Marburg virus (MARV), a member of the Filoviridae family, causes severe hemorrhagic fever in humans with case fatality rates exceeding 90%, and currently, no approved vaccines or therapeutics are available. To address this urgent need, we employed comprehensive immunoinformatics and computational approaches to design a multi-epitope subunit vaccine (MESV) capable of eliciting robust immune responses. Highly antigenic, non-allergenic, and non-toxic cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes derived from MARV glycoprotein (GP) and nucleoprotein (NP) were selected and assembled using appropriate linkers and adjuvant sequences. The designed vaccine construct exhibited favorable physicochemical characteristics, structural stability, and strong immunogenic potential. Solubility analysis predicted a score of 0.835, while structural validation revealed an ERRAT score of 94.11%, with 89.4% of residues located in the most favored regions of the Ramachandran plot. The ProSA analysis yielded a Z-score of - 5.31, confirming the reliability of the modeled structure. Molecular docking studies demonstrated strong interactions between the vaccine construct and Toll-like receptor 7, while molecular dynamics simulations confirmed the stability of the docked complex. Codon optimization and in silico cloning indicated efficient expression potential in Escherichia coli, with a codon adaptation index (CAI) of 0.9805 and GC content of 55.39%. Furthermore, immune simulations predicted a robust and sustained immune response. These computational findings suggest that the designed MESV is a promising vaccine candidate for MARV and warrants further experimental validation through in vitro and in vivo studies.
Bendiocarb (BEN), a carbamate insecticide designated as moderately hazardous (Class II) by the WHO (2009), is extensively utilized in agricultural and residential pest control, raising significant concerns about its toxicological and environmental impacts. BEN has been identified in pregnant women exposed to domestic pest-control methods, indicating possible human exposure. Owing to its lipophilicity and ecological persistence, BEN presents toxicological hazards through bioaccumulation and interactions with plasma proteins. Nonetheless, the molecular-level understanding of its interaction with serum proteins remains limited. Here, an integrative strategy that incorporates multispectroscopic and computational approaches was used to investigate the binding mechanism, structural perturbations, and dynamic behavior of BEN in interaction with BSA, a model transport protein. Spectroscopic investigations demonstrated efficient quenching of BSA intrinsic fluorescence via a static mechanism, indicating the development of a stable ground-state complex and minor but measurable conformational perturbations upon binding. The experimentally determined binding constant value (Kb ≈ 102) signifies a moderate binding affinity between BEN and BSA, aligning with the documented toxicokinetic profile of BEN as a moderately hazardous pesticide (WHO Class II). Docking and 100 ns MD simulations showed that BEN preferentially binds to Site I of BSA, generating a stable complex. Complementarily, DFT and MM/GBSA investigations indicated a narrow HOMO-LUMO gap and a favorable binding free energy (ΔGbind of -9.21 kcal·mol-1), implying moderate chemical reactivity and a thermodynamically stable BEN-BSA interaction. These findings improve understanding of BEN's bioavailability, and toxicokinetics, providing a significant foundation for environmental risk evaluation and the development of safer pesticide alternatives.
Cancer is an intrinsically heterogeneous disease characterized by distinct malignant subclones defined by specific genetic and epigenetic alterations, such as aberrant DNA methylation. Traditional bulk sequencing methods analyze large populations of cells in aggregate, yielding an averaged methylome signal that masks the rare but clinically significant epigenetic patterns driving tumor initiation, metastasis, and therapeutic resistance. The emergence of single-cell DNA methylation (scDNAme) sequencing has driven a paradigm shift in oncology by providing the resolution required to dissect this intratumoral heterogeneity. By profiling the epigenome of individual cells, scDNAme analysis enables the discovery of novel aberrant patterns, the precise reconstruction of cellular lineages, and the characterization of specific populations-such as cancer stem cells (CSCs) or drug-resistant clones-that possess distinct methylome signatures. This technological advance is not merely an incremental improvement; it is a prerequisite for understanding core cancer hallmarks, such as the evasion of growth control and resistance to apoptosis, which are frequently governed by these specific subclones. This review specifically provides a comprehensive comparative overview evaluating the technical and chemical capabilities of emerging single-cell modalities to distinguish DNA methylation profiles. By highlighting the strategic workflows of these emergent technologies alongside advanced computational tools, we emphasize how resolving these discrete cytosine variants empowers precise cell lineage tracing, the identification of refractory clones, and the implementation of locus-specific epigenetic editing therapies against causal tumor subclones.
