Identification, in silico selection, and mechanistic investigation of antioxidant peptides from corn gluten meal hydrolysate

Seven novel antioxidant peptides (AWF, LWQ, WIY, YLW, LAYW, LPWG, and LYFY) exhibiting a superior activity compared to trolox were identified through in silico screening. Among these, the four peptides (WIY, YLW, LAYW, and LYFY) displayed notably enhanced performance, with ABTS activity 2.58-3.26 times and ORAC activity 5.19-8.63 times higher than trolox. Quantum chemical calculations revealed that the phenolic hydroxyl group in tyrosine and the nitrogen-hydrogen bond in the indole ring of tryptophan serve as the critical sites for antioxidant activity. These findings likely account for the potent chemical antioxidant activity. The corn peptides also exerted a protective effect against AAPH-induced cytomorphologic changes in human erythrocytes by modulating the antioxidant system. Notably, LAYW exhibited the most pronounced cytoprotective effects, potentially due to its high content of hydrophobic amino acids.

Thermal oxidation mechanism of palmitic aicd

The oxidation and degradation of fats lead to a decrease in the nutritional value of food and pose safety concerns. Saturated fatty acids also hold a significant position in the field of lipid oxidation. In this study, the oxidation products of methyl palmitate were investigated by using gas chromatography mass spectrometry (GC-MS). Seven monohydroperoxides and 72 secondary oxidation products were detected. Combined with density functional theory (DFT) calculations, the formation mechanisms of oxidation products can be summarized into four stages. The initial stage involved the formation of monohydroperoxides and alkanes, followed by the subsequent stage involving methyl x-oxo(hydroxy)hexadecanoates. The third stage involved the formation of methyl ketones, carboxylic acids, and aldehydes, while the final stage involved lactones. Meanwhile, methyl ketones were the most abundant oxidation product, approximately 25 times more abundant than aldehydes; the calculated results agreed well with the experimental results. The establishment of a comprehensive thermal oxidation mechanism for palmitic acid provided a new foundation for future lipid oxidation analyses.

Study on the compounding optimization of surfactants and synergistic effects on the wettability of bituminous coal

To improve the wettability of surfactants on bituminous coal and to explore its wettability and wettability mechanism on bituminous coal, taking the Sandaogou bituminous coal as an example, a single factor experiment was carried out first. Through contact angle and surface tension experiments, three surfactants with good wettability were selected from among the nine surfactants and mixed in equal proportions two by two to determine the optimal compounding method and compounding concentration. The experimental results show that the compounding of nonionic and anionic, nonionic and zwitterionic, anionic and zwitterionic surfactants can have synergistic effects and significantly improve the wettability of bituminous coal. Among them, the 0.5 wt% SDS + 0.5 wt% CAB-50 (R2) compound surfactant had the best wettability on bituminous coal, and the contact angle and surface tension were only 15.24° and 23.62 mN/m, respectively. The surface electrostatic potential values of each material molecule were calculated by Materials Studio software based on the quantum chemistry method, and correlation analysis was carried out with wettability. The results show that the surface electrostatic potentials of CDEA, SDS and CAB-50 were greater than those of water and bituminous coal, and the region of maximum negative electrostatic potential corresponded to oxygen atoms, which are easier to adsorb on bituminous coal and water molecules. Then, through molecular dynamics simulation, the interaction energy and the distribution of contributions along the Z-axis of the water/compound surfactant/bituminous coal system at equilibrium were investigated, and finally, a spray dust reduction test was carried out in the Sandaogou Coal Mine. The results showed that the 0.5 wt% SDS + 0.5 wt% CAB-50 compound solution can be used as a water molecule adsorption carrier, prompting more water molecules to be embedded into coal molecules, increasing the relative concentration of water molecules on the surface of bituminous coal, restricting the diffusion of water molecules, and greatly improving the wettability. After the addition of 0.5 wt% SDS + 0.5 wt% CAB-50 as a spray agent, the concentration of total dust at the driver's position decreased from 65.14 to 9.11 mg/m3, the concentration of exhaled dust decreased from 30.07 to 3.35 mg/m3, and the efficiency of total and exhaled dust reduction compared with that of pure water was 86.01% and 89.35%, respectively.

Carbon-based molecular properties efficiently predicted by deep learning-based quantum chemical simulation with large language models

The prediction of thermodynamic properties of carbon-based molecules based on their geometrical conformation using fluctuation and density functional theories has achieved great success in the field of energy chemistry, while the excessive computational cost provides both opportunities and challenges for the integration of machine learning. In this work, a deep learning-based quantum chemical prediction model was constructed for efficient prediction of thermodynamic properties of carbon-based molecules. We constructed a novel framework - encoding the 3D information into a large language model (LLM), which in turn generates a 2D SMILES string, while embedding a learnable encoding designed to preserve the integrity of the original 3D information, providing better structural information for the model. Additionally, we have designed an equivariant learning module to encompass representations of conformations and feature learning for conformational sampling. This framework aims to predict thermodynamic properties more accurately than learning from 2D topology alone, while providing faster computational speeds than conventional simulations. By combining machine learning and quantum chemistry, we pioneer efficient practical applications in the field of energy chemistry. Our model advances the integration of data-driven and physics-based modeling to unlock novel insights into carbon-based molecules.

Novel synergistic cross-linking ameliorate ready-to-eat sea cucumber deterioration and its quantum chemical analysis

Synergistic cross-linkers could improve the taste acceptability of ready-to-eat sea cucumber (RSC). Besides, the hardness of RSC was increased by 331.00% and 266.87% after synergistic cross-linking. Synergistic cross-linking treatment could ameliorate the non-enzymatic degradation of RSC collagen and polysaccharides. Gaussian calculations results showed that dipeptides containing asparagine residues may have different reaction pathways. The main cleavage pathways of CH3CO-Asn-Gly-NHCH3 (NG) might be water-assisted side chain cyclization, stepwise cyclamide hydrolysis via a Gemdiol Intermediate, deamination, and peptide bond breakage. The relative free energy of cyclamide hydrolysis process of NG was increased by 8.2 kcal/mol after synergistic cross-linking. The mass spectrometry results showed that typical peptides could cleavage at NG, CH3CO-Asn-Lys-NHCH3 (NK) and CH3CO-Asn-Leu-NHCH3 (NL) sites after heating, which justified the breakage pattern of peptides in Gaussian calculations. It can offer a comprehensive theoretical basis for the processing of the ready-to-eat sea cucumber with storage stability.

Synthesis and ab initio conformational investigation of a series of model sulfated α-L-iduronopyranosides

Due to the all-axial orientation of the OH-groups in the 1C4 chair conformation considered standard for L-hexapyranosides, including l-iduronopyranoside - a component of many biologically and medically significant sulfated glycans, these monosaccharides can be anticipated to display unusual conformations upon the introduction of bulky and charged substituents. Herein we describe the synthesis of a series of iduronopyranoside derivatives with varying sulfation patterns, which were studied computationally using the DLPNO-MP2 approach and by means of analyzing their chemical shifts to ascertain the effects sulfation has on the conformation of the iduronopyranoside ring.

First-principles study of MXene properties with varying hydrofluoric acid concentration

With varying hydrofluoric acid (HF) concentrations under three etching conditions, we presented a comparative study of the effects of both the ordered and randomly ternary mixed terminated Ti3C2Tx surfaces with a wide variation of O/OH/F stoichiometry on the thermodynamic stability and electronic properties. Regardless of the HF concentration, an OH-rich surface is found to be thermodynamically stable and the electrical conductivity of Ti3C2Tx is substantially affected by the OH concentration. The charge density difference and electron localization function demonstrated a significant electron localization at the hydroxyl group on the O/OH/F mixed terminated surface, which could yield a locally induced dipole on the surface that renders favorable reaction sites on the functionalized surface. In addition, a large tunability in the work function (ΔΦ ∼ 3.5 eV) is predicted for Ti3C2Tx. These findings provide a pathway for strategically tuning the electronic and structural properties of Ti3C2 MXenes etched with HF.

