Search Results (3,334)

Serpentine group minerals, including lizardite, antigorite, and chrysotile, are common gangue minerals in nickel sulfide ores, and exhibit complex and often unexpected wettability that adversely affects flotation efficiency. However, how these serpentine polymorphs differ in surface hydrophobicity is still not well known, making it difficult to explain their distinct flotation behaviors. In this work, molecular dynamics (MD) simulations and experimental contact angle measurements are used to investigate the wettability of the three main serpentine polymorphs. MD simulation results reveal that the contact angles of the lizardite Si–(00$\overline{1}$) surface and Mg–(001) are 78.6° and 71.1°, respectively. Chrysotile exposes the Mg–(001) surface, with a contact angle of 74.9°. The water droplet on the antigorite surface is spread along the SiOH region. Even the Mg–OH-terminated octahedral surfaces of the three serpentine polymorphs can exhibit hydrophobicity, depending on hydroxyl orientation and oxygen bonding configuration. Contact angle measurements show that antigorite (001) is moderately hydrophobic at about 40°, while (020) is highly hydrophilic at about 10°. The combination of Derjaguin–Landau–Verwey–Overbeek (DLVO) theory and hydrophobic interactions between antigorite and air bubbles produces a net attractive force, enabling particle–bubble adhesion. This work provides new insights for controlling serpentine behavior during flotation of copper–nickel ores hosted in ultramafic rocks. Full article

Triple-negative breast cancer (TNBC) is a clinically aggressive malignancy with extremely limited effective targeted therapies. Natural products are promising alternatives for anticancer drug discovery, whereas integrated computational approaches serve as efficient tools for novel lead identification. Herein, four novel spiro-polycyclic sesquiterpene [4+2] trimers (Inubritantrimers A–D) and eight synthetic derivatives from Inula britannica L. were investigated via DFT calculations at the ωB97xD/6-311++G(2d,p) level (for geometric, electronic, spectral, and reactivity parameters), network pharmacology, molecular docking against seven core breast cancer-related targets, 500 ns all-atom molecular dynamics (MD) simulation, and MM/PBSA analysis. The results showed that the endo-type cycloaddition products had superior structural stability, with all reactions thermodynamically spontaneous (ΔG < 0). Compound 11 exhibited the most potent and balanced binding activity, with a docking free energy of −13.45 kcal/mol to MTOR; MD and MM/PBSA confirmed stable complex formation (total binding free energy −21.13 kcal/mol), driven predominantly by hydrophobic interactions. This study first established a comprehensive stereochemistry–electronic structure–property–activity relationship for this rare sesquiterpene trimer class and identified compound 11 as a promising MTOR-targeted TNBC lead. It provided a theoretical basis for developing high-efficiency, low-toxicity natural anticancer agents. Full article

Hyperoxaluria Type 1 (PH1) is a rare autosomal recessive metabolic disorder caused by mutations in the AGXT gene, leading to impaired glyoxylate metabolism and excessive oxalate accumulation, resulting in nephrolithiasis, nephrocalcinosis, and end-stage renal disease. As a rare and often neglected disease, PH1 poses a significant challenge to modern healthcare systems due to its progressive nature and limited therapeutic options. In this study, an integrated in silico approach was employed to identify pathogenic single-nucleotide polymorphisms (SNPs) and evaluate potential therapeutic candidates. Computational analyses using ConSurf, Align-GVGD, INPS-MD, CUPSAT, and iStable identified G161C and Y260C as highly deleterious variants affecting protein stability. Virtual screening, followed by ADME and toxicity assessments, identified Tauroursodeoxycholic acid (TUDCA) as a promising candidate with favorable pharmacokinetic and safety profiles. Molecular docking revealed that TUDCA exhibited higher binding affinity than the reference drug pyridoxine across native and SNP variants of AGXT proteins. Molecular dynamics simulations (300 ns) demonstrated enhanced structural stability of TUDCA-bound complexes, indicated by reduced RMSD and RMSF, improved compactness, and sustained hydrogen bonding. Furthermore, free energy landscape (FEL) and dynamic cross-correlation matrix (DCCM) analyses confirmed improved conformational stability and coordinated residue motions in SNP variant structures. Overall, these findings suggest that TUDCA may effectively stabilize structural alterations induced by pathogenic AGXT variants, highlighting its potential as a precision medicine-based therapeutic strategy for PH1. Full article

