Search Results (283)

The angiotensin II type 1 (AT₁) receptor is a key component of the renin–angiotensin system (RAS) and a validated target for cardiovascular and renal disorders. Developing small molecules with defined AT₁ versus AT₂ binding profiles remains important for both therapeutic and mechanistic studies. Here, a series of novel imidazole-based compounds was synthesized and evaluated for their binding affinities toward angiotensin II type 1 (AT₁) and type 2 (AT₂) receptors. Binding studies were conducted by measuring the displacement of radiolabeled [³H]-angiotensin II ([³H]-AII) in PLC-PRF-5 human hepatoma cells for AT₁ receptors and calf cerebellum membranes for AT₂ receptors. Structure–activity relationship (SAR) analysis revealed that sulfonamide substitution significantly enhanced AT₁ receptor affinity, whereas sterically hindered derivatives and ester-containing compounds were less active. Molecular docking studies using the AT₁ receptor crystal structure (PDB: 8TH4) rationalized the observed activity trends. The most active compound showed high AT₁ affinity (Ki = 5 nM), comparable to losartan, and all compounds displayed preferential binding for AT₁ over AT₂ receptors. Full article

Growth hormone-releasing hormone receptor 1a (GHS-R1a) is a key G protein-coupled receptor (GPCR) governing feeding and energy homeostasis. Accumulating evidence shows that GHS-R1a forms functional heterodimers with multiple metabolic-related GPCRs, including dopamine 2 receptor (D2R), melanocortin 3 receptor (MC3R), 5-hydroxytryptamine 2c receptor (5-HT2cR), orexin receptor 1 (OX1R) and cannabinoid receptor 1 (CB1R). These heterodimers undergo distinct signal transduction reprogramming, generating novel physiological effects that are not observed with individual receptors: for instance, GHS-R1a/D2R mediates an atypical calcium signaling pathway to regulate appetite, while GHS-R1a/5-HT2cR antagonizes ghrelin-induced orexigenic effects. Meanwhile, diverse detection techniques, including co-immunoprecipitation and fluorescence resonance energy transfer, have been developed to identify and validate GHS-R1a heterodimerization, laying a solid foundation for mechanistic research. This review systematically summarizes the molecular mechanisms of GHS-R1a heterodimer formation, the characteristic signal regulation patterns of different heterodimers, and their specific regulatory roles in feeding circuits. Furthermore, we discuss the existing research gaps in this field, such as the lack of in vivo detection methods for heterodimers and the unclear structural basis of dimerization. Finally, we highlight the potential of targeting specific GHS-R1a heterodimers as a novel therapeutic strategy for obesity and anorexia, providing new directions for future pharmaceutical development and clinical translation. Full article

Background/Objectives: Pathogenic variants in GNAO1, encoding the inhibitory G protein subunit Gαo, cause severe neurodevelopmental disorders that remain largely refractory to pharmacological treatments. Gαo transduces inhibitory signals downstream of multiple G protein-coupled receptors (GPCRs) involved in motor control. Here, we used gene-edited Caenorhabditis elegans models carrying goa-1 variants, the ortholog of GNAO1, to investigate GPCR contributions to Gαo-dependent locomotor phenotypes. Methods: We combined pharmacological screening of dopamine- and cannabinoid-targeting ligands in goa-1 mutants with structural analysis of ligand-binding pocket conservation and genetic perturbation of receptor function using RNAi and knockout approaches. Results: Pharmacological modulation of GPCR signaling produced non-linear and context-dependent effects. Compounds predicted to further increase excitability may instead promote phenotypic improvement, consistent with compensatory network rebalancing. Structural analyses revealed substantial divergence in ligand-binding pocket conservation for several GPCR-ligand pairs, suggesting that altered binding affinity and selectivity may also contribute to the observed phenotypic outcome. Pharmacological experiments performed in GPCR-depleted mutants allowed for the correlation of structural findings with functional effects for selected receptor-ligand pairs. Finally, genetic reduction in GPCRs coupled to stimulatory G proteins ameliorated hyperactive locomotion in goa-1 mutants, whereas reduction in GPCRs coupled to inhibitory G proteins is largely insufficient to induce or exacerbate locomotor defects. Conclusions: Our findings identify excessive excitatory GPCR input as a key modulator of motor dysfunction in the context of impaired Gαo signaling. They also show that structural conservation is a necessary but not sufficient condition to predict functional responses. Overall, this study establishes C. elegans as a suitable platform to dissect GPCR-mediated signaling and highlights the value of integrating pharmacological and genetic approaches to guide target selection in GNAO1-related disorders. Full article

