Comparative molecular field analysis and comparative molecular similarity indices analysis of human thymidine kinase 1 substrates

Thymidine kinase 1 (TK1) is a key target for antiviral and anticancer chemotherapy. Three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques was applied to analyze the phosphorylation capacity of a series of 31 TK1 substrates. The optimal predictive CoMFA model with 26 molecules provided the following values: cross-validated r(2) (q(2))=0.651, non-cross-validated r(2)=0.980, standard error of estimate (s)=0.207, F=129.3. For the optimal CoMSIA model the following values were found: q(2)=0.619, r(2)=0.994, s=0.104, F=372.2. The CoMSIA model includes steric, electrostatic, and hydrogen bond donor fields. The predictive capacity of both models was successfully validated by calculating known phosphorylation rates of five TK1 substrates that were not included in the training set. Contour maps obtained from CoMFA and CoMSIA models correlated with the experimentally developed SAR.     

3D QSAR studies on T-type calcium channel blockers using CoMFA and CoMSIA

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of isoxazolyl compounds as a potent T-type calcium channel blockers. A set of 24 structurally similar compounds served to establish the model. Four different conformations of the most active compound were used as template structures for the alignment, three of which were obtained from Catalyst pharmacophore modeling and one by using SYBYL random search option. All CoMFA and CoMSIA models gave cross-validated r(2) (q(2)) value of more than 0.5 and conventional r(2) value of more than 0.85. The predictive ability of the models was validated by an external test set of 10 compounds, which gave satisfactory pred r(2) values ranging from 0.577 to 0.866 for all models. Best predictions were obtained with CoMFA std model of Conformer no: 3 alignment (q(2)=0.756, r(2)=0.963), giving predictive r(2) value of 0.866 for the test set. CoMFA and CoMSIA contour maps were used to analyze the structural features of the ligands accounting for the activity in terms of positively contributing physicochemical properties: steric, electrostatic, hydrophobic and hydrogen bonding fields.     

Predicting anti-cancer activity of quinoline derivatives: CoMFA and CoMSIA approach

The 3D quantitative structure-activity relationships of 31 quinoline nuclei containing compounds and their biological activity have been investigated to establish various models. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies resulted in reliable and significant computational models. The obtained CoMFA model showed high predictive ability with q(2) = 0.592, r(2) = 0.966 and standard error of estimation (SEE) = 0.167, explaining majority of the variance in the data with two principal components. Predictions obtained with CoMSIA steric, electrostatic, hydrophobic, hydrogen-bond acceptor and donor fields (q(2) = 0.533, r(2) = 0.985) showed high prediction ability with minimum SEE (0.111) and four principal components. The information obtained from the CoMFA and CoMSIA contour maps can be utilized for the design and development of topoisomerase-II inhibitors for synthesis.     

CoMFA and CoMSIA 3D-QSAR analysis of diaryloxy-methano-phenanthrene derivatives as anti-tubercular agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (44 compounds) of diaryloxy-methano-phenanthrene derivatives as potent antitubercular agents. The best predictions were obtained with a CoMFA standard model (q (2)=0.625, r (2)=0.994) and with CoMSIA combined steric, electrostatic, and hydrophobic fields (q (2)=0.486, r (2)=0.986). Both models were validated by a test set of seven compounds and gave satisfactory predictive r (2) values of 0.999 and 0.745, respectively. CoMFA and CoMSIA contour maps were used to analyze the structural features of the ligands to account for the activity in terms of positively contributing physicochemical properties: steric, electrostatic, and hydrophobic fields. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for further design of phenanthrene-based analogs as anti-TB agents. The resulting contour maps, produced by the best CoMFA and CoMSIA models, were used to identify the structural features relevant to the biological activity in this series of analogs. Further analysis of these interaction-field contour maps also showed a high level of internal consistency. This study suggests that introduction of bulky and highly electronegative groups on the basic amino side chain along with decreasing steric bulk and electronegativity on the phenanthrene ring might be suitable for designing better antitubercular agents.     

Two- and three-dimensional quantitative structure-activity relationships for a series of purine nucleoside phosphorylase inhibitors

Comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis, and hologram quantitative structure-activity relationship (HQSAR) studies were conducted on a series of 52 training set inhibitors of calf spleen purine nucleoside phosphorylase (PNP). Significant cross-validated correlation coefficients (CoMFA, q(2)=0.68; CoMSIA, q(2)=0.66; and HQSAR, q(2)=0.70) were obtained, indicating the potential of the models for untested compounds. The models were then used to predict the inhibitory potency of 16 test set compounds that were not included in the training set, and the predicted values were in good agreement with the experimental results. The final QSAR models along with the information gathered from 3D contour and 2D contribution maps should be useful for the design of novel inhibitors of PNP having improved potency.     

