3D-QSAR of fungal quorum-sensing inhibiting analogs of farnesol

Thirty-four analogs with variable antifungal activity were selected to develop models for establishing three-dimensional quantitative structure-activity relationships (3D-QSAR). Comparative molecular field analysis (CoMFA) and comparative similarity indices analyses (CoMSIA) were conducted on the group of analogs to determine the structural requirements for selectivity and potency in inhibiting biofilm formation and fungal growth. The best CoMFA model predicted a q² = 0.5 and an r² = 0.991, and revealed that electrostatic properties play a significant role in potency and selectivity. The best CoMSIA model combined electrostatics, hydrogen bond acceptor and donor, and hydrophobic fields with a q² = 0.664 r² = 0.952, S = 0.099, and F = 139.892. The analyses of the contour maps from both models provide significant insight into the structural necessities for a potent compound. Therefore, manipulating various chemical properties of the substituted groups on the farnesol chain can be used to enhance the fungicidal properties of the target compound.     

Three-dimensional quantitative structure activity relationships of quorum-sensing and biofilm inhibitors in gram-negative bacteria

Thirty N-acyl homoserine lactone (AHL) analogs with variable antibacterial activity and displaying inhibition of biofilm formation were selected to develop models for establishing three-dimensional quantitative structure-activity relationships (3D-QSAR). Comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA) were carried out to determine the optimum structural requirements for selectivity and potency of quorum-sensing and bacterial biofilm inhibition. The best CoMFA model predicted a q2 value of 0.519 and an r2 value of 0.984 and revealed that electrostatic and steric properties play a significant role in potency and selectivity. The CoMSIA model predicted a q2 value of 0.411 and an r2 value of 0.938 based on a combination of steric, electrostatic, and hydrophobic effects. The analysis of the contour maps from each model provide insight into the structural requirements for increasing the activity of a compound. Consequently, manipulating the chemical and physical properties of substituted acyl groups on the homoserine lactone moiety can provide important information toward enhancing the antibacterial properties of the target chemical compound.     

Three-dimensional quantitative structure activity relationships of quorum-sensing and biofilm inhibitors in gram-negative bacteria

Thirty N-acyl homoserine lactone (AHL) analogs with variable antibacterial activity and displaying inhibition of biofilm formation were selected to develop models for establishing three-dimensional quantitative structure-activity relationships (3D-QSAR). Comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA) were carried out to determine the optimum structural requirements for selectivity and potency of quorum-sensing and bacterial biofilm inhibition. The best CoMFA model predicted a q² value of 0.519 and an r² value of 0.984 and revealed that electrostatic and steric properties play a significant role in potency and selectivity. The CoMSIA model predicted a q² value of 0.411 and an r² value of 0.938 based on a combination of steric, electrostatic, and hydrophobic effects. The analysis of the contour maps from each model provide insight into the structural requirements for increasing the activity of a compound. Consequently, manipulating the chemical and physical properties of substituted acyl groups on the homoserine lactone moiety can provide important information toward enhancing the antibacterial properties of the target chemical compound.     

3D QSAR studies of dioxins and dioxin-like compounds using CoMFA and CoMSIA

In the present study we have performed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) on structurally diverse ligands of Ah (dioxin) receptor to explore the physico-chemical requirements for binding. All CoMFA and CoMSIA models have given q(2) value of more than 0.5 and r(2) value of more than 0.84. The predictive ability of the models was validated by an external test set, which gave satisfactory predictive r(2) values. Best predictions were obtained with CoMFA model of combined modified training set (q(2) = 0.631, r(2) = 0.900), giving predictive residual value = 0.02 log unit for the test compound. Addition of CoMSIA study has elucidated the role of hydrophobicity and hydrogen bonding along with the effect of steric and electrostatic properties revealed by CoMFA. We have suggested a model comprises of four structurally different compounds, which offers a good predictability for various ligands. Our QSAR model is consistent with all previously established QSAR models with less structurally diverse ligands.     

Three-dimensional quantitative structure-activity relationship study for phenylsulfonyl carboxylates using CoMFA and CoMSIA

From both the comparative molecular field analysis (CoMFA) and the comparative molecular similarity indices analysis (CoMSIA), the paper describes two three-dimensional quantitative structure-activity relationship (3D-QSAR) models for the acute toxicity logEC50 (15 min-EC50 in micromoll(-1)) of 56 phenylsulfonyl carboxylates on Photobacterium phosphoreum. Two models yield the leave-one-out cross-validated correlation coefficient q2 values of 0.823 and 0.713, and the conventional correlation coefficient r2 values of 0.958 and 0.933, respectively. The achievement of higher q2 and r2 values of CoMFA model indicates the significance of correlation of steric and electrostatic fields with biological activities. The key features in the CoMFA contour maps are critical to trace the important properties and gain insight into the toxic mechanism of tested compounds. The quality of CoMSIA model is slightly lower than that of CoMFA in terms of q2 and r2 values. Not requiring molecular superposition, CoMSIA is faster than CoMFA in data processing.     

