Chemical Calculations Research Articles

In silico analysis of the action of saturated, monounsaturated, and polyunsaturated fatty acids against Echinococcus granulosus fatty-acid-binding protein 1

Background The zoonotic infection caused by tapeworms Echinococcus is a neglected tropical disease in poor regions with limited access to suitable sanitary conditions. Hydatid cysts produced by Echinococcus granulosus use fatty-acid-binding proteins (FABP) to obtain the fatty acids and cholesterol necessary for their survival from the host. In this work, we analyzed the behaviour of saturated, monounsaturated, and polyunsaturated fatty acids against EgFABP1. Methods We used computational biology and chemistry techniques and binding free energy estimations by molecular mechanics generalized Born surface area (MM/GBSA). Results This research has enabled us to clarify the EgFABP1 isoforms identified in the database, suggesting their potential involvement in diverse cellular activities of Echinococcus granulosus. Conversely, examining the global and local chemical reactivity of 14 fatty acids revealed that liposolubility is contingent upon the degree of unsaturation in the FAs. Additionally, FAs exhibited acceptable levels of oral absorption and bioavailability. The binding of EgFABP1 with FAs analyzed by molecular dynamics simulation showed us that these are highly stable, where the best affinity was with docosahexaenoic acid. Conclusions Our results suggest that the action of fatty acids could play an interesting role in detecting early Echinococcus granulosus.

Interplay between Catalyst Corrosion and Homogeneous Reactive Oxygen Species in Electrochemical Ozone Production.

Electrochemical ozone production (EOP), a six-electron water oxidation reaction, offers promising avenues for creating value-added oxidants and disinfectants. However, progress in this field is slowed by a dearth of understanding of fundamental reaction mechanisms. In this work, we combine experimental electrochemistry, spectroscopic detection of reactive oxygen species (ROS), oxygen-anion chemical ionization mass spectrometry, and computational quantum chemistry calculations to determine a plausible reaction mechanism on nickel- and antimony-doped tin oxide (Ni/Sb-SnO2, NATO), one of the most selective EOP catalysts. Antimony doping is shown to increase the conductivity of the catalyst, leading to improved electrochemical performance. Spectroscopic analysis and electrochemical experiments combined with quantum chemistry predictions reveal that hydrogen peroxide (H2O2) is a critical reaction intermediate. We propose that leached Ni4+ cations catalyze hydrogen peroxide into solution phase hydroperoxyl radicals (•OOH); these radicals are subsequently oxidized to ozone. Isotopic product analysis shows that ozone is generated catalytically from water and corrosively from the catalyst oxide lattice without regeneration of lattice oxygens. Further quantum chemistry calculations and thermodynamic analysis suggest that the electrochemical corrosion of tin oxide itself might generate hydrogen peroxide, which is then catalyzed to ozone. The proposed pathways explain both the roles of dopants in NATO and its lack of stability. Our study interrogates the possibility that instability and electrochemical activity are intrinsically linked through the formation of ROS. In doing so, we provide the first mechanism for EOP that is consistent with computational and experimental results and highlight the central challenge of instability as a target for future research efforts.

Understanding Defects in Amorphous Silicon with Million-Atom Simulations and Machine Learning.

The structure of amorphous silicon (a-Si) is widely thought of as a fourfold-connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it particularly interesting. Despite many attempts to explain such "dangling-bond" and "floating-bond" defects, respectively, a unified understanding is still missing. Here, we use advanced computational chemistry methods to reveal the complex structural and energetic landscape of defects in a-Si. We study an ultra-large-scale, quantum-accurate structural model containing a million atoms, and thousands of individual defects, allowing reliable defect-related statistics to be obtained. We combine structural descriptors and machine-learned atomic energies to develop a classification of the different types of defects in a-Si. The results suggest a revision of the established floating-bond model by showing that fivefold-bonded atoms in a-Si exhibit a wide range of local environments-analogous to fivefold centers in coordination chemistry. Furthermore, it is shown that fivefold (but not threefold) coordination defects tend to cluster together. Our study provides new insights into one of the most widely studied amorphous solids, and has general implications for understanding defects in disordered materials beyond silicon alone.

