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.     

3D-QSAR and docking studies of aldehyde inhibitors of human cathepsin K

In order to better understand the structural and chemical features of human cathepsin K (CatK), which is an important cysteine protease in the pathogenesis of osteoporosis, the 3D-QSAR (CoMFA) studies were conducted on recently explored aldehyde compounds with known CatK inhibitory activities. The genetic algorithm of GOLD2.2 has been employed to position 59 aldehyde compounds into the active sites of CatK to determine the probable binding conformation. Good correlations between the predicted binding free energies and the experimental inhibitory activities suggested that the identified binding conformations of these potential inhibitors are reliable. The docking results also provided a reliable conformational alignment scheme for 3D-QSAR model. Based on the docking conformations, highly predictive comparative molecular field analysis (CoMFA) was performed with q2 value of 0.723. The predictive ability was validated by some compounds that were not included in the training set. Furthermore, the CoMFA model was mapped back to the binding sites of CatK, to get a better understanding of vital interactions between the aldehyde compounds and the protease. The CoMFA field distributions are in good agreement with the structural characteristics of the binding groove of the CatK, which suggested that the n-Bu in R4 position is the favor group substitute at P1 and moderate groups in R2 group are required on P2 substitute. In addition, 3D-QSAR results also demonstrated that aldehyde is an important pharmacophore because of electrostatic effect. These results, together with the good correlations between the inhibitory activities and the binding free energies predicted by GOLD2.2, demonstrated the power of combining docking/QSAR approach to explore the probable binding conformations of compounds at the active sites of the protein target, and further provided useful information in understanding the structural and chemical features of CatK in designing and finding new potential inhibitors.     

The molecular basis for coxib inhibition of p38alpha MAP kinase

In this work, we present the results of two combined approaches, molecular docking and comparative molecular field analysis (CoMFA), to propose how the selective cyclooxygenase-2 inhibitor celecoxib could act as a p38 mitogen-activated protein (MAP) kinase inhibitor. The docking analysis revealed why celecoxib has a less favorable binding energy (DeltaG= -12.4kcal/mol) than the selective p38 MAP kinase (p38 MAPK) inhibitor, SB203580 (DeltaG= -22.2kcal/mol). The CoMFA results revealed unfavorable steric effects that can be related to the predicted lower p38 MAP kinase inhibitory activity of celecoxib. Additionally, FlexX and CoMFA results also suggested that etoricoxib, another selective COX-2 inhibitor, could inhibit p38 MAP kinase.     

3D-QSAR,molecular docking,and molecular dynamic simulations for prediction of new Hsp90 inhibitors based on isoxazole scaffold

Heat shock protein 90(Hsp90), as a molecular chaperone, play a crucial role in folding and proper function of many proteins. Hsp90 inhibitors containing isoxazole scaffold are currently being used in the treatment of cancer as tumor suppressers. Here in the present studies, new compounds based on isoxazole scaffold were predicted using a combination of molecular modeling techniques including three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking and molecular dynamic (MD) simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were also done. The steric and electrostatic contour map of CoMFA and CoMSIA were created. Hydrophobic, hydrogen bond donor and acceptor of CoMSIA model also were generated, and new compounds were predicted by CoMFA and CoMSIA contour maps. To investigate the binding modes of the predicted compounds in the active site of Hsp90, a molecular docking simulation was carried out. MD simulations were also conducted to evaluate the obtained results on the best predicted compound and the best reported Hsp90 inhibitors in the 3D-QSAR model. Findings indicate that the predicted ligands were stable in the active site of Hsp90.     

Molecular docking and 3D-QSAR studies of Yersinia protein tyrosine phosphatase YopH inhibitors

Three-dimensional quantitative structure-activity relationship (QSAR) studies were conducted on two classes of recently explored compounds with known YopH inhibitory activities. Docking studies were employed to position the inhibitors into the YopH active site to determine the probable binding conformation. Good correlations between the predicated binding free energies and the inhibitory activities were found for two subsets of phosphate mimetics: alpha-ketocarboxylic acid and squaric acid (R2=0.70 and 0.68, respectively). The docking results also provided a reliable conformational alignment scheme for 3D-QSAR modeling. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed based on the docking conformations, giving q2 of 0.734 and 0.754 for CoMFA and CoMSIA models, respectively. The 3D-QSAR models were significantly improved after removal of an outlier (q2=0.829 for CoMFA and q2=0.837 for CoMSIA). The predictive ability of the models was validated using a set of compounds that were not included in the training set. Mapping the 3D-QSAR models to the active site of YopH provides new insight into the protein-inhibitor interactions for this enzyme. These results should be applicable to the prediction of the activities of new YopH inhibitors, as well as providing structural implications for designing potent and selective YopH inhibitors as antiplague agents.     