Contrastive learning-based models such as DrugCLIP have recently emerged as scalable tools for structure-based virtual screening by embedding protein structures and small molecules into a shared representation space. While these approaches demonstrate high throughput and competitive screening performance in ligand retrieval tasks, their ability to correctly identify biologically relevant ligand-binding pockets has not been systematically evaluated. Here, we construct a benchmarking dataset comprising 42 pharmacologically diverse human protein targets with experimentally validated drug-bound structures spanning multiple target families. Using this dataset, we evaluate the pocket recognition capability of DrugCLIP and compare its performance with a traditional structure-based workflow (Fpocket combined with ESSA) and a machine learning-based method (P2Rank). DrugCLIP shows robust performance for well-characterized target classes, including kinases (10/10) and nuclear receptors (5/5), but exhibits markedly reduced accuracy for ion channels (1/4), GPCRs (3/5), and transporters (3/5). Notably, pocket prediction accuracy does not strongly correlate with structural data availability, suggesting that intrinsic pocket characteristics rather than training data abundance primarily affect model performance. Across the benchmark, DrugCLIP achieves an overall success rate of 71% (95% CI: 56–83%), compared with 79% (95% CI: 64–88%) for Fpocket+ESSA, and 93% (95% CI: 81–98%) for P2Rank. McNemar’s test showed no significant difference between DrugCLIP and Fpocket+ESSA (p = 0.508), whereas P2Rank significantly outperformed DrugCLIP (p = 0.012). Together, these results provide a quantitative evaluation of pocket recognition by contrastive learning-based models and highlight key limitations of embedding-based approaches for pocket localization. Full article

Excessive sugar intake remains a major health challenge, motivating the development of safe and effective alternatives. Thaumatin, a natural high-intensity sweet protein, elicits sweetness through activation of the sweet taste receptor (T1R2/T1R3), yet its molecular recognition mechanism remains understudied. An integrated computational strategy combining comparative modeling, protein–protein docking, and 500 ns molecular dynamics simulations (triplicates) was employed to elucidate the thaumatin–receptor binding. Structural modeling identified the closed conformation of the Venus flytrap domain (VFT) as optimal for ligand engagement. Modeling revealed a stable binding interface characterized by electrostatic complementarity and van der Waals interactions, characterized by interfacial contacts of receptors and hydrogen bonding networks. Residue-level energy decomposition highlighted key residues (W418 and E422 of T1R2; S59 of T1R3) and thaumatin residues (K67, R82, and K137) that contribute substantially to complex stabilization, consistent with experimentally reported sweetness determinants. These findings provide molecular-level insight into sweet protein recognition and establish a structural framework for rational engineering of protein-based sweeteners with enhanced potency and selectivity. Full article

X-ray free-electron lasers (XFELs) have revolutionized structural biology by enabling “diffraction-before-destruction” and capturing the ultrafast dynamics of life. However, the intrinsic sparsity and noise of XFEL diffraction snapshots, often complicated by multi-lattice overlaps, create a formidable computational bottleneck that limits data utilization and structural fidelity. Here, we present MCDPS-SFX, a robust indexing framework based on a reference-based, whole-pattern matching principle integrated with parallelized iterative refinement. By exhaustively sampling orientation space and progressively rejecting outliers, MCDPS-SFX significantly outperforms legacy algorithms—more than doubling crystal yields in heterogeneous datasets (e.g., 21,807 vs. 8792 for MOSFLM)—and achieves highly competitive yields comparable to state-of-the-art indexers, such as extracting over 90,000 lattices in the lysozyme benchmark. We demonstrate its efficacy on standard benchmarks and technically demanding G-protein-coupled receptor (GPCR) systems, including the rhodopsin–arrestin complex and the glucagon receptor. MCDPS-SFX consistently produces high-quality data statistics, enabling the high-resolution visualization of functionally critical, flexible regions such as phosphorylated receptor tails. Our results provide a powerful tool for enhancing the scientific output of XFEL experiments, offering a robust alternative for maximizing information recovery from weakly diffracting or overlapping crystalline samples. Full article

Chirality is a pervasive and functionally critical feature of biological macromolecules, yet its distributed and emergent forms remain poorly quantified in complex systems such as membrane proteins. We present Chirobiophore, a novel paradigm for capturing biochirality across scales—from atomic geometries to global structural asymmetries. Unlike traditional stereochemical metrics, Chirobiophore employs a multidimensional model-independent vector comprising Local Tetrahedral Asymmetry (LTA), Helical Path Curvature (HPC), Asymmetric Environment Score (AES), Directional Density Profile (DDP), Leaflet Asymmetry Index (LAI), and Orientation Twist Score (OTS). This framework enables coordinate-invariant comparisons of structurally diverse proteins in a continuous chirality space. We demonstrate its application to canonical, GPCR, and topologically complex membrane proteins, revealing distinct chirality signatures and functional clustering. Furthermore, we map Chirobiophore descriptors to tissue-level asymmetry indices, providing a bridge between molecular structure and morphogenetic patterning. Chirobiophore offers a unified, extensible platform for structural biology, synthetic design, and developmental modeling of chirality. Full article

