Molecular Dynamics Research Articles

Investigating the Inhibitory Mechanism of para-Sulfonato-calix[4]arenes against Polymerase and Helicase of SARS-CoV-2: Molecular Docking and Dynamics Simulation Studies

The coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a profound impact on global health and socio-economic conditions. To date, various vaccines have been administered worldwide in an effort to curb the spread of the virus. Despite vaccination efforts, there have been complications. Existing antiviral drugs have shown limited effectiveness, prompting the use of computational methods to understand the dynamics of the virus and develop suitable treatments. The current study focuses on using biocompatible para-sulfonato-calix[4]arenes to dock against two key proteins of SARS-CoV-2, namely ribonucleic acid (RNA)-dependent RNA polymerase, and helicase. Docking results indicate a strong binding affinity of these compounds to the target proteins, with higher scores compared to commonly used medications. Molecular dynamics (MD) simulation validates the docking results, showing stable protein-ligand complexes over time. The compounds are also screened for absorption, distribution, metabolism, and excretion properties and toxicity, suggesting their potential as lead candidates for inhibiting the virus’s key proteins. However, further in vivo and in vitro studies are recommended to confirm these findings.

SHARC meets TEQUILA: mixed quantum-classical dynamics on a quantum computer using a hybrid quantum-classical algorithm.

Recent developments in quantum computing are highly promising, particularly in the realm of quantum chemistry. Due to the noisy nature of currently available quantum hardware, hybrid quantum-classical algorithms have emerged as a reliable option for near-term simulations. Mixed quantum-classical dynamics methods effectively capture nonadiabatic effects by integrating classical nuclear dynamics with quantum chemical computations of the electronic properties. However, these methods face challenges due to the high computational cost of the quantum chemistry part. To mitigate the computational demand, we propose a method where the required electronic properties are computed through a hybrid quantum-classical approach that combines classical and quantum hardware. This framework employs the variational quantum eigensolver and variational quantum deflation algorithms to obtain ground and excited state energies, gradients, nonadiabatic coupling vectors, and transition dipole moments. These quantities are used to propagate the nonadiabatic molecular dynamics using the Tully's fewest switches surface hopping method, although the implementation is also compatible with other molecular dynamics approaches. The approach, implemented by integrating the molecular dynamics program package SHARC with the TEQUILA quantum computing framework, is validated by studying the cis-trans photoisomerization of methanimine and the electronic relaxation of ethylene. The results show qualitatively accurate molecular dynamics that align with experimental findings and other computational studies. This work is expected to mark a significant step towards achieving a "quantum advantage" for realistic chemical simulations.

A Computational Study of Phenothiazine Derivatives as Acetylcholinesterase Inhibitors Targeting Alzheimer's Disease.

Alzheimer's disease is a neurodegenerative disorder that affects learning, memory and behavioral turbulence in elderly patients. Acetylcholinesterase (AChE) inhibitors act as anti-Alzheimer's agents. Phenothiazine derivatives are considered momentous anti-Alzheimer's agents because of their AChE inhibitory activity. The elevated levels and increased expression of this protein have been associated with Alzheimer's disease. Coumarin-fused phenothiazines have emerged as significant anti-Alzheimer's agents due to their notable receptor inhibitory activity. Some unique phenothiazine analogs were designed, and computational studies were conducted to explore their inhibitory activity against the AChE enzyme (PDB id: 4EY7) by using the Schrodinger suite-2019-4. Docking studies were conducted by using the Glide module; binding free energies were calculated by means of the Prime MM-GBSA module, and Molecular dynamics (MD) simulation was performed by using the Desmond module of the Schrodinger suite. Glide scores were used to find out the binding affinity of the ligands with the target 4EY7. The compounds exhibited enhanced hydrophobic interactions and formed hydrogen bonds, effectively impeding Acetylcholinesterase. The Glide scores for the compounds ranged from -13.4237 to -8.43439, surpassing the standard (Donepezil) with a score of -16.9898. Interestingly, a positive value was obtained for the MM-GBSA binding of the potent inhibitor. To gain insights into the dynamic behavior of the protein A8, molecular dynamics (MD) simulations were employed. Based on the results, the study concludes that phenothiazine derivatives show promise as acetylcholinesterase inhibitors. Compounds with notable Glide scores are poised to exhibit significant anti-Alzheimer's activity, suggesting their potential therapeutic efficacy. Further in vitro and in vivo investigations are warranted to validate and explore the therapeutic potentials of these compounds.

