Afshin Fassihi

Homology modeling of human CCR5 and analysis of its binding properties through molecular docking and molecular dynamics simulation

In this study, homology modeling, molecular docking and molecular dynamics simulation were performed to explore structural features and binding mechanism of some inhibitors of chemokine receptor type 5 (CCR5), and to construct a model for designing new CCR5 inhibitors for preventing HIV attachment to the host cell. A homology modeling procedure was employed to construct a 3D model of CCR5. For this procedure, the X-ray crystal structure of bovine rhodopsin (1F88A) at 2.80Å resolution was used as template. After inserting the constructed model into a hydrated lipid bilayer, a 20ns molecular dynamics (MD) simulation was performed on the whole system. After reaching the equilibrium, twenty-four CCR5 inhibitors were docked in the active site of the obtained model. The binding models of the investigated antagonists indicate the mechanism of binding of the studied compounds to the CCR5 obviously. Moreover, 3D pictures of inhibitor-protein complex provided precious data regarding the binding orientation of each antagonist into the active site of this protein. One additional 20 ns MD simulation was performed on the initial structure of the CCR5-ligand 21 complex, resulted from the previous docking calculations, embedded in a hydrated POPE bilayer to explore the effects of the presence of lipid bilayer in the vicinity of CCR5-ligand complex. This article is part of a Special Issue entitled Protein translocation across or insertion into membranes.

Synthesis, antimicrobial evaluation and QSAR study of some 3-hydroxypyridine-4-one and 3-hydroxypyran-4-one derivatives.

A series of Mannich bases of 2-alkyl-3-hydroxy-pyridine-4-ones, namely 2-alkyl-3-hydroxy-5-N-piperidylmethyl or N,N-dialkylaminomethyl pyridine-4-ones 9, 10 and 15-18, two derivatives of N-aryl-2-methyl-3-hydroxy-pyridine-4-ones 19, 20 and two N-alkyl derivatives of maltol, 21 and 22 were prepared. They were screened for their antibacterial and antifungal activities against a variety of microorganisms using micro plate Alamar Blue((R)) assay (MABA) method. Multiple linear regressions (MLR) analysis was performed for the synthesized compounds as well as a series of pyridinone and pyranone derivatives 23-43 which have been synthesized and evaluated for antimicrobial activity by other researchers previously. Studied compounds showed a better quantitative structure-activity relationship (QSAR) model for the antimicrobial activity against Candida albicans and Staphylococcus aureus in comparison with other tested microorganisms.

Synthesis and evaluation of dextran-budesonide conjugates as colon specific prodrugs for treatment of ulcerative colitis.

Budesonide is a potent glucocorticoid with high affinity for the glucocorticoid receptor, which is now used for the treatment of inflammatory bowel diseases. Current oral formulations of budesonide present low efficacy against ulcerative colitis because of the premature drug release in the upper part of the gastrointestinal tract. The objective of this study was to develop a colon specific delivery system for budesonide to increase the efficacy in the treatment of ulcerative colitis. Dextran-budesonide conjugates were prepared with different molecular weights (MW) of dextran (10,000, 70,000 and 500,000) in the presence of dimethylaminopyridine (DMAP) using succinate spacer. The conjugates were characterized by (1)H NMR and IR spectroscopy and elemental analysis. The degree of substitution, aqueous solubility and chemical stability of conjugates in HCl 0.1N, phosphate buffer solutions pH 6.8 and 7.4 were studied. Drug release characteristics of the conjugates were also studied in the presence of the luminal contents of different segments of the rat gastrointestinal tract. Degree of substitution (DS) was dependent on the polymer MW and was 19.33, 14.29 and 11.60 mg/100 mg conjugate for MW 10,000, 70,000 and 500,000, respectively. Solubility of the drug in conjugates of MW 10,000 and 70,000 was increased with respect to the free drug and was dependent on DS. The three conjugates were found to be stable in HCl 0.1N, phosphate buffer solutions pH 6.8 and 7.4 incubated at 37 degrees C within 6 h and the rate constants for degradation of conjugates to budesonide and budesonide hemisuccinate were less than 0.006 h(-1). Less than 10% of the drug was released in contents of the stomach and small intestine, while about two-fold increase was observed after incubating the conjugates with colonic luminal contents. Conjugate prepared by dextran 70,000 showed the most desirable solubility, stability and release properties and could therefore be evaluated in vivo, for potential clinical use in the treatment of ulcerative colitis.

QSAR study of anthranilic acid sulfonamides as inhibitors of methionine aminopeptidase-2 using LS-SVM and GRNN based on principal components

Quantitative relationships between molecular structure and methionine aminopeptidase-2 inhibitory activity of a series of cytotoxic anthranilic acid sulfonamide derivatives were discovered. We have demonstrated the detailed application of two efficient nonlinear methods for evaluation of quantitative structure-activity relationships of the studied compounds. Components produced by principal component analysis as input of developed nonlinear models were used. The performance of the developed models namely PC-GRNN and PC-LS-SVM were tested by several validation methods. The resulted PC-LS-SVM model had a high statistical quality (R(2)=0.91 and R(CV)(2)=0.81) for predicting the cytotoxic activity of the compounds. Comparison between predictability of PC-GRNN and PC-LS-SVM indicates that later method has higher ability to predict the activity of the studied molecules.

