Studies of H4R antagonists using 3D-QSAR,molecular docking and molecular dynamics

Three-dimensional quantitative structure–activity relationship studies were performed on a series of 88 histamine receptor 4 (H4R) antagonists in an attempt to elucidate the 3D structural features required for activity. Several in silico modeling approaches, including comparative molecular field analysis (CoMFA), comparative similarity indices analysis (CoMSIA), molecular docking, and molecular dynamics (MD), were carried out. The results show that both the ligand-based CoMFA model (Q ² = 0.548, R _ncv² = 0.870, R _pre² = 0.879, SEE = 0.410, SEP = 0.386) and the CoMSIA model (Q ² = 0.526, R _ncv² =0.866, R _pre² = 0.848, SEE = 0.416, SEP = 0.413) are acceptable, as they show good predictive capabilities. Furthermore, a combined analysis incorporating CoMFA, CoMSIA contour maps and MD results shows that (1) compounds with bulky or hydrophobic substituents at positions 4–6 in ring A (R2 substituent), positively charged or hydrogen-bonding (HB) donor groups in the R1 substituent, and hydrophilic or HB acceptor groups in ring C show enhanced biological activities, and (2) the key amino acids in the binding pocket are TRP67, LEU71, ASP94, TYR95, PHE263 and GLN266. To our best knowledge, this work is the first to report the 3D-QSAR modeling of these H4R antagonists. The conclusions of this work may lead to a better understanding of the mechanism of antagonism and aid in the design of new, more potent H4R antagonists.     

Insights into ET<Subscript>A</Subscript> subtype selectivity of benzodiazepine endothelin receptor antagonists by 3D-QSAR approaches

ET_A subtype selective antagonists constitute a novel and potentially important class of agents for the treatment of pulmonary hypertension, heart failure, and other pathological conditions. In this paper, 60 benzodiazepine derivatives displaying potent activities against ET_A and ET_B subtypes of endothelin receptor were selected to establish the 3D-QSAR models using CoMFA and CoMSIA approaches. These models show excellent internal predictability and consistency, external validation using test-set 19 compounds yields a good predictive power for antagonistic potency. Statistical parameters of models were obtained with CoMFA-ET_A (q ² = 0.787, r ² = 0.935, r ² _pred  = 0.901), CoMFA-ET_B (q ² = 0.842, r ² = 0.984, r ² _pred  = 0.941), CoMSIA-ET_A (q ² = 0.762, r ² = 0.971, r ² _pred  = 0.958) and CoMSIA-ET_B (q ² = 0.771, r ² = 0.974, r ² _pred  = 0.953) respectively. Field contour maps (CoMFA and CoMSIA) corresponding to the ET_A and ET_B subtypes reflects the characteristic similarities and differences between these types. The results of this paper provide valuable information to facilitate structural modifications of the title compounds to increase the inhibitory potency and subtype selectivity of endothelin receptor.     

Identification of novel 5-hydroxy-1<Emphasis Type="Italic">H</Emphasis>-indole-3-carboxylates with anti-HBV activities based on 3D QSAR studies

Infection with hepatitis B virus (HBV) is a major cause of liver diseases such as cirrhosis and hepatocellular carcinoma. In our previous studies, we identified indole derivatives that have anti-HBV activities. In this study, we optimize a series of 5-hydroxy-1H-indole-3-carboxylates, which exhibited potent anti-HBV activities, using three-dimensional quantitative structure-activity relationship (3D QSAR) studies with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The lowest energy conformation of compound 3, which exhibited the most potent anti-HBV activity, obtained from systematic search was used as the template for alignment. The best predictions were obtained with the CoMFA standard model (q ² = 0.689, r ² = 0.965, SEE = 0.082, F = 148.751) and with CoMSIA combined steric, electrostatic, hydrophobic and H-bond acceptor fields (q ² = 0.578, r ² = 0.973, SEE = 0.078, F = 100.342). Both models were validated by an external test set of six compounds giving satisfactory prediction. Based on the clues derived from CoMFA and CoMSIA models and their contour maps, another three compounds were designed and synthesized. Pharmacological assay demonstrated that the newly synthesized compounds possessed more potent anti-HBV activities than before (IC₅₀: compound 35a is 3.1 μmol/l, compound 3 is 4.1 μmol/l). Combining the clues derived from the 3D QSAR studies and from further validation of the 3D QSAR models, the activities of the newly synthesized indole derivatives were well accounted for. Furthermore, this showed that the CoMFA and CoMSIA models proved to have good predictive ability.     