Three-dimensional, quantitative-structure-property-relationship study of aqueous solubility for phenylsulfonyl carboxylates using comparative-molecular-field analysis and comparative-molecular-similarity-indices analysis.

With both the comparative-molecular-field analysis (CoMFA) and the comparative-molecular-similarity-indices analysis (CoMSIA), the paper describes two five-component, three-dimensional, quantitative-structure-property-relationship (3D-QSPR) models for the aqueous solubility logSw (Sw, mol x L(-1)) of 52 phenylsulfonyl carboxylates. Two models yield the leave-one-out cross-validated correlation coefficient q2 values 0.851 and 0.821, and the conventional correlation coefficient r2 values 0.963 and 0.929, respectively. The achievement of high q2 and r2 values of the CoMFA model indicates the significance of correlation of steric and electrostatic fields with the aqueous solubility. The key features in the CoMFA contour maps are critical to trace the important properties and gain insight to the solvation mechanism of tested compounds. The quality of CoMSIA model is slightly lower than that of CoMFA in terms of q2 and r2 values. Not requiring molecular superposition, CoMSIA may be faster than CoMFA in data processing.     

Studies of 3D-quantitative structure-activity relationships on a set of nitroaromatic compounds: CoMFA,advanced CoMFA and CoMSIA

By using comparative molecular field analysis (CoMFA), advanced CoMFA and comparative molecular similarity index analysis (CoMSIA) methods, the 3D relationships between the structures of 35 nitroaromatic compounds and their toxicities have been investigated to yield statistically reliable models of considerable predictive power. In contrast to CoMFA, CoMSIA produces better results for the correlation. Moreover, the obtained CoMSIA contour maps that interpret the correlations in terms of field contributions allow physicochemical properties relevant for binding to be easily mapped back onto molecular structures, and thus elucidate structural features among ligands that are responsible for toxicities. Besides, most of the highlighted regions in CoMSIA and CoMFA contour maps are mirrored by features in the surrounding environment. Thereby, CoMFA and CoMSIA both help to give explanations of the toxic mechanism of tested compounds.     

Investigation of structural requirements for inhibitory activity at the rat and housefly picrotoxinin binding sites in ionotropic GABA receptors using DISCOtech and CoMFA

A number of widely diverse compounds that show inhibitory activities at the picrotoxinin binding sites in housefly and rat GABA receptors were investigated by using the distance comparison technique (DISCOtech) and comparative molecular field analysis (CoMFA) methods to explore the pharmacophore models and the three-dimensional quantitative structure-activity relationships (3D-QSAR) of the compounds. These compounds consist of three diverse types of noncompetitive GABA receptor antagonists, i.e., trioxabicyclooctanes and their derivatives, picrodendrins and related terpenoids, and fipronil and its analogs. For investigation of the structural requirements for inhibitory activity at the picrotoxinin binding site of GABA receptor, DISCOtech pharmacophore models containing one center of hydrophobic ring and two hydrogen bond acceptor atoms for both housefly-head and rat-brain GABA receptors were constructed, respectively. In particular, the interacting areas in housefly receptors appear to be wider than that in rat receptors, the differences between rat and housefly receptor models implicate the selectivity of noncompetitive GABA receptor antagonists. In addition, corresponding CoMFA models with good statistical indices (r(2)>0.9 and q(2)>0.5) were also obtained. These models can be used as guidance for the development of new compounds with high activities and selectivities.     

Three-dimensional quantitative structure activity relationship (3D-QSAR) analysis for in vitro toxicity of chlorophenols to HepG2 cells

In the present paper, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied to investigate two 3D-QSAR models for the cytotoxicity of chlorophenols. These models have evaluated the intensity of chlorophenols' toxicity on HepG2 cells in vitro. The CoMFA model has both high consistency and predictability. The contribution of the electrostatic field to biological activity is greater than that of the steric field. The CoMSIA model used in this study includes two fields, one is hydrophobic field, and the other is electrostatic field. The relative contribution of them is 0.789:0.211. Consisted with the CoMFA model, the CoMSIA electrostatic filed also plays a dominant role. The CoMFA and CoMSIA contour maps significantly elucidated that the electrostatic field is more important than the other fields and might be one of the reasons resulting in potential reactive mechanism involved in cell proliferation inhibition.     

