Search Results (2,574)

Hepassocin, also known as fibrinogen-like protein 1 (FGL-1), is a liver-derived secretory protein initially identified as a mitogenic factor involved in hepatocyte proliferation and liver regeneration. Increasing evidence has subsequently suggested that FGL-1 functions as a hepatokine linking hepatic metabolic stress to systemic metabolic regulation. Experimental and clinical studies have demonstrated that circulating FGL-1 levels are associated with obesity, insulin resistance, metabolic dysfunction-associated steatotic liver disease (MASLD), and type 2 diabetes mellitus (T2DM). Mechanistically, FGL-1 appears to contribute to metabolic dysfunction by impairing insulin signaling and promoting hepatic lipid accumulation, although its precise molecular targets remain incompletely defined. In addition to its metabolic roles, FGL-1 has been identified as a major ligand of lymphocyte activation gene-3 (LAG-3), implicating it in immune modulation and tumor progression, particularly in hepatocellular carcinoma (HCC). However, most available human data are observational, and conflicting findings from experimental models suggest that FGL-1 may function as a context-dependent mediator rather than a purely pathogenic factor. Given the expanding but sometimes conflicting evidence, a comprehensive understanding of FGL-1 biology may provide important insights into the complex interactions among hepatic stress responses, metabolic dysfunction, and immune regulation. This review therefore examines the current evidence regarding the physiological and pathological roles of FGL-1 and highlights key unresolved questions that may influence future translational research and therapeutic development. Full article

Diabetes mellitus remains a major metabolic disorder requiring the development of new and effective α-glucosidase inhibitors. The present study aimed to identify, design, and optimize novel 3-amino-2,4-diarylbenzo[4,5]imidazo[1,2-α]pyrimidine derivatives with promising inhibitory activity against the α-glucosidase enzyme using a comprehensive in silico strategy. Approximately 300 molecular descriptors were calculated to characterize a dataset of 32 compounds (Peytam et al.) and to investigate the structural factors governing their biological activity. Based on these descriptors, a multiple linear regression model was developed to predict the inhibitory activities of the compounds against alpha-glucosidase. The developed model demonstrated satisfactory predictive performance and was internally and externally validated to ensure its accuracy, robustness, and reproducibility. In addition, the applicability domain analysis confirmed the reliability of the predictions. Using the validated QSAR model, seven new derivatives were designed with predicted pIC₅₀ values exceeding the maximum activity of the parent compounds. The leverage analysis demonstrated that all newly designed compounds were located within the applicability domain of the model, supporting the reliability of the predictions. To further evaluate their inhibitory potential, molecular docking studies were performed to investigate the interactions between the designed compounds and the α-glucosidase active site. The docking results revealed favorable binding interactions comparable to those reported for known α-glucosidase inhibitors. Furthermore, ADMET analysis indicated generally favorable pharmacokinetic properties, although potential CYP3A4 inhibition-related pharmacokinetic risks were identified and discussed. Molecular dynamics simulations, including replicated runs and MM/GBSA binding free energy calculations, confirmed the stability of the most promising protein–ligand complexes throughout the simulation period. In conclusion, this study proposes a robust and integrated computational workflow combining descriptor generation, QSAR modeling, applicability domain analysis, molecular docking, ADMET prediction, and molecular dynamics simulations for the rational design of potential α-glucosidase inhibitors. The findings highlight the therapeutic potential of the designed derivatives and provide a valuable in silico framework for the future development of antidiabetic agents. Full article