Understanding Defects in Amorphous Silicon with Million‐Atom Simulations and Machine Learning

AbstractThe structure of amorphous silicon (a‐Si) is widely thought of as a fourfold‐connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it particularly interesting. Despite many attempts to explain such “dangling‐bond” and “floating‐bond” defects, respectively, a unified understanding is still missing. Here, we use advanced computational chemistry methods to reveal the complex structural and energetic landscape of defects in a‐Si. We study an ultra‐large‐scale, quantum‐accurate structural model containing a million atoms, and thousands of individual defects, allowing reliable defect‐related statistics to be obtained. We combine structural descriptors and machine‐learned atomic energies to develop a classification of the different types of defects in a‐Si. The results suggest a revision of the established floating‐bond model by showing that fivefold‐bonded atoms in a‐Si exhibit a wide range of local environments–analogous to fivefold centers in coordination chemistry. Furthermore, it is shown that fivefold (but not threefold) coordination defects tend to cluster together. Our study provides new insights into one of the most widely studied amorphous solids, and has general implications for understanding defects in disordered materials beyond silicon alone.

Identification of extracellular polymeric substances layer barrier in chloroquine phosphate-disturbed anammox consortia and mechanism dissection on cytotoxic behavior by computational chemistry

The over-dosing use of chloroquine phosphate (CQ) poses severe threats to human beings and ecosystem due to the high persistence and biotoxicity. The discharge of CQ into wastewater would affect the biomass activity and process stability during the biological processes, e.g., anammox. However, the response mechanism of anammox consortia to CQ remain unknown. In this study, the accurate role of extracellular polymeric substances barrier in attenuating the negative effects of CQ, and the mechanism on cytotoxic behavior were dissected by molecular spectroscopy and computational chemistry. Low concentrations (≤6.0 mg/L) of CQ hardly affected the nitrogen removal performance due to the adaptive evolution of EPS barrier and anammox bacteria. Compact protein of EPS barrier can bind more CQ (0.24 mg) by hydrogen bond and van der Waals force, among which O-H and amide II region respond CQ binding preferentially. Importantly, EPS contributes to the microbiota reshape with selectively enriching Candidatus_Kuenenia for self-protection. Furthermore, the macroscopical cytotoxic behavior was dissected at a molecular level by CQ fate/distribution and computational chemistry, suggesting that the toxicity was ascribed to attack of CQ on functional proteins of anammox bacteria with atom N17 (f–=0.1209) and C2 (f+=0.1034) as the most active electrophilic and nucleophilic sites. This work would shed the light on the fate and risk of non-antibiotics in anammox process.

Computational Chemistry: Prediction of Compound Accessibility of Targeted Synthesized Compounds.

In the present work, a series of novel pyridine carboxamides 3(a-h) were synthesized and screened with antibacterial activity. This research explores the application of Density Functional Theory (DFT) in studying biological systems at the quantum mechanical level, particularly in the context of drug design. DFT offers a streamlined approach to quantum mechanical calculations, making it indispensable in various scientific fields, and for its exceptional accuracy, reduced computational time, and cost-effectiveness has become a pivotal tool in computational chemistry. This research work highlights the integration of DFT studies with POM analyses, which effectively identify pharmacophoric sites. Moreover, the research incorporates in silico pharmacokinetics analyses to assess the pharmacokinetic properties of synthesized compounds. The paper focused on a series of compounds previously reported, aiming to provide a comprehensive understanding of their electronic structure, pharmacophoric features, and potential as drug candidates. This study not only contributes to the evolving field of computational chemistry but also holds implications for advancing drug design processes by combining theoretical insights with practical analyses. The compounds 3(a-h) were subjected to Density Functional Theory (DFT) computations using the B3LYP/6-31G(d) basis set to get optimized geometric structures. GaussViewis used to display the contributions of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). The determination of energy gaps was conducted using Gaussian 09W. The pharmacokinetic profiles were evaluated using existing techniques such as Osiris, Petra, and Molinspiration, as well as a novel platform called POM Analyse. The computational studies DFT, POM and in silico pharmacokinetics studies revealed that the studied compounds are biologically active, non-toxic, non-carcinogenic in nature and may be utilized as drug candidates. Density functional theory (DFT) investigations emphasize the exceptional stability of complex 3d, which possesses the biggest energy gap and the lowest softness. In contrast, compound 3h demonstrates poorer stability among the tested compounds, characterized by the lowest energy gap and the highest softness values. These findings are further substantiated by absolute energy calculations. The negligible energy difference in compound 3h indicates an increased transfer of electric charge within the molecule, which is associated with its enhanced biological effectiveness. The drug-likeness of the compounds is confirmed by POM and in silico pharmacokinetics investigations, with compound 3h being identified as the most biologically active among the investigated compounds.