A combined approach of docking and 3D QSAR study of beta-ketoacyl-acyl carrier protein synthase III (FabH) inhibitors

The enzyme FabH catalyzes the initial step of fatty acid biosynthesis via a type II fatty acid synthase. The pivotal role of this essential enzyme combined with its unique structural features and ubiquitous occurrence in bacteria has made it an attractive new target for the development of antibacterial and antiparasitic compounds. Three-dimensional quantitative structure-activity relationship (3D QSAR) studies such as comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) and docking simulations were conducted on a series of potent benzoylaminobenzoic acids. Docking studies were employed to position the inhibitors into the FabH active site to determine the probable binding conformation. A reasonable correlation between the predicated binding free energy and the inhibitory activity was found. CoMFA and CoMSIA were performed based on the docking conformations, giving q(2) of 0.637 and 0.697 for CoMFA and CoMSIA models, respectively. The predictive ability of the models was validated using a set of compounds that were not included in the training set and progressive scrambling test. Mapping the 3D QSAR models to the active site of FabH related that some important amino acid residues are responsible for protein-inhibitor interaction. These results should be applicable to the prediction of the activities of new FabH inhibitors, as well as providing structural understanding.     

Multiple receptor conformation docking and dock pose clustering as tool for CoMFA and CoMSIA analysis – a case study on HIV-1 protease inhibitors

Multiple receptors conformation docking (MRCD) and clustering of dock poses allows seamless incorporation of receptor binding conformation of the molecules on wide range of ligands with varied structural scaffold. The accuracy of the approach was tested on a set of 120 cyclic urea molecules having HIV-1 protease inhibitory activity using 12 high resolution X-ray crystal structures and one NMR resolved conformation of HIV-1 protease extracted from protein data bank. A cross validation was performed on 25 non-cyclic urea HIV-1 protease inhibitor having varied structures. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were generated using 60 molecules in the training set by applying leave one out cross validation method, r_loo² values of 0.598 and 0.674 for CoMFA and CoMSIA respectively and non-cross validated regression coefficient r² values of 0.983 and 0.985 were obtained for CoMFA and CoMSIA respectively. The predictive ability of these models was determined using a test set of 60 cyclic urea molecules that gave predictive correlation (r_pred²) of 0.684 and 0.64 respectively for CoMFA and CoMSIA indicating good internal predictive ability. Based on this information 25 non-cyclic urea molecules were taken as a test set to check the external predictive ability of these models. This gave remarkable out come with r_pred² of 0.61 and 0.53 for CoMFA and CoMSIA respectively. The results invariably show that this method is useful for performing 3D QSAR analysis on molecules having different structural motifs.     

Molecular modeling studies on phosphonic acid-containing thiazole derivatives: design for fructose-1,6-bisphosphatase inhibitors

Presently, an in silico modeling was carried out on a series of 63 phosphonic acid-containing thiazole derivatives as fructose-1,6-bisphosphatase (FBPase) inhibitors using CoMFA/CoMSIA and molecular docking methods. The CoMFA and CoMSIA models using 51 molecules in the training set gave r _cv² values of 0.675 and 0.619, r ² values of 0.985 and 0.979, respectively. The systemic external validation indicated that our CoMFA and CoMSIA models possessed high predictive powers with r ₀² values of 0.995 and 0.994, r _m(test)² values of 0.887 and 0.860, respectively. The 3D contour maps of the CoMFA and CoMSIA provided smooth and interpretable explanation of the structure-activity relationship for the inhibitors. Molecular docking studies revealed that a phosphonic group was essential for binding to the AMP binding site, and some key features were also identified. The analyses of the 3D contour plots and molecular docking results permitted interesting conclusions about the effects of different substituent groups at different positions of the common scaffold, which might guide the design of novel FBPase inhibitors with higher activity and bioavailability. A set of 60 new analogues were designed by utilizing the results revealed in the present study, and were predicted with significantly improved potencies in the developed models. The findings can be quite useful to aid the designing of new fructose-1,6-biphophatase inhibitors with improved biological response.     

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.     

Molecular docking and 3D QSAR studies on 1-amino-2-phenyl-4-(piperidin-1-yl)-butanes based on the structural modeling of human CCR5 receptor