Ionic conduction and cathodic properties of CaMO3 (M=Fe and Mn) electrode materials via molecular dynamics and first-principles simulations

Calcium-ion(Ca-ion) batteries are gaining ever-increasing attention for next-generation energy storage systems due to affordability, highly abundant, high energy density, high theoretical capacity, and low redox potential close to Li-ion. In this work, we deployed the first-principles and classical molecular dynamics simulations to investigate the electronic and diffusive properties of isostructural ternary perovskite CaMO3 (M= Fe and Mn). The transport properties at various temperatures from ion dynamics and electronic properties of CaMO3 perovskites are examined using classical molecular dynamics and quantum mechanical simulations, respectively. We present the microscopic origin of the diffusion of multivalent ions like Ca2+ within the crystal structure of perovskite material and the effects of two transition metals, manganese and iron. Dynamic studies of Ca-ions were performed using molecular dynamic simulation, which depicts the diffusivity and conductivity of Ca-ion in CaMO3 material. We find that the diffusivity in both the crystals increases with temperature; as a result, conductivity increases. Among both the crystals, CaFeO3 requires less activation energy for diffusion and ionic conduction than CaMnO3. Using density functional theory, we calculated specific capacity, electronic density of states, phase stability and equilibrium cell voltage, and charge transfer process during intercalation-deintercalation from first-principles calculations. The electronic behavior of these materials shows that CaFeO3 has better electronic and transport properties than CaMnO3.

Interactions of sphingomyelin with biologically crucial side chain-hydroxylated cholesterol derivatives.

Oxysterols are interesting molecules due to their dual nature, reflecting beneficial and harmful effects on the body. An issue that still needs to be solved is how slight modification of their structure owing to the location of the additional polar group in the molecules affects their biological activity. With this in mind, we selected three side chain-hydroxylated oxysterols namely: 20(S)-hydroxycholesterol (20(S)-OH), 24(S)-hydroxycholesterol (24(S)-OH), and 27-hydroxycholesterol (27-OH), and examined their behavior in mixtures with the bioactive sphingolipid - sphingomyelin (SM). Our research was based on the Langmuir monolayer technique supplemented with molecular dynamics (MD) and microscopic observation of the films texture (Brewster angle microscopy, BAM, and atomic force microscopy, AFM). Additionally, since 20(S)-hydroxycholesterol has not been studied so far, we thoroughly characterized this oxysterol in one-component monolayers. Our studies showed differences in the interactions of the studied oxysterols and sphingomyelin. Namely, it was found that 20(S)-OH binds to SM, unlike 24(S)-OH and 27-OH, which both weakly interact with SM. This distinct behavior was interpreted within the molecular dynamics as being due to weak intermolecular interactions between 20(S)-OH molecules, which allowed easy incorporation of SM into the 20(S)-OH monolayer. In contrast, the strong oxysterol-oxysterol interactions occurring in monolayers with 24(S)-OH or 27-OH make this process more difficult. This may be important in the process of bone formation/resorption. Other aspects derived from our study are: (i) the tendency of oxysterols to incorporate into lipid rafts (leading to their modification in structure and function), as well as (ii) the formation of multilayer structures, in which oxysterols are arranged in the characteristic forms of "strings of beads", which may facilitate their transport across the membrane.

