Computational Chemistry Tools Research Articles

Alcohol-DES based vortex assisted homogenous liquid-liquid microextraction approach for the determination of total selenium in food samples by hydride generation AAS: Insights from theoretical and experimental studies

This research article proposes a simple, quick, green and cheap approach for the determination of total selenium in food samples using alcohol-DES based vortex-assisted homogenous liquid-liquid microextraction (alcohol-DES-VA-HLLME) combination with hydride generation atomic absorption spectrometry (HG AAS). Analyte, complexing agent and working pH were Se(IV), Sudan-II and pH 4.0, respectively. In order to analyze the nature of the chemical interaction between Se(IV) ion and Sudan-II ligand, experimental results were supported with computational chemistry tools calculating quantum chemical descriptors like frontier orbital energies, hardness, softness, electronegativity, electrophilicity, nucleophilicity, transferred electron from the ligand to ion, electron donating power, electron accepting power, complexation energy, molecule-ion interaction energy. In addition, the alcohol-DES-VA-HLLME method was earned a good detection limit of 3.5 ng L−1 and a wide calibration curve in the concentration range of 12–300 ng L−1 (r: 0.9981). The validity of the method was evaluated by analyzing the two standard reference material (SRMs). The recoveries and relative standard deviations for 25 and 100 ng L−1 of Se(IV) (N:5) were in the range of 1.2–2.5% and 92.1–103.7% respectively, which proved acceptable. The analytical results showed that the proposed method had important features such as cheapness, green, quick extraction and reuse, which made it attractive for the determination and efficient extraction of total selenium in the food samples.

The Quest for Highly Accurate Excitation Energies: A Computational Perspective.

We provide an overview of the successive steps that made it possible to obtain increasingly accurate excitation energies with computational chemistry tools, eventually leading to chemically accurate vertical transition energies for small- and medium-size molecules. First, we describe the evolution of ab initio methods employed to define benchmark values, with the original Roos CASPT2 method, then the CC3 method as in the renowned Thiel set, and more recently the resurgence of selected configuration interaction methods. The latter method has been able to deliver consistently, for both single and double excitations, highly accurate excitation energies for small molecules, as well as medium-size molecules with compact basis sets. Second, we describe how these high-level methods and the creation of representative benchmark sets of excitation energies have allowed the fair and accurate assessment of the performance of computationally lighter methods. We conclude by discussing possible future theoretical and technological developments in the field.

Simple Automatized Tool for Exchange-Correlation Functional Fitting.

Density functional theory is, perhaps, the most popular and convenient tool in computational chemistry. DFT methods allow solving different chemical tasks with a good balance of accuracy and computational time. Dozens of existing functionals cover a majority of possible systems, and the development of new ones is still ongoing. However, despite the existence of different databases with accurate quantum-chemical data, the functional design remains a complicated and time-demanding task. Here, we propose a novel approach for simplifying and accelerating this process. The approach is based on a Bayesian search with stochastic sub-sampling that allows considering the 'history' of fitting steps, reduces the computational time for each step, and avoids overfitting to training data. Besides the general testing of the approach efficiency, we also showed an example of training specialized DFT functionals, outperforming the popular ones. The approach is presented as a free code with built-in analysis tools. Using the code with an appropriate reference database can help in constructing a DFT approximation for a highly specialized task.

In silico Studies, Synthesis and Antitubercular Activity of Some Novel Quinoline - Azitidinone Derivatives.

Diarylquinolines like Bedaquiline have shown promising antitubercular activity by their action of Mycobacterial ATPase. The structural features necessary for a good antitubercular activity for a series of quinoline derivatives were explored through computational chemistry tools like QSAR and combinatorial library generation. In the current study, 3-Chloro-4-(2-mercaptoquinoline-3-yl)-1- substitutedphenylazitidin-2-one derivatives have been designed and synthesized based on molecular modeling studies as anti-tubercular agents. 2D and 3D QSAR analyses were used to designed compounds having a quinoline scaffold. The synthesized compounds were evaluated against active and dormant strains of Mycobacterium tuberculosis (MTB) H37 Ra and Mycobacterium bovis BCG. The compounds were also tested for cytotoxicity against MCF-7, A549 and Panc-1 cell lines using MTT assay. The binding affinity of designed compounds was gauged by molecular docking studies. Statistically significant QSAR models generated by the SA-MLR method for 2D QSAR exhibited r2 = 0.852, q2 = 0.811, whereas 3D QSAR with SA-kNN showed q2 = 0.77. The synthesized compounds exhibited MIC in the range of 1.38-14.59(μg/ml). These compounds showed some crucial interaction with MTB ATPase. The present study has shown some promising results which can be further explored for lead generation.

