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.
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.
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.
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.
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