搜索结果：Computational Mechanics

Articular cartilage is a charged, multiphasic tissue whose mechanical response emerges from coupled solid-fluid-ion interactions. Modeling this complexity remains a major challenge in computational biomechanics. This scoping review maps cartilage constitutive models and provides a structured, mechanics-informed appraisal of their physiological representation, constitutive assumptions, and numerical implementation practices. Database searches (1995-2025) identified 84 eligible studies. Models were classified into monophasic, biphasic, triphasic, and other constitutive families. To systematically assess modeling assumptions, a mechanics-oriented appraisal framework structured around five evaluation axes (M1-M5) was applied, addressing constitutive closure, dissipative mechanisms, internal physical admissibility constraints, model-problem coherence, and verification/validation practices. Biphasic models dominate current practice, whereas triphasic formulations better capture osmotic and electrochemical effects. Physiological features were represented unevenly across studies: stress relaxation (86.9%), fluid exudation (69.0%), strain-dependent permeability (48.8%), zonal anisotropy (51.2%), and electrochemical coupling (16.7%). Degeneration mechanisms were incorporated in only 23.8% of studies. Across the corpus, most models demonstrated strong model-problem coherence but frequently lacked explicit admissibility constraints and robust verification and validation practices. Numerical transparency was also limited: although software platforms were often reported, solver configuration, convergence criteria, and computational cost were rarely specified. These findings highlight a persistent gap between constitutive sophistication and empirical validation. Advancing predictive cartilage modeling will require closer integration between constitutive formulation, experimental validation, parameter identifiability, and reproducible numerical implementation.

Patient-specific finite element models of hip cartilage and labrum (chondrolabral) mechanics have improved the understanding of form-function relationships underpinning hip osteoarthritis. While these models often assume the pelvis and femur are rigid bodies to reduce development and computational time, contact stresses may be overestimated when the pelvis and femur cannot deform. Recent advancements in element formulations, constitutive models, and integration of patient-specific boundary/loading conditions warrant a re-examination of this rigid body assumption. We assessed the influence of material representations for the pelvis and femur on finite element predictions of chondrolabral mechanics during level walking and squatting. Four material representations were evaluated: (1) rigid bodies, (2) deformable-inhomogeneous with CT-derived material properties, (3) deformable-two-material with distinct trabecular and cortical bone materials, and (4) deformable-one-material model. Patient-specific kinematics and joint reaction forces for level walking and squatting were derived from motion capture and musculoskeletal models, respectively. During level walking, there were no significant differences between bone material representations for maximum cartilage contact pressure, contact area, shear stress, or first principal strain. During squatting, rigid and one-material models produced significantly higher maximum contact pressures, while other metrics remained unaffected. Rigid models required significantly less computational runtime and memory than deformable models. Our findings indicate rigid bone assumptions suffice for level walking, improving efficiency in large cohort studies, but deformable bone models are likely warranted for activities that produce larger bone deformations such as squatting. This study provides guidance for selecting bone material representations that balance accuracy and computational efficiency in hip biomechanical analyses.

Accurate prediction of inhibitor selectivity across protein paralogues remains a central challenge in computational drug discovery. Here, we perform a comparative assessment of three computational methods─Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA), Absolute Binding Free Energy (ABFE) and Umbrella Sampling (US) calculations─in their ability to recapitulate PARP1 versus PARP2 selectivity for eight clinically relevant PARP enzyme inhibitors used in ovarian, breast, and prostate tumors, among others. We demonstrate how MM/PBSA calculations offer rapid and qualitative insights but show pronounced sensitivity to the chosen static conformational pose, being particularly challenging for ligands with subtle energetic differences between distinct protein paralogues. In contrast, both ABFE and US calculations using atomistic models with explicit solvent result in substantially improved agreement with experimental binding affinities. The ABFE method exhibits the strongest quantitative correlation with experimental binding free energy differences, remarkably reproducing selectivity trends even among nearly isoenergetic complexes. Notably, our structural contact analysis reveals how contact connectivity controls ligand selectivity, providing valuable mechanistic and molecular insight into the key residues that stabilize each inhibitor in both protein enzymes. Together, our multimethod computational study contributes to elucidating potential chemical modifications across the ligand chemical space to enhance potency and specificity, informing the future design and evaluation of selective inhibitors for precision oncology, including therapies targeting homologous recombination-deficient cancers.

