Journal of molecular graphics & modelling最新文献

In this research, the wetting behavior of SiO₂ modified with dodecyltrimethoxysilane (DTMS) was explored using both experimental and molecular dynamics (MD) simulation approaches. The experimental results reveal that DTMS can chemically bond to the SiO₂ surface, and the contact angle (CA) reaches the maximum value of 157.7° when the mass of DTMS is twice that of SiO₂. The different wetting behaviors caused by DTMS grafting were analyzed by CA fitting, ionic pairs, concentration distribution, molecule orientation, and interfacial interaction energy. The results demonstrate that a 25 % DTMS grafting rate resulted in a maximum CA of 158.2°, which is ascribed to the disruption of interfacial hydrogen bonding and changes in the hydration structure caused by DTMS grafting. Moreover, the above hydrophobic SiO₂ model shows a slight decrease in CA as the water temperature increases, which is consistent with the experimental findings. In contrast, an opposite change was observed for the pristine SiO₂ model. Although the higher water temperature enhances the diffusion capacity of water molecules in both models, the difference in interfacial interactions is responsible for the change in CA. We hope this finding will contribute to a deeper understanding of the wetting adjustment of SiO₂.

Endocrine-disrupting chemicals (EDCs) are substances that can disrupt the normal functioning of hormones.Using aptamers, which are biological recognition elements, biosensors can quickly and accurately detect EDCs in environmental samples. However, the elucidation of aptamer structures by conventional methods is highly challenging due to their complexity.

This has led to the development of three-dimensional aptamer structures based on different models and techniques. To do this, we developed a way to predict the 3D structures of the SS DNA needed for this sequence by starting with an aptamer sequence that has biosensor properties specific to bisphenol-A (BPA), one of the chemicals found in water samples that can interfere with hormones. In addition, we will elucidate the intermolecular mechanisms and binding affinity between aptamers and endocrine disruptors using bioinformatics techniques such as molecular docking, molecular dynamics simulation, and binding energies. The outcomes of our study are to compare modeling programs and force fields to see how reliable they are and how well they agree with results found in the existing literature, to understand the intermolecular mechanisms and affinity of aptamer-based biosensors, and to find a new way to make aptamers that takes less time and costs less.

The interactions of the micro-mechanism of hydroxymethanesulfonic acid (HMSA) with the typical small organic molecule in atmospheric (X = methanol, formaldehyde, formic acid, methyl formate, dimethyl ether, acetone) has been investigated by density functional theory (DFT), quantum theory of atoms in molecules (QTAIM), Generalized Kohn-Sham Enery Decomposition Analysis (GKS−EDA) and the atmospheric clusters dynamic code (ACDC). The results of DFT show that the stable six− to eight−membered ring structures are easily formed in HMSA−X clusters. According to the topological analysis results of the AIM theory and the IRI method, a strong hydrogen bonding interaction is present in the complex. GKS−EDA results show that electrostatic energy is the main contributor to the interaction energy as it accounts for 51 %–55 % of the total attraction energy. The evaporation rates of HMSA−HMSA and HMSA−HCOOH clusters were much lower than those of the other HMSA complexes. In addition, the Gibbs energy of formation (ΔG) of HMSA−X dimers is investigated under atmosphere temperature T = 217–298 K and p = 0.19–1.0 atm, the ΔG decreased with decreasing of the atmosphere temperature and increased with the decrease of atmospheric pressure, indicating that the low temperature and high pressure may significantly facilitate to the formation of dimers.

Deep eutectic solvents (DESs) composed of choline chloride (ChCl) and ascorbic acid (AA) were investigated using the molecular dynamics (MD) simulations. The analyses of the configuration, radial distribution function (RDFs), coordination number, spatial distribution function (SDFs), interaction energies, hydrogen bond number, and self-diffusion coefficient of the ChCl/AA binary systems of different concentrations showed that the stability of the hydrogen bond network and the mutual attraction between systems were the strongest at the experimental eutectic concentration (molar ratio of 2:1). In our simulated temperature range from 303.15 to 353.15 K, the hydrogen bonding network of ChCl/AA DES does not undergo considerable alterations, indicating that its stability was insensitive to temperature. In addition, the influence of the water content on the ChCl/AA DES system was further investigated. The simulated results revealed that the water molecules could disrupt the formation of the hydrogen bonding network by occupyin positions that are essential for the formation of hydrogen bonds within the DES system.

