Molecular modeling of triazine type MDR modulators using CoMFA and CoMSIA approaches

In the present study a series of 30 triazine derivatives was investigated by 3D QSAR methods with respect to their MDR reversing activity in vitro. Two approaches were applied and compared: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Molecular models with good predictive power were derived using steric, electrostatic and hydrophobic fields of the compounds. The results indicated the dominant role of the electrostatic and hydrophobic fields for MDR reversing activity of the investigated modulators. The obtained statistical parameters (Qcv2, Qpr2) showed that the CoMFA and CoMSIA models have similar predictivity. The CoMSIA models were slightly better than the CoMFA ones and obtained with lower number of principal components. The models were graphically interpreted using CoMFA and CoMSIA contour plots. The structural regions responsible for the differences in anti-MDR activity were analyzed in respect to their electrostatic and hydrophobic nature. An easier interpretation of the CoMSIA contour plots was noticed.     

Steric and electrostatic effects in dye-cellulose interactions by the MTD and CoMFA approaches

This paper presents the application of the MTD (minimal steric difference) analysis and CoMFA (comparative molecular field analysis) to series of anthraquinone vat, mono and disazo and disperses dyes with known affinities for cellulose fiber. A comparison of the results demonstrates that these methods usually agree with the prediction of structural features favorable for dyeing. A series of n =49 anthraquinone vat dyes was studied by MTD with r 2 between 0.903 and 0.941 and r CV 2 values in the range of 0.827-0.878. For CoMFA, r 2 =0.992, r CV 2 =0.841 were obtained; the CoMFA field is in rather good agreement with vertex attributions, by MTD for attractive and repulsive vertices. Anionic disazo dyes were studied by the CoMFA method ( n =21, r 2 =0.999, r CV 2 =0.703). Monoazo dyes (several series) were studied by CoMFA and MTD. The effect of lipophilicity on dye fiber affinity was, also, studied for these dyes. Disperse dye adsorption was analyzed by MTD and CoMFA ( n =27, r 2 =0.925, r CV 2 =0.776). Conclusions refer to the effect of structural features of dye molecules upon adsorption on cellulose fibers.     

QSAR analyses on avian influenza virus neuraminidase inhibitors using CoMFA, CoMSIA, and HQSAR

The recent wide spreading of the H5N1 avian influenza virus (AIV) in Asia, Europe and Africa and its ability to cause fatal infections in human has raised serious concerns about a pending global flu pandemic. Neuraminidase (NA) inhibitors are currently the only option for treatment or prophylaxis in humans infected with this strain. However, drugs currently on the market often meet with rapidly emerging resistant mutants and only have limited application as inadequate supply of synthetic material. To dig out helpful information for designing potent inhibitors with novel structures against the NA, we used automated docking, CoMFA, CoMSIA, and HQSAR methods to investigate the quantitative structure-activity relationship for 126 NA inhibitors (NIs) with great structural diversities and wide range of bioactivities against influenza A virus. Based on the binding conformations discovered via molecular docking into the crystal structure of NA, CoMFA and CoMSIA models were successfully built with the cross-validated q (2) of 0.813 and 0.771, respectively. HQSAR was also carried out as a complementary study in that HQSAR technique does not require 3D information of these compounds and could provide a detailed molecular fragment contribution to the inhibitory activity. These models also show clearly how steric, electrostatic, hydrophobicity, and individual fragments affect the potency of NA inhibitors. In addition, CoMFA and CoMSIA field distributions are found to be in well agreement with the structural characteristics of the corresponding binding sites. Therefore, the final 3D-QSAR models and the information of the inhibitor-enzyme interaction should be useful in developing novel potent NA inhibitors.     

Structure-based CoMFA and CoMSIA study of indolinone inhibitors of PDK1

Phosphoinositide-dependent protein kinase-1 (PDK1) is a Ser/Thr kinase which phosphorylates and activates members of the AGC kinase group known to control processes such as tumor cell growth, protection from apoptosis, and tumor angiogenesis. In this paper, CoMFA and CoMSIA studies were carried out on a training set of 56 conformationally rigid indolinone inhibitors of PDK1. Predictive 3D QSAR models, established using atom fit alignment rule based on crystallographic-bound conformation, had cross-validated (r _cv²) values of 0.738 and 0.816 and non-cross-validated (r _ncv²) values of 0.912 and 0.949 for CoMFA and CoMSIA models, respectively. The predictive ability of the CoMFA and CoMSIA models was determined using a test set of 14 compounds, which gave predictive correlation coefficients (r _pred²) of 0.865 and 0.837, respectively. Structure-based interpretation of the CoMFA and CoMSIA field properties provided further insights for the rational design of new PDK1 inhibitors. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Computational Analysis of CRTh2 receptor antagonist: A Ligand-based CoMFA and CoMSIA approach