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

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

DAT/SERT selectivity of flexible GBR 12909 analogs modeled using 3D-QSAR methods

The dopamine reuptake inhibitor GBR 12909 (1-{2-[bis(4-fluorophenyl)methoxy]ethyl}-4-(3-phenylpropyl)piperazine, 1) and its analogs have been developed as tools to test the hypothesis that selective dopamine transporter (DAT) inhibitors will be useful therapeutics for cocaine addiction. This 3D-QSAR study focuses on the effect of substitutions in the phenylpropyl region of 1. CoMFA and CoMSIA techniques were used to determine a predictive and stable model for the DAT/serotonin transporter (SERT) selectivity (represented by pK(i) (DAT/SERT)) of a set of flexible analogs of 1, most of which have eight rotatable bonds. In the absence of a rigid analog to use as a 3D-QSAR template, six conformational families of analogs were constructed from six pairs of piperazine and piperidine template conformers identified by hierarchical clustering as representative molecular conformations. Three models stable to y-value scrambling were identified after a comprehensive CoMFA and CoMSIA survey with Region Focusing. Test set correlation validation led to an acceptable model, with q(2)=0.508, standard error of prediction=0.601, two components, r(2)=0.685, standard error of estimate=0.481, F value=39, percent steric contribution=65, and percent electrostatic contribution=35. A CoMFA contour map identified areas of the molecule that affect pK(i) (DAT/SERT). This work outlines a protocol for deriving a stable and predictive model of the biological activity of a set of very flexible molecules.     

CoMFA and CoMSIA 3D-QSAR analysis of DMDP derivatives as anti-cancer agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (78 compounds) of 2, 4-diamino-5-methyl-5-deazapteridine (DMDP) derivatives as potent anticancer agents. The best prediction were obtained with a CoMFA standard model (q(2) = 0.530, r(2) = 0.903) and with CoMSIA combined steric, electrostatic, hydrophobic and hydrogen bond donor fields (q(2) = 0.548, r(2) = 0.909). Both models were validated by a test set of ten compounds producing very good predictive r(2) values of 0.935 and 0.842, respectively. CoMFA and CoMSIA contour maps were then used to analyze the structural features of ligands to account for the activity in terms of positively contributing physiochemical properties such as steric, electrostatic, hydrophobic and hydrogen bond donor fields. The resulting contour maps produced by the best CoMFA and CoMSIA models were used to identify the structural features relevant to the biological activity in this series of analogs. This study suggests that the highly electropositive substituents with low steric tolerance are required at 5 position of the pteridine ring and bulky electronegatve substituents are required at the meta-position of the phenyl ring. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for the design of deazapteridine-based analogs as anticancer agents.     

Key features for designing phosphodiesterase-5 inhibitors

Phosphodiesterase superfamily is the key regulator of 3',5'-cyclic guanosine monophosphate (cGMP) decomposition in human body. Phosphodiesterase-5 (PDE-5) inhibitors, sildenafil, vardenafil and tadalafil, are well known oral treatment for males with erectile dysfunction. To investigate the inhibitory effects of traditional Chinese medicine (TCM) compounds to PDE-5, we performed both ligand-based and structure-based studies on this topic. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies were conducted to construct three dimensional quantitative structure-activity relationship (3D-QSAR) models of series of known PDE-5 inhibitors. The predictive models had cross-validated, q(2), and non cross-validated coefficient, r(2), values of 0.791 and 0.948 for CoMFA and 0.724 and 0.908 for CoMSIA. These two 3D-QSAR models were used to predict activity of TCM compounds. Docking simulations were performed to further analyze the binding mode of training set and TCM compounds. A putative binding model was proposed based on CoMFA and CoMSIA contour maps and docking simulations; formation of pi-stacking, water bridge and specific hydrogen bonding were deemed important interactions between ligands and PDE-5. Of our TCM compounds, engeletin, satisfied our binding model, and hence, emerged as PDE-5 inhibitor candidate. Using this study as an example, we demonstrated that docking should be conducted for qualitative purposes, such as identifying protein characteristics, rather than for quantitative analyses that rank compound efficacy based on results of scoring functions. Prediction of compound activity should be reserved for QSAR analyses, and scoring functions and docking scores should be used for preliminary screening of TCM database (http://tcm.cmu.edu.tw/index.php).     