Two undescribed coumarins from Hansenia weberbaueriana

Two previously undescribed coumarins (1 and 2) were isolated from the root of Hansenia weberbaueriana which have been used to cure inflammatory diseases over thousands of years by Chinese. The structures of new findings were confirmed by comprehensive analyses of spectral evidences in HRESIMS, 1D and 2D NMR combined with chemical calculations. Compounds 1 and 2 exhibited potential anti-inflammatory properties by reducing the mRNA expression levels of TNF-α, IL-6 and IL-1β in lipopolysaccharide (LPS)-induced RAW264.7 macrophages at a concentration of 15 μM.

Prediction Challenge: Simulating Rydberg photoexcited cyclobutanone with surface hopping dynamics based on different electronic structure methods.

This research examines the nonadiabatic dynamics of cyclobutanone after excitation into the n → 3s Rydberg S2 state. It stems from our contribution to the Special Topic of the Journal of Chemical Physics to test the predictive capability of computational chemistry against unseen experimental data. Decoherence-corrected fewest-switches surface hopping was used to simulate nonadiabatic dynamics with full and approximated nonadiabatic couplings. Several simulation sets were computed with different electronic structure methods, including a multiconfigurational wavefunction [multiconfigurational self-consistent field (MCSCF)] specially built to describe dissociative channels, multireference semiempirical approach, time-dependent density functional theory, algebraic diagrammatic construction, and coupled cluster. MCSCF dynamics predicts a slow deactivation of the S2 state (10ps), followed by an ultrafast population transfer from S1 to S0 (<100fs). CO elimination (C3 channel) dominates over C2H4 formation (C2 channel). These findings radically differ from the other methods, which predicted S2 lifetimes 10-250 times shorter and C2 channel predominance. These results suggest that routine electronic structure methods may hold low predictive power for the outcome of nonadiabatic dynamics.

The Inhibition Activity of Natural Methoxyflavonoid from Inula britannica on Soluble Epoxide Hydrolase and NO Production in RAW264.7 Cells.

Soluble epoxide hydrolase (sEH) is an enzyme targeted for the treatment of inflammation and cardiovascular diseases. Activated inflammatory cells produce nitric oxide (NO), which induces oxidative stress and exacerbates inflammation. We identify an inhibitor able to suppress sEH and thus NO production. Five flavonoids 1-5 isolated from Inula britannica flowers were evaluated for their abilities to inhibit sEH with IC50 values of 12.1 ± 0.1 to 62.8 ± 1.8 µM and for their effects on enzyme kinetics. A simulation study using computational chemistry was conducted as well. Furthermore, five inhibitors (1-5) were confirmed to suppress NO levels at 10 µM. The results showed that flavonoids 1-5 exhibited inhibitory activity in all tests, with compound 3 exhibiting the most significant efficacy. Thus, in the development of anti-inflammatory inhibitors, compound 3 is a promising natural candidate.

Exploring Structure–Toxicity Relationships in Nitrobenzene and Derivatives: A Multifaceted Biochemical Investigation Using 3D–QSPR, HQSPR, Molecular Docking, and MD Simulation

AbstractNitrobenzene derivatives present an environmental concern to aquatic organisms, as well as a potential adverse effects on human health due to their toxicity. Therefore, monitoring nitrobenzene contamination levels is critical to protect both the ecosystem and the public. This study aims to quantitatively correlate the toxicity of nitrobenzene derivatives to their chemical structures using computational chemistry methods such as 3D‐QSPR, HQSPR, Molecular Docking and MD simulation. Additionally, to investigate the type of interactions and stability of nitrobenzene derivatives with the PP2 protein involved in the Phloem protein 2 (PP2). From each computational method a prediction model was extracted HQSPR (Atomic, Bonds, Donor/Acceptor, 3D‐QSPR (Steric, Electrostatic, Hydrophobic, Acceptor), and used for the design of X1 and X2 new nitrobenzene derivatives, which showed remarkable stability and better interactions. This study contributes to the understanding of the nitrobenzene derivatives’ toxicity and offers materials towards the understanding of the mechanism of action of these substances in protein–ligand interactions.