Ligand-based molecular design of 4-benzylpiperidinealkylureas and amides as CCR3 antagonists

Asthma is an inflammatory disease of the lungs. Clinical studies suggest that eotaxin and chemokine receptor-3 (CCR3) play a primary role in the recruitment of eosinophils in allergic asthma. Development of novel and potent CCR3 antagonists could provide a novel mechanism for inhibition of this recruitment process, thereby preventing asthma. With the intention of designing new ligands with enhanced inhibitor potencies against CCR3, a 3D-QSAR CoMFA study was carried out on 41 4-benzylpiperidinealkylureas and amide derivatives. The best statistics of the developed CoMFA model were r ² = 0.960, r_cv² = 0.589 r_{cv}^2 = 0.589 , n = 32 for the training set and r_pred² = 0.619 r_{pred}^2 = 0.619 , n = 9 for the test set. The generated 3D-QSAR contribution maps shed some light on the effects of the substitution pattern related to CCR3 antagonist activity.     

CoMFA and CoMSIA analyses on 1,2,3,4-tetrahydropyrrolo[3,4-b]indole and benzimidazole derivatives as selective CB2 receptor agonists

Novel classes of cannabinoid 2 receptor (CB2) agonists based on 1,2,3,4-tetrahydropyrrolo[3,4-b]indole and benzimidazole scaffolds have shown high binding affinity toward CB2 receptor and good selectivity over cannabinoid 1 receptor (CB1). A computational study of comparative molecular fields analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was performed, initially on each series of agonists, and subsequently on all compounds together, in order to identify the key structural features impacting their binding affinity. The final CoMSIA model resulted to be the more predictive, showing cross-validated r² (r_cv ²) = 0.680, non cross-validated r² (r_ncv ²) = 0.97 and test set r²( r_pred² ) = 0.93 {{\hbox{r}}^2}\left( {{\hbox{r}}_{\rm{pred}}^2} \right) = 0.{93} . The study provides useful suggestions for the design of new analogues with improved affinity.     

3D-QSAR study of c-Src kinase inhibitors based on docking

Cancer is a significant world health problem for which efficient therapies are in urgent demand. c-Src has emerged as an attractive target for drug discovery efforts toward antitumor therapies. Toward this target several series of c-Src inhibitors that showed activity in the assay have been reported. In this article, 3D-QSAR models have been built with 156 anilinoquinazoline and quinolinecarbonitrile derivative inhibitors by using CoMFA and CoMSIA methods. These studies indicated that the QSAR models were statistically significant with high predictabilities (CoMFA model, q ² = 0.590, r ² = 0.855; CoMSIA model, q ² = 0.538, r ² = 0.748). The details of c-Src kinase/inhibitor binding interactions in the crystal structure of complex provided new information for the design of new inhibitors. As a result, docking simulations were also conducted on the series of potent inhibitors. The flexible docking method, which was performed by the DOCK program, positioned all of the inhibitors into the active site to determine the probable binding conformation. The CoMFA and CoMSIA models based on the flexible docking conformations also yielded statistically significant and highly predictive QSAR models (CoMFA model, q ² = 0.507, r ² = 0.695; CoMSIA model, q ² = 0.463, r ² = 0.734). Our models would offer help to better comprehend the structure-activity relationships that exist for this class of compounds and also facilitate the design of novel inhibitors with good chemical diversity.     