3-D-QSAR CoMFA and CoMSIA studies on tetrahydrofuroyl-L-phenylalanine derivatives as VLA-4 antagonists

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on tetrahydrofuroyl-L-phenylalanine derivatives as VLA-4 antagonists. The best CoMFA and CoMSIA models that were generated using atom based alignment from a training set of twenty five tetrahydrofuroyl-L-phenylalanine derivatives, are six-component models with good statistics; CoMFA: r(2)(cv)=0.366, r(2)=0.983, s=0.099, F=172.661 and PRESS=4.435; CoMSIA: r(2)(cv)=0.528, r(2)=0.995, s=0.054, F=577.87 and PRESS=3.563. Both of these 3-D-QSAR models were validated using a test set of eleven compounds, whose predicted pIC(50) values fall within one log unit of the actual pIC(50). The contour diagrams obtained for the various CoMFA and CoMSIA field contributions can be mapped back onto structural features to explain the activity trends of the molecules analysed. Based on the spatial arrangement of the various field contributions, novel molecules with improved activity can be designed.     

CoMFA and CoMSIA 3D QSAR analysis on N1-arylsulfonylindole compounds as 5-HT6 antagonists

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series of N(1)-arylsulfonylindole compounds as 5-HT(6) antagonists. Evaluation of 20 compounds served to establish the models. The lowest energy conformer of compound 1 obtained from random search was used as template for alignment. The best predictions were obtained with CoMFA standard model (q2 = 0.643, r2 = 0.939 ) and with CoMSIA combined steric, electrostatic, hydrophobic, and hydrogen bond acceptor fields (q2 = 0.584, r2 = 0.902 ). Both the models were validated by an external test set of eight compounds giving satisfactory predictive r2 values of 0.604 and 0.654, respectively. The information obtained from CoMFA and CoMSIA 3D contour maps can be used for further design of specific 5-HT(6) antagonists.     

The 3D-QSAR study of antitumor arylsulfonylimidazolidinone derivatives by CoMFA and CoMSIA

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies for a series of arylsulfonylimidazolidinone derivatives having antitumor activity were conducted using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The in vitro cytotoxicity against human lung carcinoma (A549) exhibited a strong correlation with steric and electrostatic factors of the molecules. Four different types of models have been built using CoMFA and CoMSIA method with AM1 charge or Gasteiger-Huckel charge. By comparison of the statistical results of these models, model I obtained by CoMFA study with AM1 showed the best predictability of the antitumor activities based on the cross-validated value (0.642), conventional r2 (0.981), standard error of estimate (0.106) and PRESS value (0.170).     

Development of new CoMFA and CoMSIA 3D-QSAR models for anti-inflammatory phthalimide-containing TNFalpha modulators

In the present study, we describe a new 3D-QSAR analysis of 42 previously reported thalidomide analogues, with the ability to modulate the pro-inflammatory cytokine TNFalpha, by using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Three statistically significant models were obtained. The best resulting CoMFA and CoMSIA models have conventional r(2) values of 0.996 and 0.983, respectively. The cross-validated q(2) values are 0.869 and 0.868, respectively. The analysis of CoMFA and CoMSIA contour maps provided insight into the possible sites for structural modification of the thalidomide analogues for better activity and reduced toxicity.     

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic oxazolidinones as antibacterial agents

Oxazolidinones exemplified by eprezolid and linezolid are a new class of antibacterials that are active against Gram positive and anaerobic bacteria including methicillin-resistant Staphylococcus aureus (MRSA), methicillin-resistant Staphylococcus epidermidis (MRSE) and vancomycin resistant enterococci (VRE). In an effort to have a better antibacterial agent in the oxazolidinone class, we have performed three-dimensional quantitative structure-activity relationship (3D-QSAR) studies for a series of tricyclic oxazolidinones. 3D-QSAR studies were performed using the Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) procedures. These studies were performed using 42 compounds; the QSAR model was developed using a training set of 33 compounds. The predictive ability of the QSAR model was assessed using a test set of 9 compounds. The predictive 3D-QSAR models have conventional r(2) values of 0.975 and 0.940 for CoMFA and CoMSIA respectively; similarly, cross-validated coefficient q(2) values of 0.523 and 0.557 for CoMFA and CoMSIA, respectively, were obtained. The CoMFA 3D-QSAR model performed better than the CoMSIA model.     