The presence of an unbalanced gut microbiome and the dysregulation of bile acid signalling are considered pivotal causes of various inflammation-based diseases. The Takeda G protein-coupled receptor (TGR5), TGR5 is a bile acid-responsive receptor that modulates inflammatory signalling pathways, making it an enticing molecular target for the discovery of novel anti-inflammatory agents. Herein, a comprehensive in silico approach was employed to identify potential TGR5 agonists from sterol-rich phytocompounds present in Triphala, a traditional polyherbal formulation. Using in silico computational methods, such as molecular docking and molecular dynamics simulations (MDS), we screened the putative agonistic potential of 10 phytocompounds obtained from Terminalia chebula, Terminalia bellirica, and Phyllanthus emblica against the crystal structure of human TGR5 (PDB ID: 7XTQ). Based on binding energy and molecular interactions, ergosterol (-12.34 ± 0.17 kcal/mol) and stigmasterol (-10.35 ± 0.04 kcal/mol) were predicted to be the top and best compounds. Furthermore, the stability of these two compounds in the docked complex was analysed using MDS for 200 ns. The mean Cα RMSD values were 0.22 ± 0.02 nm for both ergosterol- and stigmasterol-bound complexes, compared to 0.21 ± 0.02 nm for the unbound apo protein. Further, the molecular mechanics/Poisson-Boltzmann surface area (MMPBSA) analysis revealed that ergosterol exhibited binding free energy (-139.868 ± 12.318 kJ/mol) comparable to that of the co-crystallised ligand R399 -93.424 ± 8.919 kJ/mol. In silico ADMET predictions indicated acceptable drug-like properties and low toxicity for both compounds. Collectively, these computational findings suggest that ergosterol is a promising putative TGR5 agonist, warranting further experimental validation of its potential role in modulating inflammation-related pathways.

DNA methylation is a central epigenetic modification regulating gene expression, chromatin structure, and disease progression. Although commonly treated as a thermally activated enzymatic process, quantum tunneling can contribute to methyl transfer in DNA methyltransferases (DNMTs). We present a multiscale computational study combining density functional theory (DFT), hybrid quantum mechanics/molecular mechanics (QM/MM), and tunneling formalisms-Wentzel-Kramers-Brillouin (WKB) analysis as a baseline and Ring Polymer Instanton (RPI) theory with instanton-Hessian refinement-applied to a Morse-fitted reaction coordinate. Near-well curvature and width are extracted from an enzyme-aligned scan and used to interpret bead-converged RPI actions and kinetic isotope effects (KIEs). In the absence of enzymatic facilitation, WKB predicts negligible tunneling probabilities and astronomically long timescales, underscoring the necessity of a quantum-mechanical treatment that includes the catalytic environment. Near the minimum, a quadratic fit gives ξe = -0.018 Å with curvature k = 460.4 kcal·mol-1·Å-2 (RMSE 4.45 kcal, N = 12). A Morse fit with fixed De = 150 kcal·mol-1 yields a = 1.03 Å-1 and re = -0.09 Å (RMSE 4.6-8.8 kcal depending on weighting), consistent with a moderately steep, slightly displaced well. With DNMT1 preorganization at 298 K, instanton + Hessian analysis of the pre-methylation geometry yields kCH3 ≈ 6.7×10-3 s-1 and kCD3 ≈ 8.2×10-7 s-1, corresponding to a large KIE (≈ 8.17×103; lnKIE ≈ 9). For the methylated state, RPI calculations (32-128 beads) converge to a modest KIE of 6.28-6.32 (lnKIE ≈ 1.84), with anchor-qualified rates kCH3 ≈ (6.1-6.7)×10-3 s-1 and kCD3 ≈ (1.0-1.1)×10-3 s-1. These results indicate that DNMT1 reshapes and narrows the barrier to enable methyl tunneling during the chemical step, while product-like geometries suppress isotope sensitivity after mark installation. Relative quantum observables are robust to prefactor choice.

