GSK3α: An Important Paralog in Neurodegenerative Disorders and Cancer.

The biological activity of the enzyme glycogen synthase kinase-3 (GSK3) is fulfilled by two paralogs named GSK3α and GSK3ß, which possess both redundancy and specific functions. The upregulated activity of these proteins is linked to the development of disorders such as neurodegenerative disorders (ND) and cancer. Although various chemical inhibitors of these enzymes restore the brain functions in models of ND such as Alzheimer's disease (AD), and reduce the proliferation and survival of cancer cells, the particular contribution of each paralog to these effects remains unclear as these molecules downregulate the activity of both paralogs with a similar efficacy. Moreover, given that GSK3 paralogs phosphorylate more than 100 substrates, the simultaneous inhibition of both enzymes has detrimental effects during long-term inhibition. Although the GSK3ß kinase function has usually been taken as the global GSK3 activity, in the last few years, a growing interest in the study of GSK3α has emerged because several studies have recognized it as the main GSK3 paralog involved in a variety of diseases. This review summarizes the current biological evidence on the role of GSK3α in AD and various types of cancer. We also provide a discussion on some strategies that may lead to the design of the paralog-specific inhibition of GSK3α.

Cellulose-Silica Nanocomposites of High Reinforcing Content with Fungi Decay Resistance by One-Pot Synthesis.

Hybrid bionanocomposites based on cellulose matrix, with silica nanoparticles as reinforcers, were prepared by one-pot synthesis of cellulose surface modified by solvent exchange method to keep the biopolymer net void for hosting inorganic nanoparticles. Neither expensive inorganic-particle precursors nor crosslinker agents or catalysts were used for effective dispersion of reinforcer concentration up to 50 wt %. Scanning electron microscopy of the nanocomposites shows homogeneous dispersion of reinforcers in the surface modified cellulose matrix. The FTIR spectra demonstrated the cellulose features even at 50 weight percent content of silica nanoparticles. Such a high content of silica provides high thermal stability to composites, as seen by TGA-DSC. The fungi decay resistance to Trametes versicolor was measured by standard test showing good resistance even with no addition of antifungal agents. This one-pot synthesis of biobased hybrid materials represents an excellent way for industrial production of high performance materials, with a high content of inorganic nanoparticles, for a wide variety of applications.

Hybrid Cellulose-Silica Materials from Renewable Secondary Raw Resources: An Eco-friendly Method.

Hybrid organic-inorganic materials based on cellulose matrix and silica particles are obtained from wastes of the local paper recycling mill and sugarcane mill as renewable secondary raw materials. The performance comparison of these hybrid materials made from secondary raw materials against the materials made from pure, raw sources is discussed. The Fourier transform infrared spectra show that cellulose features prevail even at 43 wt% silica nanoparticles in the hybrid materials. Such a high content of silica originated from sugarcane bagasse ash and hollow glass microspheres contributes to the high thermal stability of the final composites, as seen by thermogravimetric analysis with very low water absorption. This one-step approach of biobased hybrid materials represents an excellent way to produce high-performance materials with high content of inorganic nanoparticles for a wide variety of applications like energy efficient building material completely cement-free.

Ligand recognition properties of the vasopressin V2 receptor studied under QSAR and molecular modeling strategies.

The design of new drugs that target vasopressin 2 receptor (V2R) is of vital importance to develop new therapeutic alternatives to treat diseases such as heart failure, polycystic kidney disease. To get structural insights related to V2R-ligand recognition, we have used a combined approach of docking, molecular dynamics simulations (MD) and quantitative structure-activity relationship (QSAR) to elucidate the detailed interaction of the V2R with 119 of its antagonists. The three-dimensional model of V2R was built by threading methods refining its structure through MD simulations upon which the 119 ligands were subjected to docking studies. The theoretical results show that binding recognition of these ligands on V2R is diverse, but the main pharmacophore (electronic and π-π interactions) is maintained; thus, this information was validated under QSAR results. QSAR studies were performed using MLR analysis followed by ANN analysis to increase the model quality. The final equation was developed by choosing the optimal combination of descriptors after removing the outliers. The applicability domains of the constructed QSAR models were defined using the leverage and standardization approaches. The results suggest that the proposed QSAR models can reliably predict the reproductive toxicity potential of diverse chemicals, and they can be useful tools for screening new chemicals for safety assessment.