3D-QSAR study of bis-azaaromatic quaternary ammonium analogs at the blood-brain barrier choline transporter

Previously, we have developed 3D-QSAR models of the blood-brain barrier (BBB) choline transporter, a transport system that may have utility as a vector for central nervous system drug delivery. In this study, we extended the model by evaluating five bis-azaaromatic quaternary ammonium compounds for their affinity for the choline binding site on the BBB-choline transporter. The compounds, and their affinities for the transporter, were then incorporated into our existing molecular model, in order to update our knowledge on the molecular recognition factors associated with interaction of ligands at the choline binding site. The current compounds are structurally related to previous substrates that we have evaluated, but offer additional three dimensional aspects compared to the series of compounds previously utilized to define the original models. The compounds showed good affinity for the BBB-choline transporter, exhibiting inhibition constants ranging from 10 to 68 microM, as determined by the in situ rat brain perfusion method. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods were used to build the new 3D QSAR models. When the new bis-azaaromatic quaternary ammonium compounds were included in the model, the best cross-validated CoMFA q2 was found to be 0.536 and the non-cross-validated r2 was 0.818. CoMSIA hydrophobic cross-validated q2 was 0.506 and the non-cross-validated r2 was 0.804. This new model was able to better predict BBB-choline transporter affinity of hemicholinium-3 (predicted 65 microM, actual 54 microM), when compared to an earlier model (predicted 316 microM).     

3D-QSAR analysis of sialyltransferase inhibitors

A predictive 3D-QSAR model that correlates the biological activities with the chemical structures of a series of sialyltransferase inhibitors, exemplified by the sugar:nucleotide derivatives, was developed by means of comparative molecular field analysis (CoMFA). The resulting cross-validated value (q(2)=0.629), non-cross-validated value (r(2)=0.965) and standard error of estimate (SEE=0.288) indicate that the obtained pharmacophore model indeed mimics the steric and electrostatic environment where inhibitors bind to the enzyme. The developed model also possesses promising predictive ability as discerned by the testing on the external test set, and should be useful to further understand the molecular nature of inhibitor-enzyme interactions and to aid in the design of more potent sialyltransferase inhibitors.     

CoMFA and CoMSIA studies of angiotensin (AT1) receptor antagonists

Two 3D-QSAR methods--CoMFA and CoMSIA--were applied to a set of 38 angiotensin receptor (AT1) antagonists. The conformation and alignment of molecules were obtained by a novel method - consensus dynamics. The representation of biological activity, partial charge formalism, absolute orientation of the molecules in the grid, and grid spacing were also studied for their effect on the CoMFA models. The models were thoroughly validated through trials using scrambled activities and bootstrapping. The best CoMFA model had a cross-validated correlation coefficient ( q2) of 0.632, which improved with "region focusing" to 0.680. This model had a "predictive" r2 of 0.436 on a test series that was unique and with little representation in the training set. Although the "predictive" r2 of the best CoMSIA model, which included steric, electrostatic, and hydrogen bond acceptor fields was higher than that of the best CoMFA model, the other statistical parameters like q2, r2, F value, and s were unsatisfactory. The contour maps generated using the best CoMFA model were used to identify the structural features important for biological activity in these compounds.     

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 phytopathicity. 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 (q(2)) 0.532 and conventional (r(2)) 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 q(2) 0.665 and r(2) 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.     

Molecular modeling and 3D-QSAR studies of indolomorphinan derivatives as kappa opioid antagonists

Molecular modeling and 3D-QSAR studies were performed on 31 indolomorphinan derivatives to evaluate their antagonistic behaviors on kappa opioid receptor and provide information for further modification of this kind of compounds. Best predictions were obtained with CoMFA standard model (q2 = 0.693, N = 4, r2 = 0.900) and CoMSIA combined model (q2 = 0.617, N = 4, r2 = 0.904). Both models were further validated by an external test set of eight compounds with satisfactory predictions: r2 = 0.607 for CoMFA and r2 = 0.701 for CoMSIA. In addition, the 3D structure of human kappa opioid receptor was constructed based on the crystal structure of bovine rhodopsin, and the CoMSIA contour plots were then mapped into the structural model of kappa opioid receptor-GNTI complex to identify key residues, which might account for kappa antagonist potency and selectivity. The roles of nonconserved Glu297 and conserved Lys227 of human kappa opioid receptor were then discussed.     

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.