Design of High Entropy Superalloy FeNiCrCoAl Using Molecular Dynamics, Computational Thermodynamics, and Machine Learning

The development of High Entropy Superalloys (HESAs) represents a significant advancement in alloy design, offering superior mechanical properties and cost-effectiveness compared to traditional superalloys. However, the complex and time-consuming nature of HESA development, often requiring costly experimentation, poses a challenge. This study introduces a novel approach to the compositional design of HESA FeNiCrCoAl, utilizing Molecular Dynamics (MD) simulations to analyze lattice parameters, stacking fault energy (SFE), and compression strength. By integrating machine learning with computational thermodynamics, we enhance the efficiency of SFE prediction through extensive data analysis. Our findings clearly show that increasing Ni, Cr, and Co concentrations decrease lattice parameters, while higher Al concentrations increase them, validating our MD simulations. The non-equiatomic HESA FeNiCrCoAl exhibits outstanding tensile and compression strength compared to its equiatomic, with microstructural and dislocation analyses providing key insights into its deformation behavior. SFE calculations reveal that non-equiatomic HESA FeNiAlCrCo has a higher SFE than the equiatomic variant, with consistent trends observed across MD and thermodynamic analyses. Increasing concentrations of Cr and Co decreases SFE, while increasing Ni and Al concentrations increases SFE. Our machine learning models, notably the random forest algorithm, effectively predict SFE, underscoring the critical roles of Co and Cr in alloy design. The optimized HESA FeNiCrCoAl, designed for low to medium SFE, offers exceptional strength, hardness, and improved ductility, making it ideal for high-temperature applications. We propose an optimal design guide for achieving desired SFE: Ni (17-25 at.%), Cr (24-30 at.%), Al (5-15 at.%), Co (20-35 at.%), and Fe (20-35 at.%). This research provides valuable insights into the mechanical behavior of HESAs, particularly in improving creep resistance, and represents a significant step toward a deeper understanding of HESA properties and behavior.

Unravelling multi-layered structure of native SEI on lithium metal electrode and various electrolytes using reactive molecular dynamics

Lithium metal batteries, directly utilizing lithium metal as an anode, are promising as new-generation energy storage devices, and the characteristics of the solid electrolyte interphase (SEI) layer are critical to battery operation. However, the roles of electrolyte compositions on the structure and thickness of the passivating SEI film at the atomistic level have been previously overlooked, resulting in poor understanding of SEI characteristics. In this study, the formation of the native SEI layer on lithium metal electrode and various electrolyte composition is scrutinised using ReaxFF molecular dynamics (MD) simulations. The study successfully identifies different layers of the SEI film using charge distribution as a tool. The decrease in lithium density and the increase in the charge states of oxidised lithium atoms contribute to the identification of an inner layer and a transition layer within the previously known interface layer, as well as the sparse layer. Additionally, different reaction schemes are observed through the analysis of ratio between consumed lithium atoms and electrolyte molecules. Furthermore, results show the lithium salt concentration effects in suppressing the growth of the SEI between the lithium anode and the liquid electrolyte, and inhibiting the formation of key SEI components. This work provides new insights into the nature of SEI surface chemistry and potentially facilitates the design of artificial SEI layers and optimal electrolyte solutions.

Molecular dynamics simulations on the evolution of irradiation-induced dislocation loops in FeCoNiCrCu high-entropy alloy

High-entropy alloys (HEAs) have received extensive attention due to their excellent irradiation resistance. In this work, the displacement cascade simulations were performed by using the molecular dynamics (MD) method to study the dislocation loop evolution in FeCoNiCrCu HEA. The simulation results showed the dislocation loops evolution in pure Ni were dominated by Frank loops with larger size but lower density, which was caused by the absorption of prismatic dislocation loops by Frank loops. In contrast, prismatic dislocation loops were more prevailing in FeCoNiCrCu HEA with smaller size but higher density, since the interactions between dislocation loops were suppressed in HEA. To figure out the influence of HEA on dislocation loop evolution, the formation energy, interaction energy and mobility were analyzed. It was found that formation energy and interaction energy were basically the same, while the mobility of prismatic dislocation loop in HEA was much lower than that in pure Ni, which was considered as the main reason why the irradiation-induced dislocation loops were more difficult to interact and grow in HEA. The current work provides new insights into understanding the irradiation resistance from micro-mechanism in FeCoNiCrCu HEAs.