CRTh2 receptor is an important mediator of inflammatory effects and has attracted much attention as a therapeutic target for the treatment of conditions such as asthma, COPD, allergic rhinitis and atopic dermatitis. In pursuit of better CRTh2 receptor antagonist agents, 3D-QSAR studies were performed on a series of 2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl) acetic acids. There is no crystal structure information available on this protein; hence in this work, ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed by atom by atom matching alignment using systematic search and simulated annealing methods. The 3D-QSAR models were generated with 10 different combinations of test and training set molecules, since the robustness and predictive ability of the model is very important. We have generated 20 models for CoMFA and 100 models for CoMSIA based on two different alignments. Each model was validated with statistical cut off values such as q² > 0.4, r² > 0.5 and r²_pred > 0.5. Based on better q² and r²_pred values, the best predictions were obtained for the CoMFA (model 5 q² = 0.488, r²_pred = 0.732), and CoMSIA (model 45 q² = 0.525, r²_pred = 0.883) from systematic search conformation alignment. The high correlation between the cross-validated/predicted and experimental activities of a test set revealed that the CoMFA and CoMSIA models were robust. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. The generated models suggest that steric, electrostatic, hydrophobic, H-bond donor and acceptor parameters are important for activity. Our study serves as a guide for further experimental investigations on the synthesis of new CRTh2 antagonist.     

Effects of variable selection on CoMFA coefficient contour maps in a set of triazines inhibiting DHFR

Summary An example of a CoMFA study is described with the aim to discuss one of the major problems of this 3D QSAR method: lack of variable selection. It is shown that the use of nonrelevant energy parameters might produce CoMFA contour maps which poorly reflect the actual nature of the binding site and are in part statistical artefacts. The data set employed in our analysis comparises triazine inhibitors of dihydrofolate reductase (DHFR), isolated from chicken liver, which have already been the object of a QSAR study by other authors. Since three-dimensional structures of triazine-DHFR complexes are known, it was possible not only to reduce ambiguities in the superimposition of the ligands, but also to compare the resulting CoMFA contour maps with the enzyme active site.Supplementary material available: The Cartesian coordinates and the atomic charges of the PM3-optimized structures used in the CoMFA study are available as MOL2 files upon request.To whose memory this paper is dedicated.     

Non-linear modeling of bioconcentration using partition coefficients for narcotic chemicals

Bioconcentration factors (BCFs) have traditionally been used to describe the tendency of chemicals to concentrate in aquatic organisms. A reexamination of the log-log QSAR between the BCF and K OW for non-congener narcotic chemicals is presented on the basis of recommended data for fish. The model is extended to give a simple correlation between BCF and the toxicity of highly, moderately and weakly hydrophilic chemicals. For the first time, in this study an equation for calculating BCF was applied in a QSAR model for predicting the acute toxicity of chemicals to aquatic organisms.     

A combined CoMFA and CoMSIA 3D-QSAR study of benzamide type antibacterial inhibitors of the FtsZ protein in drug-resistant Staphylococcus aureus

A major problem today is bacterial resistance to antibiotics and the small number of new therapeutic agents approved in recent years. The development of new antibiotics capable of acting on new targets is urgently required. The filamenting temperature-sensitive Z (FtsZ) bacterial protein is a key biomolecule for bacterial division and survival. This makes FtsZ an attractive new pharmacological target for the development of antibacterial agents. There have been several attempts to develop ligands able to inhibit FtsZ. Despite the large number of synthesized compounds that inhibit the FtsZ protein, there are no quantitative structure–activity relationships (QSAR) that allow for the rational design and synthesis of promising new molecules. We present the first 3D-QSAR study of a large and diverse set of molecules that are able to inhibit the FtsZ bacterial protein. We summarize a set of chemical changes that can be made in the steric, electrostatic, hydrophobic and donor/acceptor hydrogen-bonding properties of the pharmacophore, to generate new bioactive molecules against FtsZ. These results provide a rational guide for the design and synthesis of promising new antibacterial agents, supported by the strong statistical parameters obtained from CoMFA (r²_pred = 0.974) and CoMSIA (r²_pred = 0.980) analyses.     