Kinetic models are useful tools for predicting the dynamic behavior of metabolic systems and to optimize the performance of bioprocesses. However, the difficulty of fitting their parameters to limited data hinders their widespread use. These models have various disparate parameters, are nonlinear, and display complex interactions. To address this challenge, this study introduces a computational framework for constructing bioreactor models integrating detailed enzyme kinetics under data-limited conditions. The framework is illustrated by the construction of a dynamic model that describes the growth kinetics of Saccharomyces cerevisiae and the production of β-ionone, an apocarotenoid extensively utilized in the flavor and fragrance industries, in batch cultivations. The model was initially formulated and described using 78 free kinetic parameters. Through the systematic application of sensitivity and identifiability analyses and reparameterization, the model was reduced to 9-parameter candidate structures. Multi-criteria decision-making techniques were then employed to select a robust model structure. When validated against an independent experimental dataset with condition-specific cofactor adjustment, the final model achieved comparable overall predictive performance compared to the original model structure, with notable improvements for key pathway metabolites, including up to 38% reduction in the normalized mean absolute error for the target product β-ionone. This framework successfully addresses the challenge of constructing predictive kinetic models from limited experiments while maintaining mechanistic interpretability, providing a quantitative tool for identifying metabolic bottlenecks and guiding metabolic engineering interventions.
Immunohistochemistry (IHC) remains a cornerstone of precision oncology, providing essential diagnostic, prognostic, and predictive molecular data. However, the traditional manual assessment of IHC slides faces persistent challenges regarding interobserver variability, reproducibility, and high diagnostic workloads. To address these limitations, artificial intelligence (AI) is increasingly integrated into computational pathology workflows. This paper outlines the current landscape of AI-assisted IHC, highlighting significant trends such as automated scoring, explainable AI, virtual staining, and multiplex analysis. We explore the transition from basic image analysis to advanced deep learning architectures capable of predicting specific IHC biomarker expression directly from standard hematoxylin and eosin morphology. Furthermore, we examine the commercial ecosystem of these tools and highlight the critical pre-analytical bottlenecks that hinder widespread clinical adoption. Finally, we present a practical case study demonstrating an automated, deep learning- based pipeline for quantifying CD34-positive myeloblasts in acute myeloid leukemia and myelodysplastic syndromes. Ultimately, while AI holds vast potential to optimize turnaround times and streamline laboratory triage, its successful clinical implementation will depend on seamless integration into existing digital pathology platforms and standardization of pre-analytical variables, moving the field from fragmented algorithms to reliable, standard-of-care diagnostic tools.
Aromatic monomers are widely used as the backbone of porous materials such as Covalent Organic Frameworks (COFs) and Metal Organic Frameworks (MOFs), as their rigid and planar structures promote crystallinity and structural order in these frameworks. In addition, aromatic monomers themselves can self-assemble into porous architectures through noncovalent interactions; however, only a limited number of such examples have been reported. Here, we investigate the self-assembly of antiaromatic dibenzopentalene (DBP) to form a stable Antiaromatic Self-assembled Porous Framework (ASPF). Density Functional Theory (DFT) calculations reveal how substitution, substituent position, and annulation influence the stability of the pentalene (PN) core. Molecular dynamics (MD) simulations further ascertain that DBP, stabilized by benzene annulation, can spontaneously organize into a porous framework even in the absence of covalent or coordination bonds. This work aims to establish antiaromatic molecules as a new class of building blocks for porous materials with potential applications.