In the present study, we have used an approach combining protein structure modeling, molecular dynamics (MD) simulation, automated docking, and 3D QSAR analyses to investigate the detailed interactions of CCR5 with their antagonists. Homology modeling and MD simulation were used to build the 3D model of CCR5 receptor based on the high-resolution X-ray structure of bovine rhodopsin. A series of 64 CCR5 antagonists, 1-amino-2-phenyl-4-(piperidin-1-yl)-butanes, were docked into the putative binding site of the 3D model of CCR5 using the docking method, and the probable interaction model between CCR5 and the antagonists were obtained. The predicted binding affinities of the antagonists to CCR5 correlate well with the antagonist activities, and the interaction model could be used to explain many mutagenesis results. All these indicate that the 3D model of antagonist-CCR5 interaction is reliable. Based on the binding conformations and their alignment inside the binding pocket of CCR5, three-dimensional structure-activity relationship (3D QSAR) analyses were performed on these antagonists using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods. Both CoMFA and CoMSIA provide statistically valid models with good correlation and predictive power. The q(2)(r(cross)(2)) values are 0.568 and 0.587 for CoMFA and CoMSIA, respectively. The predictive ability of these models was validated by six compounds that were not included in the training set. Mapping these models back to the topology of the active site of CCR5 leads to a better understanding of antagonist-CCR5 interaction. These results suggest that the 3D model of CCR5 can be used in structure-based drug design and the 3D QSAR models provide clear guidelines and accurate activity predictions for novel antagonist design.     

Molecular design of new aggrecanases-2 inhibitors

Aggrecanases-2 is a very important potential drug target for the treatment of osteoarthritis. In this study, a series of known aggrecanases-2 inhibitors was analyzed by the technologies of three-dimensional quantitative structure–activity relationships (3D-QSAR) and molecular docking. Two 3D-QSAR models, which based on comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods, were established. Molecular docking was employed to explore the details of the interaction between inhibitors and aggrecanases-2 protein. According to the analyses for these models, several new potential inhibitors with higher activity predicted were designed, and were supported by the simulation of molecular docking. This work propose the fast and effective approach to design and prediction for new potential inhibitors, and the study of the interaction mechanism provide a better understanding for the inhibitors binding into the target protein, which will be useful for the structure-based drug design and modifications.     

Exploration of a binding mode of indole amide analogues as potent histone deacetylase inhibitors and 3D-QSAR analyses

Docking simulations and three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses were conducted on a series of indole amide analogues as potent histone deacetylase inhibitors. The studies include comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Selected ligands were docked into the active site of human HDAC1. Based on the docking results, a novel binding mode of indole amide analogues in the human HDAC1 catalytic core is presented, and enzyme/inhibitor interactions are discussed. The indole amide group is located in the open pocket, and anchored to the protein through a pair of hydrogen bonds with Asp99 O-atom and amide NH group on ligand. Based on the binding mode, predictive 3D-QSAR models were established, which had conventional r2 and cross-validated coefficient values (r(cv)2) up to 0.982 and 0.601 for CoMFA and 0.954 and 0.598 for CoMSIA, respectively. A comparison of the 3D-QSAR field contributions with the structural features of the binding site showed good correlation between the two analyses. The results of 3D-QSAR and docking studies validate each other and provided insight into the structural requirements for activity of this class of molecules as HDAC inhibitors. The CoMFA and CoMSIA PLS contour maps and MOLCAD-generated active site electrostatic, lipophilicity, and hydrogen-bonding potential surface maps, as well as the docking studies, provided good insights into inhibitor-HDAC interactions at the molecular level. Based on these results, novel molecules with improved activity can be designed.     

Molecular modeling studies,synthesis and biological evaluation of dabigatran analogues as thrombin inhibitors

In this work, 48 thrombin inhibitors based on the structural scaffold of dabigatran were analyzed using a combination of molecular modeling techniques. We generated three-dimensional quantitative structure–activity relationship (3D-QSAR) models based on three alignments for both comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) to highlight the structural requirements for thrombin protein inhibition. In addition to the 3D-QSAR study, Topomer CoMFA model also was established with a higher leave-one-out cross-validation q² and a non-cross-validation r², which suggest that the three models have good predictive ability. The results indicated that the steric, hydrophobic and electrostatic fields play key roles in QSAR model. Furthermore, we employed molecular docking and re-docking simulation explored the binding relationship of the ligand and the receptor protein in detail. Molecular docking simulations identified several key interactions that were also indicated through 3D-QSAR analysis. On the basis of the obtained results, two compounds were designed and predicted by three models, the biological evaluation in vitro (IC₅₀) demonstrated that these molecular models were effective for the development of novel potent thrombin inhibitors.     