CoMFA,CoMSIA, Topomer CoMFA,HQSAR, molecular docking and molecular dynamics simulations study of triazine morpholino derivatives as mTOR inhibitors for the treatment of breast cancer

mTOR has become a promising target for many types of cancer like breast, lung and renal cell carcinoma. CoMFA, CoMSIA, Topomer CoMFA and HQSAR were performed on the series of 39 triazine morpholino derivatives. CoMFA analysis showed q² value of 0.735, r²_cv value of 0.722 and r²_pred value of 0.769. CoMSIA analysis (SEHD) showed q² value of 0.761, r²_cv value of 0.775 and r²_pred value of 0.651. Topomer CoMFA analysis showed q² value of 0.693, r² (conventional correlation coefficient) value of 0.940 and r²_pred value of 0.720. HQSAR analysis showed q²,r²and r²_pred values of 0.694, 0.920 and 0.750, respectively. HQSAR analysis with the combination of atomic number (A), bond type (B) and atomic connections showed q² and r² values of 0.655 and 0.891, respectively. Contour maps from all studies provided significant insights. Molecular docking studies with molecular dynamics simulations were carried out on the highly potent compound 36. Furthermore, four acridine derivatives were designed and docking results of these designed compounds showed the same interactions as that of the standard PI-103 which proved the efficiency of 3D-QSAR and MD/MS study. In future, this study might be useful prior to synthesis for the designing of novel mTOR inhibitors.     

3D-QSAR and molecular docking study of LRRK2 kinase inhibitors by CoMFA and CoMSIA methods

Three-dimensional quantitative structure–activity relationship (3D-QSAR) modelling was conducted on a series of leucine-rich repeat kinase 2 (LRRK2) antagonists using CoMFA and CoMSIA methods. The data set, which consisted of 37 molecules, was divided into training and test subsets by using a hierarchical clustering method. Both CoMFA and CoMSIA models were derived using a training set on the basis of the common substructure-based alignment. The optimum PLS model built by CoMFA and CoMSIA provided satisfactory statistical results (q² = 0.589 and r² = 0.927 and q² = 0.473 and r² = 0.802, respectively). The external predictive ability of the models was evaluated by using seven compounds. Moreover, an external evaluation set with known experimental data was used to evaluate the external predictive ability of the porposed models. The statistical parameters indicated that CoMFA (after region focusing) has high predictive ability in comparison with standard CoMFA and CoMSIA models. Molecular docking was also performed on the most active compound to investigate the existence of interactions between the most active inhibitor and the LRRK2 receptor. Based on the obtained results and CoMFA contour maps, some features were introduced to provide useful insights for designing novel and potent LRRK2 inhibitors.     

CoMFA and CoMSIA studies on C-aryl glucoside SGLT2 inhibitors as potential anti-diabetic agents

SGLT2 has become a target of therapeutic interest in diabetes research. CoMFA and CoMSIA studies were performed on C-aryl glucoside SGLT2 inhibitors (180 analogues) as potential anti-diabetic agents. Three different alignment strategies were used for the compounds. The best CoMFA and CoMSIA models were obtained by means of Distill rigid body alignment of training and test sets, and found statistically significant with cross-validated coefficients (q ²) of 0.602 and 0.618, respectively, and conventional coefficients (r ²) of 0.905 and 0.902, respectively. Both models were validated by a test set of 36 compounds giving satisfactory predicted correlation coefficients (r ² _pred) of 0.622 and 0.584 for CoMFA and CoMSIA models, respectively. A comparison was made with earlier 3D QSAR study on SGLT2 inhibitors, which shows that our 3D QSAR models are better than earlier models to predict good inhibitory activity. CoMFA and CoMSIA models generated in this work can provide useful information to design new compounds and helped in prediction of activity prior to synthesis.     

CoMFA and CoMSIA 3D-quantitative structure-activity relationship model on benzodiazepine derivatives, inhibitors of phosphodiesterase IV

Recently, we reported structurally novel PDE4 inhibitors based on 1,4-benzodiazepine derivatives. The main interest in developing bezodiazepine-based PDE4 inhibitors is in their lack of adverse effects of emesis with respect to rolipram-like compounds. A large effort has thus been made toward the structural optimization of this series. In the absence of structural information on the inhibitor binding mode into the PDE4 active site, 2D-QSAR (H-QSAR) and two 3D-QSAR (CoMFA and CoMSIA) methods were applied to improve our understanding of the molecular mechanism controlling the PDE4 affinity of the benzodiazepine derivatives. As expected, the CoMSIA 3D contour maps have provided more information on the benzodiazepine interaction mode with the PDE4 active site whereas CoMFA has built the best tool for activity prediction. The 2D pharmacophoric model derived from CoMSIA fields is consistent with the crystal structure of the PDE4 active site reported recently. The combination of the 2D and 3D-QSAR models was used not only to predict new compounds from the structural optimization process, but also to screen a large library of bezodiazepine derivatives.     

