Enzymatic Synthesis of Structured Lipids Enriched with Medium- and Long-Chain Triacylglycerols via Pickering Emulsion-Assisted Interfacial Catalysis: A Preliminary Exploration.

Medium- and long-chain triacylglycerol (MLCT), as a novel functional lipid, is valuable due to its special nutritional properties. Its low content in natural resources and inefficient synthesis during preparation have limited its practical applications. In this study, we developed an effective Pickering emulsion interfacial catalysis system (PE system) for the enzymatic synthesis of MLCT by trans-esterification. Lipase NS 40086 served simultaneously as a catalyst and a solid emulsifier to stabilize the Pickering emulsion. Benefitting from the sufficient oil-water interface, the obtained PE system exhibited outstanding catalytic efficiency, achieving 77.5% of MLCT content within 30 min, 26% higher than that of a water-free system. The Km value (0.259 mM) and activation energy (14.45 kJ mol-1) were 6.8-fold and 1.6-fold lower than those of the water-free system, respectively. The kinetic parameters as well as the molecular dynamics simulation and the tunnel analysis implied that the oil-water interface enhanced the binding between substrate and lipase and thus boosted catalytic efficiency. The conformational changes in the lipase were further explored by FT-IR. This method could give a novel strategy for enhancing lipase activity and the design of efficient catalytic systems to produce added-value lipids. This work will open a new methodology for the enzymatic synthesis of structured lipids.

A Highly Sensitive Model Based on Graph Neural Networks for Enzyme Key Catalytic Residue Prediction.

Determining the catalytic site of enzymes is a great help for understanding the relationship between protein sequence, structure, and function, which provides the basis and targets for designing, modifying, and enhancing enzyme activity. The unique local spatial configuration bound to the substrate at the active center of the enzyme determines the catalytic ability of enzymes and plays an important role in the catalytic site prediction. As a suitable tool, the graph neural network can better understand and identify the residue sites with unique local spatial configurations due to its remarkable ability to characterize the three-dimensional structural features of proteins. Consequently, a novel model for predicting enzyme catalytic sites has been developed, which incorporates a uniquely designed adaptive edge-gated graph attention neural network (AEGAN). This model is capable of effectively handling sequential and structural characteristics of proteins at various levels, and the extracted features enable an accurate description of the local spatial configuration of the enzyme active site by sampling the local space around candidate residues and special design of amino acid physical and chemical properties. To evaluate its performance, the model was compared with existing catalytic site prediction models using different benchmark datasets and achieved the best results on each benchmark dataset. The model exhibited a sensitivity of 0.9659, accuracy of 0.9226, and area under the precision-recall curve (AUPRC) of 0.9241 on the independent test set constructed for evaluation. Furthermore, the F1-score of this model is nearly four times higher than that of the best-performing similar model in previous studies. This research can serve as a valuable tool to help researchers understand protein sequence-structure-function relationships while facilitating the characterization of novel enzymes of unknown function.

Prediction of Plasticizer Property Based on an Improved Genetic Algorithm.

Different plasticizers have obvious differences in plasticizing properties. As one of the important indicators for evaluating plasticization performance, the substitution factor (SF) has great significance for product cost accounting. In this research, a genetic algorithm with "variable mutation probability" was developed to screen the key molecular descriptors of plasticizers that are highly correlated with the SF, and a SF prediction model was established based on these filtered molecular descriptors. The results show that the improved genetic algorithm greatly improved the prediction accuracy in different regression models. The coefficient of determination (R2) for the test set and the cross-validation both reached 0.92, which is at least 0.15 higher than the R2 of the unimproved genetic algorithm. From the results of the selected descriptors, most of the descriptors focused on describing the branching of the molecule, which is consistent with the view that the branching chain plays an important role in the plasticization process. As the first study to establish the relationship between plasticizer SF and plasticizer molecular structure, this work provides a basis for subsequent plasticizer performance and evaluation system modeling.

Feasible Cluster Model Method for Simulating the Redox Potentials of Laccase CueO and Its Variant.