Transcatheter aortic valve implantation (TAVI) is the leading treatment for aortic stenosis. Self-expanding transcatheter heart valves (THVs) are oversized to prevent paravalvular leakage and then deployed over the diseased native valve. However, this can result in incomplete expansion and elliptical deployment, which may influence thrombogenic risk and structural degeneration, although this is not fully understood. In this study, we utilized a validated in silico framework to assess the impact of THV oversizing and ellipticity on leaflet mechanics, hemodynamic shear stress and stent deformation, which are indicators of structural degeneration and thrombogenicity. We simulated self-expansion of a deformable THV stent within an idealized aortic annulus, applied pulsatile loading conditions representative of the cardiac cycle and then evaluated post-deployment frame deformation, leaflet mechanics, hemodynamics and stent fatigue. We predicted stent-frame decoupling of the supra-annular THV, with increased expansion and circularity at the functional valve level compared to the inflow. THV oversizing reduced valve expansion at the supra-annular valve level (< 90% expansion), which increased leaflet coaptation and pinwheeling, but reduced peak leaflet stresses and stent deflection compared to nominal sizing. Oversizing also altered hemodynamics, causing early mainstream flow separation, which increased leaflet oscillatory shear and viscous shear stress downstream of the THV, potentially increasing thrombogenic risk and promoting tissue degeneration. THV ellipticity induced heterogenous stent deflections, leading to variable leaflet stress distributions and coaptation mismatch. We propose that flexible THV stents may mitigate adverse effects of elliptical deployment and emphasize the importance of assessing THV expansion through fluoroscopy and considering post-TAVI balloon-dilatation to increase expansion and improve long-term functional valve performance.

Surface instabilities, such as wrinkling, folding, and creasing, have transcended their traditional perception as mechanical failures to emerge as a powerful and versatile paradigm for engineering functional surface morphologies in soft materials. This review comprehensively examines the mechanics, fabrication, and rapidly expanding applications of these instability-driven patterns. This review first elucidates the fundamental principles governing the formation of various instability modes, stemming from classical model of thin film-substrate system, and discusses advanced strategies for achieving precise morphological control, including hierarchical and spatially organized structures. Then the core of this review highlights the transformative impact of these tailored surface topographies across diverse fields. Key applications explored include the development of highly sensitive and stretchable electronic skins (E-skins), energy-harvesting triboelectric nanogenerators, deformable optoelectronic devices, physically unclonable features for advanced optical encryption and anti-counterfeiting, engineering surfaces with dynamically tunable wettability, and biomimetic constructs for biomedical engineering and artificial tissues. Finally, a forward-looking perspective on the challenges and future opportunities in this vibrant field was provided, emphasizing the potential of integrating stimuli-responsive materials, computational design, and artificial intelligence to develop the next generation of intelligent, adaptive, and multifunctional surfaces.

The Shack-Hartmann wavefront sensor (SHWS) is a widely used non-interferometric wavefront measurement technique. However, for high-slope wavefronts, spot crosstalk and asymmetric distortion cause severe matching ambiguity and centroiding errors. This creates an inherent conflict between dynamic range and reconstruction accuracy. To address this, a graph-theoretic computational model named G-SHWS is proposed. By minimizing the global pairing cost of a bipartite graph constructed between fitted and actual spots, G-SHWS drives the fitted distribution to approximate the true distribution and maps the subaperture attribution of the fitted spots to the actual spots, achieving precise spot-subaperture matching under severe aliasing. Furthermore, incorporating a Graph Attention Network (GAT) embedded with SHWS matching topology, the model utilizes a graph structure to explicitly encode the matching relationships obtained from the matching process, and combines the spatial features and intensity morphology of spots to achieve high-precision reconstruction of strongly distorted wavefronts, effectively circumventing the inherent centroiding errors under large aberrations. Experimental results demonstrate that G-SHWS extends the measurable range of SHWS to 21 times the conventional limit while maintaining a reconstruction error of less than 0.05 &#x3bb; , and remains robust under severe spot loss. These advancements significantly enhance the SHWS's ability to measure complex aberrations, providing a reliable computational framework for large dynamic range wavefront sensing.