Insights into the structure of urea-like compounds as inhibitors of the juvenile hormone epoxide hydrolase (JHEH) of the tobacco hornworm Manduca sexta: analysis of the binding modes and structure-activity relationships of the inhibitors by docking and CoMFA calculations

Substituted urea compounds are well-known as potent inhibitors of juvenile hormone epoxide hydrolase (JHEH) of the tobacco hornworm Manduca sexta. Docking simulations of 47 derivatives inside JHEH were performed to gain insight into the structural characteristics of these complexes. The obtained orientations show a strong similitude with the observed in the known X-ray crystal structures of human soluble epoxide hydrolase (sEH) complexed with dialkylurea inhibitors. In addition, the predicted inhibitor concentration (IC₅₀) of the above-mentioned compounds as JHEH inhibitors were obtained by a quantitative structure-activity relationship (QSAR) method by using comparative molecular field analysis (CoMFA) applied to aligned dataset. The best models included steric and electrostatic fields and had adequate predictive abilities. In addition, these models were used to predict the activity of an external test set of compounds that was not used for building the model. Furthermore, plots of the CoMFA fields allowed conclusions to be drawn for the choice of suitable inhibitors.     

Application of four watershed acidification models to Batchawana Watershed, Canada

Four watershed acidification models (TMWAM, ETD, ILWAS, and RAINS) are reviewed and a comparison of model performance is presented for a common watershed. The models have been used to simulate the dynamics of water quantity and quality at Batchawana Watershed, Canada, a sub-basin of the Turkey Lakes Watershed. The computed results are compared with observed data for a four-year period (Jan. 1981-Dec. 1984). The models exhibit a significant range in the ability to simulate the daily, monthly and seasonal changes present in the observed data. Monthly watershed outflows and lake chemistry predictions are compared to observed data. pH and ANC are the only two chemical parameters common to all four models. Coefficient of efficiency (E), linear (r) and rank (R) correlation coefficients, and regression slope (s) are used to compare the goodness of fit of the simulated with the observed data. The ILWAS, TMWAM and RAINS models performed very well in predicting the monthly flows, with values of r and R of approximately 0.98. The ETD model also showed strong correlations with linear (r) and rank (R) correlation coefficients of 0.896 and 0.892, respectively. The results of the analyses showed that TMWAM provided the best simulation of pH (E=0.264, r=0.648), which is slightly better than ETD (E=0.240, r=0.549), and much better than ILWAS (E=-2.965, r=0.293), and RAINS (E=-4.004, r=0.473). ETD was found to be superior in predicting ANC (E=0.608, r=0.781) as compared to TMWAM (E=0.340, r=0.598), ILWAS (E=0.275, r=0.442), and RAINS (E=-1.048, r=0.356). The TMWAM model adequately simulated SO4 over the four-year period (E=0.423, r=0.682) but the ETD (E=-0.904, r=0.274), ILWAS (E=-4.314, r=0.488), and RAINS (E=-6.479, r=0.126) models all performed poorer than the benchmark model (mean observed value).     

Acute toxicity of benzene derivatives to the tadpoles (Rana japonica) and QSAR analyses

Acute lethal toxicity (the negative logarithm of molar concentrations of 12 h acute median lethal, expressed as 12 h-log1/LC50) of 46 benzene derivatives to Rana japonica tadpoles was determined. 1-octanol/water partition coefficient (logKow)-dependent models were developed to study the toxicity of different categories chemicals. In an effort to model all chemicals, response surface analyses and stepwise multiple regression analyses were performed and successful models were obtained. A general and robust QSAR model was achieved with the combined application of variables reflecting hydrophobicity, electric property, and molecular size respectively (12h-log1/LC50 = 0.393logKow - 0.428Elumo + 0.0110Vol. + 1.362 n = 51, r2 = 0.834) using stepwise multiple regression analyses. Because of strong dissociation of carboxyl group greatly decreasing their observed toxicity, using logDow in instead of logKow the quality of the models is greatly improved. The conventional r2 and cross-validation r2(CV) were 0.914 and 0.785, respectively, indicating that QSAR was both internally consistent and highly predictive.     

A novel approach to predict aquatic toxicity from molecular structure

The main aim of the study was to develop quantitative structure-activity relationship (QSAR) models for the prediction of aquatic toxicity using atom-based non-stochastic and stochastic linear indices. The used dataset consist of 392 benzene derivatives, separated into training and test sets, for which toxicity data to the ciliate Tetrahymena pyriformis were available. Using multiple linear regression, two statistically significant QSAR models were obtained with non-stochastic (R2=0.791 and s=0.344) and stochastic (R2=0.799 and s=0.343) linear indices. A leave-one-out (LOO) cross-validation procedure was carried out achieving values of q2=0.781 (scv=0.348) and q2=0.786 (scv=0.350), respectively. In addition, a validation through an external test set was performed, which yields significant values of Rpred2 of 0.762 and 0.797. A brief study of the influence of the statistical outliers in QSAR's model development was also carried out. Finally, our method was compared with other approaches implemented in the Dragon software achieving better results. The non-stochastic and stochastic linear indices appear to provide an interesting alternative to costly and time-consuming experiments for determining toxicity.     

