Semi-empirical Quantum Mechanical Calculations Research Articles

Assessing the structural, antibacterial, and dissolution properties of 1:1 doxycycline/β-cyclodextrin complexes

A 1:1 β-cyclodextrin (βCD) inclusion complex with doxycycline (DO) as the guest molecule was prepared using kneading (KN) and coprecipitation (CoP) methods, along with a physical mixture (PM) . The complex was characterized through FTIR, ¹H NMR, UV–Vis spectroscopy, and DSC analysis. Semi-empirical quantum mechanical calculations confirmed the inclusion of DO within βCD's hydrophobic cavity. The formation constant, determined by a modified Benesi-Hildebrand equation at 25 °C, was 116.22 M⁻¹ for the βCD-DO complex. Dissolution studies revealed that the CoP method significantly enhanced DO's dissolution kinetics compared to the KN, PM methods, and free DO, highlighting CoP's effectiveness. Molecular docking indicated a stable βCD-DO complex, with binding energies from -5.9570 to -6.1423 kcal/mol, driven by hydrophilic and hydrophobic interactions. Antibacterial testing against E. coli, P. aeruginosa, S. aureus, B. cereus, and B. subtilis showed CoP had the highest efficacy, with MIC values of 5–10 µg/ml and MBC values of 10–20 µg/ml, better than KN, DO, and βCD.

Mechanistic insights into the role of cyclodextrin in the regioselective radical CH trifluoromethylation of aromatic compounds.

The regioselective radical CH trifluoromethylation of aromatic compounds have been shown to proceed in good yield and high regioselectivity when cyclodextrin (CD) is present. Yet, the reaction mechanism and the role of CD during the reaction have remained obscure. To this end, here we performed density functional theory (DFT) calculations to the conformations obtained by semiempirical quantum mechanical molecular dynamics calculations to reveal the reaction mechanism and the role of CD in controlling regioselectivity. The results show that metal salt increases the yield but do not affect the regioselectivity, which we further confirmed by an experiment. In contrast, multiple CD-substrate complex conformations and reaction pathways were obtained, and CD was shown to contribute to improving the regioselectivity by stabilizing the intermediate state via encapsulation. The present study indicates that CDs can increase the regioselectivity by stabilizing the intermediate and product states while only marginally affecting the transition state.

C–H bond dissociation enthalpy prediction with machine learning reinforced semi-empirical quantum mechanical calculations

Abstract We introduce a combined fast semi-empirical quantum mechanical and machine learning (SQM/ML) approach capable of matching the C–H bond dissociation enthalpies (BDEs) computed with the highly accurate (RO)CBS-QB3 method. The usefulness of our proposed SQM/ML model is corroborated by the fact that a single C–H BDE of a molecule is calculated in seconds and the mean absolute error amounts to only 1 to 2 kcal/mol.

Computational and experimental assessment of efficient dye adsorption method from aqueous effluents by halloysite and palygorskite clay minerals.

The removal of dyes from effluents of textile industries represents a technological challenge, due to their significant environmental impact. The application of halloysite (Hal) and palygorskite (Pal) clay minerals as adsorbents for the removal of Congo red (CR) and methylene blue (MB) was evaluated in this work. The materials were applied both in natural and acid-treated forms, and characterized by XRD, XPS, SEM-EDS, FTIR, and N2 adsorption-desorption isotherm techniques to identify their properties and main active sites. The adsorbents showed potential to remove CR (> 98%) and MB (> 85%) within 180min, using 0.3g adsorbent and initial dye concentration of 250mg L-1. Semi-empirical quantum mechanical calculations (SQM) confirmed the interaction mechanism between dyes and the adsorbents via chemisorption (- 69.0kcalmol-1 < Eads < - 28.8kcalmol-1), which was further observed experimentally due to the high fit of adsorption data to pseudo-second order kinetic model (R2 > 0.99) and Langmuir isotherm (R2 > 0.98). The use of Pal and Hal to remove dyes was proven to be economically and environmentally viable for industrial application.