Interlocking architectures in three-dimensional woven covalent organic frameworks (COFs) induce interesting molecular-scale mechanical responses, programmed through reticular chemistry and topology. Here, we use atomistic simulations to investigate the topology-driven properties of a copper-templated woven framework (COF-500-Cu) and its demetalated analogue (COF-500). The computational analysis indicates that Cu-ligand coordination in COF-500-Cu pins the interlocked ribbon topology, leading to a snap-through behavior under tension. Removal of Cu(I) allows enhanced ribbon mobility while preserving the mechanical interlocking, which becomes increasingly constrained under tension and compression due to a jamming transition. These results highlight that interlocked woven COFs can function as molecular-scale metamaterials, thereby extending their use beyond conventional chemical applications.

Interleukin-2 receptor &#x3b3; chain (IL2RG, CD132) act as a signaling subunits for various cytokine receptors, required for lymphocyte growth and maturation. Genetic variants within the IL2RG gene are associated with X-linked Severe Combined Immunodeficiency (X-SCID) disease. This study uses an in-silico method to investigate the structural and functional impacts of two IL2RG variants, Trp240Arg (W240R) and Arg226Cys (R226C), located in the extracellular domain, a region important for receptor stability. Homology modeling was generated for Wild-Type (WT) and Variant IL2RG structures and subsequently analyzed through Molecular Dynamics (MD) simulations and protein-protein docking utilizing IL-2 and IL-21 cytokines. MD simulations showed that WT-IL2RG remained stable, whereas its variant exhibited structural instability. W240R is disrupted by increased solvent exposure and reduced compactness. The IL-2 binding caused structural alterations in the WT complex in cytokine-bound states, whereas R226C reduced hydrogen bonds to decrease binding, and W240R increased flexibility to destabilize the interface. The WT complex exhibited consistent hydrogen bonding and was the most stable for IL-21, whereas both variants displayed weaker interactions and fluctuations. Further, the binding free energy Molecular Mechanics-Poisson Boltzmann Surface Area (MM/PBSA) analysis confirmed that WT complexes had better binding (IL-2: -40.87&#x2009;kJ/mol; IL-21: -45.88&#x2009;kcal/mol) compared to R226C (-35.42, -34.67&#x2009;kcal/mol) and W240R (-29.54, -33.30&#x2009;kcal/mol). The study reveals that Trp240Arg and Arg226Cys variants in IL2RG negatively affect cytokine-receptor interactions, lower binding energetics, and disturb the structural integrity of IL2RG, shedding light on the mechanisms underlying IL2RG-associated immunodeficiency disorders.

Aquaporins (AQPs) are classical water channels that also conduct small gas molecules such as O 2 $$ {\mathrm{O}}_2 $$ and CO 2 $$ {\mathrm{CO}}_2 $$ across the membrane. The hydrophobic central pore, located at the fourfold symmetry axis of an AQP tetrameric architecture, has been proposed to constitute the most optimal pathway for gas transport, although monomeric water pores can also contribute somewhat to permeation of less hydrophobic species. Here, we report a comparative molecular dynamics (MD) study of gas permeability in a plant AQP and a mammalian AQP1, taking advantage of complementary computational protocols including flooding simulations, umbrella sampling, and implicit ligand sampling. PIP2;1 AQPs, present in plants, are experimentally reported to have lower gas permeability than AQP1, which is present both in plants and animals. Using the spinach PIP2;1 (SoPIP2;1) and bovine AQP1 (bAQP1) as the models, the study unravels the specific structural features controlling the permeability of the central pore to gases. In SoPIP2;1, residue Trp79, which is highly conserved in the plant PIP2;1 family and lines directly the central pore, forms a major constriction region and the main barrier against gas permeation. Notably, the occluding conformation of the four Trp79 residues from the four monomers is stabilized by another conserved residue, Phe207 in the central pore. Sequence and structural comparisons show that both of these residues are replaced by less bulky residues in AQP1, for example, by Leu56 and Ala179, respectively, in bAQP1. The role of Phe207 residues in hindering gas permeation through SoPIP2;1 is confirmed by in silico alanine substitution, which reveals its effect on the local constriction produced by Trp79 residues. Conversely, by mutating Leu56 to tryptophan and Ala179 to phenylalanine in bAQP1, we engineer the protein to a less permeable gas channel.