Docking and QSAR Studies of Aryl-valproic Acid Derivatives to Identify Antiproliferative Agents Targeting the HDAC8.

BACKGROUND: Histone deacetylase 8 (HDAC8) is a plausible target for the development of novel anticancer drugs using a metal-chelating group and hydrophobic moieties as pharmacophores. It is known that valproic acid (administered as its salt, sodium valproate; VPANa+) is an HDAC8 inhibitor characterized by its hydrophobic chains. Nevertheless, VPA is hepatotoxic and VPA analogues might be explored for less hepatotoxic antiproliferative compounds. METHOD: In this work, docking and QSAR studies of 500 aryl-VPA derivatives as possible HDAC8 inhibitors were performed in order to explore and select potential anti-proliferative compounds. Docking results identified π-π, hydrogen bonds as the most important noncovalent interactions between HDAC8 (PDB: 3F07) and the ligands tested, whereas Belm4 was the best QSAR descriptor and classified as a 2D-BCUT descriptor. RESULT: Based on theoretical studies, compound DAVP042 was synthesized and evaluated in vitro for its antiproliferative activities on several cancer cell lines (A549-lung, MCF-7-breast, HCT116-colon and U937- lymphoid tissue) in comparison to VPA, as well as for its inhibitory activity on HDAC8 using in vitro models. DAVP042 demonstrated to have antiproliferative activity on all cancer cell lines employed, not only suggesting that this compound should be further studied, but also demonstrating that the methodology herein employed is appropriated to identify new therapeutic candidates.

QSAR/QSPR as an application of artificial neural networks.

Quantitative Structure-Activity Relationships (QSARs) and Quantitative Structure-Property Relationships (QSPRs) are mathematical models used to describe and predict a particular activity/property of compounds. On the other hand, the Artificial Neural Network (ANN) is a tool that emulates the human brain to solve very complex problems. The exponential need for new compounds in the drug industry requires alternatives for experimental methods to decrease development time and costs. This is where chemical computational methods have a great relevance, especially QSAR/QSPR-ANN. This chapter shows the importance of QSAR/QSPR-ANN and provides examples of its use.

Exploring the ligand recognition properties of the human vasopressin V1a receptor using QSAR and molecular modeling studies.

Vaptans are compounds that act as non-peptide vasopressin receptor antagonists. These compounds have diverse chemical structures. In this study, we used a combined approach of protein folding, molecular dynamics simulations, docking, and quantitative structure-activity relationship (QSAR) to elucidate the detailed interaction of the vasopressin receptor V1a (V1aR) with some of its blockers (134). QSAR studies were performed using MLR analysis and were gathered into one group to perform an artificial neural network (ANN) analysis. For each molecule, 1481 molecular descriptors were calculated. Additionally, 15 quantum chemical descriptors were calculated. The final equation was developed by choosing the optimal combination of descriptors after removing the outliers. Molecular modeling enabled us to obtain a reliable tridimensional model of V1aR. The docking results indicated that the great majority of ligands reach the binding site under π-π, π-cation, and hydrophobic interactions. The QSAR studies demonstrated that the heteroatoms N and O are important for ligand recognition, which could explain the structural diversity of ligands that reach V1aR.

Exploring QSARs for inhibitory effect of a set of heterocyclic thrombin inhibitors by multilinear regression refined by artificial neural network and molecular docking simulations.