New copper(II) (C1) and palladium(II) (C2) complexes with S,O-tetradentate ligand (L) derived from thiosalicylic and thiopropionic acids were synthesized. In cell-based assays, (C1) exhibited the most pronounced activity within the tested compound series and was therefore advanced for mechanistic evaluation in 4T1 triple-negative breast cancer cells. (C1) significantly reduced 4T1 cell viability by inducing early and late apoptosis, accompanied by mitochondrial membrane depolarization and enhanced cytochrome C release. Consistently, (C1) increased the Bax/Bcl-2 ratio, promoting a pro-apoptotic shift. In parallel, (C1) triggered autophagy, as evidenced by decreased p62 and LC3B levels, induced G0/G1 cell-cycle arrest, and suppressed proliferative signaling by downregulating Ki67, cyclin D, and phosphorylated AKT. The DNA-binding studies showed moderate to strong affinity, favoring minor groove binding, with higher affinity for (C1) than for (C2). Tryptophan fluorescence quenching indicated a strong interaction with BSA via a predominantly static mechanism, more pronounced for (C1). Molecular docking at the DNA and BSA binding sites corroborated experimental findings and suggested favorable interactions between the complexes and apoptosis-related proteins (CASP3, BAX, and BCL2). The integrated experimental and computational data identify (C1) as a biologically active compound with multimodal biological effects in vitro, supporting further structural optimization and mechanistic investigation. Full article

Intercellular communication is crucial for the coordination of skeletal muscle development. However, the intricate signaling networks that regulate chicken myogenesis are not yet fully elucidated. In this study, we utilized CellChat analysis on single-cell and single-nucleus RNA sequencing data to systematically delineate cell–cell communication patterns across five critical developmental stages of chicken skeletal muscle: embryonic day 4 (E4), day 6 (E6), day 12 (E12), day 18 (E18), and post-hatch day 30 (P30). Our findings indicate that communication architectures are highly stage-specific, with mesenchymal cells acting as the predominant signaling hub during the early embryonic stages (E4–E6), whereas fibro-adipogenic progenitors become the principal communicators during mid-to-late embryogenesis (E12–E18). At E4, the communication network was relatively simple, comprising 51 ligand–receptor pairs primarily involving the neural cell adhesion molecule, slit guidance ligand, and midkine (MK) signaling pathways between myogenic progenitors and mesenchymal cells. By E6, the network had expanded significantly, encompassing 6237 ligand–receptor pairs across 51 signaling pathways, which coincided with the emergence of multiple myogenic lineages. Peak communication complexity was observed at E12, characterized by 11,675 ligand–receptor pairs and 61 signaling pathways, reflecting the secondary wave of myogenesis. Comparative analysis across developmental stages revealed key signaling transitions: the pleiotrophin and MK pathways were predominantly active during the early phase of myogenic commitment (E4–E6), whereas the collagen, laminin, and adhesion G protein-coupled receptor L pathways were more prominent during the secondary myogenesis phase (E6–E12). Notably, a significant shift in communication patterns was observed from E12 to E18, marked by a reduction in developmental pathway signaling and an increase in immune-related communications. By P30, the communication network had stabilized into a homeostatic state, centered on interactions among myofibers, stromal cells, and the vascular system. This comprehensive atlas of intercellular communication offers novel insights into the signaling dynamics underpinning chicken skeletal muscle development. Full article

Background/Objectives: First-generation CDK4/6 inhibitors (palbociclib, ribociclib, abemaciclib) target the conserved ATP-binding pocket of CDK4 and, despite clinical success, are limited by acquired resistance and insufficient exploration of alternative regulatory sites. This study aimed to identify a putative allosteric small-molecule candidate at the CDK4 αE-helix–Cyclin D1 α1-helix protein–protein interaction (PPI) interface within the CDK4/Cyclin D1/p21 ternary complex using RapidFunnel-AI, a decision-interpretable virtual-screening pipeline. Methods: Starting from 50,000 ChEMBL 33 molecules, the pipeline sequentially applied a Q-Fold/RapidFunnel topological Tanimoto scan based on clinical CDK4/6 inhibitor motifs, fragment-level electronic-property enrichment, ADMET/PAINS filtering, dry Vina-GPU docking, hydration-mediated AutoDock-GPU (Version 1.6) docking, explicit-solvent molecular dynamics, contact-retention analysis, and MM-GBSA energy decomposition. The Q-Fold Thermo-Core surrogate model provided fragment-level enrichment, predicting the HOMO–LUMO gap (R² = 0.93) and isotropic polarizability (R² = 0.98) on QM9. Candidate selection did not rely on the lowest docking or MM-GBSA score alone, but on pose persistence, contact continuity, and energy-component consistency. Results: The workflow reduced the initial library to 43 topologically prioritized candidates, 25 ADMET/PAINS-filtered ligands, and 9 docking-derived complexes for MD validation. Ligand_020 emerged as the only candidate that preserved a persistent binding mode at Site 2 during a 500 ns simulation—an interface engagement reproduced across three independent 500 ns replicates with no full dissociation in any replicate—with a protein Cα RMSD of 2.88 ± 0.32 Å, a ligand heavy-atom RMSD of 3.56 ± 0.28 Å, and a van der Waals-dominated MM-GBSA profile (ΔGbind = −28.23 ± 3.57 kcal/mol). In contrast, palbociclib and ribociclib, forcibly placed at Site 2 as negative controls, lost most initial contacts within 5 ns and tended to detach despite more favorable MM-GBSA values. Conclusions: These results suggest that single-score docking or MM-GBSA ranking can generate false positives at shallow PPI interfaces. By integrating AI-assisted prioritization, multipocket docking, explicit-solvent MD, contact-retention analysis, and energy-component consistency, RapidFunnel-AI nominated Ligand_020 as an experimentally testable putative allosteric hit targeting the CDK4/Cyclin D1 interface, offering a reusable platform for PPI-focused oncological drug discovery. Full article

