Two- and three-dimensional QSAR studies on benzyl amide-ketoacid inhibitors of HIV integrase and their reduced analogues

The discovery of clinically relevant inhibitors of HIV-integrase for antiviral therapy has proven to be a challenging task. Ketoacid-derived inhibitors selectively inhibit the strand transfer reaction of HIV-integrase. To elucidate the structural properties required for HIV-integrase inhibitory activity, we present here the results of two- and three-dimensional (2D and 3D) QSAR studies of a series of 24 benzyl amide derivatives. The 2D-QSAR studies were performed using three methods: the multiple linear regression method (MLR), giving r ² = 0.9340 and q ² = 0.8471; the partial least squares method, with r ² = 0.9291 and q ² = 0.7896; and principle component analysis, giving r ² = 0.6496 and q ² = 0.3893. The 3D-QSAR studies were performed using the stepwise variable selection k-nearest-neighbor molecular field analysis approach; a leave-one-out cross-validated correlation coefficient (q ²) of 0.4577 and a non-cross-validated correlation coefficient (r ²) of 0.5836 were obtained. Contour maps using this approach showed that steric effects dominantly determine binding affinities. The information rendered by 2D and 3D QSAR models may lead to a better understanding of structural requirements of HIV-integrase inhibitors and can help in the design of novel potent molecules.     

Non-nucleoside inhibitors of NS5B polymerase binding to allosteric sites: 3D- QSAR and molecular docking studies

Infection caused by hepatitis C virus (HCV) is a significant world health problem for which novel therapies are in urgent demand. Nonstructural (NS5B) viral proteins have emerged as an attractive target for drug discovery efforts toward antiviral for hepatitis C virus. Toward this target several series of NS5B inhibitors that showed activity in the replicon assay have been reported. In this article, we gave a report of the NS5B allosteric sites and the corresponding non-nucleoside inhibitors, which belong to different chemical classes. Then using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods, 3-dimension quantitative structure-activity relationships (3D-QSAR) models have been built with more than two hundred benzimidazole/indole derivative inhibitors. These studies indicated that the QSAR models were statistically significant and had high predictabilities (CoMFA: q(2)=0.823, r(2)=0.942; CoMSIA: q(2)=0.817, r(2)=0.935). The flexible docking method, which was performed by the DOCK6.0 software, positioned all of the inhibitors into the allosteric site to determine the probable binding conformation. The CoMFA and CoMSIA models based on the docking conformations also yielded statistically significant and high predictive QSAR models (CoMFA: q(2)=0.509, r(2)=0.768; CoMSIA: q(2)=0.582, r(2)=0.854). Our models would offer help to better comprehend the structure-activity relationships existent for this class of compounds and also facilitate the design of new inhibitors with good chemical diversity.     

Design and quantitative structure-activity relationship of 3-amidinobenzyl-1H-indole-2-carboxamides as potent,nonchiral, and selective inhibitors of blood coagulation factor Xa

A series of 138 nonchiral 3-amidinobenzyl-1H-indole-2-carboxamides and analogues as inhibitors of the blood coagulation enzyme factor Xa (fXa) were designed, synthesized, and investigated by X-ray structure analysis and 3D quantitative structure-activity relationship (QSAR) studies (CoMFA, CoMSIA) in order to identify important protein-ligand interactions responsible for biological affinity and selectivity. Several compounds from this series are highly potent and selective inhibitors of this important enzyme linking extrinsic and intrinsic coagulation pathways. To rationalize biological affinity and to provide guidelines for further design, all compounds were docked into the factor Xa binding site. Those docking studies were based on X-ray structures of factor Xa in complex with literature-known inhibitors. It was possible to validate those binding modes by four X-ray crystal structures of representative ligands in factor Xa, while one ligand was additionally crystallized in trypsin to rationalize requirements for selective factor Xa inhibition. The 3D-QSAR models based on a superposition rule derived from these docking studies were validated using conventional and cross-validated r(2) values using the leave-one-out method and repeated analyses using two randomly chosen cross-validation groups plus randomization of biological activities. This led to consistent and highly predictive 3D-QSAR models with good correlation coefficients for both CoMFA and CoMSIA, which were found to correspond to experimentally determined factor Xa binding site topology in terms of steric, electrostatic, and hydrophobic complementarity. Subsets selected as smaller training sets using 2D fingerprints and maximum dissimilarity methods resulted in 3D-QSAR models with remarkable correlation coefficients and a high predictive power. The final quantitative SAR information agrees with all experimental data for the binding topology and thus provides reasonable activity predictions for novel factor Xa inhibitors.     

