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.     

QSAR studies on the human sirtuin 2 inhibition by non-covalent 7,5,2-anilinobenzamide derivatives

Abstract

Sirtuin 2 is a key enzyme in gene expression regulation that is often associated with tumor proliferation control and therefore is a relevant anticancer drug target. Anilinobenzamide derivatives have been discussed as selective sirtuin 2 inhibitors and can be developed further. In the present study, hologram and three-dimensional quantitative structure–activity relationship (HQSAR and 3D-QSAR) analyses were employed for determining structural contributions of a compound series containing human sirtuin-2-selective inhibitors that were then correlated with structural data from the literature. The final QSAR models were robust and predictive according to statistical validation (q² and r²_pred values higher than 0.85 and 0.75, respectively) and could be employed further to generate fragment contribution and contour maps. 3D-QSAR models together with information about the chemical properties of sirtuin 2 inhibitors can be useful for designing novel bioactive ligands.

Communicated by Ramaswamy H. Sarma     

Probing the structural requirements of A-type Aurora kinase inhibitors using 3D-QSAR and molecular docking analysis

Aurora-A, the most widely studied isoform of Aurora kinase overexpressed aberrantly in a wide variety of tumors, has been implicated in early mitotic entry, degradation of natural tumor suppressor p53 and centrosome maturation and separation; hence, potent inhibitors of Aurora-A may be therapeutically useful drugs in the treatment of various forms of cancer. Here, we report an in silico study on a group of 220 reported Aurora-A inhibitors with six different substructures. Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were carried out using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques on this series of molecules. The resultant optimum 3D-QSAR models exhibited an r _cv² value of 0.404-0.582 and their predictive ability was validated using an independent test set, ending in r _pred² 0.512-0.985. In addition, docking studies were employed to explore these protein–inhibitor interactions at the molecular level. The results of 3D-QSAR and docking analyses validated each other, and the key structural requirements affecting Aurora-A inhibitory activities, and the influential amino acids involved were identified. To the best of our knowledge, this is the first report on 3D-QSAR modeling of Aurora-A inhibitors, and the results can be used to accurately predict the binding affinity of related analogues and also facilitate the rational design of novel inhibitors with more potent biological activities.     

3D-QSAR study of 8-azabicyclo[3.2.1] octane analogs antagonists of cholinergic receptor

3D-QSAR models of Comparative of Molecular Field Analysis (CoMFA) and Comparative of Molecular Similarities Indices Analysis (CoMSIA) of 20 8-azabicyclo[3.2.1] octane (potent muscarinic receptor blocker) was performed. These benztropine analogs were optimized using ligand based alignment method. The conventional ligand-based 3D-QSAR studies were performed based on the low energy conformations employing database alignment rule. The ligand-based model gave q² value 0.819 and 0.810 and r² value 0.991 and 0.988 for CoMFA and CoMSIA, respectively, and the predictive ability of the model was validated. Results indicate that the CoMFA and CoMSIA models could be reliable model which may be used in the design of novel muscarinic antagonists as leads.     

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.     

In silico studies on p21-activated kinase 4 inhibitors: comprehensive application of 3D-QSAR analysis,molecular docking,molecular dynamics simulations,and MM-GBSA calculation

Abstract

P21-activated kinase 4 (PAK4) is a serine/threonine protein kinase, which is associated with many cancer diseases, and thus being considered as a potential drug target. In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations were performed to explore the structure-activity relationship of a series of pyrropyrazole PAK4 inhibitors. The statistical parameters of comparative molecular field analysis (CoMFA, Q ² = 0.837, R ² = 0.990, and R ² _pred = 0.967) and comparative molecular similarity indices analysis (CoMSIA, Q ² = 0.720, R ² = 0.972, and R ² _pred = 0.946) were obtained from 3D-QSAR model, which exhibited good predictive ability and significant statistical reliability. The binding mode of PAK4 with its inhibitors was obtained through molecular docking study, which indicated that the residues of GLU396, LEU398, LYS350, and ASP458 were important for activity. Molecular mechanics generalized born surface area (MM-GBSA) method was performed to calculate the binding free energy, which indicated that the coulomb, lipophilic and van der Waals (vdW) interactions made major contributions to the binding affinity. Furthermore, through 100?ns MD simulations, we obtained the key amino acid residues and the types of interactions they participated in. Based on the constructed 3D-QSAR model, some novel pyrropyrazole derivatives targeting PAK4 were designed with improved predicted activities. Pharmacokinetic and toxicity predictions of the designed PAK4 inhibitors were obtained by the pkCSM, indicating these compounds had better absorption, distribution, metabolism, excretion and toxicity (ADMET) properties. Above research provided a valuable insight for developing novel and effective pyrropyrazole compounds targeting PAK4.     

