Context:Geometrical knots are rare structural arrangements in proteins in which the polypeptide chain ties itself into a knot, which is very intriguing due to the uncertainty of their impact on the protein properties. Presently, classical molecular dynamics is the most employed technique in the few studies found on this topic, so any information on how the presence of knots affects the reactivity and electronic properties of proteins is even scarcer. Using the electronic structure methods and quantum chemical descriptors analysis, we found that the same amino-acid residues in the knot core have statistically larger values for the unknotted protein, for both hard-hard and soft-soft interaction descriptors. In addition, we present a computationally feasible protocol, where we show it is possible to separate the contribution of the geometrical knot to the reactivity and other electronic structure properties.Methods:In order to investigate these systems, we used PRIMoRDiA, a new software developed by our research group, to explore the electronic structure of biological macromolecules. We evaluated several local quantum chemical descriptors to unveil relevant patterns potentially originating from the presence of the geometrical knot in two proteins, belonging to the ornithine transcarbamylase family. We compared several sampled structures from these two enzymes that are highly similar in both tertiary structure and function, but one of them has a knot whereas the other does not. The sampling was carried out through molecular dynamics simulations using ff14SB force field along 50 ns, and the semiempirical convergence was performed with PM7 Hamiltonian.

In this chapter, we performed a historical recollection of the efforts of applying CDFT reactivity descriptors for large biological structures. We follow their time evolution until the current days, analyzing the reasons why this field of application is still incipient compared to the others used for studying chemical systems. We found that one of the most relevant issues has been associated with the particular characteristics of large biomolecules because of their electronic structure that shows emergent properties tied up to their copolymeric nature, resulting in a high energy degeneracy in the frontier molecular orbitals, for instance. Another identified problem is the lack of software with a good variety of implemented quantum chemical descriptors that can handle large output files from quantum chemical calculations of electronic structures of molecular systems with many atoms. For these reasons, the applications of electron density descriptors for systems relevant to biological processes were mainly performed on fragments of enzymes, employing the same protocols used for small organic systems. Other research groups have tried to estimate the whole structure in the calculations, being able to do so using only linear scaling electronic structure methods and semiempirical Hamiltonians but using the finite differences method, which requires higher computational effort. Recently, strategies of combinations of frontier molecular orbitals to overcome the degeneracy problem and their implementations in a new software, PRIMoRDiA, have made available approximately thirty descriptors for large biological structures, with tools for automatic visualization and data analysis. The software applications include (i) theoretical characterization of enzymatic reaction paths, (ii) ligand–protein binding processes, and (iii) conformational analysis of virus proteases. We expect that with these new implementations, the wealth of information that can be retrieved with the employment of quantum chemical descriptors could be used to unravel the chemistry behind such complex biological machinery.

In this Review, we reviewed the efforts to expand the applications of conceptual density functional theory reactivity descriptors and hard and soft acid and base principles for macromolecules and other strategies that focused on low-level quantum chemistry methods. Currently, recent applications are taking advantage of modifications of these descriptors using semiempirical electronic structures to explain enzymatic catalysis reactions, protein-binding processes, and structural analysis in proteins. We have explored these new solutions along with their implementations in the software PRIMoRDiA, discussing their impact on the field and its perspectives. We show the main issues in the analysis of the electronic structure of macromolecules, which are the application of the same calculation protocols used for small molecules without considering particularities in those large systems' electronic configuration. The major result of our discussions is that the use of semiempirical methods is crucial to obtain such a type of analysis, which can provide a powerful dimension of information and be part of future low-cost predictive tools. We expect semiempirical methods continue playing an important role in the quantum chemistry evaluation of large molecules. As computational resources advance, semiempirical methods might lead us to explore the electronic structure of even larger biological macromolecular entities and sets of structures representing larger timescales.

Three new unsymmetric isatin bishydrazone compounds; Comp. I, II, III, were synthesized by the condensation of 3,5-dichloro-salicylaldehyde, 3-bromo-5-chloro-salicylaldehyde, and 3,5-dibromo-salicylaldehyde with isatin monohydrazone, respectively. The synthesized compounds were characterized by elemental analysis, H-1-NMR, FT-IR, UV-Vis spectroscopy, and mass spectrometry technique. For studied molecules, chemical parameters like frontier orbital energies, energy gap, electronegativity, chemical potential, chemical hardness, softness, electrophilicity, nucleophilicity, electrodonating power, electroaccepting power, polarizability, and dipole moment were calculated and discussed. Investigating the validity of well-known electronic structure principles like Maximum Hardness, Minimum Polarizability, and Minimum Electrophilicity Principles in the study, it was determined which compound is more stable compared to others. In recent days, a new software having PRIMorDIA name was developed to explore reactivity and electronic structure in large biomolecules by some of the authors of this paper. Molecular docking studies for these newly synthesized molecules were performed using PRIMorDIA software. Considering the intramolecular interactions, NBO analyzes of three bishydrazone derivatives were conducted to evaluate the chemical behavior. (C) 2021 Elsevier B.V. All rights reserved.