Bacterial resistance towards antibiotics has become a major problem worldwide. Bacteria become more resistant towards available antibiotics due to quorum sensing and biofilm formation. Alkaloids exhibited potential anti-bacterial activity. In the present study, the anti-quorum sensing and antibiofilm abilities of gramine (GRM) are evaluated. Gramine, an alkaloid already reported for several biological activities includes antiviral, anti-bacterial and antitumor was evaluated for its inhibition of quorum sensing and biofilm mediated virulence in Pseudomonas aeruginosa. The anti-infective effect of GRM using Caenorhabditis elegans and Galleria mellonella models was determined. Gramine reduced violacein production by 78% in C. violaceum. GRM inhibit 84% biofilm formation in P. aeruginosa. Notably, GRM inhibits several virulence factors (Pyocyanin, Pyoverdine, HCN, Alginate and several others) of P. aeruginosa. Further validation by qRT-PCR showed that GRM significantly downregulated several virulence associated genes. In silico studies revealed the GRM interaction with the three main quorum sensing signal receptors (LasR, RhlR and PqsR) of P. aeruginosa. In vivo anti-infective experiments suggested GRM's protective effect in C. elegans and G. mellonella infection models. Our results suggests that GRM as an effective anti-biofilm and anti-infective agent.
Secreted proteins are translocated across membranes through multiple routes. In eukaryotes, secreted proteins with N-terminal signal sequences can use either the signal recognition particle and its receptor or the alternative Sec complex to cross the endoplasmic reticulum membrane. Large-scale experiments on the substrates of these pathways are primarily from the model yeast Saccharomyces cerevisiae, but less is known about conservation of translocation pathways. Here, we take a computational approach to analyze secretion signals across the fungal kingdom. Computational predictions by the Phobius model separate secreted proteins in diverse fungal species into distinct populations: cleaved signal peptides with short hydrophobic helices of 8 to 13 amino acids and transmembrane proteins with long hydrophobic helices of 16 to 27 amino acids, similarly to S. cerevisiae. These computational predictions also robustly distinguish translocation routes in S. cerevisiae: Sec-dependent translocation of native proteins is accurately predicted by the presence of a cleaved signal peptide, while conversely signal recognition particle-dependent translocation is predicted by a retained signal-anchor. Analysis of multiple hydrophobicity scales and signal peptide prediction algorithms shows that the Phobius-predicted length of the hydrophobic helix alone is an effective predictor of translocation route. Our results support the hypothesis that the Sec complex is critical for cell wall biogenesis and protein secretion across fungi.
The microbial production of pantothenic acid (d-PA) is critically limited by feedback inhibition and low activity of the key enzyme ketopantoate hydroxymethyltransferase (KPHMT). To overcome this, rational enzyme mining based on computational prediction was established. Following sequence conservation analysis, molecular dynamics simulations, and binding free energy (ΔG) calculations, five representative native KPHMT enzymes were selected for experimental validation: EcKPHMT from Escherichia coli, CgKPHMT from Corynebacterium glutamicum, MpKPHMT from Mangrovibacter plantisponsor, EpKPHMT from Enterovibrio pacificus, and BsKPHMT from Bacillus subtilis. EpKPHMT and BsKPHMT exhibited 4.25- and 4.60-fold times that of EcKPHMT, respectively, with relieved pantoate feedback inhibition (IC50: 14.07 and 19.86 mM vs. 1.08 mM) and virtually no inhibition by d-PA. The strain expressing BsKPHMT enhanced d-PA and pantoate titers by 74.30% (3.12 g/L) and 140.0% (0.84 g/L) in shake flasks, and further increased d-PA production by 55.4% in a 5 L bioreactor, over the control. This work established a predictive framework for mining superior enzymes based on in silico prediction, offering a valuable strategy for metabolic engineering of high-value chemicals.IMPORTANCEThe industrial-scale biosynthesis of pantothenic acid (d-PA) is often bottlenecked by the strict feedback inhibition of its key biosynthetic enzyme, ketopantoate hydroxymethyltransferase (KPHMT). This study describes a computational strategy for the mining and selection of naturally occurring KPHMTs with reduced feedback inhibition, providing superior genetic parts for metabolic applications. This approach provides a novel and rational framework for mining allostery-free enzymes, successfully delivering two highly efficient biocatalysts, BsKPHMT and EpKPHMT. These biocatalysts exhibit immediate potential for industrial applications, offering a direct solution to enhance the production of both pantoate and d-PA. Collectively, the integrated methodology demonstrates effective translation from fundamental discovery to practical application, presenting a generalizable model for overcoming similar metabolic bottlenecks.
