Exploring the Potential of Myrcia Genus Essential Oils: A Review of Biological Activities and Recent Advances.

The present study provides a comprehensive analysis of the chemical composition of essential oils from species of the Myrcia genus and their applications. The compiled results highlight the chemical diversity and biological activities of these oils, emphasizing their potential importance for various therapeutic and industrial applications. The findings reveal that Myrcia essential oils present a variety of bioactive compounds, such as monoterpenes and sesquiterpenes, which demonstrate antimicrobial activities against a range of microorganisms, including Gram-positive and Gram-negative bacteria, as well as yeasts. Furthermore, this study highlights the phytotoxic activity of these oils, indicating their potential for weed control. The results also point to the insecticidal potential of Myrcia essential oils against a range of pests, showing their viability as an alternative to synthetic pesticides. Additionally, species of the genus Myrcia have demonstrated promising hypoglycemic effects, suggesting their potential in diabetes treatment. This comprehensive synthesis represents a significant advancement in understanding Myrcia essential oils, highlighting their chemical diversity and wide range of biological activities. However, the need for further research is emphasized to fully explore the therapeutic and industrial potential of these oils, including the identification of new compounds, understanding of their mechanisms of action, and evaluation of safety and efficacy in different contexts.

Combined application of DP4+ and ANN-PRA to determine the relative configuration of natural products: The alpha-bisabol case study.

The combination of computational methods and experimental data from Nuclear Magnetic Resonance (NMR) is a considerably valuable tool in the elucidation of new natural product structures and, also, in the structural revision of previously reported compounds. Until recently, only classical statistical parameters were used, for example, linear correlation coefficient (R2 ), mean absolute error (MAE), or root mean square deviation (RMSD), as a way to statistically "validate" the structure pointed out by experimental NMR spectra. Regarding the resolution of the relative configuration of organic molecules, novel tools were available in the last few years to assist in the NMR elucidation process. The most relevant are DP4+, which is based on a Bayesian probability, and ANN-PRA, which is based on artificial neural networks. The combined application of these tools has become the most accurate and important alternative to solve structural and stereochemical problems in natural product chemistry. Therefore, herein, in this case study, we intended to promote these novel tools, exploring the strengths and limitations of each approach in resolving the relative configuration of the sesquiterpene alpha-bisabol. We also highlighted the advantages of the complementary use of H- and C-DP4+ to obtain optimal results in the differentiation of the stereoisomers, validating the proposal with ANN-PRA method.

Chemical Composition, Antimicrobial Properties of Siparuna guianensis Essential Oil and a Molecular Docking and Dynamics Molecular Study of its Major Chemical Constituent.

The essential oil of Siparuna guianensis was obtained by hydrodistillation. The identification of the chemical compounds was performed by gas chromatography coupled with mass spectrometry (GC/MS). Antimicrobial activity was investigated for four microorganisms: Streptococcus mutans (ATCC 3440), Enterococcus faecalis (ATCC 4083), Escherichia coli (ATCC 25922), and Candida albicans (ATCC-10231). The studies of doping and molecular dynamics were performed with the molecule that presented the highest concentration of drug-target proteins, 1IYL (C. albicans), 1C14 (E. coli), 2WE5 (E. faecalis), and 4TQX (S. mutans). The main compounds identified were: Curzerene (7.1%), γ-Elemene (7.04%), Germacrene D (7.61%), trans-ß-Elemenone (11.78%), and Atractylone (18.65%). Gram positive bacteria and fungi were the most susceptible to the effects of the essential oil. The results obtained in the simulation showed that the major compound atractylone interacts with the catalytic sites of the target proteins, forming energetically favourable systems and remaining stable during the period of molecular dynamics.

Molecular interactions of the Omicron, Kappa, and Delta SARS-CoV-2 spike proteins with quantum dots of graphene oxide.