Several non-peptide heterocyclic compounds reported as potent thrombin inhibitors in vitro were chosen to carry out a QSAR study upon them using MLR and ANN analysis. In order to identify the best QSAR models, the input for ANN consisted of those subsets of descriptors used in the MLR models. The best QSAR models contained the SIC0 descriptor as the main topological descriptor. To identify the physical and chemical properties involved in the ligand-thrombin complexes, an automated ligand-flexible docking procedure was used. The docking results suggest that the thrombin inhibition by these heterocyclic compounds is driven by π-π, hydrogen bonds and salt bridge interactions. The best Gibbs free energy of ligand binding was found at the thrombin sites S1 and D. We have shown that it is possible to build MLR models with geometries taken from two different sources (semi-empirical and MD geometries) and obtain similar results.

MLR-ANN and RTO approach to mu-opioid receptor-binding affinity. Pooling data from different sources.

One hundred and six morphinan derivatives were taken from the Drug Evaluation Committee reports to propose several quantitative structure-activity relationship models to describe the mu-receptor-binding affinity. After several procedures to reduce the descriptor number, 21 descriptors were selected for the descriptor pool by a complete Multiple Linear Regression methodology. In this procedure only three molecules were considered as outliers. Several tests changing the relation between training:predicted sets were considered to find the best relation between these sets. The higher the number of molecules in the predicted set the higher the predictive power was observed. The optimal number of descriptors was established using the Akaike's information criterion and Kubinyi fitness function parameters. The Artificial Neuron Network methodology was applied to improve the Multiple Linear Regression best result. Finally, the regression through the origin methodology was applied to establish the best model from the Artificial Neuron Network methodology. The best quantitative structure-activity relationship model was proven to be independent of chance correlation.

QSAR study on the relaxant agents from some Mexican medicinal plants and synthetic related organic compounds.

Quantitative Structure-Activity Relationship studies were performed to describe and predict the antispasmodic activity of some molecules isolated from Mexican Medicinal Flora as well as for some synthetic ones based on stilbenoid bioisosteres. The relaxant activity of these molecules was taken from experiments on rat and guinea-pig ileum tissues. Given that there is some evidence of species-specific on the relaxant effects, two data sets were proposed, one for rat ileum and the other for guinea-pig ileum. These data were statistically treated in order to find a Quantitative Structure-Activity Relationship model that could describe the corresponding biological models. The goodness of prediction for the best models was measured in terms of the Leave-One-Out Cross-Validation R(2) (LOO q(2)) and the correlation coefficients of regressions through the origin (RTO R(2)0). Results show that papaverine activity could not be used as reference in rat ileum tests; however, this molecule can be used as a good reference molecule in guinea-pig ileum tests. Our study shows that MATS5p and R8m+ descriptors are the most important descriptors in predicting the rat ileum activity and that atomic polarizability is the main atomic property. On the other hand, the R3u GETAWAY descriptor turns out to be important in predicting the guinea-pig ileum activity where the influence/distance of substituents on these molecules could describe the observed activity.

QSAR study on the antinociceptive activity of some morphinans.

Quantitative structure-activity relationship studies were performed to describe and predict the antinociceptive activity of 31 morphinan derivatives reported by the US Drug Evaluation Committee in 2005 and 2006. From these, three data sets were constructed and several models were calculated following the multiple linear regression and Leave-One-Out Cross-Validation (LOO-CV) tests. In general, these models achieved good descriptive power (approximately 92%) as well as predictive power (approximately 76%), but were unable to predict an external validation set of morphinan derivatives. When artificial neural networks were applied to these models, an improvement of the predictive and external validation values was obtained. It was observed that the results of the NN models are significantly better that those obtained by multiple linear regression. In spite that the problem under investigation can be handled adequately by a linear model, a neural network does bring slight improvements in the predictive power.

Structural preferences of neuroprotective S14G-humanin peptide analyzed by molecular modeling and circular dichroism.