Docking-based CoMFA and CoMSIA study of azaindole carboxylic acid derivatives as promising HIV-1 integrase inhibitors

Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed based on a series of azaindole carboxylic acid derivatives that had previously been reported as promising HIV-1 integrase inhibitors. Docking studies to explore the binding mode were performed based on the highly active molecule 36. The best docked conformation of molecule 36 was used as template for alignment. The comparative molecular field analysis (CoMFA) model (including steric and electrostatic fields) yielded the cross validation q ² = 0.655, non-cross validation r ² = 0.989 and predictive r ² _pred = 0.979. The best comparative molecular similarity indices analysis (CoMSIA) model (including steric, electrostatic, hydrophobic and hydrogen-bond acceptor fields) yielded the cross validation q ² = 0.719, non-cross validation r ² = 0.992 and predictive r ² _pred = 0.953. A series of new azaindole carboxylic acid derivatives were designed and the HIV-1 integrase inhibitory activities of these designed compounds were predicted based on the CoMFA and CoMSIA models.     

QSAR models of cytochrome P450 enzyme 1A2 inhibitors using CoMFA,CoMSIA and HQSAR

Quantitative structure–activity relationship (QSAR) studies were conducted on an in-house database of cytochrome P450 enzyme 1A2 inhibitors using the comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA) and hologram QSAR (HQSAR) approaches. The database consisted of 36 active molecules featuring varied core structures. The model based on the naphthalene substructure alignment incorporating 19 molecules yielded the best model with a CoMFA cross validation value q² of 0.667 and a Pearson correlation coefficient r² of 0.976; a CoMSIA q² value of 0.616 and r² value of 0.985; and a HQSAR q² value of 0.652 and r² value of 0.917. A second model incorporating 34 molecules aligned using the benzene substructure yielded an acceptable CoMFA model with q² value of 0.5 and r² value of 0.991. Depending on the core structure of the molecule under consideration, new CYP1A2 inhibitors will be designed based on the results from these models.     

Hollow fiber membrane-protected solid-phase microextraction of triazine herbicides in bovine milk and sewage sludge samples

A porous polypropylene hollow fiber membrane (HFM)-protected solid-phase microextraction (HFM-SPME) procedure in conjunction with gas chromatography/mass spectrometric analysis for use in the determination of triazine herbicides in bovine milk samples is described. A 65-microm polydimethylsiloxane-divinylbenzne (PDMS-DVB) SPME fiber was protected by an HFM. HFM-SPME experimental parameters such as fiber type, extraction time, extraction temperature and salt concentration were investigated and optimized. The relative standard deviations for the reproducibility of the optimized HFM-SPME method varied from 4.30 to 12.37%. The correlation coefficients of the calibration curves were between 0.9799 and 0.9965 across a concentration range of 0-200 microg l(-1). The method detection limits for triazines in bovine milk were in the range of 0.003-0.013 microg l(-1) and limits of quantification were in the range of 0.006-0.021 microg l(-1). The suitability of HFM-SPME was extended to the analysis of the herbicides in sewage sludge samples. The results demonstrate that HFM-SPME was an efficient pretreatment and enrichment procedure for complex matrices.     

Androgen receptor binding affinity of pesticide "active" formulation ingredients. QSAR evaluation by COREPA method

The COREPA approach for identifying the COmmon REactivity PAttern of biologically similar chemicals was employed to upgrade the recently derived affinity pattern for high androgen receptor (AR) binding affinity. The training set consisted of 28 steroidal and nonsteroidal ligands whose AR binding affinity was determined in competitive binding assays (in terms of p K i ). The interatomic distances between nucleophilic sites and their charges providing distinct and non-overlapping integral patterns for active and inactive chemicals were assumed that it was related with the endpoint, which was under study. These stereoelectronic characteristics were used to predict p K i values of pesticide "active" formulation ingredients in an attempt to identify chemicals with potential AR binding affinity.     

Comparative QSAR studies using HQSAR,CoMFA, and CoMSIA methods on cyclic sulfone hydroxyethylamines as BACE1 inhibitors

The inhibition of β-secretase (BACE1) is currently the main pharmacological strategy available for Alzheimer’s disease (AD). 2D QSAR and 3D QSAR analysis on some cyclic sulfone hydroxyethylamines inhibitors against β-secretase (IC₅₀: 0.002–2.75 μM) were carried out using hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods. The best model based on the training set was generated with a HQSAR q² value of 0.693 and r² value of 0.981; a CoMFA q² value of 0.534 and r² value of 0.913; and a CoMSIA q² value of 0.512 and r² value of 0.973. In order to gain further understand of the vital interactions between cyclic sulfone hydroxyethylamines and the protease, the analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the BACE1. The final QSAR models could be helpful in the design and development of novel active BACE1 inhibitors.