The global rise of antimicrobial resistance (AMR) demands innovative strategies to limit the spread of multidrug-resistant bacteria. Conjugative plasmids, particularly those in the incompatibility group P (IncP), play a central role in disseminating resistance genes across bacterial species via their encoded type IV secretion system (T4SS). Here, we characterize the single-stranded RNA (ssRNA) bacteriophage (ssRNA phage) PRR1, which selectively targets bacteria carrying the IncP plasmid RP4, including many Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter species, and Escherichia coli (ESKAPEE) pathogens, and assess its ability to inhibit conjugation. Using cryo-electron microscopy, we resolved the mature PRR1 virion at 3.45 Å resolution, revealing two phage maturation protein (Mat)-RNA interactions within the 3' untranslated region: a conserved interaction (Mat-U1) and a novel interaction (Mat-V1) for ssRNA phages. To characterize the PRR1-RP4 pilus interaction, we performed alanine-scanning mutagenesis and pinpointed four critical TrbC pilin residues (S12, W13, S72, and R77) for infection. Computational modeling revealed that these residues are located near the termini of the pilin at the phage-pilus interface. Notably, native and non-infectious, UV-cross-linked PRR1 was sufficient to block RP4 transfer, indicating conjugation inhibition does not require a complete infection cycle. Finally, combining PRR1 and antibiotic treatment yielded nine unique phage-resistant mutants within T4SS-associated genes on the RP4 plasmid. Eight of these mutants nearly abolished conjugation, while the trbE frameshift mutant retained ~30% of wild-type efficiency, which is pivotal to clarifying the relationship between phage infection and pilus function. Collectively, these results establish ssRNA phages as specific T4SS plasmid-targeting agents and underscore their potential to limit horizontal gene transfer in AMR pathogens.IMPORTANCEAntimicrobial resistance (AMR) spreads rapidly through horizontal gene transfer, largely driven by conjugative plasmids. Despite their central role, few strategies exist to directly block plasmid transfer. Here, we show that the IncP plasmid-dependent ssRNA phage PRR1 can inhibit the spread of antibiotic resistance genes by targeting the RP4 T4SS pilus. Structural and mutational analyses reveal previously unrecognized RNA packaging interactions and identify four pilin residues critical for infection. Remarkably, non-infectious PRR1 particles alone are sufficient to block conjugation, offering inhibition without the selective pressure from phage replication. Almost all PRR1-resistant RP4 mutants lost or had severely reduced plasmid transfer, while the remaining mutant is critical for studying the link between T4SS function and phage infection. These results highlight ssRNA phages as precise agents for limiting AMR gene dissemination.
The Microbial Dark Matter Symposium held on August 28-29, 2025, in Laguna Beach, Orange County, CA, convened a multidisciplinary group of scientists to address the vast unknowns in microbial life-from uncultured taxa and uncharacterized proteins to elusive viruses and spacefaring microbes. Set against a scenic coastal backdrop, the symposium highlighted advances in single-cell genomics, proximity ligation sequencing, and artificial intelligence-ready bioinformatics, while also probing the limits of microbial persistence, metabolism, and ecological distribution. Sessions explored microbial dark matter from multiple dimensions: cultivability, where new strategies are enabling recovery of elusive microbes; functional ambiguity, where metagenomic dark zones are illuminated by computational annotation; and genomic representation, where single-cell methods bridge gaps left by shotgun community sequencing. Researchers shared breakthroughs in identifying atmospheric microbiomes, "dark oxygen" production in groundwater ecosystems, and microbial survival on the International Space Station. The symposium emphasized integration of methods, disciplines, and ecosystems, advancing a collective push to illuminate the microbial dark matter on Earth and beyond. By highlighting emerging tools, pressing questions, and cross-domain insights, the symposium underscored the need for collaborative, open, and adaptive approaches to study the microbial unknown. The meeting marks a pivotal moment in microbiology, where cultivating knowledge of the uncultivated promises transformative understanding of life, everywhere.
