QSTR with extended topochemical atom (ETA) indices. 14. QSAR modeling of toxicity of aromatic aldehydes to Tetrahymena pyriformis

In silico aquatic toxicity prediction of chemicals toward Daphnia magna and fathead minnow using Monte Carlo approaches

The fast-increasing use of chemicals led to large numbers of chemical compounds entering the aquatic environment, raising concerns about their potential effects on ecosystems. Therefore, assessment of the ecotoxicological features of organic compounds on aquatic organisms is very important. Daphnia magna and Fathead minnow are two aquatic species that are commonly tested as standard test organisms for aquatic risk assessment and are typically chosen as the biological model for the ecotoxicology investigations of chemical pollutants. Herein, global quantitative structure-toxicity relationship (QSTR) models have been developed to predict the toxicity (pEC(LC)50) of a large dataset comprising 2106 chemicals toward Daphnia magna and Fathead minnow. The optimal descriptor of correlation weights (DCWs) is calculated using the notation of simplified molecular input line entry system (SMILES) and is used to construct QSTR models. Three target functions, TF1, TF2, and TF3 are utilized to generate 12 QSTR models from four splits, and their statistical characteristics are also compared. The designed QSTR models are validated using both internal and external validation criteria and are found to be reliable, robust, and excellently predictive. Among the models, those generated using the TF3 demonstrate the best statistical quality with R2 values ranging from 0.9467 to 0.9607, Q2 values ranging from 0.9462 to 0.9603 and RMSE values ranging from 0.3764 to 0.4413 for the validation set. The applicability domain and the mechanistic interpretations of generated models were also discussed.

Expression, purification and crystallization of a novel metagenome-derived salicylaldehyde dehydrogenase from Alpine soil

Salicylaldehyde dehydrogenase (SALD) catalyses the last reaction in the upper pathway of naphthalene degradation: the oxidation of salicylaldehyde to salicylate. This enzyme has been isolated and studied from a few organisms that belong to the betaproteobacteria and gammaproteobacteria, predominantly Pseudomonas putida. Furthermore, there is only one crystal structure of this enzyme, which was obtained from P. putida G7. Here, crystallographic studies and analysis of the crystal structure of an Alpine soil metagenome-derived SALD (SALDAP) from an alphaproteobacterium are presented. The SALDAP gene was discovered using gene-targeted sequence assembly and it was cloned into a pLATE51 vector. The recombinant protein was overexpressed in Escherichia coli BL21 (DE3) cells and the soluble protein was purified to homogeneity. The protein crystallized at 20°C and diffraction data from the crystals were collected at a resolution of 1.9 Å. The crystal belonged to the orthorhombic space group C2221, with unit-cell parameters a = 116.8, b = 121.7, c = 318.0 Å. Analysis of the crystal structure revealed its conformation to be similar to the organization of the aldehyde dehydrogenase superfamily with three domains: the catalytic, NAD+-binding and bridging domains. The crystal structure of NahF from P. putida G7 was found to be the best structural homologue of SALDAP, even though the enzymes share only 48% amino-acid identity. Interestingly, a carboxylic acid (protocatechuic acid) was found to be a putative ligand of the enzyme and differential scanning fluorimetry was employed to confirm ligand binding. These findings open up the possibility of studying the mechanism(s) of product inhibition and biocatalysis of carboxylic acids using this enzyme and other related aldehyde dehydrogenases.

Consensus QSAR modeling of toxicity of pharmaceuticals to different aquatic organisms: Ranking and prioritization of the DrugBank database compounds