Human serum albumin (HSA) plays a significant role in the transportation of steroid hormones through noncovalent interactions of low affinity. The binding between HSA and estradiol and testosterone has been a subject of investigation through experimental tools. While some studies suggest that HSA carries sex steroid hormones through a unique binding site, others propose that this interaction occurs through two or three binding sites, indicating a lack of consensus regarding the mechanisms underlying these interactions. In view of this, the present study used molecular docking, molecular dynamics, and quantum biochemistry to obtain more insights into the binding of estradiol, dihydrotestosterone, and testosterone to HSA. Molecular docking indicated that fatty acid binding sites 1 (FA1) and 6 (FA6), located respectively in subdomain IB and between subdomains IIA and IIB, are particularly promising targets for more robust investigations. The hormones exhibited considerable flexibility within subdomain IB, with dihydrotestosterone showing the greatest structural stability. This hormone also demonstrated the highest stability within FA6, which was markedly greater than that observed at FA1. Quantum mechanics calculations suggested that the three hormones exhibit similar interaction energies for the FA1 binding site, with estradiol predicting a marginally lower energy of interaction. Dihydrotestosterone was the only hormone that exhibited both the highest structural stability and the lowest energy of interaction when bound to FA6. Overall, the results suggest that the FA1 and FA6 binding sites generally do not favor the formation of strong interactions, except in the HSA-FA6:Dihydrotestosterone complex, where the hydrogen bond LEU481-(HN-main chain):DHT-(O17) played a crucial role in stabilizing conformations of both high and low theoretical energy of interaction. This observation aligns with the established fact that the interaction between HSA and sex steroid hormones is weak. Moreover, the present study found that dihydrotestosterone exhibits a heightened tendency to bind to FA6 in comparison to estradiol and testosterone. This tendency may critically regulate DHT serum transport, bioavailability, and half-life, while also creating a pharmacologically relevant hotspot for competition with fatty acids and FA6-targeting drugs, with potential implications for hormonal homeostasis and drug-hormone interactions in physiological and pathological conditions.

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Calcific aortic valve disease (CAVD) arises from coupled interactions between blood flow, tissue mechanics, and cellular signaling. Hemodynamic forces influence endothelial and interstitial cell behavior, while the resulting tissue remodeling alters valve motion and flow patterns. Capturing this two-way feedback requires models that integrate fluid-structure mechanics with biochemical regulation, yet such multiscale coupling remains technically challenging. Previous computational models have focused on isolated aspects of the disease: fluid-structure interaction (FSI) simulations reproduce valve deformation and flow, and systems biology (SB) models describe molecular signaling that drives fibrosis and calcification. However, without coupling, these approaches cannot predict how mechanical dysfunction initiates biochemical remodeling or how biochemical changes feed back on mechanics. Here, we present a proof-of-principle, multiphysics computational framework that couples three-dimensional FSI simulations of aortic valve dynamics with a mechanistic SB model of calcification signaling. The FSI module resolves pulsatile blood flow and leaflet deformation, yielding local wall shear stresses and tissue strains throughout the cardiac cycle. These mechanical quantities are used as inputs to the SB module, which comprises key biochemical pathways governing inflammation, TGF-&#x3b2;/SMAD signaling, and nitric-oxide (NO)-mediated inhibition within valvular cells. Simulations predict long-term calcification trajectories for valves of varying thickness, showing that fibrosis-induced stiffening lowers shear stress, reduces NO synthesis, and enhances TGF-&#x3b2; activation, thereby accelerating calcification. While the current one-way coupling implementation is not intended yet for clinical applications, the framework is modular and extensible, allowing for future enhancements that will advance toward this goal. These include the incorporation of additional biological pathways in the SB model and implementation of a fully two-way coupling scheme between the FSI and SB models that will increase accuracy and predictive capability of the framework. By integrating physics-based hemodynamics with systems-level biochemistry, this study demonstrates the utility of a next-generation, multiscale modeling platform for studying cardiovascular disease that unites blood flow dynamics and biochemical signaling.