The main-protease (M-pro) catalyzes a crucial step for the SARS-CoV-2 life cycle. The recent SARS-CoV-2 presents the main protease (M-pro(CoV2)) with 12 mutations compared to SARS-CoV (M-pro(CoV2)). Recent studies point out that these subtle differences lead to mobility variances at the active site loops with functional implications. We use metadynamics simulations and a sort of computational analysis to probe the dynamic, pharmacophoric and catalytic environment differences between the monomers of both enzymes. So, we verify how much intrinsic distinctions are preserved in the functional dimer of M-pro(CoV2), as well as its implications for ligand accessibility and optimized drug screening. We find a significantly higher accessibility to open binding conformers in the M-pro(CoV2) monomer compared to M-pro(CoV2). A higher hydration propensity for the M-pro(CoV2) S2 loop with the A46S substitution seems to exercise a key role. Quantum calculations suggest that the wider conformations for M-pro(CoV2) are less catalytically active in the monomer. However, the statistics for contacts involving the N-finger suggest higher maintenance of this activity at the dimer. Docking analyses suggest that the ability to vary the active site width can be important to improve the access of the ligand to the active site in different ways. So, we carry out a multiconformational virtual screening with different ligand bases. The results point to the importance of taking into account the protein conformational multiplicity for new promissors anti M-pro(CoV2) ligands. We hope these results will be useful in prospecting, repurposing and/or designing new anti SARS-CoV-2 drugs.

In this work, we performed a study to assess the interactions between the ricin toxin A (RTA) subunit of ricin and some of its inhibitors using modern semiempirical quantum chemistry and ONIOM quantum mechanics/molecular mechanics (QM/MM) methods. Two approaches were followed (calculation of binding enthalpies, ΔHbind, and reactivity quantum chemical descriptors) and compared with the respective half-maximal inhibitory concentration (IC50) experimental data, to gain insight into RTA inhibitors and verify which quantum chemical method would better describe RTA–ligand interactions. The geometries for all RTA–ligand complexes were obtained after running classical molecular dynamics simulations in aqueous media. We found that single-point energy calculations of ΔHbind with the PM6-DH+, PM6-D3H4, and PM7 semiempirical methods and ONIOM QM/MM presented a good correlation with the IC50 data. We also observed, however, that the correlation decreased significantly when we calculated ΔHbind after full-atom geometry optimization with all semiempirical methods. Based on the results from reactivity descriptors calculations for the cases studied, we noted that both types of interactions, molecular overlap and electrostatic interactions, play significant roles in the overall affinity of these ligands for the RTA binding pocket.

Obtaining reactivity information from the molecular electronic structure of a chemical system is a computationally intensive process. As a way of probing reactivity information around that, there exist electron density response variables, such as the Fukui functions (FFs), which are well-established descriptors that summarize the local susceptibility to react. These properties only require few single-point quantum chemical calculations, but even then, the intrinsic high cost and unfavorable computational complexity with respect to the number of atoms in the system makes this approach available only to small fragments and systems. In this study, we explore the computation of FFs, showing that semiempirical quantum chemical methods can be used to obtain the reactivity information equivalent to that of a Density Functional Theory (DFT) functional, for the eight entire polypeptide chains. The combination of semiempirical methods with the frozen orbital approximation allows for the obtention of these reactivity descriptors for biological systems with reasonable accuracy and speed, unlocking the utilization of these methods for such systems. These results for the frozen orbital approximation can be additionally improved when other molecular orbitals from the frontier band are employed in the computation. We also show the potential of this computational protocol in the ligand-protein complexes of HIV-1 protease, predicting which of those ligands are active inhibitors.

Plenty of enzymes with structural data do not have their mechanism of catalysis elucidated. Reactivity descriptors, theoretical quantities generated from resolved electronic structure, provide a way to predict and rationalize chemical processes of such systems. In this Application Note, we present PRIMoRDiA (PRIMoRDiA Macromolecular Reactivity Descriptors Access), a software built to calculate the reactivity descriptors of large biosystems by employing an efficient and accurate treatment of the large output files produced by quantum chemistry packages. Here, we show the general implementation details and the software main features. Calculated descriptors were applied for a set of enzymatic systems in order to show their relevance for biological studies and the software potential for use in large scale. Also, we test PRIMoRDiA to aid in the interaction depiction between the SARS-CoV-2 main protease and a potential inhibitor.

