ABSTRACT Leishmaniasis, caused by protozoa of the genus Leishmania remains a significant public health concern due to the high lethality of the visceral form if untreated. Current therapies have important limitations, including toxicity and the availability of only a single approved oral drug (miltefosine), highlighting the need for new therapeutic alternatives. N ‐benzyl spiro‐oxindole hydantoins represent promising antileishmanial candidates, combining benzyl, and oxindole cores, previously associated with anti‐ Leishmania activity, with hydantoin, a scaffold known for broad biological properties. The connection of these cores via a spiro carbon further enhances their structural potential. In this preliminary study these compounds were evaluated against Leishmania infantum promastigotes. The most active derivative exhibited an IC 50 of 37.85 µM and was subjected to docking studies to explore its possible mechanism of action. In silico predictions (SwissADME) suggested good oral bioavailability, and preliminary cytotoxicity assays in RAW 264.7 macrophages demonstrated high parasite selectivity, supporting their potential as novel antileishmanial agents.

The K3Li2Nb5O15 (KLN) compound, which crystallizes in a tetragonal tungsten bronze (TTB) structure, was synthesized and structurally resolved by the Rietveld refinement of X-ray diffraction (XRD) data, followed by characterization via Raman spectroscopy and theoretical investigation through an integrated computational approach. Density functional theory (DFT) calculations, incorporating the quasi-harmonic approximation and topological analysis based on the quantum theory of atoms in molecules (QTAIM), were employed to interpret vibrational features and to establish the crystal and electronic structures. Theoretical and experimental Raman spectra were compared, revealing significant discrepancies below 400 cm-1. These differences were attributed to the dynamic behavior of K+ and Li+ cations within the pentagonal and trigonal channels, respectively. Ab initio molecular dynamics simulations confirmed the high mobility of Li+ and the site-dependent behavior of K+, which directly impact the vibrational signatures. The combined vibrational and dynamic analyses suggest that the observed oxygen mobility, particularly in the channel regions, may influence the formation and migration of oxygen vacancies, with possible implications for ionic conductivity, gas sensing, and (photo)catalytic activity in functional oxides. This study provides a detailed atomistic understanding of the coupling between structural dynamics and vibrational spectra in complex TTB frameworks, contributing to deeper insight into phenomena rarely explored at this level.

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.

AbstractMolecular dynamics (MD) simulations produce a substantial volume of high-dimensional data, and traditional methods for analyzing these data pose significant computational demands. Advances in MD simulation analysis combined with deep learning-based approaches have led to the understanding of specific structural changes observed in MD trajectories, including those induced by mutations. In this study, we model the trajectories resulting from MD simulations of the SARS-CoV-2 spike protein-ACE2, specifically the receptor-binding domain (RBD), as interresidue distance maps, and use deep convolutional neural networks to predict the functional impact of point mutations, related to the virus’s infectivity and immunogenicity. Our model was successful in predicting mutant types that increase the affinity of the S protein for human receptors and reduce its immunogenicity, both based on MD trajectories (precision = 0.718; recall = 0.800; $$\hbox {F}_1$$ = 0.757; MCC = 0.488; AUC = 0.800) and their centroids. In an additional analysis, we also obtained a strong positive Pearson’s correlation coefficient equal to 0.776, indicating a significant relationship between the average sigmoid probability for the MD trajectories and binding free energy (BFE) changes. Furthermore, we obtained a coefficient of determination of 0.602. Our 2D-RMSD analysis also corroborated predictions for more infectious and immune-evading mutants and revealed fluctuating regions within the receptor-binding motif (RBM), especially in the $$\beta _{1}^{\prime }/\beta _{2}^{\prime }-C$$ loop. This region presented a significant standard deviation for mutations that enable SARS-CoV-2 to evade the immune response, with RMSD values of 5Å in the simulation. This methodology offers an efficient alternative to identify potential strains of SARS-CoV-2, which may be potentially linked to more infectious and immune-evading mutations. Using clustering and deep learning techniques, our approach leverages information from the ensemble of MD trajectories to recognize a broad spectrum of multiple conformational patterns characteristic of mutant types. This represents a strategic advantage in identifying emerging variants, bypassing the need for long MD simulations. Furthermore, the present work tends to contribute substantially to the field of computational biology and virology, particularly to accelerate the design and optimization of new therapeutic agents and vaccines, offering a proactive stance against the constantly evolving threat of COVID-19 and potential future pandemics.

