Electrochemical and thermodynamic investigation of diniconazole and triadimefon as corrosion inhibitors for copper in synthetic seawater

This paper presents the investigation of diniconzole and triadimefon as chemical corrosion inhibitors for freshly polished copper in synthetic seawater (3.5% NaCl solution). Determination of weight loss, polarization curves, electrochemical impedance spectroscopy (EIS), and SEM, were performed to analyze the inhibiting performance of these compounds. Polarization curves show that they act as mixed-type inhibitors. EIS indicates that an adsorption film of the inhibitors is formed on copper surface. The highest values of inhibition efficiency are respectively, 99.2% and 97.3% at 100 mg/L concentration. Thermodynamic calculation suggests that chemisorptions between the compounds and copper are accordance with Langmuir adsorption isotherm. (C) 2010 Elsevier Ltd. All rights reserved.

Docking and molecular dynamics study on the inhibitory activity of coumarins on aldose reductase

In order to explore the inhibitory mechanism of coumarins toward aldose reductase (ALR2), AutoDock and Gromacs software were used for docking and molecular dynamics studies on 14 coumarins (CM) and ALR2 protease. The docking results indicate that residues TYR48, HIS110, and TRP111 construct the active pocket of ALR2 and, besides van der Waals and hydrophobic interaction, CM mainly interact with ALR2 by forming hydrogen bonds to cause inhibitory behavior. Except for CM1, all the other coumarins take the lactone part as acceptor to build up the hydrogen bond network with active-pocket residues. Unlike CM3, which has two comparable binding modes with ALR2, most coumarins only have one dominant orientation in their binding sites. The molecular dynamics calculation, based on the docking results, implies that the orientations of CM in the active pocket show different stabilities. Orientation of CM1 and CM3a take an unstable binding mode with ALR2; their conformations and RMSDs relative to ALR2 change a lot with the dynamic process. While the remaining CM are always hydrogen-bonded with residues TYR48 and HIS110 through the carbonyl O atom of the lactone group during the whole process, they retain the original binding mode and gradually reach dynamic equilibrium.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.