Water desalination, which is a reliable method for providing drinking water and a suitable solution, as well as the membrane filtration method in wastewater treatment, has increased significantly in recent years. In this research, the separation of nitrite and nitrate ions from aqueous solutions was done using the MXene membrane of the Ti₃C₂ type using molecular dynamics simulation. In this study, various parameters, such as pore size MXene structure, characteristics of cavities, applied pressure, and flux were investigated. To investigate the removal of toxic pollutants from water, water flux, potential mean force, distribution of water molecules, and density were investigated. The results showed that the amount of penetration through the membrane increased with the increase in pressure. It was observed that by applying pressure to the system, the number of water molecules accumulated in front of the membrane decreases because they quickly pass through the membrane, which indicates the positive effect of increasing pressure on the separation rate of molecules. The permeability of this membrane was several times higher than the existing membranes in the industry. So that Mexene membranes, which consist of at least two layers, can repel ions with 100 % success.

Drug repurposing is an effective method to reduce the time and cost of drug development. Computational drug repurposing can quickly screen out the most likely associations from large biological databases to achieve effective drug repurposing. However, building a comprehensive model that integrates drugs, proteins, and diseases for drug repurposing remains challenging. This study proposes a drug repurposing method based on the ternary heterogeneous graph attention network (DRTerHGAT). DRTerHGAT designs a novel protein feature extraction process consisting of a large-scale protein language model and a multi-task autoencoder, so that protein features can be extracted accurately and efficiently from amino acid sequences. The ternary heterogeneous graph of drug-protein-disease comprehensively considering the relationships among the three types of nodes, including three homogeneous and three heterogeneous relationships. Based on the graph and the extracted protein features, the deep features of the drugs and the diseases are extracted by graph convolutional networks (GCN) and heterogeneous graph node attention networks (HGNA). In the experiments, DRTerHGAT is proven superior to existing advanced methods and DRTerHGAT variants. DRTerHGAT's powerful ability for drug repurposing is also demonstrated in Alzheimer's disease.

Scorodites are commonly used for arsenic immobilization, and it is also the main component of arsenic bearing tailings. Alkali-activated geopolymers are commonly used to landfill arsenic-bearing minerals. However, there no previous studies have explored the interaction between geopolymer molecules and the surface of scorodite. In this paper, Si(OH)₄ as a monomer molecule of geopolymer, the mechanism of adsorption and ‘ion exchange’ between Si(OH)₄ molecule and the surface of scorodite during alkali-activation is studied. Results show that the Fe-terminated scorodite (010) surface has high stability. Si(OH)₄ are more easily adsorbed on the hollow site of an Fe-terminated scorodite (010) surface, which is described as chemisorption. Compared with Si(OH)₄, NaOH is easier to adsorb on an Fe-terminated scorodite (010) surface. The co-adsorption of NaOH and Si(OH)₄ on the Fe-terminated scorodite (010) surface was studied, and also belongs to chemical adsorption. When the hydroxyl binds to the As atom, the adsorbed Si(OH)₄ is more likely to undergo an ‘ion exchange’ reaction with the surface, and the reaction is barrierless. The intermediate As(OH)₄ produced by the ‘ion exchange’ reaction can be deprotonated to form an arsenate molecule, which can occur spontaneously. This work reveals that the interaction mechanism of geopolymer molecules on surface of scorodite.