CONTEXT: The Omicron, Kappa, and Delta variants are different strains of the SARS-CoV-2 virus. Graphene oxide quantum dots (GOQDs) represent a burgeoning class of oxygen-enriched, zero-dimensional materials characterized by their sub-20-nm dimensions. Exhibiting pronounced quantum confinement and edge effects, GOQDs manifest exceptional physical-chemical attributes. This study delves into the potential of graphene oxide quantum dots, elucidating their inherent properties pertinent to the surface structures of SARS-CoV-2, employing an integrated computational approach for the repositioning of inhibitory agents. METHODS: Following rigorous adjustment tests, a spectrum of divergent bonding conformations emerged, with particular emphasis placed on identifying the conformation exhibiting optimal adjustment scores and interactions. The investigation employed molecular docking simulations integrating affinity energy evaluations, electrostatic potential clouds, molecular dynamics encompassing average square root calculations, and the computation of Gibbs-free energy. These values quantify the strength of interaction between GOQDs and SARS-CoV-2 spike protein variants. The receptor structures were optimized using the CHARM-GUI server employing force field AMBERFF14SB. The algorithm embedded in CHARMM offers an efficient interpolation scheme and automatic step size selection, enhancing the efficiency of the optimization process. The 3D structures of the ligands are constructed and optimized with density functional theory (DFT) method based on the most stable conformer of each binder. Autodock Vina Software (ADV) was utilized, where essential parameters were specified. Electrostatic potential maps (MEPs) provide a visual depiction of molecules' charge distributions and related properties. After this, molecular dynamics simulations employing the CHARM36 force field in Gromacs 2022.2 were conducted to investigate GOs' interactions with surface macromolecules of SARS-CoV-2 in an explicit aqueous environment. Furthermore, our investigation suggests that lower values indicate stronger binding. Notably, GO-E consistently showed the most negative values across interactions with different variants, suggesting a higher affinity compared to other GOQDs (GO-A to GO-D).

Interactions of Co, Cu, and non-metal phthalocyanines with external structures of SARS-CoV-2 using docking and molecular dynamics.

The new coronavirus, SARS-CoV-2, caused the COVID-19 pandemic, characterized by its high rate of contamination, propagation capacity, and lethality rate. In this work, we approach the use of phthalocyanines as an inhibitor of SARS-CoV-2, as they present several interactive properties of the phthalocyanines (Pc) of Cobalt (CoPc), Copper (CuPc) and without a metal group (NoPc) can interact with SARS-CoV-2, showing potential be used as filtering by adsorption on paints on walls, masks, clothes, and air conditioning filters. Molecular modeling techniques through Molecular Docking and Molecular Dynamics were used, where the target was the external structures of the virus, but specifically the envelope protein, main protease, and Spike glycoprotein proteases. Using the g_MM-GBSA module and with it, the molecular docking studies show that the ligands have interaction characteristics capable of adsorbing the structures. Molecular dynamics provided information on the root-mean-square deviation of the atomic positions provided values between 1 and 2.5. The generalized Born implicit solvation model, Gibbs free energy, and solvent accessible surface area approach were used. Among the results obtained through molecular dynamics, it was noticed that interactions occur since Pc could bind to residues of the active site of macromolecules, demonstrating good interactions; in particular with CoPc. Molecular couplings and free energy showed that S-gly active site residues interacted strongly with phthalocyanines with values ​​of - 182.443 kJ/mol (CoPc), 158.954 kJ/mol (CuPc), and - 129.963 kJ/mol (NoPc). The interactions of Pc's with SARS-CoV-2 may predict some promising candidates for antagonists to the virus, which if confirmed through experimental approaches, may contribute to resolving the global crisis of the COVID-19 pandemic.

Interactions of Ozone-Functionalized Activated Charcoal with SARS-Cov-2 Proteases Using Molecular Docking and Dynamics.

The high contamination by the SARS-Cov-2 virus has led to the search for ways to minimize contagion. Masks are used as part of a strategy of measures to suppress transmission and save lives. However, they are not sufficient to provide an adequate level of protection against COVID-19. Activated charcoal has an efficient antibacterial action, adsorption and low cost. Here, the interaction between two molecules of activated carbon was analyzed, interacting with two structures of the SARS-Cov-2, through docking and molecular dynamics using the platforms Autodock Vina 4.2.6, Gaussian 09 and Amber 16. As a result, the complexes from ozone-functionalized coal to viral structures happen mainly through hydrophobic interactions at the binding site of each receptor. The values of the mean square deviations of the two systems formed by ligands/receptors and showed better stability. The results of Gibbs free energy showed a better interaction between proteins and functionalized charcoal, with △Gtotal values of -48.530 and -38.882 kcal/mol. Thus, the set formed by combinations of proteins with functionalized activated carbon tends to more efficiently adsorb the protein components of the coronavirus to the pores of the activated carbon with ozone during filtration.

A Combined Molecular Docking and Density Functional Theory Nuclear Magnetic Resonance Study of Trans-Dehydrocrotonin Interacting with COVID-19 Main Protease and Severe Acute Respiratory Syndrome Coronavirus 2 3C-Like Protease.