Photoaging-induced variations in heteroaggregation of nanoplastics and suspended sediments in aquatic environments: a case study on nanopolystyrene

Photoaging of nanoplastics (NPs) and heteroaggregate with suspended sediments (SS) determines transport processes and ecological risks of NPs in aquatic environments. This study investigated the disruption of photoaging on the heteroaggregation behavior of polystyrene NPs (PSNPs) and SS in different valence electrolyte solutions and deduced the interaction mechanisms by integrating aggregation kinetics and molecular dynamics (MD) simulation. Increasing the electrolyte concentration significantly enhanced the heteroaggregation between PSNPs and SS, and the divalent electrolytes induced the heteroaggregation more efficiently. MD simulation at the molecular level revealed that PS and SS could spontaneously form clusters, and photoaged PS has a stronger potential to fold into a dense state with SS. Photoaging for 30 d retarded heteroaggregation due to the steric hindrance produced by the leached organic matter in NaCl solutions, and the critical coagulation concentration (CCC) increased by more than 85.44%. Contrarily, photoaging caused more oxygen-containing functional groups produced on the surface of PSNPs through Ca2+ bridging promoting heteroaggregation and thus destabilizing in CaCl2 solutions, the CCC decreased by 23.53% ∼ 35.29%. These findings provide mechanistic insight into the environmental process of NPs and SS and are crucial for a comprehensive understanding of the environmental fate and transport of NPs in aquatic environments.

Emergence of Synchronization-Induced Patterns in Two-dimensional Magnetic Rod Systems under Rotating Magnetic Fields

We investigate the dynamics of two-dimensional assemblies of rod-shaped magnetic colloids under the influence of an external rotating magnetic field. Using Molecular Dynamics, we simulate the formation of patterns that...

Mechanism of Compound Kushen Injection in the Treatment of Acute Myeloid Leukemia from the Analysis Perspectives

Background: Chemotherapy resistance often occurs in the conventional treatment with AML and results in poor cure rates. CKI was found to have a good therapeutic effect when it was combined with other chemotherapy drugs in the clinical treatment of AML. However, the underlying mechanism is unclear. Therefore, this study aims to preliminarily describe the pharmacological activity and mechanism of CKI through comprehensive network pharmacology methods. Objective: This study aimed to explore the possible mechanism of Compound Kushen Injection (CKI) in the treatment of acute myeloid leukemia (AML) by using network pharmacology, molecular docking, and molecular dynamics techniques. Methods: Active compounds of CKI were identified based on the Traditional Chinese Medicine Systems Pharmacy (TCMSP) database, and the related targets of the active compounds were predicted using Swiss Target Prediction; AML-related targets from Gene Cards and Online Mendelian Inheritance in Man (OMIM) were collected. Protein-protein interaction (PPI) network was constructed, and its mechanism was predicted through Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. The protein-protein interaction (PPI) network construction, module partitioning, and hub node screening were visualized by using the Cytoscape software and its plugins. These module partitionings were also verified by using molecular docking and molecular dynamics modeling. Results: Fifty-six active ingredients corresponding to 223 potential targets were identified. Biological function analysis showed that 731, 70, and 137 GO entries were associated with biological processes, cellular components, and molecular functions, respectively. A total of 163 KEGG pathways were identified. Network analysis showed that the key anti-AML targets of CKI are MAPK3, EGFR, SRC, PIK3CA, and PIK3R1 targets, which are involved in the PI3K/Akt and Ras/MAPK signaling pathways or related crosstalk pathways. Conclusion: Our results suggested that the key anti-AML targets of CKI, such as MAPK3, EGFR, SRC, PIK3CA and PIK3R1, are involved in the PI3K/Akt and Ras/MAPK signaling pathways or related crosstalk pathways. Concentrating on the dynamic and complex crosstalk regulation between PI3K/Akt and Ras/MAPK signal pathways and related signal pathways may be a new direction in anti-AML therapy in the future.