BackgroundTheileriosis, a tick-borne disease caused by Theileria annulata, leads to global economic losses in livestock, emphasizing an urgent need for the identification of new therapeutic agents with alternative mechanisms of action due to rising buparvaquone resistance combined with climate-driven spread. T. annulata lactate dehydrogenase (TaLDH) has been selected as a promising target in the current study. As approximately half of the FDA-approved veterinary drugs are shared with human medicine, integrating drug repurposing has gained importance as an emerging strategy for the management of tick-borne diseases. MethodsIn this study, in vitro and in silico approaches are integrated to screen 21 Active Pharmaceutical Ingredients (APIs) from FDA-approved drugs against TaLDH in the context of drug repurposing. Results Six of 21 the APIs showed ≥ 60% inhibition against TaLDH (over 95% purity), particularly the proton pump inhibitor omeprazole, which exhibited both the highest inhibition percentage (73.36%) and the lowest binding energy (- 6.36 kcal/mol), with a consistency between experimental and computational results. To our knowledge, this study is the first to evaluate FDA-approved human therapeutics as potential TaLDH inhibitors. Conclusion This preliminary in vitro enzymatic inhibition study suggests omeprazole as a potential therapeutic agent for theileriosis. However, comprehensive pharmacokinetic and pharmacodynamic analyses will further validate and advance these promising findings. Furthermore, APIs with ≥ 60% inhibition of TaLDH may serve as promising leads for the future development of more potent anti-theilerial agents through targeted derivatization and lead optimization. In alignment with the One Health framework, evaluating human-approved active pharmaceutical ingredients as veterinary therapeutic against parasitic diseases may offer a sustainable, time and cost-efficient strategy.
Two previously undescribed chroman derivatives, designated as lasiodiones C (1) and D (2), were obtained from Lasiodiplodia sp., an endophytic fungus derived from desert plants surviving in unique biological environments. The chemical structures of these two compounds were unambiguously elucidated based on comprehensive spectroscopic data analyses. Their absolute configurations were further confirmed by comparing the experimental circular dichroism (CD) spectra with the electronic circular dichroism (ECD) spectra calculated via quantum chemical computations. Furthermore, the cytotoxic activities of compounds 1 and 2 were assessed against two human cancer cell lines, namely HGC-2 and A549. The results indicated that compound 2 possessed moderate cytotoxic effects, with IC50 values of 13.54 μM and 14.41 μM for HGC-2 and A549 cells, respectively.
Human ornithine aminotransferase (hOAT), a pyridoxal 5'-phosphate (PLP)-dependent enzyme, plays a central role in glutamine, proline, and polyamine metabolism and is increasingly recognized as a metabolic vulnerability in multiple cancers. Previously, we established a second deprotonation strategy to achieve efficient mechanism-based inactivation of hOAT over closely related aminotransferases. Building on this concept, we report the rational design, synthesis, and mechanistic investigation of cyclopentene-based γ-aminobutyric acid analogues bearing alkyne or nitrile warheads as potent hOAT inactivators. These compounds undergo enzyme-catalyzed γ-deprotonation to form ketimine intermediates, priming for a subsequent tautomerization event that leads to irreversible inhibition. Inhibitory activity evaluation revealed pronounced stereochemical effects on binding affinity and partition ratio, with one nitrile analogue (4b) exhibiting an exceptional inactivation efficiency (kinact/KI = 111.8 mM-1·min-1) and ∼400-fold selectivity for hOAT over γ-aminobutyric acid aminotransferase. Intact protein mass spectrometry and X-ray crystallography demonstrated that alkyne-containing analogues form covalent adducts with hOAT, whereas nitrile-containing analogues generate noncovalent but tight-binding species. Kinetic isotope effect studies identified γ-deprotonation as the rate-determining step, and a complementary small-molecule mass and computational study elucidated the inactivation and turnover pathways. Collectively, these results expand the mechanistic repertoire of PLP-dependent enzyme inactivation and provide a generalizable framework for designing highly selective mechanism-based inactivators.