In general, computational simulations of enzymatic catalysis processes are thermodynamic and structural surveys to complement experimental studies, requiring high level computational methods to match accurate energy values. In the present work, we propose the usage of reactivity descriptors, theoretical quantities calculated from the electronic structure, to characterize enzymatic catalysis outlining its reaction profile using low-level computational methods, such as semiempirical Hamiltonians. We simulate three enzymatic reactions paths, one containing two reaction coordinates and without prior computational study performed, and calculate the reactivity descriptors for all obtained structures. We observed that the active site local hardness does not change substantially, even more so for the amino-acid residues that are said to stabilize the reaction structures. This corroborates with the theory that activation energy lowering is caused by the electrostatic environment of the active sites. Also, for the quantities describing the atom electrophilicity and nucleophilicity, we observed abrupt changes along the reaction coordinates, which also shows the enzyme participation as a reactant in the catalyzed reaction. We expect that such electronic structure analysis allows the expedient proposition and/or prediction of new mechanisms, providing chemical characterization of the enzyme active sites, thus hastening the process of transforming the resolved protein three-dimensional structures in catalytic information.

In this study, we have investigated the enzyme shikimate 5-dehydrogenase from the causative agent of tuberculosis, Mycobacterium tuberculosis . We have employed a mixture of computational techniques, including molecular dynamics, hybrid quantum chemical/molecular mechanical potentials, relaxed surface scans, quantum chemical descriptors and free-energy simulations, to elucidate the enzyme’s reaction pathway. Overall, we find a two-step mechanism, with a single transition state, that proceeds by an energetically uphill hydride transfer, followed by an energetically downhill proton transfer. Our mechanism and calculated free energy barrier for the reaction, 64.9 kJ mol − 1 , are in good agreement with those predicted from experiment. An analysis of quantum chemical descriptors along the reaction pathway indicated a possibly important, yet currently unreported, role of the active site threonine residue, Thr65.

The acetylation of glycerol was achieved with high conversion and selectivity towards triacetin at low temperatures and short reaction times by using acidic imidazolium salts as catalysts. Moreover, the addition of a nitro group to the imidazolium cation affords a much more competent catalyst, indicating a significant effect provided by the simple electronic change in the imidazolium cation. Theoretical calculations revealed increased polarization of the acidic hydrogen bond on the nitrated salts, which may be related to their superior catalytic behavior when compared to the non-functionalized salts. Combining the preliminary experimental and theoretical results, it is possible to suppose that the catalytic activity of acidic imidazolium salts may be better comprehended by its Bronsted acidities, but other parameters such as hardness, electronegativity, electrophilicity and ion-pair binding energy were also evaluated in order to investigate their effects in the acetylation of glycerol promoted by these acidic imidazolium salts.

A observação de divergências em resultados de estudos de Avaliação do Ciclo de Vida (ACV) que contemplam a produção de commodities agrícolas, mesmo que tenham objetivos e escopos similares, tem sido atribuída especialmente à representatividade estatística de dados utilizados na construção dos inventários de ciclo de vida. Uma vez que é frequente o uso de dados que obtidos a partir de médias nacionais na construção de ICVs de produtos agrícolas, torna-se interessante estudar a representatividade de dados nacionais agrupados ou se é aconselhável levar-se em consideração dados regionais. O presente trabalho teve como objetivo a realização de testes estatísticos relacionados com a representatividade dos dados nacionais e regionais da produção do grão de soja no Brasil e a relação entre a dispersão dos dados com as informações de região, ano, e qualidade dos dados através de métodos de Análise Multivariada. Para isso, dados de inventário de ciclo de vida de 8 trabalhos, referentes a 11 safras de cultivo de soja foram selecionados. As variáveis estudadas incluem as aplicações de defensivos e fertilizantes, uso de Diesel, emissões caracterizadas de CO2, quantidade de sementes e produtividade, o ano da safra, e origem dos dados coletados. A Análise de Componentes Principais (PCA) foi aplicada à matriz de dados para reduzir sua dimensionalidade e tornar possível a identificação de relações e agrupamentos. Análise de Variância também foi empregada, com objetivo de testar as relações quantitativas entre grupos regionais e as variáveis do estudo. A partir da PCA foi possível observar a correlação entre a produtividade, calcário e os fertilizantes fosfatados e potássicos. O uso de defensivos agrícolas e a quantidade de sementes apresentam forte correlação, e a adição de nitrogênio e uso de Diesel não apresentaram padrões significativos. Os escores de PCA indicam forte agrupamento dos inventários do Mato Grosso com o de São Paulo e considerável dissimilaridades entre os trabalhos que contemplam a produção nacional. Os testes de significância confirmaram a relação entre os dados de produtividade com a região. O mesmo foi encontrado para fósforo e potássio. Para o uso de Diesel é possível afirmar que não há diferenças. Em conclusão, verifica-se que os dados regionais não são estatisticamente similares aos dados nacionais para a avaliação de sistemas de produto da produção primária de soja no Brasil, independente da parcela de contribuição para a produção total.

