Anti-leishmanial activity of Herb. and predictions of isoeleutherin and its analogues.

Leishmaniasis is caused by protozoa of the genus , 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 anti-leishmanial potential of the extract, fractions, and isoeleutherin from , as well as the interactions of isoeleutherin and its analogs with Trypanothione Reductase (TR), in addition to predicting pharmacokinetic parameters.

In Silico Analysis of a GH3 β-Glucosidase from CACIAM 03.

Cyanobacteria are rich sources of secondary metabolites and have the potential to be excellent industrial enzyme producers. β-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.

Analysis of Kojic Acid Derivatives as Competitive Inhibitors of Tyrosinase: A Molecular Modeling Approach.

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.

Virtual Screening and Molecular Dynamics Simulations from a Bank of Molecules of the Amazon Region Against Functional NS3-4A Protease-Helicase Enzyme of Hepatitis C Virus.

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.

Docking and molecular dynamics simulation of quinone compounds with trypanocidal activity.

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.

A neural networks study of quinone compounds with trypanocidal activity.

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.

A quantum chemical and statistical study of flavonoid compounds (flavones) with anti-HIV activity.

The molecular orbital semi-empirical method AM1 was employed to calculate a set of molecular properties (variables) of 22 flavonoid compounds (flavones) with anti-HIV-1 activity and nine new compounds were proposed for anti-HIV-1 activity prediction. Pattern recognition techniques, principal component analysis (PCA), hierarchical cluster analysis (HCA), stepwise discriminant analysis (SDA) and K-nearest neighbor (KNN), were employed in order to reduce dimensionality and investigate which subset of variables could be more effective for classifying the flavones according to their degree of anti-HIV-1 activity. The PCA, HCA, SDA and KNN studies showed that the variables log P (partition coefficient), molecular volume (VOL) and electron affinity (EA) are responsible for the separation between anti-HIV-1 active and inactive compounds.