The high genetic diversity and broad host range of betacoronavirus lead to frequent zoonotic outbreaks, posing a severe threat to public health. Therefore, the development of broad-spectrum antiviral agents is critical. Our study evaluated the broad-spectrum antiviral activity of 3CL protease (3CL^pro) inhibitors AKEX0730 and AKEX0757 against seven representative betacoronavirus strains from Sarbecovirus and Merbecovirus. Their efficacy was confirmed via in vitro live virus inhibition assays, and the binding mechanism and stability with conserved viral targets were elucidated using molecular docking and molecular dynamics (MD) simulations. Our experiments demonstrated that both AKEX0730 and AKEX0757 exhibit significant broad-spectrum inhibition against coronaviruses originating from diverse hosts. These findings highlight their potential as highly potent broad-spectrum antiviral agents, holding substantial promise for the prophylaxis and treatment of emerging zoonotic coronaviruses. Full article

Amyloid-β (Aβ) aggregation is a central mechanistic feature of Alzheimer’s disease, involving heterogeneous conformational ensembles that evolve through monomeric, oligomeric, and fibrillar states. Understanding how small molecules modulate these state-dependent processes remains a major challenge in medicinal chemistry. This review examines the molecular mechanisms by which (-)-epigallocatechin-3-gallate (EGCG) perturbs Aβ aggregation, with a focus on insights derived from molecular dynamics (MD) simulations integrated with experimental data. MD studies employing structural, dynamical, and interaction-based descriptors (e.g., β-sheet content, contact maps, and salt bridge persistence) reveal that EGCG acts as a state-dependent modulator: it redistributes monomeric ensembles by masking aggregation-prone regions, induces topology switching in oligomers that suppresses seeding competence, and destabilizes protofibrillar β-sheet networks through interfacial and node-targeting interactions. Methodological analysis highlights the importance of force field selection, sampling depth, and aggregate model dependence, leading to a hierarchy of mechanistic confidence that distinguishes well-supported trends from model-specific observations. From a medicinal chemistry perspective, EGCG is best interpreted as a mechanistic probe rather than as a lead compound, informing the design of biostable modulators through principles such as bioisosteric replacement, topology control, and interfacial targeting. Collectively, this work provides a framework for translating the state-dependent aggregation mechanisms into rational therapeutic strategies. Full article

Molecular mechanics/the Poisson–Boltzmann surface area (MM-PBSA) is a popular method for binding energy estimation. Several programs have been developed for performing MM-PBSA calculations in conjunction with Gromacs, the most popular molecular dynamics (MD) software. However, current programs are limited to Linux-based systems and lack cross-platform usability. To address this, we present s_mmpbsa, a lite and cross-platform MM-PBSA program, to support binding energy calculation on native Windows platforms without a subsystem. By incorporating electrostatic screening and interaction entropy, s_mmpbsa achieves improved binding free energy calculation accuracy, validated on a dataset of HIV-1 protease inhibitor complexes. In addition, s_mmpbsa achieves enhanced performance with g_mmpbsa in the same parameters and conditions. Indeed, s_mmpbsa offers an efficient and practical solution for interaction energy calculation from MD simulations in Gromacs, providing valuable protocols for further molecular design applications such as computational enzyme design and molecular screening. Full article