We report an optical study of quinoxaline-based luminogens derived from 2,3-bis(aryl)-quinoxaline-6-carboxylic acid (1a, 1b, and 2) in solution and solid state. These compounds were fluorescent in solid films and in solutions with emission bands ranging from blue to green (445-517 nm). They showed a positive solvatochromism in the PL spectra, indicative of intramolecular charge transfer (ICT) in the excited state. 1a, 1b, and 2 exhibited Aggregation-Induced Enhanced Emission (AIEE) characteristics with a drastic change in properties by increasing alkyl chains and donor alkoxy groups. Their carboxylate potassium salts had a blueshift emission compared to the acid carboxylic form and a significant quenching in PL intensity in protic polar solvents. The salt of 1a presented an Aggregation-Induced Emission (AIE) profile, its weak fluorescence in water had an expressive enhancement by adding THF. Theoretical calculations were also carried out considering implicit solvent effects. The optimized structures of compounds and their simulated UV-vis absorption spectra showed no significant differences in water and acetonitrile. These results are in good agreement with the solvatochromism experiments. The calculated HOMO-LUMO energy gaps were 3.47 eV in both solvents, and the energy levels for HOMO and LUMO are slightly lower in water.

Bismuth complexes with dithiocarbamate ligands have attracted attention because of their biological applications, such as antimicrobial, antileishmanial, and anticancer properties. These complexes have high cytotoxic activity against cancer cells, being more active than the standard drugs cisplatin, doxorubicin, and tamoxifen. In the present study, we investigated the ability of some DFT methods to reproduce the geometries and NMR spectra of the Bi(III) dithiocarbamate complexes, selected based on their proven antitumor activity. Our investigation revealed that the M06-L/def2-TZVP/ECP/CPCM method presented good accuracy in predicting geometries, while the TPSSh/def2-SVP/ECP/CPCM method proved effective in analyzing the 13C NMR spectra of these molecules. In general, all examined methods exhibited comparable performance in predicting 1H NMR signals. Calculations were performed with the Gaussian 09 program using the def2-SVP and def2-TZVP basis sets, employing relativistic effective core potential (ECP) for Bi and using the CPCM solvent model. The exchange–correlation functionals BP86, PBE, OLYP, M06-L, B3LYP, B3LYP-D3, M06-2X, TPSSh, CAM-B3LYP, and ωB97XD were used in the study. Geometry optimizations were started from crystallographic structures available at the Cambridge Structural Database. The theoretical results were compared with experimental data using the mean root-mean-square deviation (RMSD), mean absolute deviations (MAD), and linear correlation coefficient (R2).

Leishmaniasis is reported as the second most common protozoonosis, with the highest prevalence and mortality rate. Among the Leishmania drug targets, Pteridine Reductase 1 of Leishmania major (LmPTR1) proved to be promising because Leishmania is auxotrophic for folates. Thus, this study employed a combination of ligand- and structure-based approaches to screen new benzothiazole compounds as LmPTR1 inhibitor candidates. Initially, a highly predictive quantitative structure-activity relationship (QSAR) model was able to identify the relevant hybrid descriptors, with an accuracy of over 93%. Insights into the mechanism of action indicated Phe113, His241, Leu188, Met183, and Leu226 as key residues. New commercially available compounds were screened using QSAR, docking, and pharmacokinetic properties as filters. Molecular dynamics, non-covalent interactions analysis, and quantum chemical calculation of binding enthalpy demonstrated that the lead compound (ZINC 72229720) forms a stable complex with LmPTR1, indicating it as a new promising LmPTR1 inhibitor.

Visceral leishmaniasis (VL), caused by protozoa of the genus Leishmania, remains a significant public health concern due to its potentially lethal nature if untreated. Current chemotherapy options are limited by severe toxicity and drug resistance. Derivatives of 1,2,4-oxadiazole have emerged as promising drug candidates due to their broad biological activity. This study investigated the effects of novel 1,2,4-oxadiazole derivatives (Ox1–Ox7) on Leishmania infantum, the etiological agent of VL. In silico predictions using SwissADME suggest that these compounds have high oral absorption and good bioavailability. Among them, Ox1 showed the most promise, with higher selectivity against promastigotes and lower cytotoxicity towards L929 fibroblasts and J774.G8 macrophages. Ox1 exhibited selectivity indices of 18.7 and 61.7 against L. infantum promastigotes and amastigotes, respectively, compared to peritoneal macrophages. Ultrastructural analyses revealed severe morphological damage in both parasite forms, leading to cell death. Additionally, Ox1 decreased the mitochondrial membrane potential in promastigotes, as shown by flow cytometry. Molecular docking and dynamic simulations indicated a strong affinity of Ox1 for the L. infantum CYP51 enzyme. Overall, Ox1 is a promising and effective compound against L. infantum.