The photogenerated Ir(I) compound [Cp*Ir(PMe3)] and its Ir(III) derivative [Cp*IrMe(PMe3)(solvent)]+ [Cp* = η5-(C5Me5)] have tamed the field of C-H bond activation chemistry; however, analogues using bulkier phosphines have been barely investigated. We report the synthesis and characterization of their congested versions using sterically demanding terphenyl (C6H3-2,6-Ar2) phosphine ligands. Two Ir(I) species, [Cp*Ir(PMe2ArDtbp2)] (3, ArDtbp2 = C6H3-2,6-(C6H3-3,5-tBu2)2) and [Cp*Ir(PMe2ArDipp2)] (3', ArDipp2 = C6H3-2,6-(C6H3-2,6-iPr2)2), were isolated and structurally authenticated, revealing strong metal-arene interactions that confer remarkable stability to these otherwise highly unsaturated species. Despite their apparent similarity, these complexes exhibit sharply contrasting reactivity toward methyl triflate: only the PMe2ArDtbp2 derivative undergoes clean formal oxidative addition to form the targeted cationic methyl complex [Cp*IrMe(PMe2ArDtbp2)]+ (4). Computational studies rationalize this divergent behavior in terms of steric effects beyond conventional metrics. Besides, compound 4 displays rich chemistry, including intramolecular activation of a tert-butyl group and selective intermolecular functionalization of 1,2-difluorobenzene, as well as reactions with H2 and phenylsilane. These findings evince the role of terphenyl phosphines in stabilizing reactive iridium fragments and highlight their potential for selective C-H activation under sterically congested environments.
Controlling water organization within confined spaces is essential for developing high-performance solid-state protonic and electronic devices. Here, we report a family of metalloporphyrin (MPp) based conjugated metal-organic frameworks (MOFs), MPp-Cu-O (M = Fe, Ni, and Cu), that features Janus-type subnanochannels with alternately arranged hydrophilic CuO4 linkages and hydrophobic aromatic zones. This channel design enables the chemical and spatial confinement of water molecules into moderately bound hydrogen-bond (H-bond) networks that balance adsorption stability and dynamic exchange. The confined water structures in the MOF channels promote rapid proton hopping while inducing electronic reorganization at CuO4 nodes, thereby coupling protonic and electronic transport pathways to give ultrasensitive responses to water exposure. Among the three MPp-Cu-O MOFs, NiPp-Cu-O exhibits over 6 orders of magnitude enhancement in conductivity at 90% relative humidity, with rapid response and recovery across a wide humidity range, making it one of the most sensitive chemiresistive humidity sensors reported to date. Combined spectroscopic and computational analyses reveal that nanoconfinement suppresses the formation of overcondensed H-bond networks and that the thus-formed moderately bonded water assembly in the MOF channels has sufficient connectivity for proton conduction but with low reconstruction energy to ensure fast dynamics.
A family of neutral gold(I) complexes involving N-heterocyclic carbene ligands bearing fluoro-aryl groups has been synthesized and characterized in order to evaluate its antileishmanial activity in vitro for all complexes and in vivo for the hit 34. Computational docking experiments on the potential target trypanothione reductase were performed for all complexes. Regarding complex 34, trypanothione reductase inhibition, ROS formation, as well as the impact on target genes by RT-qPCR have been addressed. Complex 34 showed almost identical activity in vitro, combined with higher selectivity than amphotericin B, the most effective drug currently used against leishmaniasis in clinical use. The high activity of this complex has been confirmed by in vivo experiments. Mechanistical studies reveal that gold complex 34 rapidly alters the expression of key genes involved in Leishmania infantum's redox homeostasis and mitochondrial function after treatment. The significant upregulation of genes in the trypanothione system suggests an early adaptive response to oxidative stress induced by this complex.
