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.     

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.     

Virtual identification of novel PPARα/γ dual agonists by 3D-QSAR,molecule docking and molecular dynamics studies

Abstract

Peroxisome proliferator-activated receptors (PPARs) are considered important targets for the treatment of Type 2 diabetes (T2DM). To accelerate the discovery of PPAR α/γ dual agonists, the comparative molecular field analysis (CoMFA) were performed for PPARα and PPARγ, respectively. Based on the molecular alignment, highly predictive CoMFA model for PPARα was obtained with a cross-validated q² value of 0.741 and a conventional r² of 0.975 in the non-cross-validated partial least-squares (PLS) analysis, while the CoMFA model for PPARγ with a better predictive ability was shown with q² and r² values of 0.557 and 0.996, respectively. Contour maps derived from the 3D-QSAR models provided information on main factors towards the activity. Then, we carried out structural optimization and designed several new compounds to improve the predicted biological activity. To investigate the binding modes of the predicted compounds in the active site of PPARα/γ, a molecular docking simulation was carried out. Molecular dynamic (MD) simulations indicated that the predicted ligands were stable in the active site of PPARα/γ. Therefore, combination of the CoMFA and structure-based drug design results could be used for further structural alteration and synthesis and development of novel and potent dual agonists. Abbreviations DM diabetes mellitus

T2DM type 2 diabetes

PPARs peroxisome proliferator-activated receptors

LBDD ligand based drug design

3D-QSAR three-dimensional quantitative structure activity relationship

CoMFA comparative molecular field analysis

PLS partial least square

LOO leave-one-out

q² cross-validated correlation coefficient

ONC optimal number of principal components

r² non-cross-validated correlation coefficient

SEE standard error of estimate

F the Fischer ratio

r²_pred predictive correlation coefficient

DBD DNA binding domain

MD molecular dynamics

RMSD root-mean-square deviation

RMSF root mean square fluctuations

Communicated by Ramaswamy H. Sarma     

Rational questing for potential novel inhibitors of FabK from Streptococcus pneumoniae by combining FMO calculation,CoMFA 3D-QSAR modeling and virtual screening

Enoyl-acyl carrier protein (ACP) reductase (ENR) is an attractive and potential target for developing selective antibacterial agents. Recent studies showed that FabK is the sole isoform of ENR in Streptococcus pneumoniae, and at the same time an X-ray crystallographic study of FabK from S. pneumoniae (SpFabK) was reported for the first time. Based on above information, the interaction mechanism and pair interaction energies between ligand and the active site of SpFabK were analyzed with the ab initio fragment molecular orbital (FMO) calculation based on the FlexX docking model at the FMO-RHF/6-31G* level. Subsequently, the first molecular docking-based 3D-QSAR model with comparative molecular field analysis (CoMFA) was established with cross-validated coefficients (q ²) up to 0.511 and regression coefficients (r ²) up to 0.986. Then integrating the 3D-QSAR CoMFA predicted model, molecular docking, and FMO pair interaction analysis structure-based virtual screening was performed, six novel and potential lead compounds were sorted out for further study.     

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.     

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,molecular docking and molecular dynamics studies of a series of RORγt inhibitors

The discovery of clinically relevant inhibitors of retinoic acid receptor-related orphan receptor-gamma-t (RORγt) for autoimmune diseases therapy has proven to be a challenging task. In the present work, to find out the structural features required for the inhibitory activity, we show for the first time a three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations for a series of novel thiazole/thiophene ketone amides with inhibitory activity at the RORγt receptor. The optimum CoMFA and CoMSIA models, derived from ligand-based superimposition I, exhibit leave-one-out cross-validated correlation coefficient (R²_cv) of .859 and .805, respectively. Furthermore, the external predictive abilities of the models were evaluated by a test set, producing the predicted correlation coefficient (R²_pred) of .7317 and .7097, respectively. In addition, molecular docking analysis was applied to explore the binding modes between the inhibitors and the receptor. MD simulation and MM/PBSA method were also employed to study the stability and rationality of the derived conformations, and the binding free energies in detail. The QSAR models and the results of molecular docking, MD simulation, binding free energies corroborate well with each other and further provide insights regarding the development of novel RORγt inhibitors with better activity.     

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.     

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

Abstract

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

Structure-based and ligand-based drug design for microsomal prostaglandin E synthase-1 inhibitors

Microsomal prostaglandin E synthase-1 (mPGES-1) has been regarded as an attractive drug for inflammation-related diseases. In search of new mPGES-1 inhibitors, we performed virtual screening using our traditional Chinese medicine and natural products database (http://tcm.cmu.edu.tw/) and constructed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) using a training set of 30 experimentally tested mPGES-1 inhibitors. The CoMFA and CoMSIA models derived were statistically significant with cross-validated coefficient values of 0.808 for CoMFA and 0.829 for CoMSIA and non-cross-validated coefficient values of 0.829 for CoMFA and 0.980 for CoMSIA. Docking and de novo evolution design gave three top derivatives, 2-O-caffeoyl tartaric acid-Evo_2, glucogallin-Evo_1 and 3-O-feruloylquinic acid-Evo_7 that have higher binding affinities than the control, glutathione. These three derivatives have interactions with Arg70, Arg73, Arg110, Arg126 and Arg38, which all are mPGES-1 key active site residues. In addition, these derivatives fit well into the CoMFA and CoMSIA models, with hydrophobic, hydrophilic and electropositive substructures mapped onto corresponding contour plots. Hence, we suggest that these three de novo compounds could be a starting basis for new mPGES-1 inhibitors.     