Glucagon-like peptide-1 (GLP-1) is an incretin hormone secreted by intestinal endocrine L cells that activates the GLP-1 receptor (GLP-1R), leading to glucose-dependent insulin secretion and suppression of glucagon release. In recent years, GLP-1R agonists (GLP-1RAs) have become one of the leading therapeutic options for the treatment of type 2 diabetes mellitus; however, for a long time clinically approved GLP-1RAs were limited to peptide drugs unsuitable for oral administration. The discovery of the “first-in-class” small molecule agonist danuglipron in 2018 demonstrated the feasibility of orally available GLP-1RAs and stimulated the development of numerous danuglipron-like compounds, some of which showed increased efficacy over the prototype. In this study, we report the design and synthesis of novel GLP-1RAs based on a regioisomeric danuglipron scaffold, 1H-benzo[d]imidazole-5-carboxylic acid. A series of 35 compounds was synthesized and evaluated in vitro for cytotoxicity and GLP-1R agonistic activity using a cAMP accumulation assay. A potent lead compound 12r (pEC₅₀ = 7.72, pCC₅₀ < 3.60) was found which is a close structural analog of danuglipron with reduced cytotoxicity and excellent selectivity over two other class B GPCRs, including GCGR and GIPR. Despite decreased potency compared to danuglipron, the obtained results hold promise for further optimization and provide valuable structure–activity relationship insights. Full article

For decades, the pharmacological identity of Panax ginseng has been primarily attributed to triterpenoid saponins known as ginsenosides. However, accumulating evidence indicates that ginseng also contains a structurally distinct lipid–protein complex, termed gintonin, enriched in lysophosphatidic acid (LPA) species. Unlike ginsenosides, which predominantly exert modulatory effects on membrane dynamics and intracellular kinase pathways, gintonin directly activates LPA G protein-coupled receptors (GPCRs), thereby inducing rapid phospholipase C (PLC) activation and intracellular Ca²⁺ mobilization. Biochemical analyses have identified major LPA species within the gintonin fraction, including C16:0, C18:1, and other unsaturated LPA species such as C18:2 stabilized within a proteinaceous matrix that may influence receptor engagement kinetics. Pharmacological studies demonstrate that gintonin preferentially activates LPA₁ and LPA₃ receptor subtypes, triggering downstream signaling cascades involving MAPK, PI3K/Akt, and Rho pathways. These receptor-mediated effects occur on a rapid temporal scale, distinguishing gintonin from the slower transcriptional and kinase-modulating actions of ginsenosides. In this review, we synthesize current evidence regarding the chemical architecture, receptor pharmacology, and signaling dynamics of gintonin and propose a dual signaling framework in which steroid-like saponins and lipid GPCR ligands represent complementary molecular axes within P. ginseng. Recognition of this layered signaling organization refines the molecular understanding of ginseng biology and highlights gintonin as a unique plant-derived GPCR ligand system.
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Allosteric modulation has emerged as a powerful strategy for achieving superior selectivity and safety in drug discovery and protein function regulation. Unlike highly conserved orthosteric sites, allosteric pockets are structurally diverse and less evolutionarily constrained, making them particularly suitable for modulation by designed miniproteins. Miniproteins can provide extended binding interfaces and high affinity for shallow, dynamic, or cryptic regulatory surfaces that are often inaccessible to small molecules. Recent advances in artificial intelligence (AI) are transforming this field through deep learning-based structure prediction and generative modeling. These AI-driven approaches enable the identification of allosteric hotspots, characterization of conformational ensembles, and de novo design of structured miniprotein binders. They are rapidly expanding the landscape for designing selective modulators across diverse allosteric targets, including GPCRs, receptor tyrosine kinases, nuclear receptors, ion channels, and other protein–protein interaction systems. This review summarizes state-of-the-art AI-driven computational methodologies for designing miniproteins as potential allosteric modulators and discusses their current challenges and future opportunities in allosteric drug discovery. Full article