One of the easier methods of wastewater treatment is adsorption due to its simplicity in implementation, environmental friendliness, and economic feasibility. Polyvinyl alcohol (PVA) looks promising as an adsorbent due to its biocompatible, non-toxic, water-soluble and eco-friendly nature. The investigation of PVA for its potential in the adsorption of pollutants has been reported in many studies but the mechanistic understanding of the adsorption is poor. The present study used a theoretical approach through density functional theory and Monte Carlo with molecular dynamics simulations to investigate the adsorption mechanism behaviors of model organic molecules (bromothymol blue (BTB), methylene blue (MB), metronidazole (MNZ) and tetracycline (TC)) on PVA surface. The quantum chemical calculations result showed that with the increase in PVA chains (2, 4, 8, 16, and 32 units), the zero-point energy decreases (from −308.79 to −4922.93 kcal/mol) while the dipole moment increases (from 4.37 to 87.52 Debye). Temperature effect on the PVA chain structures showed the same trends for all the chain units and with the increase in temperature (50–600 K), there are no appreciable changes in zero-point energy, enthalpy energy increases while Gibbs free energy decreases. Considering PVA-pollutant complexes, the effects of temperature on the structures showed that there are no appreciable changes in the zero-point energy, Gibbs free and thermal energies increase with an increase in temperature while the kinetic rate of reactions decreases with an increase in temperature. The enthalpy of the reaction showed different trends with antibiotic and dye complexes. In all the thermodynamic properties investigated and the rate of reaction, the order of affinity of the pollutants with PVA followed TC > MNZ > MB > BTB. Monte Carlo simulation was used to investigate the adsorption behavior of the pollutants on the surface of PVA. The negative adsorption energies (−366.56 to −2266.81 kcal/mol) in terms of affinity towards the pollutants on the surface of PVA followed the sequence TC > MNZ > BTB > MB and the molecular dynamic simulation results followed the same order. The obtained results give valuable insights into the mechanism and performance of PVA as an adsorbent. Most of these computational observations are in good agreement with the available experimental results.

High entropy alloy nanoparticles are envisaged as one of the most interesting materials compared to monoatomic materials due to their modulated properties in terms of their convenient surface-to-volume ratio. However, studies are still missing to unveil how composition or nanoparticle size can influence nanoparticle morphology. Based on molecular dynamics simulations, we perform a structural characterization as a function of nanoparticle size and the chemical composition of high entropy alloy nanoparticles subject to multiple annealing cycles. After the multiple thermal loads, we observe a substantial migration of copper atoms towards the np surface, consistent with the experimental results of Cu-based high entropy alloys. The resulting high entropy alloy nanoparticle behaves as a core–shell nanostructure with a rich fcc phase on the surface (50% of Cu) and 5% fcc phase in the nanoparticle core. Inspecting the nanoparticle surface, it is observed that high entropy alloy nanoparticles have a lack of surface facets, leading to a more spherical shape, quite different from mono-metallic nanoparticles with a high number of facets. Performing an average atoms simulation, it showed that nanoparticles are prone to form 111 surface facets independent of the nanoparticle size, suggesting that for high entropy alloy nanoparticles, the chemical complexity avoids the formation of surface facets. The latter can be explained in terms of the lattice distortion inducing tensile/compressive stress that drives the surface reconstruction. All in all our results match extremely well with experimental evidence of FeNiCrCoCu nanocrystalline materials, explaining the Cu segregation in terms of surface energy and mixing enthalpy criteria. We believe that our results provide a detailed characterization of high entropy nanoparticles focusing on how chemical complexity induces morphological changes compared to mono-crystalline nanoparticles. Besides, our findings are valuable for experimental works aimed at designing the shape and composition of multicomponent nanoparticles.

This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model demonstrates remarkable performance, achieving an AUC of 0.856 and 0.823 on the PepBCL Set_1 and Set_2 datasets, respectively. Additionally, it attains a Precision of 0.564 in PepBCL Set 1 and 0.527 in PepBCL Set 2, surpassing the performance of previous methods. Beyond this, we explore the application of this model in cancer therapy, particularly in identifying peptide interactions for selective targeting of cancer cells, and other fields. The findings of this study contribute to bioinformatics, providing valuable insights for drug discovery and therapeutic development.