In the present work, quantitative structure-activity relationship (QSAR) models have been developed for ecotoxicity of pharmaceuticals on four different aquatic species namely Pseudokirchneriella subcapitata, Daphnia magna, Oncorhynchus mykiss and Pimephales promelas using genetic algorithm (GA) for feature selection followed by Partial Least Squares regression technique according to the Organization for Economic Co-operation and Development (OECD) guidelines. Double cross-validation methodology was employed for selecting suitable models. Only 2D descriptors were used for capturing chemical information and model building, whereas validation of the models was performed by considering various stringent internal and external validation metrics. Interestingly, models could be developed even without using any LogP terms in contrary to the usual dependence of toxicity on lipophilicity. However, the current manuscript proposes highly robust and more predictive models employing computed logP descriptors. The applicability domain study was performed in order to set a predefined chemical zone of applicability for the obtained QSAR models, and the test compounds falling outside the domain were not taken for further analysis while making a prioritized list. An additional comparison was made with ECOSAR, an online expert system for toxicity prediction of organic pollutants, in order to prove predictability of the obtained models. The obtained robust consensus models were utilized to predict the toxicity of a large dataset of approximately 9300 drug-like molecules in order to prioritize the existing drug-like substances in accordance to their acute predicted aquatic toxicities following a scaling technique. Finally, prioritized lists of 500 most toxic chemicals obtained by respective consensus models and those predicted from ECOSAR tool have been reported.

The “ETA” Indices in QSAR/QSPR/QSTR Research

Descriptors are one of the most essential components of predictive Quantitative Structure-Activity/Property/Toxicity Relationship (QSAR/QSPR/QSTR) modeling analysis, as they encode chemical information of molecules in the form of quantitative numbers, which are used to develop mathematical correlation models. The quality of a predictive model not only depends on good modeling statistics, but also on the extraction of chemical features. A significant amount of research since the beginning of QSAR analysis paradigm has led to the introduction of a large number of predictor variables or descriptors. The Extended Topochemical Atom (ETA) indices, developed by the authors' group, successfully address the aspects of molecular topology, electronic information, and different types of bonded interactions, and have been extensively employed for the modeling of different types of activity/property and toxicity endpoints. This chapter provides explicit information regarding the basis, algorithm, and applicability of the ETA indices for a predictive modeling paradigm.

The “ETA” Indices in QSAR/QSPR/QSTR Research

Descriptors are one of the most essential components of predictive Quantitative Structure-Activity/Property/Toxicity Relationship (QSAR/QSPR/QSTR) modeling analysis, as they encode chemical information of molecules in the form of quantitative numbers, which are used to develop mathematical correlation models. The quality of a predictive model not only depends on good modeling statistics, but also on the extraction of chemical features. A significant amount of research since the beginning of QSAR analysis paradigm has led to the introduction of a large number of predictor variables or descriptors. The Extended Topochemical Atom (ETA) indices, developed by the authors' group, successfully address the aspects of molecular topology, electronic information, and different types of bonded interactions, and have been extensively employed for the modeling of different types of activity/property and toxicity endpoints. This chapter provides explicit information regarding the basis, algorithm, and applicability of the ETA indices for a predictive modeling paradigm.

Purification and characterization of NAD+ -dependent salicylaldehyde dehydrogenase from carbaryl-degrading Pseudomonas sp. strain C6

NAD+-dependent salicylaldehyde dehydrogenase (SALDH) which catalyzes the oxidation of salicylaldehyde to salicylate was purified form carbaryl-degrading Pseudomonas sp. strain C6. The enzyme was found to be a functional homotrimer (150 kDa) with subunit molecular mass of 50 kDa and contained calcium (1.8 mol/mol of enzyme). These properties were found to be unique. External addition of metal ions showed no effect on the activity and addition of chelators showed moderate inhibition of the activity. Potassium ions were found to enhance the activity significantly. SALDH showed higher affinity for salicylaldehyde (Km = 4.5 μM) and accepts mono- as well as di-aromatic aldehydes; however it showed poor activity on aliphatic aldehydes. Chloro-/nitro-substituted benzaldehydes were potent substrate inhibitors as compared to benzaldehyde and 3-hydroxybenzaldehyde, while 2-naphthaldehyde and salicylaldehyde were moderate. The kinetic data revealed that SALDH, though having broad specificity, is more efficient for the oxidation of salicylaldehyde as compared to other aromatic aldehyde dehydrogenases which gives an advantage for Pseudomonas sp. strain C6 to bioremediate carbaryl and other aromatic aldehydes efficiently.