Semi-empirical and machine learning-based prediction of site of metabolisms mediated by aldehyde oxidase

Prediction of drug clearance by aldehyde oxidase (AO) catalyzed metabolism has been of great interest in drug discovery. We report a fast, accurate, and fully-automated tool that combines semi-empirical quantum mechanical (SQM) calculations and machine-learning (ML) technique to predict AO-mediated site of metabolism (SOM). We infer that calculated probability scores together with those based on approximate C(sp2)−H bond dissociation energies accomplish accurate and immediate prediction of the number and rank ordering of SOMs. Tests on a series of 150 AO substrates demonstrate that our computational strategy can be applicable to high-throughput screening of metabolically stable compounds.

The Influence of Large Pendent Groups on Chain Anisotropy and Electrical Energy Loss of Polyimides at High Frequency through All-Atomic Molecular Simulation.

Polyimide is a potential material for high-performance printed circuit boards because of its chemical stability and excellent thermal and mechanical properties. Flexible printed circuit boards must have a low static dielectric constant and dielectric loss to reduce signal loss in high-speed communication devices. Engineering the molecular structure of polyimides with large pendant groups is a strategy to reduce their dielectric constant. However, there is no systematic study on how the large pendant groups influence electrical energy loss. We integrated all-atomic molecular dynamics and semi-empirical quantum mechanical calculations to examine the influence of pendant groups on polymer chain anisotropy and electrical energy loss at high frequencies. We analyzed the radius of gyration, relative shape anisotropy, dipole moment, and degree of polarization of the selected polyimides (TPAHF, TmBPHF, TpBPHF, MPDA, TriPMPDA, m-PDA, and m-TFPDA). The simulation results show that anisotropy perpendicular to chain direction and local chain rigidity correlate to electrical energy loss rather than dipole moment magnitudes. Polyimides with anisotropic pendant groups and significant local chain rigidity reduce electrical energy loss. The degree of polarization correlated well with the dielectric loss with a moderate computational cost, and difficulties in directly calculating the dielectric loss were circumvented.

Barrier Height Prediction by Machine Learning Correction of Semiempirical Calculations.

Different machine learning (ML) models are proposed in the present work to predict density functional theory-quality barrier heights (BHs) from semiempirical quantum mechanical (SQM) calculations. The ML models include a multitask deep neural network, gradient-boosted trees by means of the XGBoost interface, and Gaussian process regression. The obtained mean absolute errors are similar to those of previous models considering the same number of data points. The ML corrections proposed in this paper could be useful for rapid screening of the large reaction networks that appear in combustion chemistry or in astrochemistry. Finally, our results show that 70% of the features with the highest impact on model output are bespoke predictors. This custom-made set of predictors could be employed by future Δ-ML models to improve the quantitative prediction of other reaction properties.

Reliable prediction of crystal density of high nitrogen-containing organic compounds as powerful, less sensitive, eco-friendly energetic materials for dependable assessment of their performance

Many efforts have been done in recent years to seek more powerful, less sensitive, eco-friendly high nitrogen-containing organic compounds (HNCOCs). It is essential to have reliable methods for the prediction of crystal density of the new proposed HNCOCs because increasing density is associated with increased performance. This work uses structural parameters of HNCOCs and semiempirical quantum mechanical calculations using the PM6 method for reliable prediction of their densities at or near room temperature. A core correlation is introduced for the calculation of the density of HNCOCs based on the number of functional groups –NH2, >NH, –OH, and –N3 as well as the calculated density from the PM6 method. The reliability of the core correlation is improved by considering two non-additive correcting functions, which can increase or decrease the predicted values of core density. The reported densities of 137 HNCOCs as training (117 compounds) and test (20 chemicals) sets are used to establish the new model. High reliability of the core and improved models are compared with the computed results of the best available molecular surface electrostatic potential (MESP)-based method. The values of Root Mean Square Error (RMSE) for the improved model and comparative MESP-based method are 0.0472 and 0.0951 g/cm3, respectively. High reliability of the new method was also confirmed as compared to four MESP-based models for 32 compounds of external test data set where RMSE of the improved model (0.0674 g/cm3) is smaller than four MSPE-based methods, i.e. 0.126, 0.276, 0.0716, and 0.0703 g/cm3.