Developing technological pedagogical science knowledge through educational computational chemistry: a case study of pre-service chemistry teachers’ perceptions

The purpose of this descriptive case study was to develop pre-service chemistry teachers’ Technological Pedagogical Science Knowledge (TPASK) through novel computational chemistry modules. The study consisted of two phases starting with designing a computational chemistry based learning environment followed by a case study where students’ perceptions towards educational computational chemistry were explored. First, we designed an authentic research-based chemistry learning module that supported problem-based learning through the utilisation of computational chemistry methods suitable for pre-service chemistry education. The objective of the learning module was to promote learning of specific chemistry knowledge and development of scientific skills. Systematic design decisions were made through the TPASK framework. The learning module was designed for a third-year physical chemistry course taken by pre-service chemistry teachers in Chile. After the design phase, the learning module was implemented in a course, and students’ perceptions were gathered using semi-structured group interviews. The sample consisted of 22 pre-service chemistry teachers. Data were analysed through qualitative content analysis using the same TPASK framework employed in the learning module design. Based on our findings, pre-service chemistry teachers first acquired Technological Scientific Knowledge (TSK) and then developed some elements of their TPASK. Besides, they highly appreciated the combination of student-centred problem-based learning and the use of computational chemistry tools. Students felt the educational computational learning environment supported their own knowledge acquisition and expressed an interest in applying similar learning environments in their future teaching careers. This case study demonstrates that learning through authentic real-world problems using educational computational methods offers great potential in supporting pre-service teachers’ instruction in the science of chemistry and pedagogy. For further research in the TPASK framework, we propose there would be significant benefit from developing new learning environments of this nature and evaluating their utility in pre-service and in-service chemistry teacher's education.

SN2 Reactions of N2O5 with Ions in Water: Microscopic Mechanisms, Intermediates, and Products.

Reactions of dinitrogen pentoxide (N2O5) greatly affect the concentrations of NO3, ozone, OH radicals, methane, and more. In this work, we employ ab initio molecular dynamics and other tools of computational chemistry to explore reactions of N2O5 with anions hydrated by 12 water molecules to shed light on this important class of reactions. The ions investigated are Cl-, SO42-, ClO4-, and RCOO- (R = H, CH3, C2H5). The following main results are obtained: (i) all the reactions take place by an SN2-type mechanism, with a transition state that involves a contact ion pair (NO2+NO3-) that interacts strongly with water molecules. (ii) Reactions of a solvent-separated nitronium ion (NO2+) are not observed in any of the cases. (iii) An explanation is provided for the suppression of ClNO2 formation from N2O5 reacting with salty water when sulfate or acetate ions are present, as found in recent experiments. (iv) Formation of novel intermediate species, such as (SO4NO2-) and RCOONO2, in these reactions is predicted. The results suggest atomistic-level mechanisms for the reactions studied and may be useful for the development of improved modeling of reaction kinetics in aerosol particles.

Tuning Electronic Structure in Layered Hybrid Perovskites with Organic Spacer Substitution.