To evaluate how subperichondrial mobilization affects implant-tissue mechanics, glottal configuration, and vibratory behavior in computational simulations of medialization laryngoplasty (ML). We used a finite-discrete element method (FDEM) framework to simulate subperichondrial tissue-cartilage separation and implant insertion in laryngeal models reconstructed from high-resolution magnetic resonance imaging. Four dissection conditions were evaluated, ranging from no mobilization to increasing dissection length distal to the thyroplasty window. Outcome measures included change in glottal area, vocal fold medial displacement, finite element fracturing along the tissue-cartilage interface, and estimated vibratory frequency. Increasing dissection length produced increased medial displacement, progressive reductions in glottal area, fewer secondary extensions of the dissection plane during implantation, and higher estimated vibratory frequencies (range: 100.4-120.8&#x2009;Hz). These findings indicate that subperichondrial tissue mobilization alters implant-induced force transmission and modifies boundary conditions relevant to vibration. In this first application of FDEM to simulate laryngeal biomechanics in ML, subperichondrial dissection length demonstrated direct effects on model predictions relevant to implant sizing, placement strategy, and anticipated phonatory outcomes. Incorporating tissue mobilization into computational frameworks as a mechanically meaningful variable improves physiological realism and supports future development of subject-specific surgical planning tools. Level V. This study provides preclinical computational evidence using FDEM, supporting and extending clinical observations in ML.

Introduction: Sorafenib remains the only approved treatment for advanced hepatocellular carcinoma (HCC), yet its clinical use is hindered by toxicity and the emergence of drug resistance. Sorafenib's anticancer effects are largely attributed to its inhibition of multiple kinases, including c-Raf, a key player in the Ras-Raf-MEK-ERK signaling cascade that promotes cell growth and survival. Given the critical role of c-Raf in tumor progression, targeting this kinase offers a promising strategy for improving therapeutic outcomes. Developing new analogs with stronger c-Raf inhibition, better pharmacokinetics, and reduced side effects could help address the current limitations of sorafenib. Objectives: This study aimed to design novel sorafenib analogs with enhanced binding affinity and favorable pharmacokinetic profiles, specifically targeting the c-Raf kinase to increase therapeutic efficacy against HCC. By using a fragment replacement approach combined with computational methods, the goal was to identify candidates capable of forming stronger, more stable interactions with c-Raf, potentially overcoming resistance linked to sorafenib treatment. Methods: A total of 84 sorafenib analogs (A1-A84) were generated by modifying key functional groups, including the 2-picolinamide and substituted phenyl moieties known to influence kinase binding and anticancer activity. These analogs were evaluated through chemoinformatics and pharmacokinetic screening to assess their drug-likeness and safety. Molecular docking was performed to estimate their binding affinity toward c-Raf. Six top-performing analogs (A2, A6, A9, A20, A22, A63) were selected for further analysis. To evaluate their dynamic behavior, 100[Formula: see text]ns all-atom molecular dynamics simulations were conducted, followed by Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) calculations to determine binding free energies. Principal component analysis (PCA) was carried out to explore key motion patterns within the protein-ligand complexes. Results: Molecular docking showed that the selected analogs exhibited stronger binding affinities (-11.6 to -10.9[Formula: see text]kcal/mol) compared to sorafenib (-9.3[Formula: see text]kcal/mol) and regorafenib (-9.5[Formula: see text]kcal/mol). Molecular dynamics simulations substantiated the docking results. MM-PBSA results revealed that at 100[Formula: see text]ns, the binding free energy for the c-Raf-sorafenib complex was 86.751[Formula: see text]kJ/mol, while the c-Raf complexes with A2, A6, A9, A20, A22, and A63 demonstrated significantly lower free energies of -129.114, -135.637, -136.242, -127.178, -94.25, and -123.176[Formula: see text]kJ/mol, respectively, indicating stronger and more stable binding. PCA further confirmed the stability and favorable dynamic profiles of these analogs trajectory with c-Raf. Discussion: The improved binding affinities and lower free energies of the top analogs indicate that specific structural changes to sorafenib can enhance its effectiveness against c-Raf. Molecular dynamics and MM-PBSA results suggest the stability and strength of these interactions, particularly for A2, A6, and A9. Conclusion: This study identified six promising sorafenib analogs with improved binding affinity, favorable pharmacokinetic characteristics, and stable interactions with c-Raf. By focusing on c-Raf inhibition, the combined use of computational modeling, molecular simulations and mmPBSA analysis provided valuable insights for drug design. Among the candidates, A2, A6, and A9 emerged as promising drug candidates for further development, supporting the potential of targeting c-Raf to enhance therapeutic strategies against HCC.