Isolation, identification of antioxidant peptides from earthworm proteins and analysis of the structure-activity relationship of the peptides based on quantum chemical calculations

Earthworms are emerging sources of edible animal proteins. Earthworm extracts exhibit good in vivo antioxidant activity after oral administration. To better understand the antioxidant activity of earthworms, antioxidant peptides derived from earthworm proteins after gastrointestinal digestion were isolated and identified, and their structure-activity relationships were analysed in this research. Results showed that earthworm protein gastrointestinal digestion products exhibited good antioxidant activity, and 6030 peptide sequences were identified after separation using ion-exchange and gel-chromatography columns. Eleven peptides were screened using computer simulation activity scores, among which AFWYGLPCKL, WPWQMSLY, and GCFRYACGAFY showed the best antioxidant activities. Highest Occupied Molecular Orbital (HOMO) analysis indicated that N29-H10, O122-H38, and the peptide bond binding sites of serine and leucine were active sites of peptides AFWYGLPCKL, GCFRYACGAFY, and WPWQMSLY, respectively. This study provides a new understanding of substance basis of antioxidant activity in earthworms and contributes to application of earthworm proteins as antioxidants in health-foods.

QM assisted ML for 19F NMR chemical shift prediction

BACKGROUND

Ligand-observed 19F NMR detection is an efficient method for screening libraries of fluorinated molecules in fragment-based drug design campaigns. Screening fluorinated molecules in large mixtures makes 19F NMR a high-throughput method. Typically, these mixtures are generated from pools of well-characterized fragments. By predicting 19F NMR chemical shift, mixtures could be generated for arbitrary fluorinated molecules facilitating for example focused screens.

METHODS

In a previous publication, we introduced a method to predict 19F NMR chemical shift using rooted fluorine fingerprints and machine learning (ML) methods. Having observed that the quality of the prediction depends on similarity to the training set, we here propose to assist the prediction with quantum mechanics (QM) based methods in cases where compounds are not well covered by a training set.

RESULTS

Beyond similarity, the performance of ML methods could be associated with individual features in compounds. A combination of both could be used as a procedure to split input data sets into those that could be predicted by ML and those that required QM processing. We could show on a proprietary fluorinated fragment library, known as LEF (Local Environment of Fluorine), and a public Enamine data set of 19F NMR chemical shifts that ML and QM methods could synergize to outperform either method individually. Models built on Enamine data, as well as model building and QM workflow tools, can be found at https://github.com/PatrickPenner/lefshift and https://github.com/PatrickPenner/lefqm .

Density functional theory studies on the oleic acid thermal oxidation into volatile compounds

Oleic acid oxidation is one of the main sources of food flavor compounds. Volatile profiling was investigated using thermal desorption cryo-trapping combined with gas chromatography-mass spectrometry to analyze the volatile composition of oleic acid oxidation. A total of 43 volatile compounds, including aldehydes (11), ketones (2), alcohols (5), furans (2), acids (8), ester (12) and alkane (3) were identified from oleic acid during heating. Then, density functional theory (DFT) was applied to analyze the oxidative mechanism of oleic acid during heating. A total of 30 reactions were obtained and grouped into the peroxide (ROOH), alkoxy radical (RO•), and peroxide radical (ROO•) pathways. The structures of intermediates, transition states (TS), and products in each reaction were also determined. Results show that the branch chemical reactions were the key reactions in different reaction pathway. Moreover, the reaction priority of the thermal oxidation reaction of oleic acid was the peroxide radical mechanism > the peroxide mechanism > the alkoxy radical mechanism.

Contribution of molecular structures and quantum chemistry technique to root concentration factor: An innovative application of interpretable machine learning

Root concentration factor (RCF) is a significant parameter to characterize uptake and accumulation of hazardous organic contaminants (HOCs) by plant roots. However, complex interactions among chemicals, plant roots and soil make it challenging to identify underlying mechanisms of uptake and accumulation of HOCs. Here, nine machine learning techniques were applied to investigate major factors controlling RCF based on variable combinations of molecular descriptors (MD), MACCS fingerprints, quantum chemistry descriptors (QCD) and three physicochemical properties related to chemical-soil-plant system. Compared to models with variables including MACCS fingerprints or solitary physicochemical properties, the XGBoost-6 model developed by the variable combination of MD, QCD and three physicochemical properties achieved the most remarkable performance, with R2 of 0.977. Model interpretation achieved by permutation variable importance and partial dependence plots revealed the vital importance of HOCs lipophilicity, lipid content of plant roots, soil organic matter content, the overall deformability and the molecular dispersive ability of HOCs for regulating RCF. The integration of MD and QCD with physicochemical properties could improve our knowledge of underlying mechanisms regarding HOCs accumulation in plant roots from innovative structural perspectives. Multiple variables combination-oriented performance improvement of model can be extended to other parameters prediction in environmental risk assessment field.

Influence of silica-aluminum materials on heavy metals release during paper sludge pyrolysis: Experimental and theoretical studies

It is of great significance to reduce the secondary risk of heavy metals during the pyrolysis of paper sludge. This study used kaolin and alumina-silica-based xerogels to control heavy metals released during sludge pyrolysis. Pyrolyzing a mixture of sludge and 7% kaolin at 400 °C achieved high retention rates for Cu (95.85%), Zn (95.97%), Pb (97.15%), Cd (84.23%), and Cr (84.05%) when the pyrolysis tail gas was treated with 9 g of xerogel. The addition of kaolin facilitated the transformation of Cu, Zn, Pb, and Cr from the unstable fraction to the stable fraction in pyrolysis biochar, reducing their leachability. The xerogels also played a crucial role in adsorbing and stabilizing the heavy metals. The results of thermodynamic equilibrium calculations showed that Pb(g), PbS(g), PbCl2(g), PbCl(g), Zn(g), ZnCl2(g), and Cd(g) were the main gaseous products of Zn, Pb, and Cd during paper sludge pyrolysis. The Pb atoms in PbCl2 and PbS, and the Zn atoms in ZnCl2 bond with the oxygen atoms on the kaolin surface by covalent bonds, while the Cl atoms in PbCl and the Pb atoms of elemental lead form ionic bonds with H and O atoms on the kaolinite surface, respectively. These experimental and simulation results offer new ideas for controlling heavy metals during sludge pyrolysis.

Density functional theory study on the formation mechanism of CaClOH in municipal solid waste incineration fly ash

Municipal solid waste incineration (MSWI) fly ash is defined as a kind of hazardous waste because of its high levels of multiple pollutants. The main component of MSWI fly ash is CaClOH, and the characteristics have not achieved consensus. And density functional theory (DFT) was used to calculate the formation process of CaClOH in this study, which mainly included HCl adsorption on CaO (0 0 1) surface and Ca(OH)2 (0 0 1) surface and the surface reaction process. The reaction mechanism was investigated. The results showed that the maximum adsorption energies of HCl on CaO and Ca(OH)2 surfaces reached - 195.17 kJ/mol and - 83.48 kJ/mol, respectively, representing strong chemisorption. The chemisorption process was shown as the adsorption of H atom on O site, and the adsorption capacity was reflected in the adsorption range of O site. The significant electron density overlap between O site and H atom meant that a new chemical bond formed, which made the adsorption structure stable. The adsorption energy of multi-HCl adsorption on the crystal surfaces was not proportional to the number of HCl molecule, indicating that the adsorption processes were influenced by each other. After surface reaction, the H-Cl bond was broken completely, and the structure of CaO and Ca(OH)2 changed to new structures. According to transition state (TS) search, the formation of CaClOH had a higher priority, easier than that of CaCl2, explaining the presence of CaClOH in fly ash. The study provides helpful information for the solidification treatment of fly ash.