Two-dimensional layered halide organic perovskites (LHOPs) are promising candidates for many optoelectronic applications due to their interesting tunable properties. They provide a unique opportunity to control energy and charge dynamics via the independent tunability of the energy levels within the perovskite and the organic spacer for various optoelectronic applications. In the perovskite layer alone, one can replace the Pb (Sn), the halide (X = I, Br, Cl), the organic component, and the number of layers between the organic spacer layers. In addition, there are many possibilities for organic spacer layers between the perovskite layers, making it difficult for experimental methods to comprehensively explore such anextensive combinatorial space. Of particular technological interest is alignment of electronic levels between the perovskite layer and the organic spacer layer, leading to desired transfer of energy or charge carriers between perovskite and organic components. For example, as band edge absorption is almost entirely attributed to the perovskite layer, one way to demonstrate energy transfer is to observe triplet emission from organic spacers. State-of-the-art computational chemistry tools can be used to predict the properties of many stoichiometries in search for LHOPs that have the most promising electronic-structure features. In this first-principles study, we survey a group of π-conjugated organic spacer candidates for use in triplet light-emitting LHOPs. Utilizing density functional theory (DFT) and time-dependent DFT, we calculate the first singlet (S1) and triplet (T1) excitation energy in the ground-state geometry and the first triplet excitation energy in the excited-triplet-state relaxed geometry (T1*). By comparing these energies to the known lowest exciton energy level of PbnX3n+1 perovskite layers (X = I, Br, Cl), we can identify organic spacer and perovskite layer pairings for possible transfer of Wannier excitons from the inorganic perovskite lattice to spin-triplet Frenkel excitons located on the organic cation. We successfully identify ten organic spacer candidates for possible pairing with perovskite layers of specific halide composition to achieve triplet light emission across the visible energy range. Molecular dynamics simulations predict that finite temperatures and perovskite environment have little influence on the average excitation energies of the two common organic spacers naphthylethylammonium (NEA) and phenelethylammonium (PEA). We find significant thermal broadening up to 0.5 eV of the optical excitation energies appearing due to finite temperature effects. The findings herein provide insights into alignment of electronic levels of the conjugated organic spacer with the layer.

Molecular designing of naphthalene diimide based fullerene-free small organic solar cell - Acceptors with high photovoltaic performance by density functional theory.

With the help of computational chemistry tools, three non-fullerene acceptors, which are 2-methylene-malononitrile (M-1), 2-(3-methyl-5-methylene-2-thioxothiazolidin-4-ylidene) malononitrile (M-2) and 1-methyl-5-methylene-2,6-dioxo-1,2,5,6-tetrahydropyridine-3-carbonitrile (M-3), are designed with naphthalene diimide (NDI) central unit. Their different photovoltaic and optoelectronic properties like absorption spectrum, electrons density, solubility strength, reorganization energies, % ETC from donor to acceptor part, excitation energies, oscillating strength, morphology and crystallinity of device for constructing the thin film bulk hetro junction devices were computed at the WB97XD/6-31 G (d, p) level of density functional theory (DFT). Expected open circuit voltages of designed molecules are high as 4.05 eV to 4.49 eV, which are significantly larger than that of the previously reported 3-methyl-5-methylene-2-thioxothiazolidin-4-one (R) with the value of 3.60 eV at the zero current level. Charge carrier mobilities of designed molecules are high due to having low re-organization energies varying from 0.0163 eV to 0.0280 eV for electron and 0.0160 eV to 0.0190 eV for hole, strong absorption properties between the 420 nm to 550 nm in chloroform and 400 nm to 540 nm in gas phase conditions, respectively.

Computational chemistry methods to investigate the effects caused by DNA variants linked with disease

Computational chemistry offers variety of tools to study properties of biological macromolecules. These tools vary in terms of levels of details from quantum mechanical treatment to numerous macroscopic approaches. Here, we provide a review of computational chemistry algorithms and tools for modeling the effects of genetic variations and their association with diseases. Particular emphasis is given on modeling the effects of missense mutations on stability, conformational dynamics, binding, hydrogen bond network, salt bridges, and pH-dependent properties of the corresponding macromolecules. It is outlined that the disease may be caused by alteration of one or several of above-mentioned biophysical characteristics, and a successful prediction of pathogenicity requires detailed analysis of how the alterations affect the function of involved macromolecules. The review provides a short list of most commonly used algorithms to predict the molecular effects of mutations as well.

Insights Into the Origin of Life: Did It Begin from HCN and H2O?