Laccases are regarded as versatile green biocatalysts, and recent scientific research has focused on improving their redox potential for broader industrial and environmental applications. The density functional theory (DFT) quantum mechanics approach, sufficiently rigorous and efficient for the calculation of electronic structures, is conducted to better comprehend the connection between the redox potential and the atomic structural feature of laccases. According to the crystal structure of wild type laccase CueO and its variant, a truncated miniature cluster model method was established in this research. On the basic of thermodynamic cycle, the overall Gibbs free energy variations before and after the one-electron reduction were calculated. It turned out that the trends of redox potentials to increase after variant predicted by the theoretical calculations correlated well with those obtained by experiments, thereby validating the feasibility of this cluster model method for simulating the redox potentials of laccases.

Quantum Mechanical Investigation of the Oxidative Cleavage of the C-C Backbone Bonds in Polyethylene Model Molecules.

Recalcitrant plastic waste has caused serious global ecological problems. There is an urgent need to develop environmentally friendly and efficient methods for degrading the highly stable carbon skeleton structure of plastics. To that end, we used a quantum mechanical calculation to thoroughly investigate the oxidative scission of the carbon-carbon (C-C) backbone in polyethylene (PE). Here, we studied the reaction path of C-C bond oxidation via hydroxyl radical in PE. The flexible force constants and fuzzy bond orders of the C-C bonds were calculated in the presence of one or more carbocations in the same PE carbon chain. By comparison, the strength of the C-C bond decreased when carbocation density increased. However, the higher the density of carbocations, the higher the total energy of the molecule and the more difficult it was to be generated. The results revealed that PE oxidized to alcohol and other products, such as carboxylic acid, aldehyde and ketone, etc. Moreover, the presence of carbocations was seen to promote the cleavage of C-C backbones in the absence of oxygen.

Computational Assessment of the Molecular Structure and Properties for High Energy Density Fuel.

High energy density fuels (HEDFs) that have high volumetric net heat of combustion (NHOC), high stability, and high environmental resistance are greatly important in the fuel field and in military bases and aerospace applications. In this paper, molecular dynamics and quantum chemistry were used to compute the significant physical properties of candidate molecules for HEDFs, such as their enthalpies of combustion, enthalpies of vaporization, densities, and melting points. A computational protocol for evaluating these properties in the fuel field was established, including a new method for estimating the melting point. By using our protocol, we found that, to improve fuel performances such as the density and volumetric NHOC, cyclopropanation is obviously better than hydrogenation. Our protocol was verified to have good accuracy and can be used to compare and assess different target molecules as potential HEDFs.

A Quantum Mechanism Study of the C-C Bond Cleavage to Predict the Bio-Catalytic Polyethylene Degradation.

The growing amount of plastic solid waste (PSW) is a global concern. Despite increasing efforts to reduce the residual amounts of PSW to be disposed off through segregated collection and recycling, a considerable amount of PSW is still landfilled and the extent of PSW ocean pollution has become a worldwide issue. Particularly, polyethylene (PE) and polystyrene (PS) are considered as notably recalcitrant to biodegradation due to the carbon-carbon backbone that is highly resistant to enzymatic degradation via oxidative reactions. The present research investigated the catalytic mechanism of P450 monooxygenases by quantum mechanics to determine the bio-catalytic degradation of PE or PS. The findings indicated that the oxygenase-induced free radical transition caused the carbon-carbon backbone cleavage of aliphatic compounds. This work provides a fundamental knowledge of the biodegradation process of PE or PS at the atomic level and facilitates predicting the pathway of plastics' biodegradation by microbial enzymes.

Force Field Benchmark of Amino Acids. 2. Partition Coefficients between Water and Organic Solvents.