Radix pseudostellariae saponins (RPS) enhance immune responses in animals; however, the regulatory mechanisms of these effects remain unclear. This study observed that 14 days post-intranasal immunization with RPS and a Mycoplasma gallisepticum-attenuated vaccine (MGAV), MGAV-specific antibody titers were significantly increased in the blood, and chemokine (C-C motif) ligand 5 (CCL5) messenger RNA expression was significantly increased in the trachea and blood of chickens. Transcriptomic analysis demonstrated that RPS treatment significantly upregulated specific Kyoto Encyclopedia of Genes and Genomes pathways, notably the cytokine–cytokine receptor interaction pathway, which is linked to immune cell migration and involves chemokine receptor chemokine (C-C motif) receptor 4 (CCR4). This finding was corroborated at the protein level by immunohistochemical evidence showing increased CCL5 expression in tracheal tissue. In vitro studies showed that RPS enhanced the phagocytic capacity of RAW264.7 macrophages against ovalbumin, with immunofluorescence revealing time-dependent and dose-dependent CCL5 in these cells. Transwell and scratch-healing assays confirmed that RPS promoted this migration of both RAW264.7 cells and CCR4-positive lymphocytes. Collectively, the findings revealed that RPS modulated the activation, chemotaxis, and migration of macrophages and lymphocytes and is associated with the promotion of the CCL5/CCR4 signaling axis, providing novel evidence for the immune-enhancing effects of RPS by enhancing immunogenicity. Full article

Abiotic stresses, including drought, salinity, alkalinity, temperature extremes, flooding, heavy metals, and emerging pollutants, challenge plant growth and productivity by disturbing water relations, ion balance, redox homeostasis, membrane stability, energy metabolism, and developmental progression. Although substantial progress has been made in the identification of stress-responsive hormones, second messengers, kinases, transcription factors, transporters, and metabolic regulators, plant stress adaptation cannot be fully explained by linear signaling cascades or single tolerance genes. A major unresolved question is how early molecular events are reorganized into coordinated physiological and developmental outputs that support survival, recovery, and productivity. In this review, we propose an intermolecular interaction-driven adaptive remodeling framework for plant abiotic stress responses. This framework emphasizes that stress tolerance emerges from dynamic changes in receptor–ligand recognition, protein–protein interactions, calcium decoding, redox-sensitive modification, phosphorylation networks, transcriptional regulation, chromatin-associated control, and metabolite-mediated feedback. We further emphasize ROS as integrative redox switches that connect stress sensing, defense activation, senescence-related transitions, and recovery, and chromatin-associated mechanisms as regulators that may stabilize primed or memory-like adaptive states. We discuss how these interaction networks converge on core signaling hubs, including abscisic acid, reactive oxygen species, Ca²⁺, and kinase/phosphatase systems, and how they remodel stomatal behavior, root architecture, ion and pH homeostasis, redox buffering, metabolism, development, and reproductive resilience. We further highlight how natural variation, multi-omics, genome editing, high-throughput phenotyping, and field validation can translate interaction-centered stress biology into crop resilience. This perspective provides a conceptual bridge between molecular stress perception, network behavior, physiological adaptation, and climate-resilient agriculture. Full article

