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.
Abstract
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.
GPR84 is an orphan class A GPCR primarily expressed in immune cells, where it plays key roles in inflammation and metabolism. Here, we present the cryo-electron microscopy structures of the GPR84-Gi complex bound to the G protein–biased agonist DL-175, and the inactive state of GPR84 bound to the antagonist GLPG1205. Combined with signaling assays and molecular dynamics simulations, these structures elucidate the conformational landscape spanning the inactive and G protein-biased active states of GPR84, providing a mechanistic basis for biased agonism. Notably, steric interactions between DL-175 and L3366.52 selectively preclude the conformational changes required for efficient β-arrestin recruitment without compromising G protein activation. These structural insights provide a structural context for the rational design of GPR84-targeted therapeutics with precisely tuned signaling profiles. Authors here solve cryo-EM structures of the immune receptor GPR84, which reveal how a ‘biased’ drug favors one signaling pathway over another. A single steric clash inside the receptor acts as the switch, guiding the design of safer, more selective drugs.
Shota Suzuki, D. Tran, Kouki Nishikawa et al.· Nature Communications· 0 citations
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
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.
S. Peter, G. Chalhoub, Peter J. McCormick et al.· Journal of Chemical Informat...· 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
The structural stabilization and conformational modulation of inflammatory targets such as TNF-α and STAT3 are of great significance in the discovery of novel anti-inflammatory agents and provide valuable insights into ligandprotein interaction mechanisms at the molecular level. In this study, we employed an integrated in silico framework, including ensemble molecular docking, long-timescale molecular dynamics (MD) simulations, and binding free energy calculations, to investigate the atomistic interaction mechanisms of two selected small molecules, isorhamnetin and 7-methoxy-2-methyl isoflavone, against TNF-α and STAT3. Initial ensemble docking identified energetically favorable candidate binding poses across different receptor conformations. Subsequent 500-ns MD simulations suggested that these ligands may contribute to stabilization of the proteinligand complexes and reduction of local structural fluctuations. Conformational dynamics analyses, including principal component analysis (PCA), free energy landscape (FEL), and dynamic cross-correlation matrix (DCCM), revealed ligand-associated changes in conformational convergence and altered intra-protein correlated motions. Furthermore, MM-PBSA calculations provided effective binding energy estimates (without entropic contributions), particularly for the STAT3–7-methoxy-2-methyl isoflavone complex (E
effective
= −167.44 kJ/mol). These computational findings provide atomistic insights into the possible ligand-associated conformational stabilization mechanisms, offering a computational structural basis for targeting the TNF–STAT3 inflammatory axis.
Tao Li, Yuan Chen, Yingqian Zhou et al.· Journal of Computational Bio...· 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