The seminal Urey–Miller experiments showed that molecules crucial to life such as HCN could have formed in the reducing atmosphere of the Hadean Earth and then dissolved in the oceans. Subsequent proponents of the “RNA World” hypothesis have shown aqueous HCN to be the starting point for the formation of the precursors of RNA and proteins. However, the conditions of early Earth suggest that aqueous HCN would have had to react under a significant number of constraints. Therefore, given the limiting conditions, could RNA and protein precursors still have formed from aqueous HCN? If so, what mechanistic routes would have been followed? The current computational study, with the aid of the ab initio nanoreactor (AINR), a powerful new tool in computational chemistry, addresses these crucial questions. Gratifyingly, not only do the results from the AINR approach show that aqueous HCN could indeed have been the source of RNA and protein precursors, but they also indicate that just the interaction of HCN with water would have sufficed to begin a series of reactions leading to the precursors. The current work therefore provides important missing links in the story of prebiotic chemistry and charts the road from aqueous HCN to the precursors of RNA and proteins.

Quantum layer boosts machine-learning predictions

Computational chemistry lets chemists predict molecules’ properties without measuring them in the lab. Some of the most accurate computational chemistry tools use quantum chemistry, but these calculations can be time consuming. At the American Chemical Society national meeting in Orlando, Florida last week, David J. Yaron of Carnegie Mellon University described a way to speed up calculation of molecular properties by combining quantum chemistry with machine learning. By adding a quantum chemistry “layer” to a machine-learning algorithm to predict the dipole, atomic charge, and other properties of a molecule, Yaron said, his team was able to reduce calculation errors by as much as two-thirds relative to standard machine-learning algorithms. Yaron said he wants chemists to be able to model organic molecules quickly without sacrificing accuracy. When calculations have to run overnight or over a weekend it can slow down discovery, he said. Typically, machine-learning algorithms can predict an unknown molecule’s

Constructing Bridges between Computational Tools in Heterogeneous and Homogeneous Catalysis

Better catalysts are needed to address numerous challenges faced by humanity. In this Perspective, we review concepts and tools in theoretical and computational chemistry that can help to accelerate the rational design of homogeneous and heterogeneous catalysts. In particular, we focus on the following three topics: (1) identification of key intermediates and transition states in a reaction using the energetic span model, (2) disentanglement of factors influencing the relative stability of the key species using energy decomposition analysis and the activation strain model, and (3) discovery of new catalysts using volcano relationships. To facilitate wider use of these techniques across different areas, we illustrate their potentials and pitfalls when applied to the study of homogeneous and heterogeneous catalysts.

Fundamental Chemistry Behind Rechargeable Li-Based Batteries and Beyond: Theoretical and Computational Chemistry Approach

: Motivated by energy security and environmental concerns, policymakers around the world are directing attention to electrified vehicles as a partial solution to reducing fossil fuel consumption and carbon emissions. In recent years, rechargeable Li-ion batteries emerge as an important power source, and their performance highlights the adoption into the automobile electrical vehicles (EV) sector as viable alternatives to combustion engines. The success of market penetration of these environmental-friendly vehicles strongly depends on the industrialization of new energy-storage technologies that must meet at the same time, performances, security, durability and cost requirements. However, the performance of today's battery chemistries relative to the battery weight is a limiting factor – there is a gap to close between today's technology and the requirements of future battery vehicles. The next-generation energy storage systems may be based on novel chemistries, such as all-solid-state, Li-ion, Li-S, and metal-oxygen, to achieve significantly higher energy density. Materials and their interfaces in these batteries are often the key limiting factors and origins of failures. For example, the degradation at the electrolyte-electrode interfaces causes poor cyclability, low capacitance, and premature failure in these new battery systems. It is crucial to focus on research which aligned towards, robust reaction kinetics modelling and its possible validation through experiments and discovering future advanced (Li, Si, Na-ion) batteries based on material chemistry and electrolyte design. To reveal such details, a fuller understanding of how the materials composition interact at the molecular level, which is not investigated in great detail so far is highly recommended by considering cost-effective computational chemistry tools (DFT, MD). Interaction of decomposed electrolyte/salt components with Li-ion is the key source of formation of SEI layer at the anode surfac such as LiF, Li2CO3, LiO, etc. To understand the stability and chemistry of SEI layer which is in general very complex, atomic level molecular chemistry simulation were performed. The perfect SEI would be a fast forming, flexible, stable and insoluble species with minimum electronic and maximum ionic conductivity. We use computational techniques to understand the limiting factors- such as reaction kinetics and chemistry involved for solid electrolyte interphase (SEI) growth and failure mechanisms at the interfaces, for the improvement of capacity and power fade to the new energy-storage technologies Fig. 1 [1]. [1] M. Datt Bhatt and C. O’Dwyer, “Recent progress in theoretical and computational investigations of Li-ion battery materials and electrolytes,” Phys. Chem. Chem. Phys., vol. 17, no. 7, pp. 4799–4844, 2015. Figure 1