Inhibitory mode of indole-2-carboxamide derivatives against HLGPa: molecular docking and 3D-QSAR analyses

The interaction of a series of indole-2-carboxamide compounds with human liver glycogen phosphorylase a (HLGPa) have been studied employing molecular docking and 3D-QSAR approaches. The Lamarckian Genetic Algorithm (LGA) of AutoDock 3.0 was employed to locate the binding orientations and conformations of the inhibitors interacting with HLGPa. The binding models were demonstrated in the aspects of inhibitor's conformation, subsite interaction, and hydrogen bonding. The very similar binding conformations of these inhibitors show that they interact with HLGPa in a very similar way. Good correlations between the calculated interaction free energies and experimental inhibitory activities suggest that the binding conformations of these inhibitors are reasonable. The structural and energetic differences in inhibitory potencies of indole-2-carboxamide compounds were reasonably explored. Using the binding conformations of indole-2-carboxamides, consistent and highly predictive 3D-QSAR models were developed by CoMFA and CoMSIA analyses. The q2 values are 0.697 and 0.622 for CoMFA and CoMSIA models, respectively. The predictive ability of these models was validated by four compounds that were not included in the training set. Mapping these models back to the topology of the active site of HLGPa leads to a better understanding of the vital indole-2-carboxamide-HLGPa interactions. Structure-based investigations and the final 3D-QSAR results provide clear guidelines and accurate activity predictions for novel inhibitor design.     

A comparative molecular field analysis of the biotransformation of sulfides by Rhodococcus erythropolis

A comparative molecular field analysis (CoMFA) was used to model the efficacy with which the Rhodococcus erythropolis mono-oxygenase, DszC, catalyzes the enantioselective sulfoxidation of a broad range of substrates. Experimentally determined values of both the yield and enantiomeric excess for this reaction were employed to create these CoMFA models. A highly predictive CoMFA model was constructed for the prediction of enantiomeric excess of the sulfoxide product. The predictive ability of the model was demonstrated by both cross-validation of the training set (q² = 0.74) and for an external test set of substrates. The enantiomeric excesses of the members of the test set, which also included two amino acid sulfides that were structurally distinct from the membership of the training set, were predicted well by the CoMFA model. Product yield was not modelled well by any CoMFA model. Different models comparing the likely bioactive conformations of the substrates suggest that most compounds assume an ‘extended’ conformation upon binding. Contour diagrams illustrating significant substrate–enzyme interactions suggest that the model, which predicts the enantiomeric excess, is consistent with previous conclusions regarding the effect of various substrate substitutions on the enantiopurity of the product of the biotransformation.     

Docking-based 3D-QSAR (CoMFA,CoMFA-RG,CoMSIA) study on hydroquinoline and thiazinan-4-one derivatives as selective COX-2 inhibitors

A series of 26 selective COX-2 inhibitors which reported previously by our laboratory was selected to generate three-dimensional quantitative structure activity relationship (3D-QSAR) model. Active conformation of each molecule was predicted by docking studies and used for molecular alignment. Activity of 20 molecules as a train set was predicted using three methods including comparative molecular field analysis (CoMFA), CoMFA region focusing (CoMFA-RG) and comparative molecular similarity index analysis (CoMSIA). The best models of CoMFA-RG and CoMSIA revealed correlation coefficients r² of 0.955 and 0.947, the leave one out cross-validation coefficients q² of 0.573 and 0.574, respectively. In addition, CoMFA-RG and CoMSIA models were validated by a test set of six molecules with predicted coefficients r²_pred of 0.644 and 0.799, respectively. Contour maps of generated models provided fruitful information about structural aspect of molecules that affected their COX-2 inhibitory activity. Based on three models results, steric and electrostatic properties are the most important factors in controlling the activity of the molecules. Results of CoMFA-RG and CoMSIA models were utilized to design new molecules. Comparison of experimental and predicted pIC₅₀ values of designed molecules indicated that CoMFA-RG had the more predictive ability.

Communicated by Ramaswamy H. Sarma     

Molecular docking and 3D-QSAR studies on β-phenylalanine derivatives as dipeptidyl peptidase IV inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking studies were carried out to explore the binding of 73 inhibitors to dipeptidyl peptidase IV (DPP-IV), and to construct highly predictive 3D-QSAR models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The negative logarithm of IC₅₀ (pIC₅₀) was used as the biological activity in the 3D-QSAR study. The CoMFA model was developed by steric and electrostatic field methods, and leave-one-out cross-validated partial least squares analysis yielded a cross-validated value (r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} ) of 0.759. Three CoMSIA models developed by different combinations of steric, electrostatic, hydrophobic and hydrogen-bond fields yielded significant r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} values of 0.750, 0.708 and 0.694, respectively. The CoMFA and CoMSIA models were validated by a structurally diversified test set of 18 compounds. All of the test compounds were predicted accurately using these models. The mean and standard deviation of prediction errors were within 0.33 and 0.26 for all models. Analysis of CoMFA and CoMSIA contour maps helped identify the structural requirements of inhibitors, with implications for the design of the next generation of DPP-IV inhibitors for the treatment of type 2 diabetes.     

Application of comparative molecular field analysis to dopamine D2 partial agonists

Comparative molecular field analysis (CoMFA) has been applied to novel D2 partial agonists. Due to the predictability of the CoMFA model across different series of D2 partial agonists, we believe these compounds are binding to the D2 agonist receptor in the conformation and alignment described herein.