Fast Screening of Minimum Energy Crossing Points with Semiempirical Tight-Binding Methods.

The investigation of photochemical processes is a highly active field in computational chemistry. One research direction is the automated exploration and identification of minimum energy conical intersection (MECI) geometries. However, due to the immense technical effort required to calculate nonadiabatic potential energy landscapes, the routine application of such computational protocols is severely limited. In this study, we will discuss the prospect of combining adiabatic potential energy surfaces from semiempirical quantum mechanical calculations with specialized confinement potential and metadynamics simulations to identify S0/T1 minimum energy crossing point (MECP) geometries. It is shown that MECPs calculated at the GFN2-xTB level can provide suitable approximations to high-level S0/S1ab initio conical intersection geometries at a fraction of the computational cost. Reference MECIs of benzene are studied to illustrate the basic concept. An example application of the presented protocol is demonstrated for a set of photoswitch molecules.

A Data Resource for Prediction of Gas-Phase Thermodynamic Properties of Small Molecules

The thermodynamic properties of a substance are key to predicting its behavior in physical and chemical systems. Specifically, the enthalpy of formation and entropy of a substance can be used to predict whether reactions involving that substance will proceed spontaneously under conditions of constant temperature and pressure, and if they do, what the heat and work yield of those reactions would be. Prediction of enthalpy and entropy of substances is therefore of value for substances for which those parameters have not been experimentally measured. We developed a database of 2869 experimental values of enthalpy of formation and 1403 values for entropy for substances composed of stable small molecules, derived from the literature. We developed a model for predicting enthalpy of formation and entropy from semiempirical quantum mechanical calculations of energy and atom counts, and applied the model to a comprehensive database of 16,417 small molecules. The database of small-molecule thermodynamic properties will be useful for predicting the outcome of any process that might involve the generation or destruction of volatile products, such as atmospheric chemistry, volcanism, or waste pyrolysis. Additionally, the collected experimental thermodynamic values will be of value to others developing models to predict enthalpy and entropy.

Making Molecules Vibrate: Interactive Web Environment for the Teaching of Infrared Spectroscopy

Infrared spectroscopy (IR) is a staple structural elucidation and characterization technique because of its ability to identify functional groups and its ease of use. Interestingly, it allows the capture of electronic effects via their influence on the bond strength of “probes”, such as the carbonyl group and also offers a wealth of examples for discussion on the theory of electronic transitions. For this reason, IR spectroscopy is typically taught both in theoretical classes and in applied structural analysis courses. In practice, there is rarely a link between those courses, and both suffer from the lack of exploratory learning, that is, tools with which students can explore the interplay between symmetry and selection rules, as well as electronic effects and vibrational frequencies─with almost immediate feedback. In practice, this might lead to students that are well skilled in looking up vibrational frequencies in lookup tables but do not understand the links to electronic effects and reactivity. Here, we introduce a web app that leverages semiempirical quantum mechanical (or force-field based) calculations, performed on a web service, in an interactive interface to provide an environment in which students can explore how slight changes to the structure manifest in changes of the spectrum. This approach avoids the time-consuming handling of potentially hazardous materials that might not be readily available and invites students to play with spectroscopy─to “see” and “test” electronic effects that are so commonplace in organic chemistry education. As a “side effect”, our web app also provides a powerful aid for research scientists to investigate how different structural modifications, such as substitution, isomerism, or steric strain, would manifest in the infrared spectrum.

Semiempirical Molecular Modeling Analyses for Graphene/Nickel Oxide Nanocomposite

Graphene metal oxide composite show applications in many areas according to their excellent electronic properties. Accordingly, graphene was subjected to interaction with NiO as adsorbing and a complex state. Semiempirical quantum mechanical calculations at PM6 was used to study the total energy, the final heat of formation, ionization energy, total dipole moment as well the charge distribution of the surface of graphene metal oxide composite. Calculated data indicated that graphene symmetry is changed into the C1 point group, forming stable composites with NiO that the complex composites were more stable compared with adsorb state composites. The change in the partial charge of graphene is responsible for the change in total dipole moment and hence affecting the increase in the reactivity of graphene/nickel oxide composite.