Molecular docking and 3D-QSAR study on 4-(1H-indazol-4-yl) phenylamino and aminopyrazolopyridine urea derivatives as kinase insert domain receptor (KDR) inhibitors

Vascular endothselial growth factor (VEGF) and its receptor tyrosine kinase VEGFR-2 or kinase insert domain receptor (KDR) have been identified as new promising targets for the design of novel anticancer agents. It is reported that 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives exhibit potent inhibitory activities toward KDR. To investigate how their chemical structures relate to the inhibitory activities and to identify the key structural elements that are required in the rational design of potential drug candidates of this class, molecular docking simulations and three-dimensional quantitative structure-activity relationship (3D-QSAR) methods were performed on 78 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives as KDR inhibitors. Surflex-dock was used to determine the probable binding conformations of all the compounds at the active site of KDR. As a result, multiple hydrophobic and hydrogen-bonding interactions were found to be two predominant factors that may be used to modulate the inhibitory activities. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR models were developed based on the docking conformations. The CoMFA model produced statistically significant results with the cross-validated correlation coefficient q² of 0.504 and the non-cross-validated correlation coefficient r² of 0.913. The best CoMSIA model was obtained from the combination of steric, electrostatic and hydrophobic fields. Its q² and r² being 0.595 and 0.947, respectively, indicated that it had higher predictive ability than the CoMFA model. The predictive abilities of the two models were further validated by 14 test compounds, giving the predicted correction coefficients r_pred² of 0.727 for CoMFA and 0.624 for CoMSIA, respectively. In addition, the CoMFA and CoMSIA models were used to guide the design of a series of new inhibitors of this class with predicted excellent activities. Thus, these models may be used as an efficient tool to predict the inhibitory activities and to guide the future rational design of 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives-based novel KDR inhibitors with potent activities.     

Design,synthesis and biological evaluation of novel oseltamivir derivatives as potent neuraminidase inhibitors

Neuraminidase (NA) is one of the particular potential targets for novel antiviral therapy. In this work, a series of neuraminidase inhibitors with the cyclohexene scaffold were studied based upon the combination of 3D-QSAR, molecular docking, and molecular dynamics techniques. The results indicate that the built 3D-QSAR models yield reliable statistical information: the correlation coefficient (r²) and cross-validation coefficient (q²) of CoMFA (comparative molecular field analysis) are 0.992 and 0.819; the r² and q² of CoMSIA (comparative molecular similarity analysis) are 0.992 and 0.863, respectively. Molecular docking and MD simulations were conducted to confirm the detailed binding mode of enzyme-inhibitor system. The new NA inhibitors had been designed, synthesized, and their inhibitory activities against group-1 neuraminidase were determined. One agent displayed excellent neuraminidase inhibition, with IC₅₀ value of 39.6?μM against NA, while IC₅₀ value for oseltamivir is 61.1?μM. This compound may be further investigated for the treatment of infection by the new type influenza virus.     

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.     

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.     

CoMFA and CoMSIA 3D-QSAR analysis on hydroxamic acid derivatives as urease inhibitors

Urease (EC 3.5.1.5) serves as a virulence factor in pathogens that are responsible for the development of many diseases in humans and animals. Urease allows soil microorganisms to use urea as a source of nitrogen and aid in the rapid break down of urea-based fertilizers resulting in phytopathiCIT000y. It has been well established that hydroxamic acids are the potent inhibitors of urease activity. The 3D-QSAR studies on thirty five hydroxamic acid derivatives as known urease inhibitors were performed by Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods to determine the factors required for the activity of these compounds. The CoMFA model produced statistically significant results with cross-validated (q²) 0.532 and conventional (r²) correlation coefficients 0.969.The model indicated that the steric field (70.0%) has greater influence on hydroxamic acid inhibitors than the electrostatic field (30.0%). Furthermore, five different fields: steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor assumed to generate the CoMSIA model, which gave q² 0.665 and r² 0.976.This model showed that steric (43.0%), electrostatic (26.4%) and hydrophobic (20.3%) properties played a major role in urease inhibition. The analysis of CoMFA and CoMSIA contour maps provided insight into the possible modification of the hydroxamic acid derivatives for improved activity.     

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.     

CoMFA and CoMSIA analysis of 2,4-thiazolidinediones derivatives as aldose reductase inhibitors

Diabetes remains a life-threatening disease. The clinical profile of diabetic subjects is often worsened by the presence of several long-term complications, for example neuropathy, nephropathy, retinopathy, and cataract. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of 2,4-thiazolidinediones derivatives as aldose reductase (ALR2) inhibitors. Molecular ligand superimposition on a template structure was finished by the database alignment method. The 3D-QSAR models resulted from 44 molecules gave q ² values of 0.773 and 0.817, r ² values of 0.981 and 0.979 for CoMFA and CoMSIA, respectively. The contour maps from the models indicated that a large volume group next to the R-substituent will increase the ALR2 inhibitory activity. In fact, adding a -CH₂COOH substituent at the R-position would generate a new compound with higher predicted activity.     