Nucleation and growth dynamics of nanobubbles on smooth and rough surfaces

<sec>The interfacial nanobubbles (INBs) have been confirmed to exist, and have significant potential for applications in fields such as mineral flotation, aquaculture, and wastewater treatment. However, the microscopic nucleation process of INBs is still poorly understood. This study investigates the nucleation process and growth dynamics of INBs on smooth and rough surfaces under different levels of gas supersaturation. Molecular dynamics (MD) simulations using GROMACS software package are conducted to observe the microscopic nucleation process and the temporal evolution of the geometric characteristics of the INBs. Additionally, a growth dynamics model for INBs is derived based on the Epstein-Plesset gas diffusion theory, and the predictions from the model are compared with the MD simulation data.</sec><sec>The results indicate that on smooth homogeneous surfaces, the curvature radius and width of INBs increase progressively with time after nucleation. This growth process is well captured by the theoretical model, indicating that the gas diffusion theory provides an accurate description of INB growth dynamics. In addition, the contact angle (measured on the gas side) during INB growth is not constant but increases initially before stabilizing. This phenomenon is caused by reducing solid-gas interfacial tension due to higher Laplace pressure, thus leading the contact angle to increase as the INB radius grows. Furthermore, on smooth homogeneous surfaces, INBs are observed to nucleate at 81 ns, 17 ns, 6 ns, and 1.3 ns under gas supersaturation levels of 100, 120, 150, and 200, respectively. This demonstrates that higher gas supersaturation significantly shortens the nucleation time. Additionally, as gas supersaturation increases, the growth rate of INBs after nucleation will also accelerate. However, at a gas supersaturation level of 50, no nucleation occurrs during the simulation period of 200 ns. Theoretical analysis reveals that the INBs can only nucleate and grow when the radius of gas aggregates exceeds the critical nucleation radius (<inline-formula><tex-math id="M1">\begin{document}$ {R}_{{\mathrm{critical}}} = \dfrac{\sigma }{\zeta {P}_{0}} $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M2">\begin{document}$ \sigma $\end{document}</tex-math></inline-formula> is the liquid-gas interfacial tension, <inline-formula><tex-math id="M3">\begin{document}$ \zeta $\end{document}</tex-math></inline-formula> is the gas supersaturation level, and <inline-formula><tex-math id="M4">\begin{document}$ {P}_{0} $\end{document}</tex-math></inline-formula> is the ambient pressure). As gas supersaturation decreases, <inline-formula><tex-math id="M5">\begin{document}$ {R}_{{\mathrm{c}}{\mathrm{r}}{\mathrm{i}}{\mathrm{t}}{\mathrm{i}}{\mathrm{c}}{\mathrm{a}}{\mathrm{l}}} $\end{document}</tex-math></inline-formula> increases, thus significantly increasing the difficulty of nucleation.</sec><sec>On rough surfaces, pits with widths of 1 nm, 2 nm, 4 nm, and 10 nm are introduced. At a gas supersaturation of 50,where no INB nucleation occurrs on the smooth surfaces, gas nuclei rapidly form within the pits. However, only gas nuclei in pits with widths larger than 2 nm can grow into INBs. This is because in the growth process the pinning effect at the pit edges causes the curvature radius of the gas nucleus to initially decrease and then increase. Only when the minimum curvature radius exceeds the critical nucleation radius, can gas nuclei develop into INBs.</sec><sec>The findings of this study provide more in-depth insights into the nucleation mechanism of INBs, and practical guidance for controlling their generation, and they also deliver theoretical support for relevant applications such as mineral flotation and other industrial processes.</sec>

Mechanical property prediction of random copolymers using uncertainty-based active learning

The copolymer, a widely used material in our daily lives, presents a significant challenge in targeted sequence design. While recent advancements in computational simulation and data science offer a promising avenue for addressing this complex issue, challenges persist in labeled data scarcity. In this study, we introduce an uncertainty-based active learning framework for predicting the properties of random copolymers. We found that the active learning strategy allowed for labeling only 40 data points within the design space of 1550 data points, drastically reducing the labeling efforts by 97%. Most data selected by active learning were positioned on the design space’s periphery, transforming the learning task into an interpolation problem. Through integrating active learning and molecular dynamics, we successfully overcame the combinatorial explosion problem in copolymer sequence design, streamlining the data labeling process and culminating in a highly accurate model. This research demonstrates data science’s potential in polymer design, especially when facing data scarcity.