3D-QSAR studies on caspase-mediated apoptosis activity of phenolic analogues

Phenols and its analogues are known to induce caspase-mediated apoptosis activity and cytotoxicity on various cancer cell lines. In the current work, two types of molecular field analysis techniques were used to perform the three dimension quantitative structure activity relationship (3D-QSAR) modeling between structural characters and anticancer activity of two sets of phenolic compounds, which are comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Then two 3D-QSAR models for two sets of phenolic analogues were obtained with good results. The first QSAR model, which was derived from CoMFA for phenols with caspase-mediated apoptosis activity against L1210 cells, had good predictability (q ² = 0.874, r ² = 0.930), and the other one was derived from CoMSIA for electron-attracting phenols with cytotoxicity in L1210 cell (q ² = 0.836, r ² = 0.950). In addition, the CoMFA and CoMSIA contour maps provide valuable guidance for designing highly active phenolic compounds.     

In silico QSAR studies of anilinoquinolines as EGFR inhibitors

Members of the epidermal growth factor receptor (EGFR) family of proteins are frequently overactive in solid tumors. A relatively new therapeutic approach to inhibit the kinase activity is the use of ATP-competitive small molecules. In silico techniques were employed to identify the key interactions between inhibitors and their protein receptors. A series of EGFR inhibitory anilinoquinolines was studied within the framework of hologram quantitative structure activity relationship (HQSAR), density functional theory (DFT)-based QSAR, and three-dimensional (3D) QSAR (CoMFA/CoMSIA). The HQSAR analysis implied that substitutions at certain sites on the inhibitors play an important role in EGFR inhibition. DFT-based QSAR results suggested that steric and electronic interactions contributed significantly to the activity. Ligand-based 3D-QSAR and receptor-guided 3D-QSAR analyses such as CoMFA and CoMSIA techniques were carried out, and the results corroborated the previous two approaches. The 3D QSAR models indicated that steric and hydrophobic interactions are dominant, and that substitution patterns are an important factor in determining activity. Molecular docking was helpful in identifying a bioactive conformer as well as a plausible binding mode. The docked geometry-based CoMFA model with steric and electrostatic fields effect gave q ² = 0.66, r ² = = 0.94 with r ² _predictive = 0.72. Similarly, CoMSIA with hydrophobic field gave q ² = 0.59, r ² = 0.85 with r ² _predictive = 0.63. Bulky groups around site 3 of ring “C”, and hydrophilic and bulky groups at position 6 of ring “A” are desirable, with a hydrophobic and electron-donating group at site 7 of ring “A” being helpful. Accordingly, potential EGFR inhibitors may be designed by modification of known inhibitors.     

3D-QSAR studies of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas as CCR5 receptor antagonists

3D-QSAR studies on the derivatives of 1-(3,3-diphenylpropyl)-piperidinyl amide and urea as CCR5 receptor antagonists were performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) methods to rationalize the structural requirements responsible for the inhibitory activity of these compounds. The global minimum energy conformer of the template molecule, the most active and pharmacokinetically stable molecule of the series, was obtained by systematic search and used to build structures of the molecules in the dataset. The best predictions for the CCR5-receptor were obtained with the CoMFA standard model (q ² = 0.787, r ² = 0.962) and CoMSIA model combined steric, electrostatic and hydrophobic fields (q ² = 0.809, r ² = 0.951). The predictive ability of CoMFA and CoMSIA were determined using a test set of 12 compounds giving predictive correlation coefficients of 0.855 and 0.83, respectively, indicating good predictive power. Further, the robustness of the model was verified by bootstrapping analysis. The contour maps produced by the CoMFA and CoMSIA models were used to identify the structural features relevant to the biological activity in this series. Based on the CoMFA and CoMSIA analysis, we have identified some key features in the series that are responsible for CCR5 antagonistic activity which may be used to design more potent 1-(3,3-diphenylpropyl)-piperidinyl derivatives and predict their activity prior to synthesis. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

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.     