Aromatic Stacking Facilitated Self-Assembly of Ultrashort Ionic Complementary Peptide Sequence: β-Sheet Nanofibers with Remarkable Gelation and Interfacial Properties.

Understanding peptide self-assembly mechanisms and stability of the formed assemblies is crucial for the development of functional nanomaterials. Herein, we have adopted a rational design approach to demonstrate how a minimal structural modification to a nonassembling ultrashort ionic self-complementary tetrapeptide FEFK (Phe4) remarkably enhanced the stability of self-assembly into β-sheet nanofibers and induced hydrogelation. This was achieved by replacing flexible phenylalanine residue (F) by the rigid phenylglycine (Phg), resulting in a constrained analogue PhgEPhgK (Phg4), which positioned aromatic rings in an orientation favorable for aromatic stacking. Phg4 self-assembly into stable β-sheet ladders was facilitated by π-staking of aromatic side chains alongside hydrogen bonding between backbone amides along the nanofiber axis. The contribution of these noncovalent interactions in stabilizing self-assembly was predicted by in silico modeling using molecular dynamics simulations and semiempirical quantum mechanics calculations. In aqueous medium, Phg4 β-sheet nanofibers entangled at a critical gelation concentration ≥20 mg/mL forming a network of nanofibrous hydrogels. Phg4 also demonstrated a unique surface activity in the presence of immiscible oils and was superior to commercial emulsifiers in stabilizing oil-in-water (O/W) emulsions. This was attributed to interfacial adsorption of amphiphilic nanofibrils forming nanofibrilized microspheres. To our knowledge, Phg4 is the shortest ionic self-complementary peptide rationally designed to self-assemble into stable β-sheet nanofibers capable of gelation and emulsification. Our results suggest that ultrashort ionic-complementary constrained peptides or UICPs have significant potential for the development of cost-effective, sustainable, and multifunctional soft bionanomaterials.

A Computational Study on the Effects of Molecular Structures of Di-n-butyldithiophosphate and of its Derivatives on the Stability of Their Complex Compounds with Rare-Earth Elements

The stability of complex compounds formed from the ligand di-n-butyldithiophosphate (DBDTP) and its derivatives, with ions of rare-earth elements (REEs), such as gadolinium ion (Gd3+), is an important factor in the separation and purification processes of the elements using solvent extraction method. The complex stability is dependent, one of which, on the partial charge of the donor atom (S atom in this case) in the molecule of DBDTP or its derivatives. The more negative the partial charge of the donor atom, the more stable is the complex compound formed. The purpose of this study is to explore the effect of electron donating, and of electron withdrawing groups, as well as the effect of the structure of the butyl group in the molecules of DBDTP and or its derivatives on the partial charge of the donor atom. The method used was the semi empirical quantum mechanical calculations, i.e. the Austin Model 1 (AM1). The results of the study showed that the electron withdrawing group of -CN had resulted in the most positive charge on the donor atom, if it is on the second carbon atom of the butyl group in the DBDTP and or its derivatives. Conversely, in the same carbon atom position, the donating electron group of -CH=CH2 had generated the most negative partial charge on the donor atom. Furthermore, the results of this study also revealed that the sec-butyl isomer produced the most negative partial charge on the donor atom, among other isomers.

Structural diversification during glucosinolate breakdown: mechanisms of thiocyanate, epithionitrile and simple nitrile formation.