Arthropod-borne viruses within the Flaviviridae family such as Zika (ZIKV) and dengue (DENV) are responsible for major outbreaks in tropical countries, and there are no specific treatments against them. Naringenin and 7-O-methyl naringenin are flavonoids that can be extracted from geopropolis, a natural material that the Brazilian Jandaira stingless bee (Melipona subnitida Ducke) produces to protect its nest. Here, these flavonoids were tested against ZIKV and DENV using Vero cells as a cellular model to perform a cytotoxicity assay and to define the effective concentrations of TCID50 as the readout method. The results demonstrated the antiviral activity of the compounds against both viruses upon the treatment of infected cells. The tested flavonoids had antiviral activity comparable with 6-methylmercaptopurine riboside (6-MMPr), used here as a positive control. In addition, to identify the possible action mechanism of the antiviral candidates, we carried out a docking analysis followed by a molecular dynamics simulation to elucidate naringenin and 7-O-methyl naringenin binding sites to each virus. Altogether, these results demonstrate that both flavonoids have potent antiviral effects against both viruses and warrant further in vivo trials.

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.

Background The SARS-CoV-2 pandemic reverberated, posing health and social hygiene obstacles throughout the globe. Mutant lineages of the virus have concerned scientists because of convergent amino acid alterations, mainly on the viral spike protein. Studies have shown that mutants have diminished activity of neutralizing antibodies and enhanced affinity with its human cell receptor, the ACE2 protein. Methods Hence, for real-time measuring of the impacts caused by variant strains in such complexes, we implemented E-Volve, a tool designed to model a structure with a list of mutations requested by users and return analyses of the variant protein. As a proof of concept, we scrutinized the spike-antibody and spike-ACE2 complexes formed in the variants of concern, B.1.1.7 (Alpha), B.1.351 (Beta), and P.1 (Gamma), by using contact maps depicting the interactions made amid them, along with heat maps to quantify these major interactions. Results The results found in this study depict the highly frequent interface changes made by the entire set of mutations, mainly conducted by N501Y and E484K. In the spike-Antibody complex, we have noticed alterations concerning electrostatic surface complementarity, breaching essential sites in the P17 and BD-368-2 antibodies. Alongside, the spike-ACE2 complex has presented new hydrophobic bonds. Discussion Molecular dynamics simulations followed by Poisson-Boltzmann calculations corroborate the higher complementarity to the receptor and lower to the antibodies for the K417T/E484K/N501Y (Gamma) mutant compared to the wild-type strain, as pointed by E-Volve, as well as an intensification of this effect by changes at the protein conformational equilibrium in solution. A local disorder of the loop α1′/β1′, as well its possible effects on the affinity to the BD-368-2 antibody were also incorporated to the final conclusions after this analysis. Moreover, E-Volve can depict the main alterations in important biological structures, as shown in the SARS-CoV-2 complexes, marking a major step in the real-time tracking of the virus mutant lineages. E-Volve is available at http://bioinfo.dcc.ufmg.br/evolve.

Objetivo: Realizar o estudo teórico a nível DFT das propriedades estruturais e termodinâmicas dos complexos [BiX3(L)], onde X= F, Cl ou Br; L=4,5 difenil-1,3,4-tiadiazólio-2-tiolato (L1), 4-fenil-5-(clorofenil)1,3,4tiadiazólio-2-tiolato (L2) e 4-fenil-5-(4-metilfenil)-1,3,4-tiadiazólio-2-tiolato (L3) com a finalidade de avaliar a geometria e estabilidade dos complexos em fase gasosa e em solução. Métodos: Uma busca conformacional da interação do BiCl3 com o ligante L1 foi realizada. A partir do confórmero mais estável, as estruturas dos demais complexos foram construídas e otimizadas a nível M06-2X/def2-TZVP com potencial efetivo do caroço (ECP) para o Bi. As frequências vibracionais dos complexos e ligantes foram calculadas para obtenção do mínimo de energia e dos parâmetros termodinâmicos de reação de complexação em fase gasosa e em metanol. Resultados: Os cálculos mostraram que BiCl3 se coordena ao átomo de N com o ligante na posição axial e é estabilizado por possível interação fraca Bi⋯S. Os parâmetros termodinâmicos indicam que apenas as reações de complexação [BiF3(L2)] e [BiF3(L3)] em metanol não são espontâneas. Conclusão: Os resultados obtidos indicam que a força de interação Bi−N depende principalmente da natureza dos trihaletos e não dos ligantes. Os parâmetros termodinâmicos mostram maior afinidade do BiBr3 pelos ligantes, resultando em complexos mais estáveis.