Bulk, overlap and surface effects of swift heavy ions in CeO2

Formation of tracks of swift heavy ions decelerating in the electronic stopping regime in CeO2 was studied, combining the Monte Carlo code TREKIS with molecular dynamics. We show that strong lattice disordering (melting) followed by structure recovery form finally a damaged ion track consisting of a discontinuous crystalline region in CeO2. Normal ion impacts result in appearance of spherical crystalline hillocks on CeO2 surface. The solid-vacuum interface strongly suppresses the recrystallization of the near-surface layers, forming conically shaped tracks with several tens of nanometers lengths. Grazing ion irradiation induces intensive material expulsion from the surface forming finally grooves surrounded by nanohillocks. The processes of surface nanostructures formation is similar to those observed previously in CaF2 which has the similar crystalline structure, however requires much longer recrystallization time. Recent experimental data confirm the simulation results.

A novel cobweb-like sub-grain structured Al-Cu-Mg alloy with high strength-plasticity synergy

Al-Cu-Mg alloys, as the most widely used lightweight structural materials, have been recognized as promising candidates in the transportation field for a low-carbon economy. However, the tensile strength and plasticity of alloys cannot be simultaneously improved to satisfy the requirements of continuously upgraded transportation vehicles. In this work, inspired by high-tensile strength and high plasticity of cobweb structure, a novel cobweb-like sub-grain structure was developed in Al-Cu-Mg alloys by a successive solution, high-strain-rate rolling (4.4 s-1), cryogenic treatment (–196°C) and aging process (SRCA). Notably, the tensile strength and plasticity of this alloy were superior to those reported in the current study. An ultrahigh Vickers hardness and tensile strength value of 206.2 Hv and 619.6 MPa, which were 39.8% and 31.8% higher than those of traditional T6 heat-treated Al-Cu-Mg alloys, were obtained after SRCA. Meanwhile, an increase in the elongation of this alloy from 4.31% to 8.23% (increase of 90.9%) was also achieved. More importantly, the high strength-plasticity (“double high”) Al-Cu-Mg alloy was attributed to a cobweb-like sub-grain structure, which was proposed for the first time by utilizing reverse thinking to enhance plasticity through elevating dislocations, due to the formation of high-density dislocations from high-strain-rate rolling and rearrangement effect of dislocations from cryogenic treatment. Furthermore, the strength-plasticity mechanism was verified using in-situ tensile electron back scatter diffraction (EBSD), molecular dynamics (MD) simulations, and crystal plasticity (CP) models. The results indicated that the cobweb-like sub-grain structure, resembling countless walls, formed barriers that hindered dislocation migration towards high-angle grain boundaries (HAGBs) and absorbed them, thereby reducing the occurrence of stress concentration zones, i.e., the dislocation absorption and stress-strain sharing mechanisms. Additionally, the strengthening mechanism was associated with synergistic strengthening by multiscale microstructures, including micron-sized grains, micron-sized high-density dislocation lattices, and nanosized Al2CuMg phases, which were activated by successive deformation processes. Consequently, the concept of biomimetic structure design, which may serve as an effective method for achieving structural materials with high strength-plasticity synergy, can be extended to transportation fields, such as railway tracks and body structure design.

Synthesis, Antileishmanial Activity, and in silico Study of 2-Hydroxy-3-(1,2,3-triazolyl)propyl Vanillin Derivatives

This study details the preparation, antileishmanial assay, and in silico analysis of twenty 2-hydroxy-3-(1,2,3-triazolyl)propyl vanillin derivatives. These compounds were synthesized in three steps and evaluated against Leishmania infantum, Leishmania amazonensis, and Leishmania braziliensis promastigotes. Compounds 3s and 3t were the most effective, showing good activity against all Leishmania species tested. Molecular docking indicated that all compounds bind favorably to the sterol 14α-demethylase (CYP51) enzyme from L. infantum. ADMET (absorption, distribution, metabolism, excretion and toxicity) analysis indicated good oral bioavailability, non-blood-brain barrier penetration, and high gastrointestinal absorption. Posaconazole and compounds 3e, 3s, and 3t remained stable in the CYP51 binding region during 100 ns molecular dynamics (MD) simulations. Root mean square deviation (RMSD) and root mean square fluctuations (RMSF) analyses from the MD trajectory revealed significant conformational fluctuations of the CYP51 N-terminal, suggesting occasional expulsion of 3e, potentially explaining its higher IC50 (half-maximal inhibitory concentration) values. Pairwise decomposition analyses from molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculations highlighted the importance of hydrophobic residues in interacting with the synthesized derivatives.