SummarySecondary metabolism is characterized by an impressive structural diversity. Here, we have addressed the mechanisms underlying structural diversification upon damage‐induced activation of glucosinolates, a group of thioglucosides found in the Brassicales. The classical pathway of glucosinolate activation involves myrosinase‐catalyzed hydrolysis and rearrangement of the aglucone to an isothiocyanate. Plants of the Brassicaceae possess specifier proteins, i.e. non‐heme iron proteins that promote the formation of alternative products by interfering with this reaction through unknown mechanisms. We have used structural information available for the thiocyanate‐forming protein from Thlaspi arvense (TaTFP), to test the impact of loops protruding at one side of its β‐propeller structure on product formation using the allylglucosinolate aglucone as substrate. In silico loop structure sampling and semiempirical quantum mechanical calculations identified a 3L2 loop conformation that enabled the Fe2+ cofactor to interact with the double bond of the allyl side chain. Only this arrangement enabled the formation of allylthiocyanate, a specific product of TaTFP. Simulation of 3,4‐epithiobutane nitrile formation, the second known product of TaTFP, required an alternative substrate docking arrangement in which Fe2+ interacts with the aglucone thiolate. In agreement with these results, substitution of 3L2 amino acid residues involved in the conformational change as well as exchange of critical amino acid residues of neighboring loops affected the allylthiocyanate versus epithionitrile proportion obtained upon myrosinase‐catalyzed allylglucosinolate hydrolysis in the presence of TaTFP in vitro. Based on these insights, we propose that specifier proteins are catalysts that might be classified as Fe2+‐dependent lyases.

In Silico Identification of JMJD3 Demethylase Inhibitors.

In the search for new demethylase inhibitors, we have developed a multistep protocol for in silico screening. Millions of poses generated by high-throughput docking or a 3D-pharmacophore search are first minimized by a classical force field and then filtered by semiempirical quantum mechanical calculations of the interaction energy with a selected set of functional groups in the binding site. The final ranking includes solvation effects which are evaluated in the continuum dielectric approximation (finite-difference Poisson equation). Application of the multistep protocol to JMJD3 jumonji demethylase has resulted in a dozen low-micromolar inhibitors belonging to five different chemical classes. We have solved the crystal structure of JMJD3 inhibitor 8 in the complex with UTX (a demethylase in the same subfamily as JMJD3) which validates the predicted binding mode. Compound 8 is a promising candidate for future optimization as it has a favorable ligand efficiency of 0.32 kcal/mol per nonhydrogen atom.

Interface Interactions of the Bowman–Birk Inhibitor BTCI in a Ternary Complex with Trypsin and Chymotrypsin Evaluated by Semiempirical Quantum Mechanical Calculations

Black‐eyed pea trypsin and chymotrypsin inhibitor (BTCI) is a small protein from Bowman–Birk protease inhibitor family, which simultaneously inhibits trypsin and chymotrypsin. Through the inhibition of trypsin‐ and chymotrypsin‐like sites on the 20S subunit of human proteasome, BTCI acts as a potent anticarcinogenic agent inducing apoptosis in breast cancer cells. Because of the lack of crystallographic information of the BTCI‐proteasome complex, we analyze here the BTCI‐chymotrypsin and BTCI‐trypsin interfaces using computations. We adopt the corrected semiempirical quantum‐mechanical methods in combination with implicit description of the water environment. Firstly, we carefully check the representativeness of smaller systems by fragmentation and analyzing the convergence of the overall interaction energies. Then, we use the “virtual glycine scan” technique to understand the binary complex formation, identifying the most contributing amino acid side chains in the interfaces. Besides detailed quantification of all important residue contributions, the importance of Lys26 and Phe53 in the BTCI and Asp186 (trypsin) and Ser195 (chymotrypsin) residues is confirmed. In summary, we have worked out an accurate and efficient in silico protocol for protein–protein interfaces, which can be later used for studying the inhibition of 20S proteasome.

How does graphene nanosheet affect the pharmaceutical adsorption? A comprehensive insight from molecular dynamics simulation, quantum mechanics and experimental study

Tetracycline (TTC) is a type of antimicrobials that have been utilized to remedy illness. TTCs, having been released into the environs, increase the potential hazard to ecosystems and physical environments. This work has evaluated the superiority of the new generation carbon allotropes family adsorbent, graphene nanosheet (GNS) to remove TTC from the water. The pH of the adsorption system, shaking time, the initial concentration of TTC and the temperature of the solution effects were experimentally investigated. To investigate the behavior of TTC on GNS surface, the simulation of GNS and TTC was done by LAMMPS. The Semi-empirical quantum mechanics calculations were also accomplished to recognize the reliability of the MD simulation applied here. The experimental, theoretical and computational study has been performed showing TTC could be efficiently adsorbed on graphene surface via π- π interaction in low and electrostatic interactions at higher temperatures. We envisage these results to build a basis for the design of a novel system based adsorption as an economical remedy for the elimination of pharmaceuticals from wastewater. Our obtained finding is a criterion for addressing experimental work, which is generally confined by the problems associated with creating novel adsorptive systems of a specific new generation of water purification.

