3D-QSAR study of sulfonamide inhibitors of human carbonic anhydrase II

X-ray crystallographic structures as a source of ligand alignment in 3D-QSAR

Three-dimensional quantitative structure-activity relationships (3D-QSAR) analyses are methods correlating a pharmacological property with a mathematical representation of a molecular property distribution around three-dimensional molecular models for a set of congeners. 3D-QSAR methods are known to be highly sensitive to ligand conformation and alignment method. The current study collects 32 unique positions of congeneric ligands co-crystallized with the binding domain of AMPA receptors and aligns them using protein coordinates. Thus, it allows for a unique opportunity to consider a ligands' orientation aligned by their mode of binding in a native molecular target. Comparative molecular field analysis (CoMFA) models were generated for this alignment and compared with the results of analogous modeling using standard structure-based alignment or obtained in docking simulations of the ligands' molecules. In comparison with classically derived models, the model based on X-ray crystallographic studies showed much better performance and statistical significance. Although the 3D-QSAR methods are mainly employed when crystallographic information is limited, the current study underscores the importance that the selection of inappropriate molecular conformations and alignment methods can lead to generation of erroneous models and false conclusions.

Investigation of antigen-antibody interactions of sulfonamides with a monoclonal antibody in a fluorescence polarization immunoassay using 3D-QSAR models

A three-dimensional quantitative structure-activity relationship (3D-QSAR) model of sulfonamide analogs binding a monoclonal antibody (MAb_SMR) produced against sulfamerazine was carried out by Distance Comparison (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA). The affinities of the MAb_SMR, expressed as Log₁₀IC₅₀, for 17 sulfonamide analogs were determined by competitive fluorescence polarization immunoassay (FPIA). The results demonstrated that the proposed pharmacophore model containing two hydrogen-bond acceptors, two hydrogen-bond donors and two hydrophobic centers characterized the structural features of the sulfonamides necessary for MAb_SMR binding. Removal of two outliers from the initial set of 17 sulfonamide analogs improved the predictability of the models. The 3D-QSAR models of 15 sulfonamides based on CoMFA and CoMSIA resulted in q²_cv values of 0.600 and 0.523, and r² values of 0.995 and 0.994, respectively, which indicates that both methods have significant predictive capability. Connolly surface analysis, which mainly focused on steric force fields, was performed to complement the results from CoMFA and CoMSIA. This novel study combining FPIA with pharmacophore modeling demonstrates that multidisciplinary research is useful for investigating antigen-antibody interactions and also may provide information required for the design of new haptens.

3D-QSAR STUDIES OF SUBSTITUTED 4-ARYL/HETEROARYL-4H-CHROMENES AS APOPTOSIS INDUCERS USING CoMFA AND CoMSIA

Three-dimensional (3D) quantitative structure–activity relationships (QSARs) of 36 apoptosis inducers, substituted 4-aryl/heteroaryl-4H-chromenes with anticancer activity against human breast cancer cell lines T47D, have been studied by using methods of comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA). The established 3D-QSAR models in training set show not only significant statistical quality, but also predictive ability, with high correlation coefficient (R2) values and cross-validation coefficient (q2) values: CoMFA (R2, q2: 0.944, 0.747), CoMSIA (R2, q2: 0.944, 0.704). Moreover, the predictive abilities of the CoMFA and CoMSIA models were further confirmed by a test set, giving the predictive correlation coefficients ([Formula: see text] values) of 0.845 and 0.851, respectively. Based on the CoMFA and CoMSIA contour map analyses, some key factors responsible for anticancer activity of this series of compounds have been found as follows: the steric interaction plays a decisive role in determining the anticancer activities of these compounds; bulky groups as substituent R 1 are not tolerated; in addition to a steric moderation, higher degree of electropositivity and hydrophobicity on the terminal alkyl of substituent R 2 might be favorable to the activity; the substituent R 3 should be hydrophobic; bulky and strong electron withdrawing groups for the substituent R 4 are not advantageous to the activity; simultaneously introducing large electronegative atoms as hydrogen-acceptors to the first atoms of the substituents R 5 and R 6 may increase the activity, but substituents R 5 and R 6 with a linking group – OCH 2 O – may decrease the activity. Such results can offer some useful theoretical references for understanding the action mechanism, designing more potent derivatives, and predicting their activities prior to synthesis.

Quantitative structure-activity relationship models with receptor-dependent descriptors for predicting peroxisome proliferator-activated receptor activities of thiazolidinedione and oxazolidinedione derivatives

A quantitative structure-activity relationship study has been carried out, in which the relationship between the peroxisome proliferator-activated receptor alpha and the peroxisome proliferator-activated receptor gamma agonistic activities of thiazolidinedione and oxazolidinedione derivatives and quantitative descriptors, V(site) calculated in a receptor-dependent manner is modeled. These descriptors quantify the volume occupied by the optimized ligands in regions that are either common or specific to the superimposed binding sites of the targets under consideration. The quantitative structure-activity relationship models were built by forward stepwise linear regression modeling for a training set of 27 compounds and validated for a test set of seven compounds, resulting in a squared correlation coefficient value of 0.90 for peroxisome proliferator-activated receptor alpha and of 0.89 for peroxisome proliferator-activated receptor gamma. The leave-one-out cross-validation and test set predictability squared correlation coefficient values for these models were 0.85 and 0.62 for peroxisome proliferator-activated receptor alpha and 0.89 and 0.50 for peroxisome proliferator-activated receptor gamma respectively. A dual peroxisome proliferator-activated receptor model has also been developed, and it indicates the structural features required for the design of ligands with dual peroxisome proliferator-activated receptor activity. These quantitative structure-activity relationship models show the importance of the descriptors here introduced in the prediction and interpretation of the compounds affinity and selectivity.

Some QSAR studies for a group of sulfonamide Schiff base as carbonic anhydrase CA II inhibitors

In the present study, quantitative structure–activity-relationship (QSAR) study on a group of sulfonamide Schiff-base inhibitors of Carbonic Anhydrase (CA) enzyme has been carried out using Codessa Pro methodology and software. Linear regression QSAR models of the biological activity (Ki) of 38 inhibitors of carbonic anhydrase CA-II isozyme were established with 12 different molecular descriptors which were selected from more than hundreds of geometrical, topological, quantum-mechanical, and electronic types of descriptors and calculated using Codessa Pro software. Among the models presented in this study, statistically the most significant one is a five-parameter equation with correlation coefficient, R² values of ca. 0.840, and the cross-validated correlation coefficient, R² values of ca. 0.777. The obtained models allowed us to reveal some physicochemical and structural factors, which are strongly correlated with the biological activity of the compounds.