Ricin is a potent toxin derived from the castor bean plant and comprises two subunits, RTA and RTB. Because of its cytotoxicity, ricin has alarmed world authorities for its potential use as a chemical weapon. Ricin also affects castor bean agribusiness, given the risk of animal and human poisoning. Over the years, many groups attempted to propose small-molecules that bind to the RTA active site, the catalytic chain. Despite such efforts, there is still no effective countermeasure against ricin poisoning. The computational study carried out in the present work renews the discussion about small-molecules that may inhibit this toxin. Here, a structure-based virtual screening protocol capable of discerning active RTA inhibitors from inactive ones was performed to screen over 2 million compounds from the ZINC database to find novel scaffolds that strongly bind into the active site of the RTA. Besides, a novel score method based on ligand undocking force profiles and semi-empirical quantum chemical calculations provided insights into the rescore of docking poses. Summing up, the filtering steps pointed out seven main compounds, with the SCF00-451 as a promising candidate to inhibit the killing activity of such potent phytotoxin.

The role of knots in proteins remains elusive. Some studies suggest an impact on stability; the difficulty in comparing systems to assess this effect, however, has been a significant challenge. In this study, we produced and analyzed molecular dynamic trajectories considering three different temperatures of two variants of ornithine transcarbamylase (OTC), only one of which has a 31 knot, in order to evaluate the relative stability of the two molecules. RMSD showed equilibrated structures for the produced trajectories, and RMSF showed subtle differences in flexibility. In the knot moiety, the knotted protein did not show a great deal of fluctuation at any temperature. For the unknotted protein, the residue GLY243 showed a high fluctuation in the corresponding moiety. The fraction of native contacts (Q) showed a similar profile at all temperatures, with the greatest decrease by 436 K. The investigation of conformational behavior with principal component analysis (PCA) and dynamic cross-correlation map (DCCM) showed that knotted protein is less likely to undergo changes in its conformation under the conditions employed compared to unknotted. PCA data showed that the unknotted protein had greater dispersion in its conformations, which suggests that it has a greater capacity for conformation transitions in response to thermal changes. DCCM graphs comparing the 310 K and 436 K temperatures showed that the knotted protein had less change in its correlation and anti-correlation movements, indicating stability compared to the unknotted.

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.

Herein, molecular modeling techniques were used with the main goal to obtain candidates from a drug database as potential targets to be used against SARS-CoV-2. This novel coronavirus, responsible by the COVID-19 outbreak since the end of 2019, became a challenge since there is not vaccine for this disease. The first step in this investigation was to solvate the isolated S-protein in water for molecular dynamics (MD) simulation, being observed a transition from "up" to "down" conformation of receptor-binding domain (RBD) of the S-protein with angle of 54.3 and 43.0 degrees, respectively. The RBD region was more exposed to the solvent and to the possible drugs due to its enhanced surface area. From the equilibrated MD structure, virtual screening by docking calculations were performed using a library contained 9091 FDA approved drugs. Among them, 24 best-scored ligands (14 traditional herbal isolate and 10 approved drugs) with the binding energy below -8.1 kcal/mol were selected as potential candidates to inhibit the SARS-CoV-2 S-protein, preventing the human cell infection and their replication. For instance, the ivermectin drug (present in our list of promise candidates) was recently used successful to control viral replicationin vitro.MD simulations were performed for the three best ligands@S-protein complexes and the binding energies were calculated using the MM/PBSA approach. Overall, it is highlighted an important strategy, some key residues, and chemical groups which may be considered on clinical trials for COVID-19 outbreak.

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.