Epilepsy is a debilitating neurological disorder that impacts approximately 50 million people worldwide. The treatment of epilepsy with antiepileptic drugs has not achieved effective seizure management and thus requires new therapeutic options. This study investigated the catechins' affect on epilepsy-related molecular targets using a computational method that combined network pharmacology, molecular docking, and molecular dynamics (MDs) simulation. We fetched 84 catechins-related and 5356 disease-associated targets from various databases, yielding 31 common targets. The protein-protein interaction (PPI) network of 31 common targets identified 10 hub genes, including ALB, INS, brain-derived neurotrophic factor (BDNF), PTGS2, tumor necrosis factor (TNF), IL1B, FOS, IL6, LEP, and FGF2. Further, the functional enrichment analysis revealed that these common targets have a high prevalence in multiple pathways and gene ontology functions. Furthermore, "compound-target" and "compound-gene-pathway" networks were constructed and analyzed. Network pharmacology data show TNF, IL1B, and IL6 could influence epilepsy treatment by regulating several pathways. The Cresset Flare Pro+ docking study unveiled that the lead catechin, epigallocatechin gallate (EGCG), exhibited the highest Lead Finder (LF) dG scores of -10.2, -9.40, and -8.15&#xa0;kcal/mol against TNF, IL6, and IL1B, respectively. The electrostatic complementarity and Molecular Mechanics with Generalized Born and surface area (MMGBSA) results supported the docking results. Further, the stability of EGCG-bound complexes was analyzed using a 300&#xa0;ns MD simulation. The principal component analysis yielded promising results for the EGCG-2AZ5 and EGCG-1ALU complexes collective motion. These findings provide computational evidence suggesting that EGCG has a promising scaffold for designing multi-target molecules that could modulate epilepsy, meriting further experimental validation.

Intrathecal (IT) injection is an effective way to deliver drugs to the brain bypassing the blood-brain barrier (BBB). To evaluate and optimize IT drug delivery, it is necessary to understand the cerebrospinal fluid (CSF) dynamics in the central nervous system (CNS). In combination with experimental measurements, computational modeling plays an important role in reconstructing CSF flow in the CNS. Existing models have provided valuable insights into the CSF dynamics; however, most neglect the effects of tissue mechanics, focus on partial geometries, or rely on measured CSF flow rates under specific conditions, leaving full-CNS CSF flow field predictions across different physiological states underexplored. Here, we propose a comprehensive multiphysics computational model of the CNS with three key features: (1) it is implemented on a fully closed geometry of CNS; (2) it includes the interaction between CSF and poroelastic tissue as well as the compliant spinal dura mater; (3) it has potential for predictive simulations because it only needs data on cardiac blood pulsation into the brain. Our simulations under physiological conditions demonstrate that our model reproduces key features of CSF pulsation, including the craniocaudal attenuation and phase shift of CSF flow along the spinal subarachnoid space (SAS). When applied to the simulation of IT drug delivery, our model successfully captures the intracranial pressure (ICP) elevation during injection and subsequent recovery after injections. The proposed multiphysics model provides a unified and extensible framework that allows parametric studies of CSF flow dynamics and optimization of IT injections, serving as a strong foundation for integration of additional physiological mechanisms. The online version contains supplementary material available at 10.1186/s12987-026-00804-7.

We report an innovative design concept for small-molecule fluorescent sensors that are highly selective for protein targets. The sensor's enhanced selectivity stems from a "C-clamping" paradigm where three different binding domains cooperatively interact with a protein target. We established the sensor's performance both in vitro and in live cells using epidermal growth factor receptor (EGFR) tyrosine kinase as a proof-of-concept target. Detailed combined quantum mechanics/molecular mechanics computational studies strongly support both the experimentally established specificity and the "C-clamp" binding mechanism. To demonstrate the sensor's practical utility, we developed a single-compound assay for the quantitative profiling of EGFR kinase inhibitors upon fluorescent imaging in live cells. When bound to the protein target, the sensor is emissive in the near-infrared region and yields a turn-on quantitative fluorescent response toward small-molecule tyrosine kinase inhibitors. Furthermore, this sensing system produces differentiated responses to a series of clinically relevant EGFR kinase inhibitors in native environments. Overall, we envision that this work will empower the development of small-molecule systems for highly specific protein recognition and sensing, become an invaluable tool for assessing small-molecule/protein engagement, and be extended toward live-cell screening and fluorescent imaging of other important biomolecular targets.