For the development of drugs that treat SARS-CoV-2, the fastest way is to find potential molecules from drugs already on the market. Unfortunately, there is currently no specific drug or treatment for COVID-19. Among all structural proteins in SARS-CoV, the spike protein is the main antigenic component responsible for inducing host immune responses, neutralizing antibodies, and/or protecting immunity against virus infection. Molecular docking is a technique used to predict whether a molecule will bind to another. It is usually a protein to another or a protein to a binding compound. Natural products are potential binders in several studies involving coronavirus. The structure of the ligand plays a fundamental role in its biological properties. The nuclear magnetic resonance technique is one of the most powerful tools for the structural determination of ligands from the origin of natural products. Nowadays, molecular modeling is an important accessory tool to experimentally got nuclear magnetic resonance data. In the present work, molecular docking studies aimed is to investigate the limiting affinities of trans-dehydrocrotonin molecule and to identify the main amino acid residues that could play a fundamental role in their mechanism of action of the SARS-CoV spike protein. Another aim of this work is all about to evaluate 10 hybrid functionalities, along with three base pairs using computational programs to discover which ones are more reliable with the experimental result the best computational method to study organic compounds. We compared the results between the mean absolute deviation (MAD) and root-mean-square deviation (RMSD) of the molecules, and the smallest number between them was the best result. The positions assumed by the ligands in the active site of the spike glycoprotein allow assuming associations with different local amino acids.

Hormones Nanofiltration in Carbon Nanotubes and Boron Nitride Nanotubes Using Uniform External Electric Field Through Molecular Dynamics.

Hormones are a dangerous group of molecules that can cause harm to humans. This study based on classical molecular dynamics proposes the nanofiltration of wastewater contaminated by hormones from a computer simulation study, in which the water and the hormone were filtered in two single-walled nanotube compositions. The calculations were carried out by changing the intensities of the electric field that acted as a force exerting pressure on the filtration along the nanotube, in the simulation time of 100 ps. The hormones studied were estrone, estradiol, estriol, progesterone, ethinylestradiol, diethylbestrol, and levonorgestrel in carbon nanotubes (CNTs) and boron nitride (BNNTs). The most efficient nanofiltrations were for fields with low intensities in the order of 10-8 au and 10-7 au. The studied nanotubes can be used in membranes for nanofiltration in water treatment plants due to the evanescent field potential caused by the action of the electric field inside. Our data showed that the action of EF in conjunction with the van der Walls forces of the nanotubes is sufficient to generate the attractive potential. Evaluating the transport of water molecules in CNTs and BNNTs, under the influence of the electric field, a sequence of simulations with the same boundary conditions was carried out, seeking to know the percentage of water molecules filtered in the nanotubes.

Density functional theory for the thermodynamic gas-phase investigation of butanol biofuel and its isomers mixed with gasoline and ethanol.

Herein, we present the results of our study on the thermodynamic properties of the isomers of butanol (n-butanol, 2-butanol, i-butanol, and t-butanol) to evaluate their thermodynamic potential as a complementary biofuel and/or substitute for ethanol and gasoline. The Gaussian09W software was used to perform molecular geometry optimization calculations using density functional theory with the B3lyp hybrid function using the base set 6-311++g(d,p) and the compound methods G3, G4, and CBS-QB3. Calculations of the fundamental frequency of the molecules were performed to obtain the molecular vibration modes for the respective frequencies. These calculations provided thermodynamic parameters such as the entropy, enthalpy, and specific molar heat at constant pressure, all as a function of the temperature. The parameter values obtained by each method were compared to the experimental values available in the literature. The results showed good accuracy, especially those obtained at the B3lyp/6-311++g(d,p) level for n-butanol. The error between the theoretical and experimental values for the combustion enthalpy of n-butanol was less than 4% at 298.15 K; due to the good prediction of its thermodynamic properties, we used n-butanol as a model for the prediction of other thermodynamic properties. We started a molecular docking study of four ligands, namely, n-butanol, ethanol, propanol, heptane, isooctane, and methanol interacting with butanol isomers. The highest values of affinity energy found were for N-butanol. The possible formation of hydrogen bonds, associations by means of London forces, hydrogen, and alkyl interactions were analyzed. n-Butanol was added to ethanol-gasoline mixtures in the temperature range of 298.15 to 600 K and the results suggest that n-butanol has a higher calorific value than gasoline-ethanol mixtures in G30E, G40E, G50E, G60E, G70E, G80E, G90E, and E100 blends. As such, n-butanol releases greater amounts of heat during combustion and is thus a viable alternative to biofuels.