Theoretical exploration and experimental regulation of the degradation of Δ9-tetrahydrocannabinol in hemp seed oil by density functional theory

Δ⁹-tetrahydrocannabinol (Δ⁹-THC) in hemp seed oil is a psychoactive cannabinoid, and the content of Δ⁹-THC can be reduced. Density functional theory (DFT) was used to simulate the degradation path of Δ⁹-THC, and the ultrasonic treatment was used to degrade the Δ⁹-THC in hemp seed oil. Results found that the reaction of Δ⁹-THC degradation to cannabinol (CBN) was a spontaneous exothermic reaction, which required a certain amount of external energy to initiate reaction process. Through the surface electrostatic potential analysis, the minimum value of electrostatic potential of Δ⁹-THC was -37.68 kcal/mol, and the maximum value was 40.98 kcal/mol. The frontier molecular orbitals analysis found that the energy level difference of Δ⁹-THC was lower than that of CBN, indicating that the reactivity of Δ⁹-THC was stronger. The degradation process of Δ⁹-THC could be divided into two stages, which needed to cross the reaction energy barriers of 3197.40 and 3087.24 kJ/mol, respectively. Ultrasonic treatment was used to degrade Δ⁹-THC standard solution, it was found that Δ⁹-THC can be effectively degraded into CBN through intermediate. Subsequently, ultrasonic technology was applied to hemp seed oil, under the conditions of ultrasonic power 150 W and ultrasonic time 21 min, the Δ⁹-THC was degraded to 10.00 mg/kg.

Deep exploration of lipid oxidation into flavor compounds: A density functional theory study on (E)-2-decenal thermal oxidative reaction

Unsaturated fatty aldehydes are the main products of fatty acid oxidation, and could be further oxidized to form volatile compounds with shorter carbon chains. Therefore, studying the oxidation of unsaturated fatty aldehydes is an important way to reveal the mechanism of food flavor formation during heating. In this study, volatile profiling of (E)-2-decenal during heating was firstly investigated by using thermal-desorption cryo-trapping combined with gas chromatography-mass spectrometry (GC-MS). A total of 38 volatile compounds were detected. Then, twenty-one reactions in the heating process of (E)-2-decenal were obtained by using density functional theory (DFT) calculations, and grouped into three oxidation pathways, namely, peroxide pathway, peroxyl radical pathway and alkoxy radical pathway. Meanwhile, the priority of these three pathways was the alkoxy radical reaction pathway > peroxide pathway > peroxyl radical reaction pathway. Moreover, the calculated results agreed well with the experimental results.

Source code and simulation data for the prediction of the electrodeposition mechanism of nanostructured metallic coatings

This data article presents a simulation model based on quantum mechanics and energy potentials for obtaining simulation data that allows, from the perspective of materials informatics, the prediction of the electrodeposition mechanism for forming nanostructured metallic coatings. The development of the research is divided into two parts i) the formulation (Quantum mechanical model and Corrected model for electron prediction; using a modified Schrödinger equation) and ii) the implementation of the theoretical prediction model (Discretization of the model). For the simulation process, the finite element method (FEM) was used considering the equation of electric potential and electroneutrality with and without the inclusion of quantum leap. We also provide the code to perform QM simulations in CUDA®, and COMSOL® software, the simulation parameters, and data for two metallic arrangements of chromium nanoparticles (CrNPs) electrodeposited on commercial steel substrate. (CrNPs-AISI 1020 steel and CrNPs-A618 steel). Data collection shows the direct relationship between applied potential (V_DC), current (A), concentration (ppm), and time (s) for the homogeneous formation of the coating during the electrodeposition process, as estimated by the theoretical model developed. Their potential reuse data is done to establish the precision of the theoretical model in predicting the formation and growth of nanostructured surface coatings with metallic nanoparticles to give surface-mechanical properties.

A combined experimental and computational chiroptical approach to establish the biosynthesis and absolute configuration of licochalcone L

Often, chiral natural products exist as single stereoisomers; however, simultaneous occurrences of both enantiomers can exist in nature, resulting in scalemic or racemic mixtures. Ascertaining natural products' absolute configuration (AC) is pivotal for attributing their specific biological signature. Specific rotation data commonly characterize chiral non-racemic natural products; however, measurement conditions, viz., solvent and concentration, can influence the sign of specific rotation values, especially when characterizing natural products possessing small specific rotation values. For example, licochalcone L, a minor constituent of Glycyrrhiza inflata, was reported with a specific rotation of [α]_D²²= +13 (c 0.1, CHCl₃); however, not establishing the AC and the reported zero specific rotation for an identical compound, licochalcone AF1, resulted in debatable chirality and its biogenesis. In this study, a combined experimental and computational chiroptical approach involving specific rotation and electronic circular dichroism (ECD) data, supported by time-dependent density functional theory (TDDFT), were effectively utilized to establish the AC of licochalcone L as the (E, 2″S)-isomer. Establishing the 2″S absolute configuration permitted the conception of a reasonable biosynthetic pathway involving intramolecular '5-exo-tet' ring opening of a chiral oxirane to form chiral licochalcone L in G. inflata.

Photolysis mechanism of eleven insecticides under simulated sunlight irradiation: Kinetics, pathway and QSAR

Insecticides are widely used in crop protection against insects and frequently detected in aquatic environment. Photolysis kinetics are directly related with exposure assessment and risk assessment. However, the photolysis mechanism of neonicotinoid insecticides with different structures has not been studied and compared systematically in the literature. In this paper, the photolysis rate constants in water were determined for eleven insecticides under irradiation of simulated sunlight. At the same time, the photolysis mechanism and effect of dissolved organic matter (DOM) on their photolysis were studied. The results showed that photolysis rates of eleven insecticides vary in a large range. The photolysis rates of nitro-substituted neonicotinoids and butenolide insecticide are much faster than that of cyanoimino-substituted neonicotinoids and sulfoximine insecticide. The ROS scavenging activity assays reveal that direct photolysis dominates the degradation of seven insecticides and, on the other hand, self-sensitized photolysis dominates four insecticides. The shading-effect from DOM can reduce the direct photolysis rates, on the other hand, ROSs generated by triplet-state DOM (³DOM*) can also accelerate photolysis of insecticides. According to the photolytic products identified from HPLC-MS, these eleven insecticides have different photolysis pathways. Six insecticides are degraded from the removal of nitro group from their parent compounds and four insecticides are degraded through ·OH reaction or singlet oxygen (¹O₂) reaction. QSAR (quantitative structure-activity relationship) analysis showed that photolysis rate was directly related to the energy gap between the highest occupied molecular orbital to the lowest unfilled molecular orbital (E_gap = E_LUMO-E_HOMO) and dipole moment (δ). These two descriptors reflect the chemical stability and reactivity of insecticides. The pathways developed from identified products and the molecular descriptors of QSAR models can well verify the photolysis mechanisms of eleven insecticides.

Molecular design and experimental study of deep eutectic solvent extraction of keratin derived from feathers

Feather keratin has a complex structure, hard texture and must be treated to improve its bioavailability. In this paper, according to the designability of DES, some deep eutectic solvents (DESs) were prepared to degrade feathers and extract keratin. Calculations by quantum chemical methods showed that DESs were considered molecular scissors with the ability to break initial hydrogen bonds and form new bonds only when the Gibbs free energy change for the degradation process was ΔG < 0, i.e., hydrogen binding energy ΔE < -0.3038 kcal/mol. Then, the degradation mechanism was predicted to provide guidance for the molecular design of DES. Finally, experimental results showed that the same ratio of choline chloride-based DESs had higher catalytic performance, in which [ChCl][P][ZnCl₂] 1:5:2 was used with a high yield of keratin of 85.46 %. DES had a high catalytic performance after multiple recycling cycles and this method has no H₂S gas generation, which improves the atomic utilization.