The persistence of an immunosuppressive microenvironment remains a formidable challenge for cancer immunotherapy, particularly in tumors with immune-excluded or immune-desert phenotypes. Increasing evidence indicates that chronic inflammation and tumor progression are intrinsically linked through shared signaling hubs, including NF-κB and PI3K/Akt. Osthole, a natural coumarin compound, has been reported to exhibit both potent anti-inflammatory and antitumor activities; however, whether these effects reflect a coordinated regulation of the inflammation–cancer axis remains unclear. In this study, we deployed an integrative framework founded on network pharmacology, molecular docking, and rigorous molecular dynamics simulations, complemented by literature-based evidence synthesis, to computationally explore the potential mechanisms underlying Osthole’s dual activities. Our analysis revealed that Osthole’s predicted targets are significantly enriched in signaling pathways bridging inflammatory and oncogenic processes, most notably the PI3K/Akt, NF-κB, and TGF-β/Smad pathways. Crucially, MD simulations provided supportive computational evidence, suggesting that Osthole forms stable, energetically favorable complexes with core protein hubs (AKT1, RELA, and TGFB1) under the simulated conditions. Evidence from representative inflammatory and tumor models supports the biological plausibility of these predictions, including suppression of pro-inflammatory signaling, mitigation of maladaptive tissue remodeling, and induction of apoptosis. Furthermore, in hepatocellular carcinoma models, Osthole-mediated apoptosis appeared linked to HMGB1-related inflammatory signaling, highlighting its potential to modulate the local immune niche. Collectively, this convergence of systems-level predictions and dynamic structural evidence identifies Osthole as a promising multi-target candidate for the coordinated regulation of inflammation-associated tumor progression, providing a robust rationale for further experimental validation. Full article

The catalytic influence of γ-aminobutyric acid (GABA) on Zn²⁺ electroreduction at a mercury electrode was investigated in an acetate buffer. Electrochemical measurements, including DC polarography and differential capacity, indicate that GABA facilitates charge transfer through the formation of “cap-pair” surface bridges. This acceleration is reflected in a systematic increase in the standard rate constant and the transfer coefficient. Molecular dynamics simulations complement these findings by characterizing the conformational properties of GABA, showing a transition toward more folded forms in concentrated environments. Moreover, MD simulations demonstrate that GABA reduces the Zn²⁺ solvation number, providing a structural pathway that lowers the dehydration barrier prior to charge transfer. These observations correlate with the measured decrease in diffusion coefficients as the neurotransmitter concentration increases. The results establish a direct link between the zwitterionic adsorption of GABA and the reduction in the energetic barrier in the zinc electroreduction process. Full article

Numerical simulation of metal microstructure evolution is essential for material design and performance optimization. This paper reviews major simulation methods for key evolution mechanisms, including recrystallization, grain growth, slip, twinning, and phase transformation. The reviewed methods are classified into atomistic models, discrete-field models, and continuous-field models. Molecular dynamics (MD) is discussed as an independent atomistic approach, with emphasis on its role in revealing atomic-scale mechanisms, calibrating mesoscale parameters, and bridging atomistic, mesoscale, and continuum simulations. Discrete-field methods, including Monte Carlo, cellular automata, and vertex models, are compared with continuous-field methods, including artificial neural networks, phase field models, finite element methods, and level-set methods. Furthermore, a semi-quantitative evaluation matrix based on accuracy, computational cost, scalability, and applicability is established to clarify the practical trade-offs among different methods. The results show that no single method is universally optimal; instead, method selection should depend on the dominant physical mechanism, target length scale, required accuracy, and available computational resources. This review provides methodological guidance for multiscale microstructure simulation and supports future applications in precision machining, additive manufacturing, and process parameter optimization. Full article

Background: Thrombotic disorders remain one of the leading causes of global mortality, necessitating the discovery of anticoagulants with broader therapeutic windows and multi-target efficacy. This study aimed to identify FDA-approved drugs capable of simultaneously inhibiting three critical nodes of the coagulation cascade: Factor X (F10), Proteinase-activated receptor 1 (PAR1) and Prothrombin (F2). Methods: High-confidence 3D structures of coagulation cascade proteins were established using AlphaFold2 and validated via MolProbity (Favored regions > 91%). A library of 1657 compounds from the Zinc database was screened using PyRx, followed by rigorous ADMET profiling to evaluate pharmacokinetic viability. The structural integrity and binding kinetics of the top candidate drugs were further analyzed through Molecular Dynamics simulation for 100 ns. Results: Virtual screening and downstream analysis identified 30 multi-target drugs. Avodart and Naldemedine were observed to have superior pharmacokinetic equilibrium. Compared to the other two drugs (Digoxin and Ledipasvir), Avodart and Naldemedine showed high affinity, higher adherence to drug likeness, lower metabolic inhibition risks and lack of acute toxicity, and were therefore the most suitable candidates. The 100 ns MD simulations revealed Avodart and Naldemedine to have the highest level of interaction stability and favorable MM-GBSA energies with Factor X, whereas Ledipasvir and Digoxin exhibited significant structural instability. Conclusions: The study proposes Avodart and Naldemedine as promising candidates for drug repurposing in antithrombotic therapy. This study provides a computational blueprint for the development of next-generation, broad-spectrum anticoagulants. Full article

