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Introduction: Leishmaniasis is caused by protozoa of the genus Leishmania, classified as tegumentary and visceral. The disease treatment is still a serious problem, due to the toxic effects of available drugs, the costly treatment and reports of parasitic resistance, making the search for therapeutic alternatives urgent. This study assessed the in vitro anti-leishmanial potential of the extract, fractions, and isoeleutherin from Eleutherine plicata, as well as the in silico interactions of isoeleutherin and its analogs with Trypanothione Reductase (TR), in addition to predicting pharmacokinetic parameters. Methods: From the ethanolic extract of E. plicata (EEEp) the dichloromethane fraction (FDEp) was obtained, and isoeleutherin isolated. All samples were tested against promastigotes, and parasite viability was evaluated. Isoeleutherin analogues were selected based on similarity in databases (ZINK and eMolecules) to verify the impact on structural change. Results and Discussion: The extract and its fractions were not active against the promastigote form (IC50 > 200 µg/mL), while isoeleutherin was active (IC50 = 25 µg/mL). All analogues have high intestinal absorption (HIA), cell permeability was moderate in Caco2 and low to moderate in MDCK. Structural changes interfered with plasma protein binding and blood-brain barrier permeability. Regarding metabolism, all molecules appear to be CYP3A4 metabolized and inhibited 2-3 CYPs. Molecular docking and molecular dynamics assessed the interactions between the most stable configurations of isoeleutherin, analogue compound 17, and quinacrine (control drug). Molecular dynamics simulations demonstrated stability and favorable interactions with TR. In summary, fractionation contributed to antileishmanial activity and isoleutherin seems to be promising. Structural alterations did not contribute to improve pharmacokinetic aspects and analogue 17 proved to be more promising than isoeleutherin, presenting better stabilization in TR.

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Cyanobacteria are rich sources of secondary metabolites and have the potential to be excellent industrial enzyme producers. ß-glucosidases are extensively employed in processing biomass degradation as they mediate the most crucial step of bioconversion of cellobiose (CBI), hence controlling the efficiency and global rate of biomass hydrolysis. However, the production and availability of these enzymes derived from cyanobacteria remains limited. In this study, we evaluated the ß-glucosidase from Microcystis aeruginosa CACIAM 03 (MaBgl3) and its potential for bioconversion of cellulosic biomass by analyzing primary/secondary structures, predicting physicochemical properties, homology modeling, molecular docking, and simulations of molecular dynamics (MD). The results showed that MaBgl3 derives from an N-terminal domain folded as a distorted ß-barrel, which contains the conserved His-Asp catalytic dyad often found in glycosylases of the GH3 family. The molecular docking results showed relevant interactions with Asp81, Ala271 and Arg444 residues that contribute to the binding process during MD simulation. Moreover, the MD simulation of the MaBgl3 was stable, shown by analyzing the root mean square deviation (RMSD) values and observing favorable binding free energy in both complexes. In addition, experimental data suggest that MaBgl3 could be a potential enzyme for cellobiose-hydrolyzing degradation.

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Tyrosinases belong to the functional copper-containing proteins family, and their structure contains two copper atoms, in the active site, which are coordinated by three histidine residues. The biosynthesis of melanin in melanocytes has two stages depending on the actions of the natural substrates L-DOPA and L-tyrosine. The dysregulation of tyrosinase is involved in skin cancer initiation. In the present study, using molecular modeling tools, we analyzed the inhibition activity of tyrosinase activity using kojic acid (KA) derivatives designed from aromatic aldehydes and malononitrile. All derivatives showed conformational affinity to the enzyme active site, and a favorable distance to chelate the copper ion, which is essential for enzyme function. Molecular dynamics simulations revealed that the derivatives formed promising complexes, presenting stable conformations with deviations between 0.2 and 0.35 Å. In addition, the investigated KA derivatives showed favorable binding free energies. The most stable KA derivatives showed the following binding free energies: -17.65 kcal mol-1 (D6), -18.07 kcal mol-1 (D2), -18.13 (D5) kcal mol-1, and -10.31 kcal mol-1 (D4). Our results suggest that these derivatives could be potent competitive inhibitors of the natural substrates of L-DOPA (-12.84 kcal mol-1) and L-tyrosine (-9.04 kcal mol-1) in melanogenesis.

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Hepatitis C virus (HCV) infection is a disease that affects approximately 3% of the global population and requires new therapeutic agents without the inconvenience associated with current anti-HCV treatment. This paper reports on a study of a virtual screening and a molecular dynamics simulation of compounds derived from natural products from the Amazon region that are potentially effective against the NS3-4A enzyme of HCV, which plays an important role in the replication process of this virus. According to the results of the molecular docking calculations and subsequent consensual analysis, the best scored compounds showed interactions between hydrogen and residues of the catalytic triad as well as interactions with residues that guide ligands to the active site of the enzyme. They also showed stability in the molecular dynamics simulation, as the structures preserved important interactions at the active site of the enzyme. The root mean square deviation (RMSD) values were stabilized at the end of the simulation time. Such compounds are considered promising as novel therapies against HCV.

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In this work, two different docking programs were used, AutoDock and FlexX, which use different types of scoring functions and searching methods. The docking poses of all quinone compounds studied stayed in the same region in the trypanothione reductase. This region is a hydrophobic pocket near to Phe396, Pro398 and Leu399 amino acid residues. The compounds studied displays a higher affinity in trypanothione reductase (TR) than glutathione reductase (GR), since only two out of 28 quinone compounds presented more favorable docking energy in the site of human enzyme. The interaction of quinone compounds with the TR enzyme is in agreement with other studies, which showed different binding sites from the ones formed by cysteines 52 and 58. To verify the results obtained by docking, we carried out a molecular dynamics simulation with the compounds that presented the highest and lowest docking energies. The results showed that the root mean square deviation (RMSD) between the initial and final pose were very small. In addition, the hydrogen bond pattern was conserved along the simulation. In the parasite enzyme, the amino acid residues Leu399, Met400 and Lys402 are replaced in the human enzyme by Met406, Tyr407 and Ala409, respectively. In view of the fact that Leu399 is an amino acid of the Z site, this difference could be explored to design selective inhibitors of TR.

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This work investigates neural network models for predicting the trypanocidal activity of 28 quinone compounds. Artificial neural networks (ANN), such as multilayer perceptrons (MLP) and Kohonen models, were employed with the aim of modeling the nonlinear relationship between quantum and molecular descriptors and trypanocidal activity. The calculated descriptors and the principal components were used as input to train neural network models to verify the behavior of the nets. The best model for both network models (MLP and Kohonen) was obtained with four descriptors as input. The descriptors were T5 (torsion angle), QTS1 (sum of absolute values of the atomic charges), VOLS2 (volume of the substituent at region B) and HOMO-1 (energy of the molecular orbital below HOMO). These descriptors provide information on the kind of interaction that occurs between the compounds and the biological receptor. Both neural network models used here can predict the trypanocidal activity of the quinone compounds with good agreement, with low errors in the testing set and a high correctness rate. Thanks to the nonlinear model obtained from the neural network models, we can conclude that electronic and structural properties are important factors in the interaction between quinone compounds that exhibit trypanocidal activity and their biological receptors. The final ANN models should be useful in the design of novel trypanocidal quinones having improved potency.

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