Correlating metal ionic characteristics with biosorption capacity using QSAR model

The relationship between metal ionic characteristics and the maximum biosorption capacity (q(max)) was established using QSAR model based on the classification of metal ions (soft, hard and borderline ions). Ten kinds of metal ions (Ag(+), Cs(+), Zn(2+), Pb(2+), N(i2+), Cu(2+), Co(2+), Sr(2+), Cd(2+), Cr(3+)) were selected and the waste biomass of Saccharomyces cerevisiae obtained from a local brewery was used as biosorbent. Eighteen parameters of physiochemical characteristics of metal ions were selected and correlated with q(max). Classification of metal ions could improve the QSAR models and different characteristics were significant in correlating with q(max), such as polarizing power Z(2)/r or the first hydrolysis constant |logK(OH)| or ionization potential IP. X(m)(2)r seemed to be suitable for metal ions including soft ions, and Z(2)/r, |logK(OH)| and IP suitable for only soft ions or metal ions excluding soft ions. It provided a new way to predict the biosorptive capacity of metal ions.     

The removal of reactive black 5 from aqueous solutions by cotton seed shell

This study investigated the removal of Reactive Black 5 (RB5) textile dye from aqueous solutions using cotton (Gossypium hirsutum) seed shell (CSS) as low cost adsorbents. The data were described according to the Freundlich and Langmuir isotherm models. Of these, the Langmuir model provided the best fit for the experimental data. The highest measured adsorption density was 12.19 mg/g at pH 2. An equilibrium adsorption rate of RB5 by CSS (q(e) = 11.879 mg/g) was observed at 30 minutes. In order to evaluate the adsorption kinetic mechanisms, pseudo first and second order rate kinetic models and an intraparticle diffusion model were applied, with the pseudo second order model providing an excellent fit for the data.     

Mechanism-based quantitative structure-activity relationships for the inhibition of substituted phenols on germination rate of Cucumis sativus

Comparative inhibition activity (GC50) of 42 structurally diverse substituted phenols on seed germination rate of Cucumis sativus was investigated. Quantitative structure-activity relationships (QSARs) were developed by using hydrophobicity (1-octanol/water partition coefficient, logKow) and electrophilicity (the energy of the lowest unoccupied molecule orbital, Eluma) for the toxicity of phenols according to their modes of toxic action. Most phenols elicited their response via a polar narcotic mechanism and a highly significant log Kow-based model was obtained (GC50 = 0.92 log Kow + 1.99, r2 0.84, n = 29). The inclusion of E(lumo) greatly improved the predictive power of the polar narcotic QSAR (GC50 = 0.88 log Kow - 0.30E(lumo) + 1.99, r2 = 0.93, n = 29). pKa proved to be an insignificant influencing factor in this study. Poor correlation with hydrophobicity and strong correlation with electrophilicity were observed for the nine bio-reactive chemicals. Their elevated toxicity was considerably underestimated by the polar narcotic logKow-dependent QSAR. The nine chemicals consist of selected nitro-substituted phenols, hydroquinone, catechol and 2-aminophenol. Their excess toxic potency could be explained by their molecular structure involving in vivo reaction with bio-macromolecules. Strong dissociation of carboxyl group of the four benzoic acid derivatives greatly decreased their observed toxicity. In an effort to model all chemicals including polar narcotics and bio-reactive chemicals, a response-surface analysis with the toxicity, logKow and E(lumo) was performed. This resulted in a highly predictive two-parameter QSAR for most of the chemicals (GC50 = 0. 70 logKow - 0.66E(lumo) + 2.17, r2 = 0.89, n = 36). Catechol and 2,4-dinitrophenol proved to be outliers of this model and their much high toxicity was explained.     

Separation of the strength and selectivity of the microbiological effect of synthetic dyes by spectral mapping technique

The growth inhibitory effect of 30 synthetic dyes on 22 bacteria (test organisms) belonging to various taxonomic groups was determined. The strength (potency) and selectivity of the biological effect were separated by the spectral mapping technique, reducing the dimensionality of the selectivity maps to two by the nonlinear mapping technique. The relationship between biological effect and physicochemical parameters of dyes was elucidated by stepwise regression analysis. It has been established that the strength of the effect of anthracene and trityl derivatives was higher than that of azobenzene dyes and significantly depended on the hydrophobicity of the compound. The selectivity of the effect also depended on hydrophobicity and on the nonpolar unsaturated surface area of the dyes. Gram negative and Gram positive bacteria differed in the strength and selectivity of their response to dyes indicating the marked impact of the taxonomical position on the response. Contrary to other multivariate mathematical statistical methods biological activity may be divided by SPM into potency and selectivity values, therefore, application of the technique in future QSAR studies is highly recommended.