Application of PC-ANN and PC-LS-SVM in QSAR of CCR1 antagonist compounds: A comparative study

Principal component regression (PCR), principal component-artificial neural network (PC-ANN), and principal component-least squares-support vector machine (PC-LS-SVM) as regression methods were investigated for building quantitative structure-activity relationships for the prediction of inhibitory activity of some CCR1 antagonists. Nonlinear methods (PC-ANN and PC-LS-SVM) were better than the PCR method considerably in the goodness of fit and predictivity parameters and other criteria for evaluation of the proposed model. These results reflect a nonlinear relationship between the principal components obtained from molecular descriptors and the inhibitory activity of this set of molecules. The maximum variance in activity of the molecules, in PCR method was 45.5%, whereas nonlinear methods, PC-ANN and PC-LS-SVM, could explain more than 93.7% and 95.6% variance in activity data respectively.

QSAR study of isatin analogues as in vitro anti-cancer agents

Quantitative structure activity relationships (QSAR) of anti-cancer isatin derivatives were discovered by multiple linear regressions (MLR) and genetic algorithm partial least squares (GA-PLS) methods. Topological, chemical, geometrical and functional groups descriptors were found to be effective parameters on the cytotoxic activity. The positive effects of the number of halogen atoms and the number of total secondary carbons, and the negative effects of the number of secondary amides, and the number of ketones on the anti-cancer activity were in agreement with previous SAR studies. Hansch analysis showed the importance of lipophilic R(3) and R(5) substituents. Between MLR and GA-PLS, MLR represented superior results with a high statistical quality (R(2)=0.92 and Q(2)=0.90) for predicting the activity of the compounds.

Validated QSAR analysis of some diaryl substituted pyrazoles as CCR2 inhibitors by various linear and nonlinear multivariate chemometrics methods

Quantitative relationships between structures of 26 diaryl substituted pyrazoles as CCR2 inhibitors and their activities were investigated by four linear and nonlinear methods namely MLR, PLS, GRNN and LS-SVM. The obtained models were able to describe about 83%, 87%, 86%, and 0.91% of the variance in the experimental activity of molecules in training set, respectively. The accuracy and predictability of the proposed models were illustrated using various evaluation techniques. Some of them were: cross-validation, validation through an external test set, and Y-randomization. Furthermore, various criteria suggested by Tropsha and Roy were applied for evaluation of predictability of developed models. A comparison between the four different developed methods indicates that LS-SVM can be preferred as supreme model.

QSAR Study of p56 lck Protein Tyrosine Kinase Inhibitory Activity of Flavonoid Derivatives Using MLR and GA-PLS

Quantitative relationships between molecular structure and p56lck protein tyrosine kinase inhibitory activity of 50 flavonoid derivatives are discovered by MLR and GA-PLS methods. Different QSAR models revealed that substituent electronic descriptors (SED) parameters have significant impact on protein tyrosine kinase inhibitory activity of the compounds. Between the two statistical methods employed, GA-PLS gave superior results. The resultant GA-PLS model had a high statistical quality (R2 = 0.74 and Q2 = 0.61) for predicting the activity of the inhibitors. The models proposed in the present work are more useful in describing QSAR of flavonoid derivatives as p56lck protein tyrosine kinase inhibitors than those provided previously.

QSAR Analysis for Some Diaryl‐substituted Pyrazoles as CCR2 Inhibitors by GA‐Stepwise MLR

Quantitative relationships between calculated molecular structure and 26 diaryl‐substituted pyrazoles CCR2 inhibitors were investigated by GA‐stepwise multiple linear regression. In multiple linear regression analysis, the quantitative structure–activity relationship models were constructed by grouping descriptors and also dual selection of variables using genetic algorithm and stepwise selection methods from each group of the pool of all calculated descriptors. The accuracy of the proposed multiple linear regression model was demonstrated using the following evaluation techniques: cross‐validation, validation through an external test set, and Y‐randomization. Furthermore, the domain of applicability that shows the area of reliable predictions was defined. The prediction results were in good agreement with the experimental values.

Application of partial least squares and radial basis function neural networks in multivariate imaging analysis-quantitative structure activity relationship: study of cyclin dependent kinase 4 inhibitors.

The detailed application of multivariate image analysis (MIA) method for the evaluation of quantitative structure activity relationship (QSAR) of some cyclin dependent kinase 4 inhibitors is demonstrated. MIA is a type of data mining methods that is based on data sets obtained from 2D images. The purpose of this study is to construct a relationship between pixels of images of investigated compounds as independent and their bioactivities as a dependent variable. Partial least square (PLS) and principal components-radial basis function neural networks (PC-RBFNNs) were developed to obtain a statistical explanation of the activity of the molecules. The performance of developed models were tested by several validation methods such as external and internal tests and also criteria recommended by Tropsha and Roy. The resulted PLS model had a high statistical quality (R2 = 0.991 and R2(CV) = 0.993) for predicting the activity of the compounds. Because of high correlation between values of predicted and experimental activities, MIA-QSAR proved to be a highly predictive approach.