Pharmacophore generation and atom-based 3D-QSAR of

ABSTRACT: BACKGROUND: Coronary heart disease continues to be the leading cause of mortality and a significant cause of morbidity and account for nearly 30% of all deaths each year worldwide. High levels of cholesterol are an important risk factor for coronary heart disease. The blockage of 3-hydroxy-3-methylglutaryl-coenzyme (HMG-CoA) reductase activity by small molecule inhibitors has been shown to inhibit hypercholesterolemia. METHODS: This article describes the development of a robust pharmacophore model and the investigation of structure activity relationship analysis of 43 N-iso-propyl pyrrole-based derivatives reported for HMG-CoA reductase inhibition. A five point pharmacophore model was developed and the generated pharmacophore model was used to derive a predictive atom-based 3D-QSAR model for the studied dataset. RESULTS: The obtained 3D-QSAR model has an excellent correlation coefficient value (r2 = 0.9566) along with good statistical significance as shown by high Fisher ratio (F = 143.2). The model also exhibited good predictive power confirmed by the high value of cross validated correlation coefficient (q2 = 0.6719). CONCLUSIONS: The QSAR model suggests that electron withdrawing character is crucial for the HMG-CoA reductase inhibitory activity. In addition to the electron withdrawing character, hydrogen bond donating groups, hydrophobic and negative ionic groups positively contribute to the HMG-CoA reductase inhibition. These findings provide a set of guidelines for designing compounds with better HMG-CoA reductase inhibitory potential.     

Quantitative structure-activity relationship of human neutrophil collagenase (MMP-8) inhibitors using comparative molecular field analysis and X-ray structure analysis.

A set of 90 novel 2-(arylsulfonyl)-1,2,3, 4-tetrahydroisoquinoline-3-carboxylates and -hydroxamates as inhibitors of the matrix metalloproteinase human neutrophil collagenase (MMP-8) was designed, synthesized, and investigated by 3D-QSAR techniques (CoMFA, CoMSIA) and X-ray structure analysis. Docking studies of a reference compound are based on crystal structures of MMP-8 complexed with peptidic inhibitors to propose a model of its bioactive conformation. This model was validated by a 1. 7 A X-ray structure of the catalytic domain of MMP-8. The 3D-QSAR models based on a superposition rule derived from these docking studies were validated using conventional and cross-validated r2 values using the leave-one-out method, repeated analyses using two randomly chosen cross-validation groups plus randomization of biological activities. This led to consistent and highly predictive 3D-QSAR models with good correlation coefficients for both CoMFA and CoMSIA, which were found to correspond to experimentally determined MMP-8 catalytic site topology in terms of steric, electrostatic, and hydrophobic complementarity. Subsets selected as smaller training sets using 2D fingerprints and maximum dissimilarity methods resulted in 3D-QSAR models with remarkable correlation coefficients and a high predictive power. This allowed to compensate the weaker zinc binding properties of carboxylates by introducing optimal fitting P1' residues. The final QSAR information agrees with all experimental data for the binding topology and thus provides clear guidelines and accurate activity predictions for novel MMP-8 inhibitors.     

3D-QSAR CoMFA on cyclin-dependent kinase inhibitors

Several series of cyclin-dependent kinase inhibitors previously prepared in our laboratory were compared using 3D-QSAR (CDK1) and docking (CDK2) techniques. Evaluation of our own library of 93 purine derivatives served to establish the model which was validated by evaluation of an external library of 71 compounds. The best predictions were obtained with the CoMFA standard model (q(2) = 0.68, r(2) = 0.90) and with the CoMSIA combined steric, electrostatic, and lipophilic fields (q(2) = 0.74, r(2) = 0.90). The CDK1 3D-QSAR model was then superimposed to the ATP/CDK2 binding site, giving direct contour maps of the different fields. Although too few compounds were evaluated on CDK5 to derive a 3D-QSAR model, some interesting SARs have been deduced. Comparison of the results obtained from both methods helped with understanding the specific activity of some compounds and designing new specific CDK inhibitors.     

Three-dimensional quantitative structure–activity relationship (CoMSIA) analysis of bis-coumerine analogues as urease inhibitors

As a basis for predicting structural features that may lead to the design of more potent and selective inhibitors of urease, the three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were carried out on a series of 30 bis-coumerine analogs, which are known urease inhibitors. Five different properties: steric, electrostatic, hydrophobic, H-bond donor, and H-bond acceptor, assumed to cover the major contributions to ligand binding, were used to generate the 3D-QSAR model. A significant cross-validated correlation coefficient q2 (0.51), r ² (0.962) for CoMSIA were obtained, indicating the statistical significance of this class of compounds. Actual urease inhibitory activities of this class, and the predicted values were in good agreement with the experimental results. This model offer insight into the structural requirements for activity of bis-coumerine analogues as urease inhibitors, since there is only speculative knowledge of their target in protein.     

3D-QSAR studies of some tetrasubstituted pyrazoles as COX-II inhibitors

Pharmacophore mapping studies were undertaken for a series of molecules belonging to tetrasubstituted pyrazoles as canine COX-II inhibitors. A six point pharmacophore with 3 hydrogen bond acceptors (A), one hydrophobic group (H) and two aromatic rings (R) as pharmacophoric feature was developed. The pharmacophoric hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r2 = 0.958. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.852 was observed between experimental and predicted activity values of test set molecules. The geometry and features of pharmacophore model describe the key structure-activity relationship of COX-II inhibitors, can predict their activities, and can thus be used to design novel inhibitors.     