Background/Objectives: Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a major global health challenge, exacerbated by the emergence of multidrug-resistant strains and limited efficacy of existing therapies. Given the involvement of multiple essential mycobacterial proteins, multitarget drug discovery represents a rational therapeutic strategy. Methods: In this study, an integrated in silico pipeline combining machine learning–based quantitative structure–activity relationship modeling, graph neural network–driven drug–target affinity prediction, molecular docking, molecular dynamics (MD) simulations, and pharmacokinetic–toxicity profiling was employed to identify potential antitubercular leads from natural products. Results: A curated library of over 0.69 million compounds from the COCONUT database was systematically screened against seven essential M. tuberculosis protein targets. Machine learning and heterogeneous graph neural network models effectively captured complex ligand–protein interaction patterns, enabling high-confidence multitarget prioritization. Structure-based docking and MM-GBSA analyses revealed favorable binding affinities, further supported by 100 ns Molecular Dynamics simulations demonstrating stable binding and conformational integrity. In silico ADMET and toxicity predictions identified pharmacokinetically balanced candidates, while density functional theory calculations corroborated favorable electronic properties. Conclusions: Notably, a myricetin-based flavonoid glycoside exhibited consistent multitarget binding and dynamic stability across all targets. Overall, this study underscores the potential of integrated artificial intelligence and structure-based approaches in accelerating natural product-based antitubercular drug discovery and supports further experimental validation of prioritized leads. Full article

Objectives: This study aimed to elucidate the multi-target therapeutic mechanisms of Camellia sinensis phytochemicals in periodontitis using an integrative multi-scale molecular modeling strategy. Methods: An integrated in silico strategy was employed, incorporating network-based pharmacological analysis, protein interaction network evaluation, molecular docking assessment, density functional theory (DFT) computations, molecular dynamics (MD) trajectory analysis, MM/PBSA-derived binding energy estimation, and residue-level energetic contribution profiling. Overlapping targets between C. sinensis and periodontitis-associated genes were identified, followed by topological screening to determine crucial hub proteins. The most promising target was subjected to detailed structural and energetic evaluation. Results: Intersection analysis identified 23 common targets, with AKT1, myeloperoxidase (MPO), MMP2, MMP3, MMP9, STAT1, IL2, BCL2, ESR1, and SERPINE1 emerging as central hubs. Functional enrichment highlighted AGE–RAGE and JAK–STAT signaling pathways and extracellular matrix remodeling processes. Docking revealed MPO as the most favorable core target. Gallate-containing catechins, particularly (−)-gallocatechin gallate (−9.63 kcal/mol) and gallocatechin 3-O-gallate (−9.52 kcal/mol), exhibited more favorable binding affinities than the standard inhibitor 4-ABAH (−6.02 kcal/mol). DFT analysis demonstrated moderate HOMO–LUMO gaps (4.31–4.78 eV) and favorable dipole moments supporting electronic stability and reactivity. MD simulations confirmed stable complex formation over 100 ns, with persistent hydrogen bonding and consistent ligand retention. MM/PBSA calculations further validated a favorable binding of (−)-gallocatechin gallate (−27.66 ± 7.53 kcal/mol) and gallocatechin 3-O-gallate (−26.09 ± 8.96 kcal/mol), comparable to or exceeding 4-ABAH (−25.88 ± 4.44 kcal/mol). Conclusions: C. sinensis phytochemicals, particularly gallate-containing catechins, exhibit stable, energetically favorable interactions with MPO, supporting their potential as competitive inhibitors that modulate oxidative stress and inflammatory pathways in periodontitis. Full article