16th National Physical Chemistry Meeting (16ENQF) - 5th Computational Chemistry Symposium (5CCS)

16th National Physical Chemistry Meeting (16ENQF) - 5th Computational Chemistry Symposium (5CCS)

A theoretical study on toluene oxidization by OH radical

Toluene, a prominent member of volatile organic compounds (VOCs), exerts a substantial adverse influence on both human life and the environment. In the context of advanced oxidation processes, the ·OH radical emerges as a highly efficient oxidant, pivotal in the elimination of VOCs. This study employs computational quantum chemistry methods (G4MP2//B3LYP/6-311++G(d,p)) to systematically investigate the degradation of toluene by ·OH radicals in an implicit solvent model, and validates the rationale of choosing a single-reference method using T1 diagnostics. Our results suggest three possible reaction mechanisms for the oxidation of toluene by ·OH: firstly, the phenyl ring undergoes a hydrogen abstraction reaction followed by direct combination with ·OH to form cresol; secondly, ·OH directly adds to the phenyl ring, leading to ring opening; thirdly, oxidation of sidechain to benzoic acid followed by further addition and ring opening. The last two oxidation pathways involve the ring opening of toluene via the addition of ·OH, significantly facilitating the process. Therefore, both pathways are considered feasible for the degradation of toluene. Subsequently, the UV-H2O2 system was designed to induce the formation of ·OH for toluene degradation and to identify the optimal reaction conditions. It was demonstrated that ·OH and 1O2 are the primary active species for degrading toluene, with their contribution ranking as ·OH > 1O2. The intermediates in the mixture solution after reactions were characterized using GC–MS, demonstrating the validity of theoretical predictions. A comparative study of the toluene consumption rate revealed an experimental comprehensive activation energy of 10.33 kJ/mol, which is consistent with the preliminary activation energies obtained via theoretical analysis of these three mechanisms (0.56 kJ/mol to 13.66 kJ/mol), indicating that this theoretical method can provide a theoretical basis for experimental studies on the oxidation of toluene by ·OH.

Softeners doped with asphaltene dispersants acting in synergy with resins: Molecular mechanisms underlying asphalt rejuvenation

In confronting the global climate change, reusing waste asphalt materials stands out as a key strategy for the transportation sector to preserve natural resources and reduce the carbon footprint. Recycling additives or rejuvenators are usually required in this practice, especially for boosting the recycling rate. Given the imperative need for a general compositional design principle of rejuvenators, we previously proposed an innovative formulation concept by combining softeners with asphaltene dispersants, and reported that the use of dispersants largely reduced the total quantity of rejuvenators needed and thus would minimize the environmental impacts. The present study is aimed for probing the molecular mechanisms underlying this observation, with additional interest focused on the synergistic effect of the dispersants with resins and also on the impacts due to their self-association. Selection of representative materials consisted of three types of dispersants including an ionic liquid, a phenolic amphiphile, and an ester plasticizer, in combination with an aromatic oil as the softener. Long-time molecular dynamics (MD) simulation was employed to inspect the aggregation of asphaltenes. Quantum chemical (QC) computation was performed to provide insights into the source of different dispersing behaviors. The results indicated that the three dispersants all acted in concert with resins in dispersing asphaltenes. The dispersion characteristics of asphaltenes could be viewed as a net result of the two competing mechanisms of the dispersing capability and self-association of the dispersant used. The ionic liquid exhibited much higher propensity to self-association at increasing concentrations than the phenolic dispersant, and yet the optimum performance was achieved for both when they were added at low dosages. The ester plasticizer showed minimal self-association and yielded improved dispersion with increasing concentrations. In all cases, the dispersing functionality mainly originated from the presence or formation of significant electron-withdrawing sites, collectively due to the highly electronegative cation and anion, hydrogen bonding, and polar groups. Comparison of the dispersing power was also conducted from different perspectives. The findings are expected to provide molecular insights for formulating versatile and more environmentally benign asphalt rejuvenators.