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)     

3D-QSAR studies of checkpoint kinase 1 inhibitors based on molecular docking and CoMFA

Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on a series of substituted 1,4-dihydroindeno[1,2-c]pyrazoles inhibitors, using molecular docking and comparative molecular field analysis (CoMFA). The docking results from GOLD 3.0.1 provide a reliable conformational alignment scheme for the 3D-QSAR model. Based on the docking conformations and alignments, highly predictive CoMFA model was built with cross-validated q ² value of 0.534 and non-cross-validated partial least-squares analysis with the optimum components of six showed a conventional r ² value of 0.911. The predictive ability of this model was validated by the testing set with a conventional r ² value of 0.812. Based on the docking and CoMFA, we have identified some key features of the 1,4-dihydroindeno[1,2-c]pyrazoles derivatives that are responsible for checkpoint kinase 1 inhibitory activity. The analyses may be used to design more potent 1,4-dihydroindeno[1,2-c]pyrazoles derivatives and predict their activity prior to synthesis.     

Three-dimensional quantitative structure-activity relationships of pyrrolopyridinone as cell division cycle kinase inhibitors by CoMFA and CoMSIA

Seventy-five 1,5,6,7-tetrahydro-pyrrolo[3,2-C]pyridinone derivatives displaying potent activities against Cdc7 kinase were selected to establish 3D-QSAR models using CoMFA and CoMSIA methods. Internal and external cross-validation techniques were investigated as well as some measures including region focusing, progressive scrambling, bootstraping and leave-group-out. The satisfactory CoMFA model predicted a q ² value of 0.836 and an r ² value of 0.950, indicating that electrostatic and steric properties play a significant role in potency. The best CoMSIA model, based on a combination of steric, electrostatic and H-bond acceptor effects, predicted a q ² value of 0.636 and an r ² value of 0.907. The models were graphically interpreted using contour plots which provided insight into the structural requirements for increasing the activity of a compound. The final 3D-QSAR results could be used for rational design of potent inhibitors against Cdc7 kinase.     

Steroidal Metabolites Transformed by <Emphasis Type="Italic">Marchantia polymorpha</Emphasis> Cultures Block Breast Cancer Estrogen Biosynthesis

Suspension of cultured cells of Marchantia polymorpha have the potential to hydrogenate the olefinic bonds present in androst-1,4-dien-3,17-dione (boldione, 1) to afford dihydroandrost-3,17-dione derivatives including: androst-4-ene-3,17-dione (androstenedione, 4-AD, 2), 5α-androstane-3,17-dione (androstenedione, AD, 4), and the less abundant metabolite 5α-androst-1-ene-3,17-dione (1-androstenedione, 1-AD, 3). After isolation and purification, these metabolites were characterized on the basis of spectroscopic analyses using 1D and 2D NMR as well as mass spectrometry. Cytotoxicity of the biotransformation products against breast adenocarcinoma cells (MCF-7) was assessed by a 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide assay and cell death (apoptosis or necrosis) was assayed by acridine orange/ethidium bromide staining. Aromatase (cytochrome P450 19 enzyme, CYP19) inhibitory activity was measured by a tritiated water release assay and by direct measurement of bio-transformed steroids using the tritium labeled substrate ³H-androst-4-ene-3,17-dione. CYP19 mRNA expression in MCF-7 cells was analyzed by real-time PCR. Steroidal products 3 and 4 revealed a highly significant inhibition of MCF-7 cell growth that was predominantly due to apoptosis not necrosis. Steroidal products 3 and 4 are both potent inhibitors of aromatase activity and CYP19 mRNA expression, while 2 is a known substrate for aromatase. These data establish that metabolites 3 and 4 are potent chemical agents against breast cancer via aromatase inhibitory mechanism. Results were interpreted via virtual docking of the biotransformation products to the human placental aromatase active site.     

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.     

Influence of cell culture conditions on aromatase activity in human genital skin fibroblasts

Summary Because the measurement of aromatase activity in cultured human genital skin fibroblasts has been proposed as a means of studying estrogen production in men, we investigated the influence of culture conditions on aromatase activity. Genital skin fibroblasts were seeded onto culture plates at a density of 1×10⁶ cells/plate and aromatase activity was determined over a 1-mo. period. Enzyme activity rose slowly over the first 14 d but then rose rapidly to a 10-fold higher plateau by Day 28. The rise in aromatase activity was similar whether activity was normalized for protein or for DNA content. When cells were seeded at the usual density of 1×10⁶ or at 0.25×10⁶ cells/plate, aromatase activity was consistently lower during the first 2 wk in cells plated at lower density, but thereafter the levels of enzyme activity in the two groups converged. In cells plated at the lower density, the lower activity observed in the first 2 wk was associated with a lower V _max . Preincubation of cells plated at one density with conditoned medium from cells plated at the other density did not change the relatve levels of activity in the two groups. By contrast, dihydrotestosterone (DHT) receptor binding and 5α-reductase activity were similar at all time points, despite differences in plating density. In additional experiments, the culture medium was replaced daily rather than every 3rd d, and aromatase activity was assayed on Day 7. In cells fed daily, DNA and protein content were twice that of cells fed every 3rd d. By contrast, aromatase activity declined to 30% of the in the latter group. DHT and dexamethasone receptor binding and 5α-reductase activity were similar in the two groups. In summary, factors such as plating density, culture density, and frequency of media replacement dramatically influence aromatase activity in cultured human genital skin fibroblasts. Therefore, the interpretation of aromatase activity data obtained from cultured cells in relation to physiologic or pathologic states should be viewed with appropriate caution. The work was supported in part by grants R01 DK 35339 and R01 DK 00180 from the National Institutes of Health, Bethesda, MD, and by RR 00035 from CLINFO Systems at the Johns Hopkins University School of Medicine, Baltimore, MD.