Recently, there has been great interest in AI-based approaches for de novo design of novel drug candidates. However, the generation of useful lead drug candidate compounds requires more than predicting engagement with the desired protein target. Candidate molecules must also be anchored in the real world of medicinal chemistry for their synthesis and modification as well as satisfying multiple drug development-related criteria. Here, we present Nevermore, an AI target-conditioned, database-grounded workflow for prioritizing candidate ligands from large compound libraries. Nevermore uses a geometry-aware protein–ligand affinity oracle to score target-specific binding and perform sparse integer edits in count-based Morgan fingerprint space. Nevermore then retrieves the most structurally similar molecules from public chemical databases. This design enables multi-objective search over predicted affinity and absorption, distribution, metabolism, excretion, and toxicity (ADMET) proxies while keeping all candidates anchored to valid database compounds. We evaluated Nevermore’s performance across three biologically distinct targets: Menin, a protein-interaction target relevant to leukemia; SARS-CoV-2 M^pro, a viral cysteine protease relevant to antiviral discovery; and epidermal growth factor receptor (EGFR), a kinase-superfamily oncology target with extensive experimentally tested compounds. Nevermore retrieved candidate sets with favorable predicted affinity–property trade-offs. These results support database-grounded fingerprint steering as a practical computational strategy for lead prioritization and for generating testable molecular hypotheses, although the prioritized candidates remain predictions, requiring follow-up experimental validation. Full article

Exercise-induced regulation of cortical GPCR pathways in diabetic obesity remains unclear. This study aimed to investigate exercise-associated GPCR-related transcriptomic pathway changes in the cerebral cortex of diabetic-obese rats. Cerebral cortical samples from male Zucker Fatty Diabetes Mellitus (ZFDM) rats subjected to a 12-week swimming program were examined using RNA sequencing, functional enrichment, GSOAP clustering, and STRING-based protein–protein interaction (PPI) analysis. Exercise training reduced fasting glucose and body weight. RNA sequencing identified 817 exercise-responsive transcripts (403 upregulated and 414 downregulated; p < 0.05), including 48 associated with GPCR signaling. Results showed that these 48 genes mapped to three major GPCR-related networks: cAMP signaling, with increased Adcyap1r1, Gipr, Tshr, and Vipr2 and decreased Vip, Chrm1, Gabbr2, and Sst; calcium signaling, with increased Ntsr1 and Trhr and decreased Chrm1; and neuroactive ligand–receptor interaction, with increased Trh, Trhr, and Crh and decreased Opr-related transcripts. These findings provide hypothesis-generating evidence for interpreting cortical GPCR-related transcriptomic pathway associations in diabetic-obese conditions. Full article

This review examines the emerging role of machine learning (ML) in pharmaceutical chemistry, with emphasis on molecular design, synthetic feasibility, and structure–property–performance (SPP) relationships. By enabling pre-synthesis prediction of physicochemical properties, reaction pathways, and pharmaceutical performance, ML can reduce empirical trial-and-error experimentation and support more efficient exploration of chemical space. A structured narrative review design with PRISMA-aligned systematic search elements was used to evaluate 101 studies, enabling transparent literature identification, eligibility screening, and thematic synthesis across heterogeneous ML applications in pharmaceutical chemistry. This review examines structure–property relationships (SPRs) and property–performance relationships (PPRs), with emphasis on key pharmaceutical endpoints such as solubility, permeability, stability, dissolution, and bioavailability. An integrated SPP framework is proposed to connect molecular structure, intermediate properties, and final performance outcomes while incorporating retrosynthetic analysis and experimental feedback and closed-loop optimization. Recent frontier developments are also discussed, including molecular foundation models, multimodal language–graph models, diffusion-based molecular generation, E(3)-equivariant models, and MolMIM-like latent-space optimization. This review also covers co-folding and joint ligand–protein modeling, Boltz-2-like affinity prediction, AlphaFold 3-related biomolecular interaction modeling, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction. Key limitations include dataset leakage, benchmark inconsistency, assay variability, conformational and protonation-state effects, reproducibility challenges, regulatory constraints, and the gap between computational prediction and prospective experimental validation. Future progress is expected to depend on hybrid physics–ML models, uncertainty-aware prospective validation, autonomous experimentation, explainable artificial intelligence, and sustainability-aware molecular design. Overall, ML is evolving from a predictive tool into a chemically informed decision-support framework for rational, synthesis-aware, and experimentally validated pharmaceutical development. Full article