Towards practical and massively parallel quantum computing emulation for quantum chemistry

Quantum computing is moving beyond its early stage and seeking for commercial applications in chemical and biomedical sciences. In the current noisy intermediate-scale quantum computing era, the quantum resource is too scarce to support these explorations. Therefore, it is valuable to emulate quantum computing on classical computers for developing quantum algorithms and validating quantum hardware. However, existing simulators mostly suffer from the memory bottleneck so developing the approaches for large-scale quantum chemistry calculations remains challenging. Here we demonstrate a high-performance and massively parallel variational quantum eigensolver (VQE) simulator based on matrix product states, combined with embedding theory for solving large-scale quantum computing emulation for quantum chemistry on HPC platforms. We apply this method to study the torsional barrier of ethane and the quantification of the protein-ligand interactions. Our largest simulation reaches 1000 qubits, and a performance of 216.9 PFLOP/s is achieved on a new Sunway supercomputer, which sets the state-of-the-art for quantum computing emulation for quantum chemistry.

Mechanistic investigation of the influence of defects on armchair unburned carbon for PbCl2 adsorption

As the largest consumer of coal energy, coal-fired power plants emit large amounts of PbCl2 each year, which is of wide concern due to its high toxicity, global migration, and accumulation. Unburned carbon is considered a promising adsorbent for effective PbCl2 removal. However, there is a problem that the current unburned carbon model cannot show the structure of carbon defects on the actual unburned carbon surface. Therefore, it is important to construct defective unburned carbon models with practical significance. In addition, the adsorption mechanism of PbCl2 by an unburned model is not studied deeply enough and the reaction mechanism is not clear yet. This has seriously affected the development of effective adsorbents. To reveal the adsorption mechanism of PbCl2 on unburned carbon, the adsorption mechanism of PbCl2 on defective unburned carbon surfaces was analyzed by using the density flooding theory to investigate the adsorption process of PbCl2 on different unburned carbon models. This will provide theoretical guidance for the design and development of adsorbents for the removal of PbCl2 from coal-fired power plants.

Quantum chemical elucidation of a sevenfold symmetric bacterial antenna complex

The light-harvesting complex 2 (LH2) of purple bacteria is one of the most studied photosynthetic antenna complexes. Its symmetric structure and ring-like bacteriochlorophyll arrangement make it an ideal system for theoreticians and spectroscopists. LH2 complexes from most bacterial species are thought to have eightfold or ninefold symmetry, but recently a sevenfold symmetric LH2 structure from the bacterium Mch. purpuratum was solved by Cryo-Electron microscopy. This LH2 also possesses unique near-infrared absorption and circular dichroism (CD) spectral properties. Here we use an atomistic strategy to elucidate the spectral properties of Mch. purpuratum LH2 and understand the differences with the most commonly studied LH2 from Rbl. acidophilus. Our strategy exploits a combination of molecular dynamics simulations, multiscale polarizable quantum mechanics/molecular mechanics calculations, and lineshape simulations. Our calculations reveal that the spectral properties of LH2 complexes are tuned by site energies and exciton couplings, which in turn depend on the structural fluctuations of the bacteriochlorophylls. Our strategy proves effective in reproducing the absorption and CD spectra of the two LH2 complexes, and in uncovering the origin of their differences. This work proves that it is possible to obtain insight into the spectral tuning strategies of purple bacteria by quantitatively simulating the spectral properties of their antenna complexes.

Relationship between photolysis mechanism and photo-enhanced toxicity to Vibrio Fischeri for neonicotinoids with cyano-amidine and nitroguanidine structures

Neonicotinoids are widely used pesticides that contaminate aquatic environments. Although these chemicals can be photolyzed under sunlight radiation, it is unclear for the relationship between photolysis mechanism and toxicity change in aquatic organisms. This study aims to determine the photo-enhanced toxicity of four neonicotinoids with different main structures (acetamiprid, and thiacloprid for cyano-amidine structure, imidacloprid and imidaclothiz for nitroguanidine). To Achieve the goal, photolysis kinetics, effect of dissolved organic matter (DOM) and reactive oxygen species (ROSs) scavengers on photolysis rates, photoproducts, and photo-enhanced toxicity to Vibrio fischeri were investigated for four neonicotinoids. The results showed direct photolysis plays a key role in the photo-degradation of imidacloprid and imidaclothiz (photolysis rate constants are 7.85 × 10^-3 and 6.48 × 10^-3 min^-1, respectively), while the photosensitization process of acetamiprid and thiacloprid was dominated by ·OH reactions and transformation (photolysis rate constants are 1.16 × 10^-4 and 1.21 × 10^-4 min^-1, respectively). All four neonicotinoid insecticides exerted photo-enhanced toxicity to Vibrio fischeri, indicating photolytic product(s) posed greater toxicity than their parent compounds. The addition of DOM and ROS scavengers influenced photo-chemical transformation rates of parent compounds and their intermediates, leading to diverse effects on photolysis rates and photo-enhanced toxicity for the four insecticides as a result of different photo-chemical transformation processes. Based upon the detection of chemical structures of intermediates and Gaussian calculations, we observed different photo-enhanced toxicity mechanisms for the four neonicotinoid insecticides. Molecular docking was used to analyze the toxicity mechanism of parent compounds and photolytic products. A theoretical model was subsequently employed to describe the variability of toxicity response to each of the four neonicotinoids.

An improved parameterization procedure for NDDO-descendant semi-empirical methods

CONCEPT

MNDO-based semi-empirical methods in quantum chemistry have found widespread application in the modelling of large and complex systems. A method for the analytic evaluation of first and second derivatives of molecular properties against semi-empirical parameters in MNDO-based NDDO-descendant models is presented, and the resultant parameter Hessian is compared against the approximant currently used in parameterization for the PMx models.

METHODS

As a proof of concept, the exact parameter Hessian is employed in a limited reparameterization of MNDO for the elements C, H, N, O and F using 1206 molecules for reference data (heats of formation, ionization energies, dipole moments and reference geometries). The correctness of our MNDO implementation was verified by comparing the calculated molecular properties with the MOPAC program.

QuantumTox: Utilizing quantum chemistry with ensemble learning for molecular toxicity prediction

Molecular toxicity prediction plays an important role in drug discovery, which is directly related to human health and drug fate. Accurately determining the toxicity of molecules can help weed out low-quality molecules in the early stage of drug discovery process and avoid depletion later in the drug development process. Nowadays, more and more researchers are starting to use machine learning methods to predict the toxicity of molecules, but these models do not fully exploit the 3D information of molecules. Quantum chemical information, which provides stereo structural information of molecules, can influence their toxicity. To this end, we propose QuantumTox, the first application of quantum chemistry in the field of drug molecule toxicity prediction compared to existing work. We extract the quantum chemical information of molecules as their 3D features. In the downstream prediction phase, we use Gradient Boosting Decision Tree and Bagging ensemble learning methods together to improve the accuracy and generalization of the model. A series of experiments on various tasks show that our model consistently outperforms the baseline model and that the model still performs well on small datasets of less than 300.

Directed regulation mechanism on chlorine substitution of PCDD/Fs isomers based on quantum chemical computation

BACKGROUND

Chlorine substitution has been considered as one of the key steps of polychlorinated dibenzodioxin/furan (PCDD/Fs) generation. The introduction of oxygen carriers (OCs), especially in chemical looping combustion (CLC), provides the platform of directed regulation for the chlorine substitution process.

METHODS

Density functional theory (DFT) calculations with code VASP 5.4 were employed to investigate the free energy of PCDD/Fs adsorption on different surfaces. 12378-PCDD, which is the product of a one-step chlorine substitution for toxic 2378-PCDD, has been selected as the calculation case, and the regulation mechanisms on the inter-isomeric conversion of 12378-PCDD were identified by calculating the energy barrier and action angle.