Enantiomeric C-6 fluorinated swainsonine derivatives as highly selective and potent inhibitors of α-mannosidase and α-l-rhamnosidase: design, synthesis and structure-activity relationship study

Six C-6 fluorinated d-swainsonine derivatives and their enantiomers have been designed based on initial docking calculations, and synthesized from enantiomeric ribose-derived aldehydes, respectively. Glycosidase inhibition assay of these derivatives with d-swainsonine (1) and l-swainsonine (ent-1) as contrasts found that the C-6 fluorinated d-swainsonine derivatives with C-8 configurations as R (α) showed specific and potent inhibitions of jack bean α-mannosidase (model enzyme of Golgi α-mannosidase II); whereas their enantiomers with C-8 configurations as S (β) were powerful and selective α-l-rhamnosidase inhibitors. Molecular docking calculations found the C-6 fluorinatedd-swainsonine derivatives 21, 24 and 25 with highly coincident binding conformations with d-swainsonine (1) in their interactions with the active site of α-mannosidase (PDB ID: 1HWW). Reliability of the docking results were confirmed by Molecular Dynamics (MD) simulation. Additionally, solid interactions with residues Gln-392 and Tyr-393 in the active site of α-l-rhamnosidase (PDB ID: 3W5N) were proved to be vital for potent α-l-rhamnosidase inhibitions of the l-swainsonine derivatives. The role of C-6 fluorines in swainsonine derivatives well demonstrated the “mimic effect” of fluorine to hydrogen by minimal influence on the binding conformations and effective compensation for any possible lost interactions. This work contributes to a comprehensive understanding of the structure-activity relationship (SAR) of the fluorinated swainsonines and ever reported branched swainsonines, and has laid good foundation for development of more potent α-mannosidase and α-l-rhamnosidase inhibitors.

Computational profiling and pharmacokinetic modelling of Febuxostat: Evaluating its potential as a therapeutic agent for diabetic wound healing.

Diabetic wounds, a significant complication of Type 2 Diabetes Mellitus (T2DM), face delayed healing due to impaired inflammation, angiogenesis, and collagen synthesis. This study explores Febuxostat, a xanthine oxidase inhibitor for its therapeutic potential in wound healing. Combining computational approaches and in-vitro assays, the study evaluates its effects on key wound healing pathways, cell viability, migration. The potential of Febuxostat in diabetic wound healing was studied using in-silico tools for Molecular docking and ADMET profiling, alongside Molecular dynamics (MD) simulations. Toxicity was assessed with OSIRIS Explorer, and biological activity was predicted using the PASS tool. In-vitro MTT and scratch assays on L929 cells further validated cytotoxicity and wound healing efficacy. Docking analysis revealed strong binding affinities to key wound healing targets, including VEGF (-9.11 kcal/mol) and NFKβ (-8.62 kcal/mol). Pharmacokinetic studies highlighted favorable skin permeability, supporting topical applications. Toxicity predictions indicated a safe profile. Molecular dynamics simulations demonstrated stable protein-ligand complexes, particularly with VEGF. Cytotoxicity studies on L929 cells revealed an IC50 of 6.08 μM and the scratch assay demonstrated significant wound healing activity, highlighting its effectiveness in promoting cell migration and closure. Febuxostat shows remarkable potential in enhancing diabetic wound healing by promoting cell migration, targeting wound-healing proteins, as demonstrated through in-silico and in-vitro studies. This drug is poised to effectively treat diabetic wounds, accelerating healing and reducing complications. Rigorous pre-clinical and clinical evaluations are essential to validate its safety, efficacy, and therapeutic potential.

Role of Zr in Cu-rich single-phase and nanocomposite Cu-Zr: Molecular dynamics and experimental study

Role of Zr in Cu-rich single-phase and nanocomposite Cu-Zr: Molecular dynamics and experimental study

A deep neural network potential model for theoretically predicting thermal transport, mechanical properties of multi-layered graphitic carbon nitride with molecular dynamics

A deep neural network potential model for theoretically predicting thermal transport, mechanical properties of multi-layered graphitic carbon nitride with molecular dynamics