its affinity predictions complement rather than replace physics- and empirical-based docking approaches for GPCR AM screening.
T.D. Thompson, Yinglong Miao· bioRxiv· 0 citations
Whether binding specificity and partner selection in protein-protein interactions (PPIs) can be reliably inferred from static structures or require more dynamic, pathway-resolved energetic analyses remains an open question. To explore this, we focus on the ornithine decarboxylase (ODC)-antizyme isoform 1 (Az1)-antizyme inhibitor (AzIN) system, a well-characterized competitive PPI network that plays a critical role in regulating polyamine homeostasis. By combining extensive all-atom molecular dynamics simulations with biochemical experiments and the development of a new tool, we uncover key dynamic features of the static and recognition pathway interaction. Based on these, we designed novel antizyme isoforms (NAZs). Our analysis, using residue-resolved energetic landscapes, reveals critical determinants of binding specificity and partner selection that static structures alone cannot capture. These insights guide the engineering of NAZs that either directly engage ODC or modulate Az1 availability. This work provides a new perspective, demonstrating that dynamic energetic landscapes, rather than static structures, are key to understanding and modulating competitive protein recognition. Additionally, our DyResEL tool enables broader, more detailed analyses of energetic contributions, offering a versatile approach for exploring PPIs in various biological contexts.
Baolin Guo, Qian Xue, Fan Yang et al.· Journal of Chemical Informat...· 0 citations