RESULTS

It was found that the chlorine substitution of 12378-PCDD, particularly in 4# position, 9# position, and 6# position, emerged a tendency to increase the difficulty in turn, which conforms to the principle of distal preference. Besides, the influence from CaO adsorption on the crystalline surface of the iron-based oxygen carrier (OC) has been analyzed and it was verified that CaO adsorption can significantly increase the energy barrier for the chlorine substitution of 12378-PCDD. Meanwhile, the action angle was proposed to evaluate the parameters of adsorption process, and the adsorption of CaO can not only change the action angle between the 12378-PCDD molecule and the lattice surface, but also can modulate the energy barrier order of chlorine substitution among PCDD isomers. In addition, the loading component modulation was carried out to further confirm the feasibility of modulating the chloride substitution pathway, which proved the influence degree of loading component. And accordingly, the stretching analysis of the inactive component provides a theoretical basis for the subsequent study of the directional regulation of the PCDDs isomer generation pathway. Finally, the chlorine substitution rules and directed regulation mechanisms of PCDD/Fs isomers were obtained, which provides a modification direction for the structural components of OCs.

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted ¹³C and ¹H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted ¹³C and ¹H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of ¹H and ¹³C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches

Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein-protein interfaces. Here in this review, we provide an overview of the progress that has been made in virtual screening methodology and technology on multiple fronts in recent years. The advent of ultra-large virtual screens, in which hundreds of millions to billions of compounds are screened, has proven to be a powerful approach to discover highly potent hit compounds. However, these developments are just the tip of the iceberg, with new technologies and methods emerging to propel the field forward. Examples include novel machine-learning approaches, which can reduce the computational costs of virtual screening dramatically, while progress in quantum-mechanical approaches can increase the accuracy of predictions of various small molecule properties.

Mass spectrometric stochastic dynamic 3D structural analysis of mixture of steroids in solution - Experimental and theoretical study

There is explored, herein, functional relation: Experimental mass spectrometric phenomenon, obeying a certain scientific law ⇔ 3D molecular conformations and electronic structures of analytes obtained for quantum chemical theories. The paper answers to questions: (a) What evidence claims these actual relations among measurable and theoretical parameters, experimental factors and molecular properties; (b) how the provided evidence is collected and used; and (c) how empirical proof relates to assign and explain mass spectrometric phenomena of steroids afforded by our innovative stochastic dynamic mass spectrometric formula, D″_SD = 2.6388.10^-17.(<I²>-<I>²), quantum chemical 3D conformations, electronic structures and energetics of molecules, respectively. The paper address issue concerning empirical evidence at very high-to-exact level of assignment of 3D molecular conformations of steroids to experimental mass spectrometric fragment ions, accounting precisely for (i) effect of protonation; (ii) intramolecular rearrangement for A-D rings of steroidal skeleton and proton transfer effect, if any; in addition to (iii) examination of enantiomers of steroids in mixture with different stereochemistry, (R) and (S), of a set of six atoms of the molecular backbone of hydrocortisone (1), deoxycorticosterone (2), progesterone (3) and methyltestosterone (4), respectively. Results from testosterone (5) are discussed, as well. There are used ultra-high resolution atmospheric pressure chemical ionization mass spectrometric data on analytes (1)-(4) at ng.(mL)^-1 concentration levels in mixtures in solution obtained for positive operation mode. High accuracy static and molecular dynamic quantum chemical computations and chemometrics are also utilized. Experimental 3D structural parameters of steroids obtained for stochastic dynamic diffusion theory are correlated with available crystallographic data.

Mercury/oxygen reaction mechanism over CuFe2O4 catalyst

CuFe₂O₄ is regarded as a promising candidate of catalyst for Hg⁰ oxidation in industrial flue gas. However, the microcosmic reaction mechanism governing mercury oxidation on CuFe₂O₄ remains elusive. Herein, experiments and quantum chemistry calculations were conducted for understanding the chemical reaction mechanism of oxygen-assisted mercury oxidation on CuFe₂O₄. CuFe₂O₄ shows the optimal catalytic activity towards mercury oxidation at 150 ºC. The reactivity difference of different lattice oxygen species is associated with its atomic coordination environment. The lattice oxygen coordinating with two octahedral Cu atoms and a tetrahedral Fe atom shows higher catalytic activity towards mercury oxidation than other lattice oxygen atoms. The inverse spinel structure of CuFe₂O₄ is favorable for O₂ activation due to the Jahn-Teller effect, thereby promoting mercury oxidation. O₂ molecule preferably adsorbs on iron active site and dissociates into active oxygen species. Hg⁰ oxidation is a three-step reaction process: Hg⁰ adsorption, Hg(ads) → HgO(ads), and HgO desorption. The energy barrier of mercury oxidation by chemisorbed oxygen is lower than that of mercury oxidation by lattice oxygen. The chemisorbed oxygen preserves higher reactivity towards mercury oxidation than lattice oxygen. Hg(ads) → HgO(ads) is the rate-determining step of mercury oxidation by chemisorbed oxygen because of the higher energy barrier of 116.94 kJ/mol. This work could provide the theoretical guidance for the diversified structure design of highly-efficient catalysts used for elemental mercury oxidation.

Atmospheric persistence and toxicity evolution for fluorinated biphenylethyne liquid crystal monomers unveiled by in silico methods

It is essential to understand the atmospheric fate of liquid crystal monomers (LCMs), an important component in liquid crystal displays (LCDs); however, limited information is available at present. In this study, the atmospheric reaction mechanism, kinetics and toxicity evolution of three fluorinated biphenylethyne LCMs (1,2,3-trifluoro-5-(2-(4-methylphenyl)ethynyl)benzene (m-TEB), 1,2,3-trifluoro-5-(2-(4-ethylphenyl)ethynyl)benzene (e-TEB), 1,2,3-trifluoro-5-(2-(4-propylphenyl)ethynyl)benzene (p-TEB)) are investigated by theoretical calculations. Results show that the initial reactions of·OH addition to -C ≡ C- groups and hydrogen abstraction from alkyl groups (-CH₃, -C₂H₅, -C₃H₇) are dominant pathways. The resulting transformation products (TPs) for m-TEB are mainly highly oxidized multi-functional compounds such as benzil-based compounds, benzoic acid, alcohols, aldehydes, diketone and epoxy compounds. Results also show that some TPs exhibit higher aquatic toxicity than the parent. The calculated rate constants of m-TEB, e-TEB and p-TEB with·OH at 298 K are in the ranges of (1.3 -8.6) × 10^-12 cm³ molecule^-1 s^-1, and the corresponding atmospheric half-lives are 3.8-9.3, 2.2-5.4 and 0.6-1.4 days, respectively. This evidences that m-TEB and e-TEB may have atmospheric persistence and could undergo long-range transport. The results herein could be helpful for clarifying the atmospheric fates, persistence and risks of fluorinated LCMs with ethynyl benzene center.

Exploring the inhibitory potential of novel bioactive compounds from mangrove actinomycetes against nsp10 the major activator of SARS-CoV-2 replication

The current study reveals the inhibitory potential of novel bioactive compounds of mangrove actinomycetes against nsp10 of SARS-CoV-2. A total of fifty (50) novel bioactive (antibacterial, antitumor, antiviral, antioxidant, and anti-inflammatory) compounds of mangrove actinomycetes from different chemical classes such as alkaloids, dilactones, sesquiterpenes, macrolides, and benzene derivatives are used for interaction analysis against nsp10 of SARS-CoV-2. The six antiviral agents sespenine, xiamycin c, xiamycin d, xiamycin e, xiamycin methyl ester, and xiamycin A (obeyed RO5 rule) are selected based on higher binding energy, low inhibition constant values, and better-docked positions. The effective hydrogen and hydrophobic (alkyl, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi$$\end{document}π–sigma, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi$$\end{document}π–\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi$$\end{document}π T shaped and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi$$\end{document}π-alkyl) interaction analysis reveals the four antivirals sespenine, xiamycin C, xiamycin methyl ester, and xiamycin A are supposed to be the most auspicious inhibitors against nsp10 of SARS-CoV-2. Quantum chemistry methods such as frontier molecular orbitals and molecular electrostatic potential are used to explain the thermal stability and chemical reactivity of ligands. The toxicity profile shows that selected ligands are safe by absorption, distribution, metabolism, excretion, and toxicity profiling and also effective for inhibition of nsp10 protein of SARS-CoV-2. The molecular dynamic simulation investigation of apo and halo forms of nsp10 done by RMSD of C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α atoms of nsp10, all amino acid residues RMSF, count total number of hydrogen bonds and radius of gyration (R_g). MD simulations reveal the complexes are stable and increase the structural compactness of nsp10 in the binding pocket. The lead antiviral compounds sespenine, xiamycin C, xiamycin methyl ester, and xiamycin A are recommended as the most promising inhibitors against nsp10 of SARS-CoV-2 pathogenicity.

