Molecular dynamics simulation techniques and their application to aroma compounds/cyclodextrin inclusion complexes: A review

Preparation, structural analysis of alcohol aroma compounds/β-cyclodextrin inclusion complexes and the application in strawberry preservation

The dynamic combination of aromas and cyclodextrins is an important means to achieve their stability and controllability, and accurately revealing their interaction rules is the key to designing and constructing high-quality aroma nanocarriers. In this study, the inclusion mechanism between alcohol aroma compounds with different structures and β-cyclodextrin (β-CD) was studied by combining molecular dynamics simulation and experimental methods. Results showed that the selected alcohol aroma compounds formed inclusion complexes (ICs) with β-CD in a 1:1 ratio, while alcohol aroma compounds containing cyclic structures were more tightly bound to β-CD. Van der Waals forces were the primary forces driving the formation and stabilization of the ICs. Cinnamyl alcohol/β-CD ICs showed the most significant antimicrobial effect and notably prolonged the shelf life of strawberries. This study aimed to provide theoretical support for precisely designing and preparing highly stable flavours and fragrances, as well as expanding their application range.

Hyaluronic acid-based multifunctional bio-active coating integrated with cinnamaldehyde/hydroxypropyl-β-cyclodextrin inclusion complex for fruit preservation

Natural preservatives such as cinnamaldehyde (CIN) are garnering increasing interest to replace their synthetic counterparts in maintaining fruit freshness and safety. However, their long-term effectiveness and widespread application have been greatly limited due to high volatility and potent aroma. To address these challenges, we developed a viable and simple strategy to prepare a multifunctional active coating for fruit preservation by incorporating host-guest inclusion complex of CIN and 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) CIN@HP-β-CD into hyaluronic acid (HA), a natural polysaccharide with exceptional film-forming properties. The as-prepared HA/CIN@HP-β-CD coatings exhibited universal surface affinity, excellent antimicrobial performance, and satisfactory antioxidant properties with no potential toxicity. Release kinetic studies have demonstrated that CIN in the coating is continuously and slowly released. Furthermore, freshness preservation experiments on bananas and fresh-cut apples demonstrated that the developed coating is effective in preserving the color of fruit, decreasing the weight loss rate, preventing the microorganism's growth, and significantly extending the period of freshness, exhibiting the potential for application in fruit preservation.

MDs-NP: a property prediction model construction procedure for naphtha based on molecular dynamics simulation

In the context of carbon neutrality and carbon peaking, molecular management has become a focus of the petrochemical industry. The key to achieving molecular management is molecular reconstruction, which relies on rapid and accurate calculation of oil properties. Focusing on naphtha, we proposed a novel property prediction model construction procedure (MDs-NP) employing molecular dynamics simulations for property collections and gamma distribution from real analytical data for calculating mole fractions of simulation mixtures. We calculated 348 sets of mixture properties data in the range of 273 K-300 K by molecular dynamics simulations. Molecular feature extraction was based on molecular descriptors. In addition to descriptors based on open-source toolkits (RDKit and Mordred), we designed 12 naphtha knowledge (NK) descriptors with a focus on naphtha. Three machine learning algorithms (support vector regression, extreme gradient boosting and artificial neural network) were applied and compared to establish models for the prediction of the density and viscosity of naphtha. Mordred and NK descriptors + support vector regression algorithm achieved the best performance for density. The selected RDKFp and NK descriptors + artificial neural network algorithm achieved the best performance for viscosity. Using ablation studies, T, P_w and CC(C)C are three effective descriptors in NK that can improve the performance of the property prediction models. MDs-NP has the potential to be extended to more properties as well as more-complex petroleum systems. The models from MDs-NP can be used for rapid molecular reconstruction to facilitate construction of data-driven models and intelligent transformation of petrochemical processes.

Identification of Helicobacter pylori-carcinogenic TNF-alpha-inducing protein inhibitors via daidzein derivatives through computational approaches

Gastric cancer is considered a class 1 carcinogen that is closely linked to infection with Helicobacter pylori (H. pylori), which affects over 1 million people each year. However, the major challenge to fight against H. pylori and its associated gastric cancer due to drug resistance. This research gap had led our research team to investigate a potential drug candidate targeting the Helicobacter pylori-carcinogenic TNF-alpha-inducing protein. In this study, a total of 45 daidzein derivatives were investigated and the best 10 molecules were comprehensively investigated using in silico approaches for drug development, namely pass prediction, quantum calculations, molecular docking, molecular dynamics simulations, Lipinski rule evaluation, and prediction of pharmacokinetics. The molecular docking study was performed to evaluate the binding affinity between the target protein and the ligands. In addition, the stability of ligand-protein complexes was investigated by molecular dynamics simulations. Various parameters were analysed, including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), hydrogen bond analysis, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM). The results has confirmed that the ligand-protein complex CID: 129661094 (07) and 129664277 (08) formed stable interactions with the target protein. It was also found that CID: 129661094 (07) has greater hydrogen bond occupancy and stability, while the ligand-protein complex CID 129664277 (08) has greater conformational flexibility. Principal component analysis revealed that the ligand-protein complex CID: 129661094 (07) is more compact and stable. Hydrogen bond analysis revealed favourable interactions with the reported amino acid residues. Overall, this study suggests that daidzein derivatives in particular show promise as potential inhibitors of H. pylori.

Molecular Dynamics Simulations of Supramolecular Polymers within Nanoconfinements for Enhanced Oil Recovery

Supramolecular polymers offer promising potential for enhanced oil recovery (EOR) advancing techniques. Current instrumental analyses face limitations in capturing instantaneous intracomplex motions due to temporal and spatial constraints. The molecular mechanism of supramolecular polymer transport behavior within nanoconfinement is not yet fully understood. Therefore, the self-assembly mechanism of β-cyclodextrin (β-CD) and adamantane (ADA)-modified supramolecular polymers (p-AA-β-CD-ADA) was delved into in this work. Further exploration focuses on the translocation dynamics of p-AA-β-CD-ADA within nanoconfinement under external driving forces. Results suggest that β-CD and ADA in p-AA-β-CD-ADA were assembled into nodes in the form of a host and a guest, combining with a "node-rebar-cement" interaction model encapsulating the formation mechanism of these supramolecular polymers. The heightened density of the hydrate layers at the nanoscale pore throats acts as a constraining factor, resulting in restricted mobility and altered dynamics of the supramolecular polymers. During passage through nanopore throats, host-guest molecules within the supramolecular polymer experience noncovalent dissociation. Notably, these supramolecular polymers exhibit remarkable self-healing capabilities, reinstating their assembly state upon traversing pore throats. This work provides a molecular-level comprehension of the potential utility of supramolecular polymers in EOR processes, offering valuable information for the molecular design of polymers employed for EOR in low-permeability reservoirs.