Rational Design, Synthesis and Evaluation of γ-CD-Containing Cross-Linked Polyvinyl Alcohol Hydrogel as a Prednisone Delivery Platform.

This study describes the in-silico rational design, synthesis and evaluation of cross-linked polyvinyl alcohol hydrogels containing γ-cyclodextrin (γ-CDHSAs) as platforms for the sustained release of prednisone (PDN). Through in-silico studies using semi-empirical quantum mechanical calculations, the effectiveness of 20 dicarboxylic acids to generate a specific cross-linked hydrogel capable of supporting different amounts of γ-cyclodextrin (γ-CD) was evaluated. According to the interaction energies calculated with the in-silico studies, the hydrogel made from PVA cross-linked with succinic acids (SA) was shown to be the best candidate for containing γ-CD. Later, molecular dynamics simulation studies were performed in order to evaluate the intermolecular interactions between PDN and three cross-linked hydrogel formulations with different proportions of γ-CD (2.44%, 4.76% and 9.1%). These three cross-linked hydrogels were synthesized and characterized. The loading and the subsequent release of PDN from the hydrogels were investigated. The in-silico and experimental results showed that the interaction between PDN and γ-CDHSA was mainly produced with the γ-CDs linked to the hydrogels. Thus, the unique structures and properties of γ-CDHSA demonstrated an interesting multiphasic profile that could be utilized as a promising drug carrier for controlled, sustained and localized release of PDN.

Comparative study of the antioxidant properties of monocarbonyl curcumin analogues C66 and B2BrBC in isoproteranol induced cardiac damage

AimTo test the antioxidant properties of the newly synthesized (2E,6E)-2,6-bis(2-bromobenzylidene)cyclohexanone (B2BrBC) in parallel with C66 in rats with cardiac hypertrophy. Materials and methodsThe protective effects of both C66 and B2BrBC against oxidative stress in rats with cardiac hypertrophy, was studied by evaluating the activity of antioxidant enzymes, the relationship between the ratio of the activities of the antioxidant enzymes R = SOD/(GPx + CAT) and levels of thiols and lipid peroxidation in the heart. In order to gain better understanding of the antioxidant properties of the studied compounds, computational methods were utilized. The properties of selected structurally related derivatives were obtained on optimized geometries for ground states, using semi-empirical PM3 quantum mechanical calculations. Key findingsThe ratio R shows disequilibrium in rats with induced hypertrophy (p < 0.001). Coextending changes were detected in total and free sulfhydryl group content (p = 0.011 for t-SH and p = 0.008, for free SH, respectively). The results with the B2BrBC, indicated strong thiol prevention reflected in the levels of both t-SH and f-SH. Taking into account the HOMO energies of B2BrBC (−9.398 eV) and C66 (−9.667), it can be concluded that B2BrBC has lower HOMO energy, which makes it a better electron donor and a better antioxidant. SignificanceThe obtained results indicated that the antioxidant ability of B2BrBC is positively associated with the catalytic SOD and GPx activities expressed through preserved t-SH levels. It seems plausible that for a compound to exhibit antioxidant activity, as most of the 2,6-bis(benzylidene)cyclohexanones do, they should be good electron donors. Impact statementUnderstanding the relationship between cardiac hypertrophy induced oxidative injuries and supporters of endogenous reparatory machinery will help in establishing the beneficial role of adequate antioxidant supplementation. In this study reliable data on the preventive effects of newly synthesized symmetric monocarbonyl curcumin analogue B2BrBC and its role in the prevention of oxidative injuries on three levels (enzymatic, protein and lipid), in the heart hypertrophic onset, were obtained.