Umami taste in lager beer not only determined body fullness and the backbone of aftertaste, but also affected the controllability and interpretability of flavor expression across the entire brewing process. Based on stage-wise sampling, peptidomic profiles were established on wort fermentation day 0, day 1, day 3, and day 9. A total of 25,592 peptides were identified by reversed-phase liquid chromatography–quadrupole time-of-flight mass spectrometry (RPLC-QTOF-MS). Molecular informatics screening was performed using UMPred-FRL (a feature representation learning-based meta-predictor for umami peptides) and TastePeptides-Meta (a one-stop platform for taste peptides and prediction models), yielding 7255 potential umami peptides. From these, 145 peptides were further selected for molecular docking. In addition, 6 representative umami peptides were selected for receptor-level validation and structural analysis. Mechanistically, the umami receptor taste receptor type 1 member 1/taste receptor type 1 member 3 (T1R1/T1R3) belonged to class C G protein-coupled receptor (GPCR) and relied on the extracellular Venus flytrap (VFT) domain for ligand capture. Ligand-induced VFT conformational convergence transmitted changes to the transmembrane region and triggered signal transduction. Docking and energy decomposition indicated that the ionic group primarily contributed to orientation and anchoring. Salt-bridge or hydrogen-bond networks were formed around Lys228, Arg240, Glu206, Asp210, Asn141, and Gln138, thereby reducing conformational freedom. Meanwhile, hydrophobic side chains obtained major binding gains within a hydrophobic microenvironment formed by Val135, Ile137, Leu165, Tyr166, Trp78, and His79. These results reflected a synergistic mode in which charge pairing enabled positioning and hydro-phobic complementarity promoted VFT closure. To experimentally confirm sensory relevance, 6 representative peptides were individually spiked into 4 brewing-stage beer samples, which produced a clear stratification pattern across stages. Notably, peptides with favorable docking-derived binding propensity did not necessarily enhance umami perception, and several longer peptides showed persistent negative sensory shifts, supporting that binding affinity alone could not be treated as a proxy for perceived umami in the beer matrix. At the node level, the cumulative abundance of umami peptides showed a significant positive correlation with umami scores, with a Pearson correlation coefficient of r = 0.963 and p = 0.037. This result indicated good linear consistency between umami peptide content and the upward shift in umami taste in lager beer. Umami peptide clusters were further proposed as a more appropriate functional unit, and an umami peptide cluster database spanning the full process was constructed. This database provided a reusable resource for process control and flavor prediction. Full article

Modern advances in artificial intelligence have accelerated the development of computational tools for protein–ligand structure prediction, yet their real-world performance remains uneven across receptor classes and ligand chemotypes. Recently published cryo-EM structures of several different psychedelics bound to the serotonin 5HT_2A receptor provide a unique opportunity to explore how modern AI-based modeling performs in a pharmacologically important GPCR system. Here, we compare three major approaches: AI-based protein–ligand cofolding (Boltz-2), a leading AI-driven docking module (Uni-Mol Docking v2), and a widely used classical physics-based docking pipeline (AutoDock Vina) across a series of tryptamine and phenethylamine psychedelics. Predicted binding poses were comparatively assessed through structural alignment with these newly available cryo-EM complexes. Additionally, calcium-mobilization assays were performed to provide a coarse functional readout for comparison with computationally predicted binding affinities. This study integrates methodological review with exploratory benchmarking to illustrate how different modeling paradigms behave on a shared receptor–ligand test set. Our results highlight substantial variation between modeling strategies, with AI-based cofolding often producing global binding orientations more closely resembling experimental structures, and classical docking showing greater variability across ligands, while still outperforming AI-driven docking on average. These observations underscore both the growing utility and current limitations of AI-assisted structure prediction in serotonergic drug discovery, and emphasize the importance of careful, experimentally anchored evaluation as such tools continue to advance. Full article

Maternal nutrient restriction (MNR) heightens disease susceptibility in offspring through epigenetic modifications that alter the development of essential organs. This study investigates how restriction alters the fetal sheep hepatic methylome and its potential regulatory influence on gene expression. Using a monozygotic twin model generated through embryo splitting, we examined hepatic DNA methylation responses to maternal nutrient restriction (50% vs. 100% NRC nutritional requirements; n = 4 per group) from gestational day (GD) 35 to 135 in pregnant sheep. At GD 135, conceptus (fetal–placental unit) development was assessed; although fetal weight was unaffected (p > 0.10), restricted fetuses exhibited reduced liver mass (p < 0.05). Whole-genome bisulfite sequencing (WGBS) of fetal liver identified 1,636,305 differentially methylated CpG sites (dmCpGs) in the Group-Level Analyses and 42,231 dmCpGs in the Twin-Pair Analyses. At the Group-Level, 40,533 promoter, 126,667 exonic, and 785,381 intronic sites were identified, whereas the Twin-Pair subset contained 1314, 7116, and 22,239, respectively. Site-level shifts and functional enrichment across features highlighted GPCR–cAMP/calcium–PI3K/AKT signaling, phosphoinositide metabolism, ECM/integrin–focal adhesion networks, thyroid hormone signaling, and Rho-family GTPases. These findings indicate that maternal nutrient restriction modifies the fetal hepatic methylome through coordinated signaling, metabolic, and structural reconfigurations that create conditions conducive to metabolic disease. Full article