Three-dimensional structures, dynamics and calcium-mediated interactions of the exopolysaccharide, Infernan, produced by the deep-sea hydrothermal bacterium Alteromonas infernus

The exopolysaccharide Infernan, from the bacterial strain GY785, has a complex repeating unit of nine monosaccharides established on a double-layer of sidechains. A cluster of uronic and sulfated monosaccharides confers to Infernan functional and biological activities. We characterized the 3-dimensional structures and dynamics along Molecular Dynamics trajectories and clustered the conformations in extended two-fold and five-fold helical structures. The electrostatic potential distribution over all the structures revealed negatively charged cavities explored for Ca²⁺ binding through quantum chemistry computation. The transposition of the model of Ca²⁺complexation indicates that the five-fold helices are the most favourable for interactions. The ribbon-like shape of two-fold helices brings neighbouring chains in proximity without steric clashes. The cavity chelating the Ca²⁺ of one chain is completed throughout the interaction of a sulfate group from the neighbouring chain. The resulting is a 'junction zone' based on unique chain-chain interactions governed by a heterotypic binding mode.

Chemical reactivity prediction: current methods and different application areas

The ability to predict chemical reactivity of a molecule is highly desirable in drug discovery, both ex vivo (synthetic route planning, formulation, stability) and in vivo: metabolic reactions determine pharmacodynamics, pharmacokinetics and potential toxic effects, and early assessment of liabilities is vital to reduce attrition rates in later stages of development. Quantum mechanics offer a precise description of the interactions between electrons and orbitals in the breaking and forming of new bonds. Modern algorithms and faster computers have allowed the study of more complex systems in a punctual and accurate fashion, and answers for chemical questions around stability and reactivity can now be provided. Through machine learning, predictive models can be built out of descriptors derived from quantum mechanics and cheminformatics, even in the absence of experimental data to train on. In this article, current progress on computational reactivity prediction is reviewed: applications to problems in drug design, such as modelling of metabolism and covalent inhibition, are highlighted and unmet challenges are posed.

The adsorption mechanism of heavy metals from coal combustion by modified kaolin: Experimental and theoretical studies

Experimental and theoretical studies are combined to analyze the adsorption properties of modified kaolin for heavy metal (Pb, Cd, Zn and Cr) from coal combustion. The results indicate that the retention effect of kaolin for Pb, Cd, Zn and Cr has been significantly enhanced after intercalation-exfoliation combined with acid/alkali modification, which is mainly attributed to more active sites for adsorption, richer porosity and more effective in retarding coking of coal ash. The higher oxygen concentration is positive to the enrichment of heavy metals at 900-1200 ℃, while the coking of coal ash and the thermal conversion of additives become the main factors affecting the absorption at 1200-1300 ℃. The acid/alkali modification effectively promotes the inductive effect of electron transfer between modified kaolin and heavy metals to form stable chemical adsorption. The electron transfer induction of modified kaolin for Pb, Cd is higher than Zn, Cr at 900-1000 ℃, while the adsorption activity of mullite and cristobalite for Zn, Cr is stronger than Pb, Cd at 1200-1300 ℃. In addition, Pb, Cd and Zn are more readily adsorbed as oxides by additives at 900-1300 °C. The results shed new light on strengthening the adsorption activity of kaolin to Pb, Cd, Zn and Cr in high temperature.

Study on the mechanism of the effect of temperature on the decomposition reaction of SFn (n = 1-6) under discharge conditions

The study on the mechanism of the effect of temperature on the decomposition reaction of SF_n (n = 1-6) under discharge conditions is very important in studying the potential fault of SF₆ high voltage switch equipment and perfecting the chemical kinetic model of SF_n discharge. In this paper, structural optimizations, thermal correction to Gibbs free energy for the reactants, and products were performed at the B3LYP/6-311 +  + G(d,p) theory level. The single-point energies of all species were collected at the CCSD(T)/aug-cc-PVTZ level. The electric and thermal decomposition mechanism of SF_n under discharge conditions of 298-10,000 K were studied, respectively. The conclusion drawn was that in the temperature range of 298-10,000 K, the thermal decomposition homopolytic reaction △G began to decline from 200 kJ/mol, while the △G of the other two heterogenous reactions began to decrease from 1000 kJ/mol and 2000 kJ/mol, showing a downward trend of an almost similar slope. The electrolysis of SF_n is related to electron energy. When the electron energy is low, SF_n + e → SF_n^- series reactions occur, and △G of R12, R20, R28, R36, and R44 increases with temperature rise, while △G of R4 decreases with temperature. When the electron energy is high, one of SF_n^-  → SF_n-1^-  + F, SF_n^-  → SF_n-1 + F^-, and SF_n^-  → SF_n-1 + F + e will occur, and the reactions that occur at various temperature ranges as the temperature rises vary. When the second electron hits the SF_n^-, the SF_n^-  + e → SF_n-1^-  + F reaction will occur. The △G of this reaction slowly decreases with an increase in temperature. This study in clearer terms explains the decomposition process and mechanism of SF_n at different temperatures.

SAMPL7 physical property prediction from EC-RISM theory

Inspired by the successful application of the embedded cluster reference interaction site model (EC-RISM), a combination of quantum–mechanical calculations with three-dimensional RISM theory to predict Gibbs energies of species in solution within the SAMPL6.1 (acidity constants, pK_a) and SAMPL6.2 (octanol–water partition coefficients, log P) the methodology was applied to the recent SAMPL7 physical property challenge on aqueous pK_a and octanol–water log P values. Not part of the challenge but provided by the organizers, we also computed distribution coefficients log D_7.4 from predicted pK_a and log P data. While macroscopic pK_a predictions compared very favorably with experimental data (root mean square error, RMSE 0.72 pK units), the performance of the log P model (RMSE 1.84) fell behind expectations from the SAMPL6.2 challenge, leading to reasonable log D_7.4 predictions (RMSE 1.69) from combining the independent calculations. In the post-submission phase, conformations generated by different methodology yielded results that did not significantly improve the original predictions. While overall satisfactory compared to previous log D challenges, the predicted data suggest that further effort is needed for optimizing the robustness of the partition coefficient model within EC-RISM calculations and for shaping the agreement between experimental conditions and the corresponding model description.

Active sites of peptides Asp-Asp-Asp-Tyr and Asp-Tyr-Asp-Asp protect against cellular oxidative stress

The protective effects of the peptides Asp-Asp-Asp-Tyr (DDDY) and Asp-Tyr-Asp-Asp (DYDD) against AAPH-induced HepG2 cells are unclear. Our objective was to investigate the active sites of these peptides and their cellular antioxidant mechanism. DDDY and DYDD show a direct free radical scavenging effect in reducing ROS levels and maintained cellular antioxidant enzymes at normal levels. The quantum chemistry analysis of the electronic properties of antioxidant activity showed that DYDD has a greater energy in the highest occupied molecular orbital than DDDY, and O₅₈-H₅₉ and N₁₀-H₁₂ were identified as the active antioxidant sites in DYDD and DDDY, respectively, indicating that the inconsistent arrangement of amino acids affects the distribution of the highest occupied orbital energy as well as the active sites; thus, influences the antioxidant activity of peptides. It provide valuable insights into the antioxidant active sites of peptides.