The partitioning of amino acids between water and apolar environments is of vital importance in protein function and drug delivery. Here we present an extensive benchmark for octanol/water (log Poct), chloroform/water (log Pclf), and cyclohexane/water (log Pchx) partition coefficients of neutral amino acid side chain analogues (SCAs) with Amber families of ff99SB-ILDN, ff03, ff14SB, fb15, and ff15ipq, CHARMM 27, GROMOS 53A6, and OPLS-AA/L force fields. A root-mean-square error (RMSE) of 0.4-1.3 log units from experiment is observed for the tested FFs, of which Amber ff94 lineages of ff99SB-ILDN, ff14SB, and fb15 perform best with an RMSE and mean signed error (MSE) of about 0.5 and 0.2 log units, respectively, a performance comparable with quantum mechanical SMD calculations. This finding retains the possibility of modeling proteins in varied environments with one set of classical molecular mechanical force fields. All the FFs tend to overestimate log P, except for GROMOS 53A6 underestimating log Pclf and log Pchx. These discrepancies are mainly due to the larger overestimated solvation free energies in water (Δ Gwat) relative to that in organic solvents (Δ Goct, Δ Gclf, and Δ Gchx); for GROMOS 53A6, it is due to the underestimated Δ Gwat and Δ Goct. The latest water models of "FB" and "OPC" families paired with the recent Amber fb15 do not show an obvious improvement for Δ Gwat and log P calculations. The van der Waals interaction between amino acids and cyclohexane is found to be too strong (overestimated) systematically. Scaling protein-water interactions lead to more favorable Δ Gwat, thereby lowering log P and resulting in a better performance for Amber ff03ws, while such scaling seems a bit too much for Amber ff99SBws. This, along with our previous work ( Zhang et al. J. Chem. Inf. MODEL: 2018 , 58 , 1037 - 1052 ), may aid in the development and systematic improvements of classical force fields to model proteins in aqueous and nonaqueous phases accurately.

Comparative Assessment of Computational Methods for Free Energy Calculations of Ionic Hydration.

Experimental observations for ionic hydration free energies are highly debated mainly due to the ambiguous absolute hydration free energy of proton, ΔGhyd*(H+). Hydration free energies (HFEs) of the 112 singly charged ions in the Minnesota solvation database were predicted by six methods with explicit and implicit solvent models, namely, thermodynamic integration (TI), energy representation module (ERmod), three-dimensional reference interaction site model (3D-RISM), and continuum solvation models based on the quantum mechanical charge density (SMD) and on the Poisson-Boltzmann (PB) and generalized Born (GB) theories. Taking the solvent Galvani potential of water into account, the resulting real HFEs from TI calculations for the generalized Amber force field (GAFF) modeled ions best match the experiments based on ΔGhyd*(H+) = -262.4 kcal/mol (Randles Trans. Faraday Soc . 1956 , 52 , 1573 - 1581 ), in agreement with our previous work on charged amino acids (Zhang et al. J. Phys. Chem. Lett. 2017 , 8 , 2705 - 2712 ). The examined computational methods show an accuracy of ∼7 kcal/mol for the GAFF-modeled ions, except for SMD with a higher accuracy of ∼4 kcal/mol. A biased deficiency in modeling anionic compounds by GAFF is observed with a larger standard deviation (SD) of 9 kcal/mol than that for cations (SD ∼ 4 kcal/mol). The relatively cheap ERmod and 3D-RISM methods reproduce TI results with good accuracy, although ERmod yields a systematic underestimation for cations by 9 kcal/mol; PB and GB generate relative (but not absolute) HFEs comparable to the TI predictions. Computational accuracy is found to be more limited by the accuracy of force fields rather than the models themselves.

Modeling Coordination-Directed Self-Assembly of M2L4 Nanocapsule Featuring Competitive Guest Encapsulation.

Exploring the mechanism of self-assembly and guest encapsulation of nanocapsules is highly imperative for the design of sophisticated molecular containers and multistimuli-responsive functional materials. Here we present a molecular dynamics simulation protocol with implicit solvent and simulated annealing techniques to investigate the self-assembly and competitive guest (C60 and C70 fullerenes) encapsulation of a M2L4 nanocapsule that is self-assembled by the coordination of mercury cations and bent bidentate ligands. Stepwise formation of the nanocapsule and competitive fullerene encapsulation during dynamic structural changes in the self-assembly were detected successfully. Such processes were driven by coordination bonding and π-π stacking and obey the minimum total potential energy principle. Potential of mean force calculations for guest binding to the M2L4 nanocapsule explained the mechanism underlying the competitive encapsulations of C60 and C70. This work helps design new functional nanomaterials capable of guest encapsulation and release.