Theoretical investigation on action mechanism and mollifying efficacy of propellant stabilizers.

This project performed quantum chemical computation, through kinetic and thermodynamic analyses to compare relative reactivity, reaction rate, and equilibrium composition from the possible pathways in connection with stabilizer-nitrodioxide reactions to determine the stability of the materials for practical application. Corresponding achievements have promoted the use of N-methyl-p-nitroaniline (MNA) and dinitrophenyl malonamide series (M3, M4, and M5) stabilizers as high priorities for selection. The Gaussian 09 program (G09) (Frisch et al 2009) and density functional theory (DFT) calculations with the B3LYP/6-31G(d,p) function were performed to obtain related geometric and thermodynamic energy data for the molecular systems in this study. The synchronous transit-guided quasi-Newton method (STQN) (Peng and Schlegel Isr J Chem 33:49, 1993) was applied through the QST3 procedure to identify single imaginary frequency-valued transition-state species. The related reaction rate constant (k) and pre-exponential factor (A) were obtained, based on transition state theory (Su 2008), using Eqs. 11 and 12.

Variation of Activation Volume as an Indicator of the Difference in Clusterization Phenomenon Induced by H-Bonding and F-Π Stacking Interactions in Enantiomers and a Racemate of Flurbiprofen.

In this paper, X-ray diffraction (XRD), differential scanning calorimetry (DSC), broadband dielectric (BDS), and Fourier transform infrared (FTIR) spectroscopy supported by molecular dynamics (MD) simulations and quantum chemical computations were applied to investigate the structural and thermal properties, molecular dynamics, and H-bonding pattern of R-, S-, and RS-flurbiprofen (FLP). Experimental data indicated various spatial molecular arrangements in crystalline forms of examined systems, which seemed to disappear in the liquid state. Surprisingly, deeper analysis of high-pressure dielectric data revealed unexpected variation in the activation volume of pure enantiomers and a racemate. MD simulations showed that it is an effect of the clusterization phenomenon and a higher population of small associates in the former samples. Moreover, theoretical consideration exposed the particular role of unspecific F-Π interactions as a driving force underlying local molecular arrangements of molecules in the liquid and the crystal lattice of R-, S-, and RS-FLP.

The Gourava Co-Indices of Graph

This Paper explores the concept of Gourava co-indices, inspired by the analogous discussions on Zagreb indices in prior literature. Gourava co-indices are a novel extension of graph theoretical concepts, focusing on the relationships and patterns within molecular structures. Moreover, we present several key relations and properties associated with Gourava co- indices, providing a comprehensive framework for further research and practical applications in areas such as drug discovery, material science, and computational chemistry.

Bayesian Mechanistic Inference, Statistical Mechanics, and a New Era for Monte Carlo.

On the one hand, much of computational chemistry is concerned with "bottom-up" calculations which elucidate observable behavior starting from exact or approximated physical laws, a paradigm exemplified by typical quantum mechanical calculations and molecular dynamics simulations. On the other hand, "top down" computations aiming to formulate mathematical models consistent with observed data, e.g., parametrizing force fields, binding or kinetic models, have been of interest for decades but recently have grown in sophistication with the use of Bayesian inference (BI). Standard BI provides an estimation of parameter values, uncertainties, and correlations among parameters. Used for "model selection," BI can also distinguish between model structures such as the presence or absence of individual states and transitions. Fortunately for physical scientists, BI can be formulated within a statistical mechanics framework, and indeed, BI has led to a resurgence of interest in Monte Carlo (MC) algorithms, many of which have been directly adapted from or inspired by physical strategies. Certain MC algorithms─notably procedures using an "infinite temperature" reference state─can be successful in a 5-20 parameter BI context which would be unworkable in molecular spaces of 103 coordinates and more. This Review provides a pedagogical introduction to BI and reviews key aspects of BI through a physical lens, setting the computations in terms of energy landscapes and free energy calculations and describing promising sampling algorithms. Statistical mechanics and basic probability theory also provide a reference for understanding intrinsic limitations of Bayesian inference with regard to model selection and the choice of priors.