S14G-humanin (S14G-HN) is one of the latest of a new family of neuropeptides with protective action against Alzheimer's disease insults. The structure of S14G-HN was studied with both spectroscopic techniques and molecular dynamics simulation. Secondary structure predictions and modeling of backbone conformation were carried out. Side chain reconstruction, homology modeling and molecular dynamics (MD) simulations were performed on four different models. A beta strand tendency in residues 5 to 10 and a propensity to adopt turn or irregular conformation in residues 13 to 17 was found. Circular dichroism experimental studies of S14G-HN in aquaeous solution and in different 2,2,2-trifluoroethanol (TFE) concentrations were also performed. In the absence of TFE and at low TFE concentrations, CD spectra are indicative of a small degree of ordering in the peptide. On further increment of TFE concentration, changes occur that indicate the formation of a structured conformation. Both experimental and computational results indicate that S14G-HN has a reduced helical propensity, in contrast with wild type humanin, as well as a higher conformational flexibility.

Effect of water molecules on the fluorescence enhancement of Aflatoxin B1 mediated by Aflatoxin B1:beta-cyclodextrin complexes. A theoretical study.

In order to explain the observed fluorescence enhancement of Aflatoxin B1 (AFB1) when forming AFB1:beta-cyclodextrin (AFB1:beta-CD) inclusion complexes, we have performed a theoretical (quantum chemistry calculations) study of AFB1 and AFB1:beta-CD in vacuum and in the presence of aqueous solvent. The AM1 method was used to calculate the absorption and emission wavelengths of these molecules. With the help of density functional theory (DFT) and time-dependent DFT (TDDFT) vibrational frequencies and related excitation energies of AFB1 and AFB1.(H2O)m = 4,5,6,11 were calculated. On the basis of these calculations we propose a plausible mechanism for the fluorescence enhancement of AFB1 in the presence of beta-CD: (1) before photoexcitation of AFB1 to its S1 excited state, there is a vibrational coupling between the vibrational modes involving the AFB1 carbonyl groups and the bending modes of the nearby water molecules (CG + WM); (2) these interactions allow a thermal relaxation of the excited AFB1 molecules that results in fluorescence quenching; (3) when the AFB1 molecules form inclusion complexes with beta-CD the CG + WM interaction decreases; and (4) this gives rise to a fluorescence enhancement.

Laser ablation synthesis of selenium superoxide anion SeO4- via selenium trioxide photolysis. Time-of-flight mass spectrometry and ab initio calculations.

Laser desorption/ionisation and laser ablation of solid selenium trioxide, as well as the gas-phase behaviour of selenium trioxide, were studied. Selenium trioxide undergoes photochemical decomposition and, from the mass spectra obtained by laser desorption/ionisation time-of-flight mass spectrometry (LDI-TOF-MS), the following species were identified: O-, O2-, O3-, SeO-, SeO2-, SeO3-, SeO4-, Se2O7-, Se3O11-, and Se4O14-. Formation of the selenium superoxide SeO4- anion is described in this work for the first time. In addition, low-abundance selenium species such as Se2O8H2-, Se3O11H-, and Se4O15H2- were also detected. The stoichiometry of all ions was confirmed via isotopic pattern modeling and/or post-source decay (PSD) analysis. Photolysis of selenium trioxide leads partly to ozone formation. It was found that the most likely mechanisms of selenium superoxide formation are oxidation of selenium trioxide with ozone and/or reactive oxygen radicals, or photolysis of selenium trioxide tetramer (SeO3)4. Therefore, ab initio calculations were performed to support the mass spectrometric evidence and to suggest probable geometries for selenium superoxide anion SeO4- and diselenium superoxide anion Se2O7-, as well as to provide insight into and/or predict possible formation pathways. It has been found that both cyclic and non-cyclic peroxide structures of SeO4- and Se2O7- ions are possible. In addition, the SeO4 structure was also calculated guided by thermodynamic considerations using Gaussian-2 methodology, and the inferred stability of the SeO4 neutral molecule was supported by ab initio calculations.