Experimental Protocols for Generating Focused Mutant Libraries and Screening for Thermostable Proteins.

Many proteins are rapidly deactivated when exposed to high or even ambient temperatures. This cannot only impede the study of a particular protein, but also is one of the major reasons why enzyme catalysis is still widely unable to compete with established chemical processes. Furthermore, differences in protein stability are a challenge in synthetic biology, when individual modules prove to be incompatible. The targeted stabilization of proteins can overcome these hurdles, and protein engineering techniques are more and more reliably supported by computational chemistry tools. Accordingly, algorithms to predict the differences in folding energy of a mutant compared to the wild-type, ΔΔGfold, are used in the highly successful FRESCO workflow. The resulting single mutant prediction library consists typically of a few hundred amino acid exchanges, and after combining the most successful hits we so far obtained stabilized mutants which exhibited increases in apparent melting temperature of 20-35°C and showed vastly increased half-lives, as well as resistance to cosolvents. Here, we report a detailed protocol to generate these mutant libraries experimentally, covering the entire workflow from primer design, through mutagenesis, protein production and screening, to mutation combination strategies. The individual parts of the method are furthermore applicable to many other scenarios besides protein stabilization, and these protocols are valuable for any project requiring individual or semi high-throughput site-directed mutagenesis, protein expression and purification, or generation of mutant combination libraries.

First-Principles Investigation of Lithium Polysulfide Structure and Behavior in Solution

The lithium–sulfur battery is a promising next generation energy storage technology that could meet the demands of modern society with a theoretical specific energy near 2500 W h kg–1. However, this battery chemistry faces unique problems such as the parasitic polysulfide shuttle reaction, which hinders battery performance severely. This shuttle phenomenon is caused by solubilities of intermediate reaction products in the electrolyte during the reduction chemistry of the battery. With molecular simulation and computational chemistry tools, we studied the thermodynamics, solvation structure, and dynamics of the long-chain lithium polysulfide species Li2S6 and Li2S8 in dimethoxyethane and 1,3-dioxolane to gain a deeper fundamental understanding of this process. We determined the structure of the first solvation shell for Li+ as well as those of Li2S6, Li2S8 closed, and Li2S8 linear in pure solvents and solvents with extra Li+ added. The lithium polysulfide species were found not to favor dissociation and wo...

Multiscale Molecular Simulations Applied to Nucleic Acid-Dendrimer Interactions Studies

As shown in the present review, molecular dynamics simulations can contribute by studying at theoretical level dendrimer-nucleic acid complexes at different conditions, such as pH or ionic strength. Therefore, different cell conditions such as the stay at the cytoplasm and the transit towards endosomes can be addressed. The influence of different terminal groups of dendrimers to DNA/RNA binding can also be evaluated using molecular simulations and especially, by using free energy methods, which aim to determine affinity of dendrimers for nucleic acids. The development of a library of terminal groups for dendrimers may represent a significant contribution to the design of new dendrimers. In this regard, protein-DNA interactions of structure databases have been analyzed as a way to identify suitable residues that can be incorporated as terminal groups of dendrimers. In summary, computational chemistry and biology tools will aim the design of new dendrimers for different kinds of cargo molecules.