Voltage-gated sodium channels are central to electrical excitability, and Nav1.7 is a major therapeutic target implicated in pain disorders and sensory signaling. Within the channel pore, permeating Na⁺ ions experience dynamically fluctuating hydration and coordination environments that may influence local ion–protein interactions. Identifying chemically distinct coordination states from molecular dynamics (MD) simulations is an important prerequisite for future higher-level electronic structure investigations. In this study, we present a reproducible workflow for identifying and classifying Na⁺ hydration–coordination microstates in the Nav1.7 pore using explicit-solvent molecular dynamics simulations. A geometrically defined pore region was used to quantify pore hydration and Na⁺ inner-shell coordination based on a 3.2 Å Na–O distance criterion. Na⁺ configurations were classified according to ligand identity into water-only (W), mixed protein–water (PW), and protein-only (P) microstates. Analysis of a 2 ns proof-of-principle simulation revealed a persistently hydrated pore environment, with Na⁺ coordination dominated by water-rich states and a smaller but distinct population of protein-contact configurations. These observations demonstrate that local coordination environments are chemically heterogeneous and cannot be fully described by hydration number alone. Representative structures from each microstate class were extracted to provide candidate configurations for future quantum mechanical, Quantum Mechanics/Molecular Mechanics (QM/MM), or density functional theory investigations of ion–ligand interactions in confined pore environments. The present work establishes a transparent and reproducible microstate-selection framework and does not report quantum mechanical energies, free-energy landscapes, or converged microstate populations. More broadly, the workflow provides a practical strategy for reducing complex MD ensembles into chemically interpretable coordination states suitable for subsequent higher-level analysis. Full article

Amyotrophic lateral sclerosis (ALS) remains an intractable motor neuron (MN) disease with a growing patient population and few effective treatments. Here, we review how extracellular phosphoglycerate kinase 1 (ePgk1) improves neurite outgrowth of MNs (NOMN) and axonal growth, both in vitro and in vivo. Our group first elucidated a novel non-canonical function of ePgk1 as a cross-tissue mediator between nerve and muscle tissues. We then discovered that neural membranous Enolase 2 (Eno2) serves as a receptor of ligand ePgk1 and that ePgk1-Eno2 interaction suppresses the Rac1-GTP/p-Pak1-T423/p-P38-T180/pMK2-T334/p-Limk1-S323 axis, reducing p-Cofilin and promoting NOMN and axonal growth, finally suggesting that the 419th aspartic acid residue of Eno2 mediates this interaction. In a crucial preclinical step, we truncated two short 16-amino-acid derivatives from Pgk1, FD-1/-2, each mediating neuroprotection comparable to that of full-length 417-amino-acid Pgk1 in ALS animal models, in terms of improvements of innervated neuromuscular junction, MN cell bodies, motor performance, and endpoint prolongation. In this context, we also discuss the opposite function driven by Eno1-plasminogen interaction and by Eno2-ePgk1 interaction; the latter results in unfavorable for tumorigenesis. Unlike intracellular Pgk1 roles, ePgk1 is an extracellular factor with anti-angiogenic properties, further positioning ePgk1 and its FD-1/-2 as promising protein/peptide drugs for ALS treatment. Full article