Investigation on the binding mode of benzothiophene analogues as potent factor IXa (FIXa) inhibitors in thrombosis by CoMFA,docking and molecular dynamic studies

Recently, benzothiophenes attract much attention of interest due to its possible inhibitory activity targeting FIXa, a blood coagulation factor that is essential for the amplification or consolidation phase of blood coagulation. To explore this inhibitory mechanism, three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) studies on a series of 84 benzothiophene analogues, for the first time, were performed. As a result, a highly predictive CoMFA model was developed with the q²?=?0.52, r²?=?0.97 and r²_pred?=?0.81, respectively. The CoMFA contour maps, the docking analysis, as well as the MD simulation results are all in a good agreement, proving the reliability and robustness of the model. These models and the information, we hoped, would be helpful in screening and development of novel drugs against thrombosis prior to synthesis.     

CoMFA based de novo design of Pyrrolidine Carboxamides as Inhibitors of Enoyl Acyl Carrier Protein Reductase from Mycobacterium tuberculosis

InhA, the enoyl acyl carrier protein reductase (EACP reductase) from Mycobacterium tuberculosis, is one of the key enzymes involved in the mycobacterial fatty acid elongation cycle and has been validated as an effective target for the development of anti-microbial agents. We report here, comparative molecular field analysis (CoMFA) studies and subsequent de novo ligand design using the LeapFrog program on pyrrolidine carboxamides, which have been reported as selective inhibitors of EACP reductase from Mycobacterium tuberculosis. The CoMFA model, constructed from the inhibitors used in this study has been successfully used to rationalize the structure-activity relationship of pyrrolidine carboxamides. The CoMFA model produced statistically significant results with cross-validated and conventional correlation coefficients of 0.626 and 0.953 respectively. Further, the predictive ability of CoMFA model was determined using a test set which gave predictive correlation coefficient r ² _pred of 0.880, indicating good predictive power. Finally, Leapfrog was used to propose 13 new pyrrolidine carboxamide analogues, based on the information derived from the CoMFA contour maps. The designed molecules showed better predicted activity using the CoMFA model with respect to the already reported systems; hence suggesting that newly proposed molecules in this series of compounds may be more potent and selective toward EACP reductase inhibition.     

Design of novel quinazolinone derivatives as inhibitors for 5HT7 receptor

To study the pharmacophore properties of quinazolinone derivatives as 5HT₇ inhibitors, 3D QSAR methodologies, namely Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were applied, partial least square (PLS) analysis was performed and QSAR models were generated. The derived model showed good statistical reliability in terms of predicting the 5HT₇ inhibitory activity of the quinazolione derivative, based on molecular property fields like steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields. This is evident from statistical parameters like q² (cross validated correlation coefficient) of 0.642, 0.602 and r² (conventional correlation coefficient) of 0.937, 0.908 for CoMFA and CoMSIA respectively. The predictive ability of the models to determine 5HT₇ antagonistic activity is validated using a test set of 26 molecules that were not included in the training set and the predictive r² obtained for the test set was 0.512 & 0.541. Further, the results of the derived model are illustrated by means of contour maps, which give an insight into the interaction of the drug with the receptor. The molecular fields so obtained served as the basis for the design of twenty new ligands. In addition, ADME (Adsorption, Distribution, Metabolism and Elimination) have been calculated in order to predict the relevant pharmaceutical properties, and the results are in conformity with required drug like properties.     

3D-QSAR (CoMFA,CoMSIA), molecular docking and molecular dynamics simulations study of 6-aryl-5-cyano-pyrimidine derivatives to explore the structure requirements of LSD1 inhibitors

Recently, Histone Lysine Specific Demethylase 1 (LSD1) was regarded as a promising anticancer target for the novel drug discovery. And several small molecules as LSD1 inhibitors in different structures have been reported. In this work, we carried out a molecular modeling study on the 6-aryl-5-cyano-pyrimidine fragment LSD1 inhibitors using three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking and molecular dynamics simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to generate 3D-QSAR models. The results show that the best CoMFA model has q² = 0.802, r²_ncv = 0.979, and the best CoMSIA model has q² = 0.799, r²_ncv = 0.982. The electrostatic, hydrophobic and H-bond donor fields play important roles in the models. Molecular docking studies predict the binding mode and the interactions between the ligand and the receptor protein. Molecular dynamics simulations results reveal that the complex of the ligand and the receptor protein are stable at 300 K. All the results can provide us more useful information for our further drug design.