Comparative QSAR studies on peptide deformylase inhibitors

Comparative quantitative structure–activity relationship (QSAR) analyses of peptide deformylase (PDF) inhibitors were performed with a series of previously published (British Biotech Pharmaceuticals, Oxford, UK) reverse hydroxamate derivatives having antibacterial activity against Escherichia coli PDF, using 2D and 3D QSAR methods, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR). Statistically reliable models with good predictive power were generated from all three methods (CoMFA r ² = 0.957, q ² = 0.569; CoMSIA r ² = 0.924, q ² = 0.520; HQSAR r ² = 0.860, q ² = 0.578). The predictive capability of these models was validated by a set of compounds that were not included in the training set. The models based on CoMFA and CoMSIA gave satisfactory predictive r ² values of 0.687 and 0.505, respectively. The model derived from the HQSAR method showed a low predictability of 0.178 for the test set. In this study, 3D prediction models showed better predictive power than 2D models for the test set. This might be because 3D information is more important in the case of datasets containing compounds with similar skeletons. Superimposition of CoMFA contour maps in the active site of the PDF crystal structure showed a meaningful correlation between receptor–ligand binding and biological activity. The final QSAR models, along with information gathered from 3D contour and 2D contribution maps, could be useful for the design of novel active inhibitors of PDF. Figure Superimposition of comparative molecular field analysis (CoMFA) contour plot in the active site of peptide deformylase (PDF)     

Structural determinants of benzodiazepinedione/peptide-based p53-HDM2 inhibitors using 3D-QSAR, docking and molecular dynamics

As a tumor suppressor, p53 protein regulates the cell cycle and is involved in preventing tumorgenesis. The protein level of p53 is under the tight control of its negative regulator human double minute 2 (HDM₂) via ubiquitination. Therefore, the design of inhibitors of HDM₂ has attracted much interest of research on developing novel anticancer drugs. Presently, two classes of molecules, i.e., the 1,4-benzodiazepine-2,5-diones (BDPs) and N-Acylpolyamine (NAPA) derivatives were studied by three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling approaches including the comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) as promising p53-HDM₂ inhibitors. Based on both the ligand-based and receptor-guided (docking) alignments, two optimal 3D-QSAR models were obtained with good predictive power of q ² = 0.41, r ² _pred = 0.60 for BDPs, and q ² = 0.414, r ² _pred = 0.69 for NAPA analogs, respectively. By analysis of the model and its related contour maps, it is revealed that the electrostatic interactions contributed much larger to the compound binding affinity than the steric effects. And the contour maps intuitively suggested where to modify the molecular structures in order to improve the binding affinity. In addition, molecular dynamics simulation (MD) study was also carried out on the dataset with purpose of exploring the detailed binding modes of ligand in the HDM₂ binding pocket. Based on the CoMFA contour maps and MD-based docking analyses, some key structural aspects responsible for inhibitory activity of these two classes of compounds were concluded as follows: For BDPs, the R₁ and R₃ regions should have small electronegativity groups; substituents R₂ and R₄ should be larger, and R₃ substituent mainly involves in H-bonds forming. For NAPA derivatives, bulky and electropositive groups in ring B and ring A, small substituent at region P is favorable for the inhibitory activity. The models and related information, we hope, may provide important insight into the inhibitor-p53-HDM₂ interactions and be helpful for facilitating the design of novel potent inhibitors.     

3D-QSAR study of microsomal prostaglandin E<Subscript>2</Subscript> synthase(mPGES-1) inhibitors

Microsomal prostaglandin E₂ synthase (mPGES-1) has been identified recently as a novel target for treating pain and inflammation. The aim of this study is to understand the binding affinities of reported inhibitors for mPGES-1 and further to design potential new mPGES-1 inhibitors. 3D-QSAR-CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) - techniques were employed on a series of indole derivatives that act as selective mPGES-1 inhibitors. The lowest energy conformer of the most active compound obtained from systematic conformational search was used as a template for the alignment of 32 compounds. The models obtained were used to predict the activities of the test set of eight compounds, and the predicted values were in good agreement with the experimental results. The 3D-QSAR models derived from the training set of 24 compounds were all statistically significant (CoMFA; q ² = 0.89, r ² = 0.95, , and CoMSIA; q ² = 0.84, r ² = 0.93, , ). Contour plots generated for the CoMFA and CoMSIA models reveal useful clues for improving the activity of mPGES-1 inhibitors. In particular, substitutions of an electronegative fluorine atom or a bulky hydrophilic phenoxy group at the meta or para positions of the biphenyl rings might improve inhibitory activity. A plausible binding mode between the ligands and mPGES-1 is also proposed.     