The innate immune response is a critical defense mechanism by which vertebrates recognize and eliminate invading pathogens. Pattern recognition receptors (PRRs) detect pathogen-associated molecular patterns and activate downstream signaling pathways. NOD1, a classic PRR of the NLR family, recruits the adaptor protein RIPK2 to initiate antibacterial signaling. In this study, we cloned and characterized the NOD1 gene from snakehead (Channa argus). Briefly, the full-length NOD1 cDNA is 2829 bp encoding 943 amino acids, showing high homology with Perciformes. The qPCR analysis revealed widespread NOD1 gene expression in various tissues, with significant upregulation in the gill (p < 0.05) and spleen (p < 0.05) following bacterial infection. Overexpression of the NOD1 gene activated the NF-κB signaling pathway in a dose- and time-dependent manner, and specifically responded to the bacterial ligand iE-DAP but not to other tested ligands. Furthermore, NOD1 synergized with the downstream adaptor RIPK2 to enhance NF-κB activity, and direct protein interaction between NOD1 and RIPK2 was confirmed by co-immunoprecipitation. Taken together, these findings demonstrate that snakehead NOD1 plays a critical role in the host antimicrobial immune response. Full article

Cholangiocarcinoma (CCA) in Northeast Thailand is characterized by late diagnosis and poor prognosis, creating a critical need for effective early-detection biomarkers. This study utilized a multi-omics approach to identify novel diagnostic targets and improve CCA screening. Initial serum proteomic and transcriptomic analyses revealed significant upregulation of the luteinizing hormone/choriogonadotropin receptor (LHCGR) in CCA patients, correlating with advanced disease stages. Interaction network analysis subsequently identified its circulating ligand, beta-human chorionic gonadotropin (β-hCG), as a highly translatable clinical target. The protein expression of β-hCG was assessed via immunohistochemistry (IHC) in 100 tissue samples, and serum levels of β-hCG, alongside routine markers (CA19-9, AFP, and CEA), were quantified in a cohort of 405 individuals, including 153 CCA patients. IHC confirmed significantly higher β-hCG expression in tumor tissues compared to adjacent areas (p < 0.0001). Serum β-hCG levels were significantly elevated in CCA patients and correlated with tumor volume and reduced overall survival. Diagnostically, a combined multiparameter panel (β-hCG, carbohydrate antigen 19-9, carcinoembryonic antigen, and alpha-fetoprotein) yielded excellent accuracy in distinguishing CCA from healthy controls (AUC: 0.962) and hepatocellular carcinoma cases (AUC: 0.890). However, discriminatory efficiency was notably lower when differentiating CCA from benign biliary diseases (AUC: 0.680) and liver metastases (AUC: 0.705). In conclusion, activation of the LHCGR signaling axis is a novel pathophysiological feature in CCA. When integrated into a multi-marker blood panel, circulating β-hCG serves as a valuable complementary risk-stratification and prognostic tool, though further optimization is required to overcome limited specificity in the presence of confounding liver pathologies before routine clinical implementation. Full article

The rate of fatty acid (FA) uptake by cells depends on the presence of the CD36 receptor on the cell surface. However, unesterified FAs cannot circulate freely in plasma; they are bound to serum albumin. The molecular mechanisms of FA transfer from albumin to CD36 remain poorly understood. This study used macromolecular docking and molecular dynamics methods to investigate the interaction of the CD36 receptor with human serum albumin (HSA) loaded with oleic acid at the FA1-7 fatty acid-binding sites, with the aim of identifying potential mechanisms of FA transfer from HSA to CD36. The data obtained indicate that the interaction of HSA with CD36 does not result in direct FA transfer, but rather causes a local weakening of the affinity of individual FA sites on HSA. A comparative analysis was performed between the interaction interfaces predicted by macromolecular docking and those generated by AlphaFold 3. To further evaluate the influence of ligand nature, an additional molecular docking of HSA loaded with saturated (palmitic, PALM) and polyunsaturated (arachidonic, ARA) acids to the CD36 receptor was performed. This revealed a marked sensitivity of the protein–protein interface architecture to the type of lipid ligand, with the effect of ARA being more pronounced than PALM. Conversely, an alternative structure prediction using the AlphaFold3 algorithm demonstrated the opposite trend, indicating high geometric invariance and reproducibility of the complex. Ultimately, the proposed dynamic mechanism expands our understanding of the multi-stage processes governing FA transport across the endothelium. Full article