Automatic procedure for generating symmetry adapted wavefunctions

Automatic detection of point groups as well as symmetrisation of molecular geometry and wavefunctions are useful tools in computational quantum chemistry. Algorithms for developing these tools as well as an implementation are presented. The symmetry detection algorithm is a clustering algorithm for symmetry invariant properties, combined with logical deduction of possible symmetry elements using the geometry of sets of symmetrically equivalent atoms. An algorithm for determining the symmetry adapted linear combinations (SALCs) of atomic orbitals is also presented. The SALCs are constructed with the use of projection operators for the irreducible representations, as well as subgroups for determining splitting fields for a canonical basis. The character tables for the point groups are auto generated, and the algorithm is described. Symmetrisation of molecules use a projection into the totally symmetric space, whereas for wavefunctions projection as well and partner function determination and averaging is used. The software has been released as a stand-alone, open source library under the MIT license and integrated into both computational and molecular modelling software.Graphical abstract.

Investigating Protein-Peptide Interactions Using the Schrödinger Computational Suite.

The Schrödinger software suite contains a broad array of computational chemistry and molecular modeling tools that can be used to study the interaction of peptides with proteins. These include molecular docking using Glide and Piper, relative binding free energy predictions with FEP+, conformational searches using MacroModel and Desmond, and structural refinement using Prime and PrimeX. In this review we provide a comprehensive overview of these tools and describe their potential application in the identification and optimization of peptide ligands for proteins.

Molecular aspects of squaraine dyes aggregation and its influence on spectroscopic properties

Molecular aggregates of two squaraine dyes of different ability for hydrogen-bond formation are investigated with computational chemistry tools. Oligomers up to hexamers are included into the present analysis. Potential energy surface scan for dimers and small oligomers indicates a preference for regular staircase and zig-zag structures. A decomposition of interaction energy of the dimers with SAPT0 procedure allows to notice a clear predominance of dispersion-bound structures in the case of 2,4-bis(4-dimethylaminophenyl)squaraine and susceptibility of the 2,4-bis(aminophenyl)squaraine for hydrogen-bond formation, what is consistent with predictions based on molecular structure. An influence of aggregation on spectroscopic properties is analyzed. An elongation of the squaraine oligomer leads to a shift on the maximum absorption wavelength and an increase of the intensity of the signal. Moreover, in the case of the 2,4-bis(aminophenyl)squaraine, the new interaction-induced signals appear in the long-wavelength part of the UV–Vis spectrum.

Loss of stability and hydrophobicity of presenilin 1 mutations causing Alzheimer's disease.

Nearly 200 mutations in the gene coding for presenilin 1 (PSEN1) cause early-onset Alzheimer's disease, yet the molecular mechanism remains obscure. As a meta-analysis, we compiled available clinical and biochemical data for PSEN1 variants and correlated these to chemical properties of the mutants. We found statistically significant relationships between relative Aβ42 levels and clinical age of onset. We then computed chemical properties of the mutants from a variety of computational chemistry tools. Relative Aβ42 levels correlated significantly (95% confidence or more from p-values of linear regression) with loss of hydrophobicity for four different regression analyses (squared correlation coefficient of linear regression R(2) of 0.41-0.53) and with increased polarity (R(2) = 0.47, 0.59) and loss of protein stability (R(2) = 0.39, 0.63) for two independent data sets. Age of onset of patients carrying PSEN1 variants correlated with increased polarity (R(2) = 0.49, 0.40) and loss of stability (R(2) = 0.75, 0.44) of the protein for both data sets. These relations suggest that mutants impair the membrane-associated structural integrity of presenilin by reducing hydrophobic membrane association and overall protein stability. This explains why the many mutations that spread out across the protein and far from the catalytic aspartates can cause disease. The identified molecular determinants of clinical age of symptom onset may be relevant to future presenilin-modulating therapies specifically directed towards increasing the structural integrity and packing of the protein. Close to 200 mutations in presenilin 1 (PSEN1) cause Alzheimer's disease, but the biochemical relating these to disease remains debated. The chemical properties of PSEN1 variants were computed and correlated against clinical age of symptom onset. Loss of stability and hydrophobicity and gain of polarity relate to disease onset, suggesting that mutants impair the membrane structure of PSEN1 and that therapies should increase PSEN1 structural integrity.