Elucidation of binding mode and three dimensional quantitative structure–activity relationship studies of a novel series of protein kinase B/Akt inhibitors

Protein kinase B (PKB; also known as Akt kinase) is located downstream in the PI-3 kinase pathway. Overexpression and constitutive activation of PKB/Akt leads to human prostate, breast and ovarian carcinomas. A series of 69 PKB/Akt inhibitors were examined to explore their binding modes using FlexX, and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies based on comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed to provide structural insights into these compounds. CoMFA produced statistically significant results, with cross-validated q ² and non-cross validated correlation r ² coefficients of 0.53 and 0.95, respectively. For CoMSIA, steric, hydrophobic and hydrogen bond acceptor fields jointly yielded ‘leave one out’ q ²  = 0.51 and r ²  = 0.84. The predictive power of CoMFA and CoMSIA was determined using a test set of 13 molecules, which gave correlation coefficients, of 0.58 and 0.62, respectively. Molecular docking revealed that the binding modes of these molecules in the ATP binding sites of the Akt kinase domain were very similar to those of the co-crystallized ligand. The information obtained from 3D contour maps will allow the design of more potent and selective Akt kinase inhibitors.     

Use of molecular modeling, docking, and 3D-QSAR studies for the determination of the binding mode of benzofuran-3-yl-(indol-3-yl)maleimides as GSK-3β inhibitors

Molecular modeling and docking studies along with three-dimensional quantitative structure relationships (3D-QSAR) studies have been used to determine the correct binding mode of glycogen synthase kinase 3β (GSK-3β) inhibitors. The approaches of comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) are used for the 3D-QSAR of 51 substituted benzofuran-3-yl-(indol-3-yl)maleimides as GSK-3β inhibitors. Two binding modes of the inhibitors to the binding site of GSK-3β are investigated. The binding mode 1 yielded better 3D-QSAR correlations using both CoMFA and CoMSIA methodologies. The three-component CoMFA model from the steric and electrostatic fields for the experimentally determined pIC₅₀ values has the following statistics: R²(cv) = 0.386 nd SE(cv) = 0.854 for the cross-validation, and R² = 0.811 and SE = 0.474 for the fitted correlation. F (3,47) = 67.034, and probability of R² = 0 (3,47) = 0.000. The binding mode suggested by the results of this study is consistent with the preliminary results of X-ray crystal structures of inhibitor-bound GSK-3β. The 3D-QSAR models were used for the estimation of the inhibitory potency of two additional compounds.     

Discovery of FIXa inhibitors by combination of pharmacophore modeling,molecular docking,and 3D-QSAR modeling

Human Coagulation Factor IXa (FIXa), specifically inhibited at the initiation stage of the blood coagulation cascade, is an excellent target for developing selective and safe anticoagulants. To explore this inhibitory mechanism, 86 FIXa inhibitors were selected to generate pharmacophore models and subsequently SAR models. Both best pharmacophore model and ROC curve were built through the Receptor–Ligand Pharmacophore Generation module. CoMFA model based on molecular docking and PLS factor analysis methods were developed. Model propagations values are q²?=?0.709, r²?=?0.949, and r²_pred?=?0.905. The satisfactory q² value of 0.609, r² value of 0.962, and r²_pred value of 0.819 for CoMSIA indicated that the CoMFA and CoMSIA models are both available to predict the inhibitory activity on FIXa. On the basis of pharmacophore modeling, molecular docking, and 3D-QSAR modeling screening, six molecules are screened as potential FIXa inhibitors.