Field- and Gaussian-based 3D-QSAR studies on barbiturate analogs as MMP-9 inhibitors

Matrix metalloproteinase-9 (MMP-9) is one of the important enzymes belongs to gelatinase family and involved in multiple cellular processes including proliferation, angiogenesis, and metastasis. Various studies show that N-substituted homopiperazine barbiturates are promising and also selective for the MMP-9 inhibition. In this study, we selected and reported 49 barbiturate derivatives as inhibitors of MMP-9 and performed structure-based 3D-QSAR studies to elucidate the important structural features responsible for binding affinity. Crystal structure of MMP-9 complexed with barbiturate available in PDB was selected and performed quantum-polarized ligand docking for the reported inhibitors. Receptor-based alignment obtained from the docked poses were used for field- and Gaussian-based 3D-QSAR study. The results of both field and Gaussian models gave the good predictive correlation coefficient (test set) q ² of 0. 77 and 0.85, and the conventional correlation coefficient (training set) r ² of 0.845 and 0.928, respectively. The results of 3D-QSAR and docking studies validate each other and provided insight into the structural requirements for MMP-9 inhibition. The outcomes of the contour maps for both steric and Gaussian models results indicate that steric and hydrophobic interactions are major contributing factor for the activity and these findings will be useful to design the new inhibitors against MMP-9.     

Toward the quantitative prediction of T-cell epitopes: coMFA and coMSIA studies of peptides with affinity for the class I MHC molecule HLA-A*0201

A set of 102 peptides with affinity for the class I MHC HLA-A0201 molecule was subjected to three-dimensional quantitative structure-affinity relationship (3D QSAR) studies using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). A test set of 50 peptides was used to determine the predictive value of the models. The CoMFA models gave q(2) and r(2)pred below 0.5. The best CoMSIA model has q(2) = 0.542 and r(2)pred = 0.679, and includes hydrophobic, steric, and H-bond donor fields. The hydrophobic interactions play a dominant role in peptide-MHC molecule binding. CoMSIA coefficient contour maps were used to analyze the structural features of the peptides accounting for the affinity in terms of the three positively contributing physicochemical properties: local hydrophobicity, steric bulk and hydrogen-bond-donor ability.     

3D-QSAR studies of JNK1 inhibitors utilizing various alignment methods

We report our three-dimensional quantitative structure activity relationship (3D-QSAR) studies of the series of anilinopyrimidine derivatives of JNK1 inhibitors. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied using different alignment methods. The ligand-based atom-by-atom matching alignment has produced better values for CoMFA (q(2) = 0.646 and r(2) = 0.983), while in CoMSIA it has achieved only lower statistical values. The pharmacophore-based model has produced (q(2) = 0.568, r(2) = 0.938) and (q(2) = 0.670, r(2) = 0.982) for CoMFA and CoMSIA models, respectively. As the model was based on the receptor-guided alignment, all the compounds were optimized within the receptor, resulting in q(2) = 0.605 and r(2) = 0.944 for CoMFA, and q(2) = 0.587 and r(2) = 0.863 for CoMSIA. Molecular Dynamic simulation studies suggested that the generated models were consistent with the low-energy protein ligand conformation. The CoMFA and CoMSIA contour maps indicated that the substitutions of the electropositive groups in the phenyl ring, and an addition of hydrophobic groups in the pyrimidine ring, are important to enhance the activity of this series. Moreover, the virtual screening analysis against NCI database yields potentials hits, and the results obtained would be useful to synthesize selective and highly potent c-Jun N-terminal kinase 1 analogs.     

Molecular docking and 3D-QSAR studies of 2-substituted 1-indanone derivatives as acetylcholinesterase inhibitors

Aim： To explore the binding mode of 2-substituted 1-indanone derivatives with acetylcholinesterase （ACHE） and provide hints for the future design of new de- rivatives with higher potency and specificity. Methods： The GOLD-docking con- formations of the compounds in the active site of the enzyme were used in subse- quent studies. The highly reliable and predictive three-dimensional quantitative structure-activity relationship （3D-QSAR） models were achieved by comparative molecular field analysis （CoMFA） and comparative molecular similarity analysis （CoMSIA） methods. The predictive capabilities of the models were validated by an external test set. Moreover, the stabilities of the 3D-QSAR models were veri- fied by the leave-4-out cross-validation method. Results： The CoMFA and CoMSIA models were constructed successfully with a good cross-validated coef- ficient （q2） and a non-cross-validated coefficient （r2）. The q2 and r2 obtained from the leave- 1-out cross validation method were 0.784 and 0.974 in the CoMFA model and 0.736 and 0.947 in the CoMSIA model, respectively. The coefficient isocontour maps obtained from these models were compatible with the geometrical and physi- cochemical properties of ACHE. Conclusion： The contour map demonstrated that the binding affinity could be enhanced when the small protonated nitrogen moi- ety was replaced by a more hydrophobic and bulky group with a highly partial positive charge. The present study provides a better understanding of the inter- action between the inhibitors and ACHE, which is helpful for the discovery of new compounds with more potency and selective activity.