In-silico investigation of tubulin binding modes of a series of novel antiproliferative spiroisoxazoline compounds using docking studies

Interference with microtubule polymerization results in cell cycle arrest leading to cell death. Colchicine is a well-known microtubule polymerization inhibitor which does so by binding to a specific site on tubulin. A set of 3, 4-bis [substituted phenyl]-4H-spiro [indene-2, 5-isoxazol]-1[3H]-one derivatives with known antiproliferative activities were evaluated for their tubulin binding modes. 3D structures of the derivatives were docked into the colchicine binding site of tubulin using GOLD 5.0 program under flexible ligand and semi-flexible receptor condition. The spiroisoxazoline derivatives bind tubulin in a similar manner to colchicine by establishing at least a hydrogen bonding to Cys[241] as well as hydrophobic interactions with Leu[255], Ile[378] and Lys[254] and few other residues at the binding pocket. It can be concluded that the spiroisoxazoline core structure common to the studied derivatives is a suitable scaffold for placing the antitubulin pharmacophoric groups in appropriate spatial positions required for tubulin binding activity

Comparison of different 2D and 3D-QSAR methods on activity prediction of histamine H3 receptor antagonists

Histamine H3 receptor subtype has been the target of several recent drug development programs. Quantitative structure-activity relationship [QSAR] methods are used to predict the pharmaceutically relevant properties of drug candidates whenever it is applicable. The aim of this study was to compare the predictive powers of three different QSAR techniques, namely, multiple linear regression [MLR], artificial neural network [ANN], and HASL as a 3D QSAR method, in predicting the receptor binding affinities of arylbenzofuran histamine H3 receptor antagonists. Genetic algorithm coupled partial least square as well as stepwise multiple regression methods were used to select a number of calculated molecular descriptors to be used in MLR and ANN-based QSAR studies. Using the leave-group-out cross-validation technique, the performances of the MLR and ANN methods were evaluated. The calculated values for the mean absolute percentage error [MAPE], ranging from 2.9 to 3.6, and standard deviation of error of prediction [SDEP], ranging from 0.31 to 0.36, for both MLR and ANN methods were statistically comparable, indicating that both methods perform equally well in predicting the binding affinities of the studied compounds toward the H3 receptors. On the other hand, the results from 3D-QSAR studies using HASL method were not as good as those obtained by 2D methods. It can be concluded that simple traditional approaches such as MLR method can be as reliable as those of more advanced and sophisticated methods like ANN and 3D-QSAR analyses

In silico evaluation of cross linking effects on denaturant meq values and delta Cp upon protein unfolding

Important thermodynamic parameters including denaturant equilibrium m values [meq] and heat capacity changes [delta Cp] can be predicted based on changes in Solvent Accessible Surface Area [SASA] upon unfolding. Cross links such as disulfide bonds influence the stability of the proteins by decreasing the entropy gain as well as reduction of SASA of unfolded state. The aim of the study was to develop mathematical models to predict the effect of cross links on delta SASA and ultimately on meq and delta Cp based on in silico methods. Changes of SASA upon computationally simulated unfolding were calculated for a set of 45 proteins with known meq and delta Cp values and the effect of cross links on delta SASA of unfolding was investigated. The results were used to predict the meq of denaturation for guanidine hydrochloride and urea, as well as delta Cp for the studied proteins with overall error of 20%, 31% and 17%, respectively. The results of the current study were in close agreement with those obtained from the previous studies

Solubility prediction of sulfonamides at various temperatures using a single determination

Solubility of sulphamethoxazole, sulphisoxazole and sulphasalazine in six solvents namely water,methanol, ethanol, 1-propanol, acetone and chloroform were determined at 15, 25, 37 and 45 °C. Two models derived from the Hildebrand solubility approach are proposed for solubility prediction at different temperatures using a single determination. The experimental data of the present work as well as data gathered from the literature have been employed to investigate the accuracy and prediction capability of the proposed models. The overall percent deviations between the predicted and experimental values were 10.78 and 14.63% which were comparable to those of the classical two and three parameter models. The proposed models were much superior to the two pure predictive models i.e., the ones which do not require experimental solubility determination, as the overall percent deviations produced by the latter models were 150.09 and 161.00%