New Pyranone Derivatives and Sesquiterpenoid Isolated from the Endophytic Fungus Xylaria sp. Z184.

The fungus Xylaria sp. Z184, harvested from the leaves of Fallopia convolvulus (L.) Á. Löve, has been isolated for the first time. Chemical investigation on the methanol extract of the culture broth of the titles strain led to the discovery of three new pyranone derivatives, called fallopiaxylaresters A-C (1-3), and a new bisabolane-type sesquiterpenoid, named fallopiaxylarol A (4), along with the first complete set of spectroscopic data for the previously reported pestalotiopyrone M (5). Known pyranone derivatives (6-11), sesquiterpenoids (12-14), isocoumarin derivatives (15-17), and an aromatic allenic ether (18) were also co-isolated in this study. All new structures were elucidated by the interpretation of HRESIMS, 1D, 2D NMR spectroscopy, and quantum chemical computation approach. The in vitro antimicrobial, anti-inflammatory, and α-glucosidase-inhibitory activities of the selected compounds and the crude extract were evaluated. The extract was shown to inhibit nitric oxide (NO) production induced by lipopolysaccharide (LPS) in murine RAW264.7 macrophage cells, with an inhibition rate of 77.28 ± 0.82% at a concentration of 50 μg/mL. The compounds 5, 7, and 8 displayed weak antibacterial activity against Staphylococcus areus subsp. aureus at a concentration of 100 μM.

Massively scalable workflows for quantum chemistry: BigChem and ChemCloud.

Electronic structure theory, i.e., quantum chemistry, is the fundamental building block for many problems in computational chemistry. We present a new distributed computing framework (BigChem), which allows for an efficient solution of many quantum chemistry problems in parallel. BigChem is designed to be easily composable and leverages industry-standard middleware (e.g., Celery, RabbitMQ, and Redis) for distributed approaches to large scale problems. BigChem can harness any collection of worker nodes, including ones on cloud providers (such as AWS or Azure), local clusters, or supercomputer centers (and any mixture of these). BigChem builds upon MolSSI packages, such as QCEngine to standardize the operation of numerous computational chemistry programs, demonstrated here with Psi4, xtb, geomeTRIC, and TeraChem. BigChem delivers full utilization of compute resources at scale, offers a programable canvas for designing sophisticated quantum chemistry workflows, and is fault tolerant to node failures and network disruptions. We demonstrate linear scalability of BigChem running computational chemistry workloads on up to 125 GPUs. Finally, we present ChemCloud, a web API to BigChem and successor to TeraChem Cloud. ChemCloud delivers scalable and secure access to BigChem over the Internet.

Unveiling geniposide from Paederia foetida as a potential antihypertensive treatment: an integrated approach involving in vivo and computational methods

BackgroundHypertension is one of the burning topics in today’s world. Natural product can open a new window in the treatment as they are lesser side effect compared to synthetic compounds. Paederia foetida a naturally occurring plant has proven its biological importance in many aspects. In this present study, the ethanolic extract of Paederia foetida has effectively proven its antihypertensive activity against Amphetamine-induced hypertension.ResultsThe study was carried out for 4 weeks with five different groups where the groups receiving Paederia foetida (400 mg/kg) for 4 weeks result in decrease in blood pressure and was found helpful in maintaining the sodium and potassium balance in rat’s serum. Amphetamine induces decreasing sodium level that can be countered by Paederia foetida whole plant extract. Geniposide, an active ingredient present in this plant, is having antihypertensive activity, so it was docked against different PDB IDs (3OLL, 3OLS, 5DX3, 5DXE & 6PIT), to find its anti-hypertension effectiveness through computational chemistry. The docking investigations identified that estrogen receptor (PDB ID: 3OLS) exhibited the highest possibility to be the site of action. Docking score of Geniposide with 3OLS was -8.91 which is quit comparable with the internal ligand Estradiol.ConclusionTo assess the binding affinity of Geniposide with the estrogen receptor, an additional molecular dynamics simulation was conducted. The result strongly suggests that Geniposide has the potential to function as an activator of estrogen receptor through of β-ligand binding. This key finding reveals that Geniposide may serve as a potential in the treatment of hypertension by modulating the activity of the estrogen receptor.