Study on the structure-activity relationship of watermelon seed antioxidant peptides by using molecular simulations

Our previous studies had shown that watermelon seed antioxidant peptides (WSAPs: P1-P5) possessed good activities. However, the structure-activity relationship of P1 is still unclear. Quantum chemistry and molecular docking were used to investigate the antioxidant mechanism of P1. The active site of P1 is located at C₆H₁₄ on Arg. P1 can bind to DPPH/ABTS through hydrogen bond and hydrophobic interaction. Compared with P2-P4, P1 has the strongest antioxidant capacity. The molecular simulation showed that P1 could enhance the stability of Keap1 by interacting with Asn382, Arg380 and Tyr 334 in the active sites. Compared with the model group, the expression of Keap1 was down-regulated (p < 0.05), while the expression of Nrf2 and HO-1 was up-regulated (p < 0.05) after P1 treatment. P1 has the potential ability to activate the signaling pathway Keap1-Nrf2 and improve the antioxidant defense system. This study provides a new perspective for the rational design and mechanism of antioxidant peptides.

Fate of chromium with the presence of HCl and steam during oxy-coal combustion: Quantum chemistry and experimental study

Quantum chemistry combined with kinetic simulation and drop tube furnace (DTF) experiments were conducted to reveal the transformation behavior of chromium at the presence of steam and HCl under oxy-coal combustion. A completed kinetic system Cr‒O‒H‒Cl containing 107 elementary reactions was firstly proposed. The unknown microcosmic reaction paths and corresponding Arrhenius parameters were calculated via quantum chemistry. Kinetic simulations on the basis of Cr‒O‒H‒Cl system clarified that HCl promoted the oxidation of chromium to hexavalent CrO₂Cl₂. Coexistence of HCl and steam divided the transition of chromium into two stages. At the early stage, reaction rate between chromium and steam was faster than chromium with HCl, chromium mainly transformed to CrO(OH)₂. Hereafter, HCl was dominant in the transformation of chromium, then chromium mainly presented as CrO₂Cl₂. Moreover, DTF experimental results indicated that introduction of HCl into combustion atmosphere induced more chromium release. Presence of steam reinforced the effect of HCl on chromium attributing to the significant transition of CrO_x(OH)_y to CrO₂Cl₂. Although CaO and Fe₂O₃ both exhibited good reactivity with chromium, presence of HCl largely suppressed chromium capture by CaO and Fe₂O₃. Moreover, the inhibition effect of HCl on Fe₂O₃ was stronger than CaO for Cr capture.

MLatom 2: An Integrative Platform for Atomistic Machine Learning

Atomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our MLatom 2 software package, which provides an integrative platform for a wide variety of AML simulations by implementing from scratch and interfacing existing software for a range of state-of-the-art models. These include kernel method-based model types such as KREG (native implementation), sGDML, and GAP-SOAP as well as neural-network-based model types such as ANI, DeepPot-SE, and PhysNet. The theoretical foundations behind these methods are overviewed too. The modular structure of MLatom allows for easy extension to more AML model types. MLatom 2 also has many other capabilities useful for AML simulations, such as the support of custom descriptors, farthest-point and structure-based sampling, hyperparameter optimization, model evaluation, and automatic learning curve generation. It can also be used for such multi-step tasks as Δ-learning, self-correction approaches, and absorption spectrum simulation within the machine-learning nuclear-ensemble approach. Several of these MLatom 2 capabilities are showcased in application examples.

Quantum-mechanical property prediction of solvated drug molecules: what have we learned from a decade of SAMPL blind prediction challenges?

Joint academic-industrial projects supporting drug discovery are frequently pursued to deploy and benchmark cutting-edge methodical developments from academia in a real-world industrial environment at different scales. The dimensionality of tasks ranges from small molecule physicochemical property assessment over protein-ligand interaction up to statistical analyses of biological data. This way, method development and usability both benefit from insights gained at both ends, when predictiveness and readiness of novel approaches are confirmed, but the pharmaceutical drug makers get early access to novel tools for the quality of drug products and benefit of patients. Quantum-mechanical and simulation methods particularly fall into this group of methods, as they require skills and expense in their development but also significant resources in their application, thus are comparatively slowly dripping into the realm of industrial use. Nevertheless, these physics-based methods are becoming more and more useful. Starting with a general overview of these and in particular quantum-mechanical methods for drug discovery we review a decade-long and ongoing collaboration between Sanofi and the Kast group focused on the application of the embedded cluster reference interaction site model (EC-RISM), a solvation model for quantum chemistry, to study small molecule chemistry in the context of joint participation in several SAMPL (Statistical Assessment of Modeling of Proteins and Ligands) blind prediction challenges. Starting with early application to tautomer equilibria in water (SAMPL2) the methodology was further developed to allow for challenge contributions related to predictions of distribution coefficients (SAMPL5) and acidity constants (SAMPL6) over the years. Particular emphasis is put on a frequently overlooked aspect of measuring the quality of models, namely the retrospective analysis of earlier datasets and predictions in light of more recent and advanced developments. We therefore demonstrate the performance of the current methodical state of the art as developed and optimized for the SAMPL6 pKa and octanol-water log P challenges when re-applied to the earlier SAMPL5 cyclohexane-water log D and SAMPL2 tautomer equilibria datasets. Systematic improvement is not consistently found throughout despite the similarity of the problem class, i.e. protonation reactions and phase distribution. Hence, it is possible to learn about hidden bias in model assessment, as results derived from more elaborate methods do not necessarily improve quantitative agreement. This indicates the role of chance or coincidence for model development on the one hand which allows for the identification of systematic error and opportunities toward improvement and reveals possible sources of experimental uncertainty on the other. These insights are particularly useful for further academia-industry collaborations, as both partners are then enabled to optimize both the computational and experimental settings for data generation.

A theoretical and experimental case study of the hydrogen bonding predilection of S-methylcysteine

S-containing amino acids can lead to two types of local NH···S interactions which bridge backbone NH sites to the side chain to form either intra- or inter-residue H-bonds. The present work reports on the conformational preferences of S-methyl-L-cysteine, Cys(Me), using a variety of investigating tools, ranging from quantum chemistry simulations, gas-phase UV and IR laser spectroscopy, and solution state IR and NMR spectroscopies, on model compounds comprising one or two Cys(Me) residues. We demonstrate that in gas phase and in low polarity solution, the C- and N-capped model compound for one Cys(Me) residue adopts a preferred C5-C6γ conformation which combines an intra-residue N-H···O=C backbone interaction (C5) and an inter-residue N-H···S interaction implicating the side-chain sulfur atom (C6γ). In contrast, the dominant conformation of the C- and N-capped model compound featuring two consecutive Cys(Me) residues is a regular type I β-turn. This structure is incompatible with concomitant C6γ interactions, which are no longer in evidence. Instead, C5γ interactions occur, that are fully consistent with the turn geometry and additionally stabilize the structure. Comparison with the thietane amino acid Attc, which exhibits a rigid cyclic side chain, pinpoints the significance of side chain flexibility for the specific conformational behavior of Cys(Me).

Highly-accurate quartic force fields for the prediction of anharmonic rotational constants and fundamental vibrational frequencies

The CcCR quartic force field (QFF) methodology is capable of computing B₀ and C₀ rotational constants to within 35 MHz (0.14%) of experiment for triatomic and larger molecules with at least two heavy atoms. Additionally, the same constants for molecules with four or more atoms agree to within 20 MHz (0.12%) of experiment for the current test set. This work also supports previous claims that the same QFF methodology can produce fundamental vibrational frequencies with a deviation less than 5.7 cm^-1 from experiment. Consequently, this approach of augmenting complete basis set extrapolated energies with treatments of core electron correlation and scalar relativity produces some of the most accurate rovibrational spectroscopic data available.