The main goal of this work is to verify the possibility of the improving the process variables in CO2 capture process using ionic liquids as sorbent, by changing the anions and/or cation type of the Ionic liquids. ANOVA models a PCA were performed for CO2 capture capacity, viscosity, melting degradation temperature and chemical information of the ionic liquids. The results shows that tunning of ionic liquids are statistically significant and the resultants insights could be used to predict new sorbents.

The Ricin Toxic A chain RTA is a subunity of Ricin protein, which is a ribossome inactivator very toxic for humans. This protein is found in castor plant that is raw material for several valuable products. Hence, it is plenty interest in inactivation of RTA toxicity activity to insure industry biosafety[1]. The field computational molecular modeling provide useful tools to explore drug design, one of them that is growing for biological systems is the Reactivity Descriptors (RDs) developed by Hard and Soft Acid Base Theory, and Fukui’s Frontier Molecular Orbital. The Conceptual Density Functional Theory rises in the process of establishing correspondence between the RDs and coefficients of the fundamental differential equation of DFT[2]. These descriptions cover global molecule’s trends of charge transfer, polarization and electrostatic long range interactions[3]. Also, local reactivity trends derived from electron density changes as electron number vary[4]. Such descriptors are well established for small molecules, currently being applied for reaction rationalization[5], QSAR[6] and regioselectivity[4] studies. For large/biological system the application of RDs can be exemplified in the literature as: protonation state determination[7]; docking ligand score functions and evaluation of residue interactions[8]. Although, these studies relies on quantum mechanical methods with lack of electron correlation or without computing properly the protein and solvent environment effects on active sites. However, it is difficult to demand full ab initio treatment for such large systems as proteins. In the present work, we propose the use of Fragment Molecular Orbital (FMO)[9] method for efficient ab initio calculation to obtain local reactivity descriptors of RTA. FMO is a fragmentation scheme for quantum mechanical (QM) calculations that divides a large system in several monomers[9]. The FMO does not depend on empirical fitted parameters and are reported for providing accurate energies, orbital and densities for large system when compared with full ab initio calculations[10]. Then, we use the frontier molecular orbitals densities of RTA obtained with FMO to calculate the Fukui indices in order to describe local reactivity. The first calculations were performed using the 2-nbody FMO method[11] in GAMESS program, at HF/STO-3G level of theory without counting the solvent effect. The fragmentation was carried out using two protein residues by monomer. The Fukui indices for the electrophilic attack susceptibility from the electron density of a neutral charge state of RTA and a negatively charged. The results show that there is a spatial concentration of that descriptor, which is shown in Figure 1 by the rendered scalar field volume around the protein atoms. That region could indicate an active site for a electrophile ligand to react or dock. The FMO method allow us to calculate the descriptors using ab-initio or DFT treatment for a molecule containing more than 4000 atoms, accounting the electronic structure of the entire protein. 12 a 17/Nov, 2017, Águas de Lindóia/SP, Brasil Figure 1: Volume rendering of Fukui’s function of electrophilic susceptibility on RTA protein structure representation.

Nowadays ionic liquids (IL) are target of massive research in CO2 captures technologies, due to its thermic degradation resistance, low vapour pressure and low requirement of power for regeneration. All these properties, including solubility capacity, are known to vary with the nature of the IL cation and anion. The main goal of this study was calculate electronic indicators for ILs that have thermodynamic data available and verify the existence of correlation between then, providing a possible new way to get information of these processes.

This study presents a greenhouse gases emissions assessment of soybean cultivation in southern Brazil based on life cycle inventory. Although there are currently some studies on this topic, it is focused in the country level. Nevertheless, there are differences among the producing regions and it's estimated that for each 20 kg of soybeans produced in Brazil, one is produced in Rio Grande do Sul state. In a previous study, a life cycle inventory of soybean cultivated in this Brazilian state was developed, nevertheless, the influence of land use change along the life cycle was not taken into account. Therefore, the current study discusses the influence of direct land use change over the greenhouse gas emissions. The functional unit (FU) employed was 1 kg of soybean harvested for a cradle to gate study. For the soybean cultivation, in the scenario related to no land use change (scenario 1), 0.352 kg CO2/FU was emitted. This value increases up to 205% in scenario 2 (in this case, the actual scenario was that 15.4% of soybean cropland area replaced grassland) and 892% in scenario 3 (all land transformation was over forest). In scenario 1, soybean cultivation was responsible for the higher share of the greenhouse gases emissions (42%). The highest contributions in soybean cultivation for greenhouse gases emissions were: liming (37%), fertilization (19%) and seeding (9%). (C) 2016 Elsevier Ltd. All rights reserved.