Receptor based 3D-QSAR to identify putative binders of Mycobacterium tuberculosis Enoyl acyl carrier protein reductase

In the current study, the applicability and scope of 3D-QSAR models (CoMFA and CoMSIA) to complement virtual screening using 3D pharmacophore and molecular docking is examined and applied to identify potential hits against Mycobacterium tuberculosis Enoyl acyl carrier protein reductase (MtENR). Initially CoMFA and CoMSIA models were developed using series of structurally related arylamides as MtENR inhibitors. Docking studies were employed to position the inhibitors into MtENR active site to derive receptor based 3D-QSAR models. Both CoMFA and CoMSIA yielded significant cross validated q² values of 0.663 and 0.639 and r² values of 0.989 and 0.963, respectively. The statistically significant models were validated by a test set of eight compounds with predictive r² value of 0.882 and 0.875 for CoMFA and CoMSIA. The contour maps from 3D-QSAR models in combination with docked binding structures help to better interpret the structure activity relationship. Integrated with CoMFA and CoMSIA predictive models structure based (3D-pharmacophore and molecular docking) virtual screening have been employed to explore potential hits against MtENR. A representative set of 20 compounds with high predicted IC₅₀ values were sorted out in the present study.     

Structural insights of PA-824 derivatives: ligand-based 3D-QSAR study and design of novel PA824 derivatives as anti-tubercular agents

Abstract

With the purpose of designing novel chemical entities with improved inhibitory potencies against drug-resistant Mycobacterium tuberculosis, the 3D- quantitative structure–activity relationship (QSAR) studies were carried out on biphenyl analogs of the tuberculosis (TB) drug, PA-824. Anti-mycobacterial activity (MABA) was considered for the 3D-QSAR studies using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches. The best CoMFA and CoMSIA models were found statistically significant with cross-validated coefficients (q²) of 0.784 and 0.768, respectively, and conventional coefficients (r²) of 0.823 and 0.981, respectively. The cross-validated and the external validation results revealed that both the CoMFA and CoMSIA models possesses high accommodating capacities and they would be reliable for predicting the pMIC values of new PA-824 derivatives. Based on the models and structural insights, a series of new PA-824 derivatives were designed and the anti-mycobacterial activities of the designed compounds were predicted based on the best 3D-QSAR model. The predicted data results suggest the designed compounds are more potent than existed ones.     

Understanding the structure-activity and structure-selectivity correlation of cyclic guanine derivatives as phosphodiesterase-5 inhibitors by molecular docking, CoMFA and CoMSIA analyses

Molecular docking and 3D-QSAR analyses were performed to understand how PDE5 and PDE6 interact with a series of (49) cyclic guanine derivatives. Using the conformations of the compounds revealed by molecular docking, CoMFA and CoMSIA analyses resulted in the first quantitative structure-activity relationship (QSAR) and first quantitative structure-selectivity relationship (QSSR) models (with high cross-validated correlation coefficient q(2) and conventional correlation coefficient r(2) values) for predicting the inhibitory activity against PDE5 and the selectivity against PDE6. The high q(2) and r(2) values, along with further testing, indicate that the obtained 3D-QSAR and 3D-QSSR models will be valuable in predicting both the inhibitory activity and selectivity of cyclic guanine derivatives for these protein targets. A set of 3D contour plots drawn based on the 3D-QSAR and 3D-QSSR models reveal some useful clues to improve both the activity and selectivity by modifying structures of the compounds. It has been demonstrated that both the steric and electrostatic factors should appropriately be taken into account in future rational design and development of more active and more selective PDE5 inhibitors for the therapeutic treatment of erectile dysfunction (ED).     

3D-QSAR studies on fluroquinolones derivatives as inhibitors for tuberculosis

A quantitative structure activity relationship (QSAR) study was performed on the fluroquinolones known to have anti-tuberculosis activity. The 3D-QSAR models were generated using stepwise variable selection of the four methods - multiple regression (MR), partial least square regression (PLSR), principal component regression (PCR) and artificial neural networks (kNN-MFA). The statistical result showed a significant correlation coefficient q(2) (90%) for MR model and an external test set of (pred_r(2)) -1.7535, though the external predictivity showed to improve using kNN-MFA method with pred_r(2) of -0.4644. Contour maps showed that steric effects dominantly determine the binding affinities. The QSAR models may lead to a better understanding of the structural requirements of anti-tuberculosis compounds and also help in the design of novel molecules.     

3D-QSAR and molecular docking studies of 2-pyrimidinecarbonitrile derivatives as inhibitors against falcipain-3

The three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on a series of falcipain-3 inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. A training set containing 42 molecules served to establish the QSAR models. The optimum CoMFA and CoMSIA models obtained for the training set were statistically significant with cross-validated correlation coefficients r(cv)(2) (q(2)) of 0.549 and 0.608, and conventional correlation coefficients (r(2)) of 0.976 and 0.932, respectively. An independent test set of 12 molecules validated the external predictive power of both models with predicted correlation coefficients (r(pred)(2)) for CoMFA and CoMSIA as 0.697 and 0.509, respectively. The docking of inhibitors into falcipain-3 active site using GOLD software revealed the vital interactions and binding conformation of the inhibitors. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of falcipain-3 active site, which suggests that the information rendered by 3D-QSAR models and the docking interactions can provide guidelines for the development of improved falcipain-3 inhibitors.     

Three-dimensional quantitative structure–activity relationship study on paullones as CDK inhibitors using CoMSIA and CoMFA

3D-QSAR studies were conducted on a series of paullones as CDK inhibitors using three-dimensional quantitative structure-activity relationship (3D-QSAR) methods. Two methods were compared: the widely used comparative molecular field analysis (CoMFA) and the recently reported comparative molecular similarity indices analysis (CoMSIA). Systematic variations of some parameters in CoMSIA and CoMFA were performed to search for the best 3D-QSAR model. The computed results showed that the 3D-QSAR models from CoMSIA were clearly superior to those from CoMFA. Using the best model from CoMSIA analysis, a significant cross-validated q² was obtained and the predicted biological activities of the five compounds in the test set were in good agreement with the experimental values. The correlation results obtained from CoMSIA were graphically interpreted in terms of field contribution maps allowing physicochemical properties relevant for binding to be easily mapped back onto molecular structures. The features in the CoMSIA contour maps intuitively suggested where to modify a molecular structure in terms of physicochemical properties and functional groups in order to improve its binding affinity, which is very important for improving our understanding of the ligand-receptor interactions and in helping to design compounds with improved activity.