Melatonin, an indolic neuromodulator with putative oncostatic and proposed anti-inflammatory properties, primarily demonstrated in preclinical models, is produced at extrapineal sites—most notably in the gut. Its canonical actions are mediated by high-affinity GPCRs (MT1/MT2) and by NQO2, a cytosolic enzyme with a melatonin-binding site (historically termed “MT3”). A growing body of work highlights a bidirectional interaction between the gut microbiota and host melatonin. We integrated two lines of work: (i) three clinical cohorts—cardiac arrhythmias (n = 111; 46–75 y), epilepsy (n = 77; 20–59 y), and stage III–IV solid cancers (25–79 y)—profiled with stool 16S rRNA sequencing, SCFA measurements, and circulating melatonin/urinary 6-sulfatoxymelatonin and (ii) an age-spanning cognitive cohort with melatonin phenotyping, microbiome analyses, and exploratory immune/metabolite readouts, including a novel observation of melatonin binding on bacterial membranes. Across all three disease cohorts, we observed moderate-to-severe dysbiosis, with reduced alpha-diversity and shifted beta-structure. The core dysbiosis implicated tryptophan-active taxa (Bacteroides/Clostridiales proteolysis and indolic conversions) and depletion of SCFA-forward commensals (e.g., Faecalibacterium, Blautia, Akkermansia, and several Lactobacillus/Bifidobacterium spp.). Synthesised literature indicates that typical human gut commensals rarely secrete measurable melatonin in vitro; rather, their metabolites (SCFAs, lactate, and tryptophan derivatives) regulate host enterochromaffin serotonin/melatonin production. In arrhythmia models, dysbiosis, bile-acid remodelling, and autonomic/inflammatory tone align with melatonin-sensitive antiarrhythmic effects. Epilepsy exhibits circadian seizure patterns and tryptophan–metabolite signatures, with modest and heterogeneous responses to add-on melatonin. Cancer cohorts show broader dysbiosis consistent with melatonin’s oncostatic actions. In the cognitive cohort, the absence of dysbiosis tracked with preserved learning across ages, and exploratory immunohistochemistry suggested melatonin-binding sites on bacterial membranes in ~15–17% of samples. A unifying microbiota–tryptophan–melatonin axis plausibly integrates circadian, electrophysiologic, and immune–oncologic phenotypes. Practical levers include fiber-rich diets (to drive SCFAs), light hygiene, and time-aware therapy, with indication-specific use of melatonin. Our conclusions regarding microbiota–melatonin crosstalk rely primarily on local paracrine effects within the gut mucosa (where melatonin concentrations are 10–400× plasma levels), whereas systemic chronotherapy conclusions depend on circulating melatonin amplitude and phase. This original research article presents primary data from four prospectively enrolled clinical cohorts (total n = 577). Full article

Expanding the chemical diversity of DNA–encoded libraries (DELs) is crucial for identifying binders to emerging drug targets using DEL technology. In the present study, as part of our ongoing efforts to develop on–DNA diazide platforms (D–DAPs)—platform molecules possessing both aromatic and aliphatic azide groups on a single core reactive scaffold—we designed and synthesized a new compact diazide platform, designated as a compact D–DAP (C–D–DAP). This molecule is based on a low–molecular–weight reactive scaffold, 3–azido–5–(azidomethyl)benzoic acid, to facilitate small–molecule drug discovery targeting enzymes and G protein–coupled receptors (GPCRs). Furthermore, we established two distinct stepwise warhead construction strategies that exploit the chemoselective transformations of the azide groups in the C–D–DAP, which exhibit different reactivities. In addition, four virtual DELs were generated based on stepwise warhead elaboration from the C–D–DAP scaffold. Comparative chemical diversity analysis against bioactive compounds from ChEMBL revealed that these virtual libraries populate structural regions that are sparsely represented among known molecules. Each virtual library also occupies a distinct region of structural space relative to the others and displays intermediate quantitative estimate of drug–likeness (QED) values. Full article