The intervertebral disc (IVD) plays a fundamental role in load absorption and redistribution during daily activities. Its mechanical behavior arises from the intricate interplay between the structural anisotropy of the annulus fibrosus (AF) and osmotic swelling driven by fixed charge density in the nucleus pulposus (NP). This behavior is further governed by fluid redistribution through the porous matrix and across its boundaries, including interactions with the adjacent endplates and the surrounding physiological environment. Despite advances in IVD modeling, few computational tools accurately capture these coupled mechanisms while accounting for progressive degeneration under realistic loading conditions. This study introduces a microstructure-informed and degeneration sensitive finite element model of the human IVD, integrating regional fiber architecture, biphasic fluid-solid interactions, and osmotic swelling within a unified mechanistic framework. A multiscale calibration strategy is employed to identify the solid, osmotic, and fluid transport parameters, based on targeted mechanical experiments. Degenerative changes are incorporated at both macroscopic (e.g., IVD height loss) and microscopic (e.g., fiber uncrimping, proteoglycan depletion, and increased matrix porosity) levels. Model predictions are compared against physiological loading scenarios representative of everyday life-including lying down, standing upright, and trunk motions-revealing the evolving contribution of each mechanism with degeneration, while model robustness is assessed through a parametric sensitivity analysis. This framework provides a mechanistic understanding of the evolving roles of IVD constituents across degeneration and defines representative parameter sets for different degenerative states, providing a basis for future patient-adapted modeling approaches. It also enables the exploration of degeneration-dependent mechanical responses and loading sensitivities, offering perspectives for improved mechanobiological understanding of IVD degeneration.
Despite great advances in the chemistry of low-valent silicon species, a silicon/sulfur analogue of an acyl carbene, namely a silathioacyl silylene, has remained elusive. Herein, we report that the reaction of dialkyldisilyne 1 with cyclic thiourea 2a affords 3, an N-heterocyclic carbene (NHC) complex of a silathioacyl silylene. The solid-state structure of 3 reveals a three-membered ring with a markedly elongated Si─S bond and an Si-Si single bond, consistent with hitherto unknown intramolecular coordination of the silanethione sulfur to the silylene center, while computational studies indicate negligible disilathiirene character. Compound 3 exhibits silylene-like reactivity toward xylyl isocyanide to give an NHC complex of an imino-substituted silanethione.
The chemokine receptor type 5 (CCR5) plays a crucial role in HIV-1 entry into host cells, making it a key therapeutic target. This study aimed to identify novel CCR5 inhibitors through pharmacophore-guided virtual screening and molecular dynamics (MD) simulations using ZINC-derived compounds. The 3D structure of CCR5 (PDB ID: 4MBS) was used as the target receptor. A ligand-based pharmacophore model was generated using the Pharmit server, with Maraviroc as a reference molecule. Virtual screening of ZINC compounds was conducted based on pharmacophore features and Lipinski's Rule of Five. The top 10 compounds were subjected to molecular docking, ADMET analysis, and MD simulations using GROMACS to evaluate stability and binding dynamics. Among all screened ligands, ZINC000000867238 exhibited the strongest binding affinity (-10.0 kcal/mol), forming hydrogen bonds with Tyr251 and Glu283, and hydrophobic interactions with Trp86, Phe182, and Tyr108. The compound demonstrated favorable ADMET characteristics, including high absorption, non-mutagenicity, and absence of hERG inhibition. MD simulation confirmed its stability, with RMSD fluctuations between 0.20-0.38 nm, low RMSF deviations in active site residues, and a compact radius of gyration (∼2.48 nm) compared to apo-CCR5. The integrated computational approach identified ZINC000000867238 as a potent and stable CCR5 inhibitor candidate, warranting further in vitro and in vivo validation as a potential HIV-1 entry blocker.