Kinetics, mechanisms and toxicity of the degradation of imidaclothiz in soil and water

Hydrolysis and photolysis are important degradation pathways of pesticides while their degradation in soil is a principal metabolic pathway. These processes might generate toxic chemicals and pose threats to the environment. As a member of the controversial neonicotinoid pesticides, the environmental fate and toxicity of imidaclothiz and its potential metabolites have remained ambiguous. Laboratory experiments were conducted to study the degradation kinetics, mechanisms and toxicity of imidaclothiz in aqueous solutions (pH = 4, 7, 9 buffer solutions and ultra-pure water) and soil (black soil, red soil and fluvo-aquic soil) under different conditions (25 ℃ and 50 ℃). The results showed that imidaclothiz was fairly stable in water and soil under natural conditions. Based on the mass accuracy of the parent chemical and conserved fragment ions, ten candidate degradation products were filtered out using UHPLC-QTOF-MS and the UNIFI system. Then, two of the candidates were synthesized, analysed, and compared with standards to confirm. The microscopic mechanisms of three degradation reactions (imidaclothiz degraded to M216, M216 degraded to M217 and M216 degraded to M198) were elucidated using theoretical calculations. The toxicity data from experiments and the ECOSAR prediction showed that imidaclothiz had low toxicity to Daphnia magna and Danio rerio and had high toxicity to Apis mellifera, with 50% of the degradation products in this study exhibiting higher toxicities to aquatic organisms than the parent chemical.

Cadmium ion-chlorophyll interaction - Examination of spectral properties and structure of the cadmium-chlorophyll complex and their relevance to photosynthesis inhibition

Chlorophyll was shown to spontaneously form a complex with cadmium, which is incorporated at the central position of the chlorophyll molecule porphyrin ring, where it replaces magnesium. The rate of complex formation depended on the ratio of Cd2+ ions to chlorophyll concentration in the solution. In solutions with chlorophyll concentration of C = 1 × 10^-5 M and Cd²⁺ concentrations of C = 1 × 10^-5 M, C = 1 × 10^-3 M and C = 9 × 10^-3 M, Cd-Chl complex formation was completed after 200 h, 50 h and 33 h, respectively. The formation of Cd-Chl complex followed the second order over all substrates reaction order, first order over Cd²⁺ concentration and first over Chl concentration. The pseudo second order reaction rate constant k, when Cd²⁺ concentration was equal Chl concentration have been obtained as k = 1.510 ± 0.023 × 10^-4 M^-1min^-1. Quantum chemistry computations showed that Cd-chlorophyll complex existed in two conformations in the methanol solution with cadmium ion placed either below or above the coordination plane. Two times smaller overlap integral of the Chl fluorescence spectrum with the Cd-Chl absorption spectrum I_Chl,Cd-Chl= 2.4223 × 10^-13 cm³/M in comparison with the overlap integral of the Chl fluorescence spectrum with the Chl absorption spectrum I_Chl,Chl= 4.6210 × 10^-13 cm³/M (twice lower probability of energy transfer Chl∗ → Cd-Chl than Chl∗ → Chl) and lower Förster critical distance for resonance energy transfer: R_{oChl→Cd-Chl}= 46.773 Å, R_oChl→Chl= 52.086 Å, indicated that in plants intoxicated with cadmium, taken up from the contaminated soil, the energy transfer between Chl and Cd-Chl in antennas will be disturbed, which may be one of the reasons for the inhibition of photosynthesis.

Mechanism of absorption wavelength shifts in anion channelrhodopsin-1 mutants

Using a quantum mechanical/molecular mechanical approach, we show the mechanisms of how the protein environment of Guillardia theta anion channelrhodopsin-1 (GtACR1) can shift the absorption wavelength. The calculated absorption wavelengths for GtACR1 mutants, M105A, C133A, and C237A are in agreement with experimentally measured wavelengths. Among 192 mutant structures investigated, mutations at Thr101, Cys133, Pro208, and Cys237 are likely to increase the absorption wavelength. In particular, T101A GtACR1 was expressed in HEK293T cells. The measured absorption wavelength is 10 nm higher than that of wild type, consistent with the calculated wavelength. (i) Removal of a polar residue from the Schiff base moiety, (ii) addition of a polar or acidic residue to the β-ionone ring moiety, and (iii) addition of a bulky residue to increase the planarity of the β-ionone and Schiff base moieties are the basis of increasing the absorption wavelength.

Stretching force constants as descriptors of energy and geometry of F···HF hydrogen bonds

In this work applicability of proton donor group stretching vibration force constants k_s and intermolecular stretching force constants k_σ for evaluations of hydrogen bond strength and geometry are discussed. For a set of 30 complexes with F···HF hydrogen bonds in a wide range 0.5-48 kcal/mol by means of quantum chemical calculations equilibrium geometries, complexation energies, vibrational frequencies and corresponding force constants were calculated (MP2/aug-cc-pVTZ). It is shown, that properties of a hydrogen bond are more strictly correlated with the values of force constants than with vibrational frequencies. Easy-to-use equations for estimations of hydrogen bond energy ∆E and geometry (r_FH, r_FF) based on k_s and k_σ values are proposed.

Singlet oxygen production abilities of oxidated aromatic compounds in natural water

Singlet oxygen (¹O₂) is well known to be formed through energy transfer from excited state organic matters to O₂, playing an important role in the transformations of contaminants. However, the contribution of small oxidated aromatic compounds (OACs) to the production of ¹O₂ in surface water is unclear. In this study, 28 OACs were selected to investigate the correlations between their photochemical production abilities of ¹O₂ and molecular structures. Our results showed that the steady-state concentrations and quantum yields of ¹O₂ (Φ_1O2) generated by OACs were in the range of 7.0 × 10^-14-1.4 × 10^-12 M and 2.2 × 10^-4-4.7 × 10^-2, respectively, indicating that the photochemical production abilities of ¹O₂ by OACs varied greatly with types and positions of functional groups on the molecule. More importantly, the observed photochemical production of ¹O₂ was most notable in cases of molecules containing -OCH₃ group and benzoquinone. A good quantitative structure-property relationship model was established between ¹O₂ producing ability, energy of the lowest unoccupied molecular orbitals (E_LUMO) and the most positive net charge of hydrogen atoms (qH⁺) of OACs. In addition, the role of ¹O₂ produced by 2, 6-dimethoxy-1, 4-benzoquinone, the OAC with the highest Φ_1O2, in the photodegradation of organic contaminants was validated by the enhanced degradation of atorvastatin under simulated sunlight, suggesting that OACs ubiquitously existed in surface water may greatly affect the fate and ecological risks of organic contaminants.

Predicting in silico electron ionization mass spectra using quantum chemistry

Compound identification by mass spectrometry needs reference mass spectra. While there are over 102 million compounds in PubChem, less than 300,000 curated electron ionization (EI) mass spectra are available from NIST or MoNA mass spectral databases. Here, we test quantum chemistry methods (QCEIMS) to generate in silico EI mass spectra (MS) by combining molecular dynamics (MD) with statistical methods. To test the accuracy of predictions, in silico mass spectra of 451 small molecules were generated and compared to experimental spectra from the NIST 17 mass spectral library. The compounds covered 43 chemical classes, ranging up to 358 Da. Organic oxygen compounds had a lower matching accuracy, while computation time exponentially increased with molecular size. The parameter space was probed to increase prediction accuracy including initial temperatures, the number of MD trajectories and impact excess energy (IEE). Conformational flexibility was not correlated to the accuracy of predictions. Overall, QCEIMS can predict 70 eV electron ionization spectra of chemicals from first principles. Improved methods to calculate potential energy surfaces (PES) are still needed before QCEIMS mass spectra of novel molecules can be generated at large scale.