The increasing emission of harmful gases into the atmosphere represents a major environmental challenge, driving the need for efficient air purification materials. Poly(methyl methacrylate) (PMMA) has emerged as a promising candidate due to its favorable physicochemical properties and adsorption potential. In this study, the interactions between PMMA and selected sulfur-containing pollutants (CH₃SH, COS, CS₂, H₂S, and SO₂) were systematically investigated using a multiscale computational approach. Initial structural exploration was performed using extended tight-binding (xTB) methods, followed by refinement at the density functional theory (DFT) level, while molecular dynamics (MD) simulations were employed to capture the dynamic behavior of the systems. The results suggest that all investigated gases exhibit attractive interactions with PMMA, with interaction strength strongly dependent on molecular polarity and electronic structure. Among the studied systems, SO₂ shows the strongest binding, while CS₂ exhibits the weakest interaction. Energy decomposition based on symmetry-adapted perturbation theory (SAPT) and electronic structure analyses suggest that electrostatic and donor–acceptor interactions play a dominant role for strongly interacting systems, whereas weaker interactions are primarily governed by dispersion forces. Full article

Crystallization and blockage in tunnel drainage systems represent a major challenge in the operation and maintenance of tunnels in karst regions. This study focuses on a tunnel in Guilin, Guangxi, employing a combined approach of field investigation, laboratory characterization, and molecular dynamics (MD) simulations to explore the atomic-scale mechanism of CaCO₃ crystallization within the drainage system. Field investigations reveal that the groundwater is dominated by Ca²⁺ and HCO₃⁻ ions, and the crystalline products consist primarily of high-crystallinity single-phase calcite, characterized by typical rhombohedral geometric structures and heterogeneous stacking. Molecular dynamics simulations indicate that the CaCO₃ nucleation process is accompanied by the desolvation of Ca²⁺, while background electrolyte ions exert distinct regulatory effects on the nucleation kinetics. SO₄²⁻ participates in cluster construction through strong coordination, inducing the formation of loose, chain-like aggregates; conversely, Cl⁻ delays cluster coalescence primarily through charge shielding and steric hindrance effects. Additionally, Na⁺ influences the overall solution dynamics and the stability of pre-nucleation clusters by constructing stable hydration shells and providing charge neutralization. This research reveals the formation mechanism of tunnel crystallization from a microscopic perspective, providing theoretical support for the prevention and control of crystallization in tunnel drainage systems. Full article

Iron oxides, including hematite ($\alpha$-${\mathrm{Fe}}_2$${\mathrm{O}}_3$), magnetite (${\mathrm{Fe}}_3$${\mathrm{O}}_4$), and maghemite ($\gamma$-${\mathrm{Fe}}_2$${\mathrm{O}}_3$), play central roles in catalysis, corrosion, environmental remediation, magnetic nanotechnology, and energy storage. Molecular dynamics simulations have become an essential tool for understanding their structural, magnetic, and interfacial behavior at the atomic scale. This review provides a comprehensive overview of MD methodologies applied to these materials, spanning classical force fields, reactive force fields, ab initio molecular dynamics, and emerging machine learning interatomic potentials. Particular emphasis is placed on facet-dependent surface chemistry, especially the contrast between compact (111) and open (110) planes, and on adsorption processes involving water, nitrogen-containing molecules, and representative organic compounds. The review highlights recent advances in force field development, redox modeling, and multiscale simulation strategies while critically identifying limitations related to charge transfer, mixed valence, vacancy ordering, and magnetic–chemical coupling. Finally, future perspectives are outlined toward quantitatively predictive, facet-resolved, and magnetically aware simulations of iron oxide interfaces. These developments are expected to tightly link atomistic insights with experimental observations and guide the rational design of iron oxide-based functional materials. Full article