3D-QSAR studies on the toxicity of substituted benzenes to Tetrahymena pyriformis: CoMFA, CoMSIA and VolSurf approaches

Three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis were performed on the toxicity of a large set of substituted benzenes toward ciliate Tetrahymena pyriformis. The 3D-QSAR studies were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and VolSurf techniques. The optimal CoMFA and CoMSIA models obtained from the training set were all statistically significant with correlation coefficients (R(2)) greater than 0.79 and absolute error less than 0.33 in log units. The predictive ability of the models was externally evaluated through the prediction of a test set (20 percent of the whole data set) that were not included in the training set. A simple and fairly good predictive linear model based on VolSurf descriptors was also developed that showed an adequate prediction power of the toxicity (pIGC50) of substituted benzenes. Validation, reliability and robustness of models were also evaluated by leave-one-out, leave-four-out, bootstrapping and progressive scrambling approaches. The results confirmed that in addition to hydrophobic effects, electrostatic and H-bonding interactions also play important roles in the toxicity of substituted benzenes. The information obtained from CoMFA and CoMSIA 3-D contour maps could be useful to explain the toxicity mechanism of substituted benzenes.

Aquatic toxicity assessment of esters towards the Daphnia magna through PCA-ANFIS

The widespread production of esters combined with their ability to migrate in different compartments, makes their environmental toxicity important. In this background, the multivariate image analysis-quantitative structure-toxicity relationship (MIA-QSTR) method coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA-ANFIS) was applied to assess the toxicity of esters to Daphnia magna. In MIA-QSTR, pixels of chemical structures (2D images) stand for descriptors, and structural changes account for the variance in toxicities. The ANFIS procedure was capable of correlating the inputs (PCA scores) with the toxicities accurately. The PCA-ANFIS also was statistically validated for its predictive power using cross-validation, applicability domain and Y-scrambling evaluation procedures. The satisfactory results (R p (2) = 0.926, Q LOO (2) = 0.887, R L25%O (2) = 0.843, RMSELOO = 0.320 and RMSEL25%O = 0.379) suggests that the QSTR model could be proposed as an alternative method for aquatic toxicity assessment of esters allowing possible application in the European Union regulation REACH.

QSTR with extended topochemical atom (ETA) indices. 16. Development of predictive classification and regression models for toxicity of ionic liquids towards Daphnia magna

Ionic liquids have been judged much with respect to their wide applicability than their considerable harmful effects towards the living ecosystem which has been observed in many instances. Hence, toxicological introspection of these chemicals by the development of predictive mathematical models can be of good help. This study presents an attempt to develop predictive classification and regression models correlating the structurally derived chemical information of a group of 62 diverse ionic liquids with their toxicity towards Daphnia magna and their interpretation. We have principally used the extended topochemical atom (ETA) indices along with various topological non-ETA and thermodynamic parameters as independent variables. The developed quantitative models have been subjected to extensive statistical tests employing multiple validation strategies from which acceptable results have been reported. The best models obtained from classification and regression studies captured necessary structural information on lipophilicity, branching pattern, electronegativity and chain length of the cationic substituents for explaining ecotoxicity of ionic liquids towards D. magna. The derived information can be successfully used to design better ionic liquid analogues acquiring the qualities of a true eco-friendly green chemical.

Straightforward MIA-QSTR evaluation of environmental toxicities of aromatic aldehydes to Tetrahymena pyriformis

Aldehydes are toxic environmental contaminants which cause severe health hazards. There is a growing need by industries and regulatory agencies for the development of tools able to assess the potential hazardous effects of chemicals on living organisms. In this background, multivariate image analysis combined with quantitative structure-toxicity relationships (MIA-QSTR) was used to evaluate the toxicity of aromatic aldehydes to Tetrahymena pyriformis. The techniques of genetic algorithm-partial least squares (GA-PLS) were applied effectively as MIA descriptor selection and mapping tools. In MIA-QSTR evaluation, pixels of 2D images of chemical structures could be used to recognize physicochemical information and predict changes in the toxicities. The resulting MIA-QSTR explains 90.3% leave-one-out predicted variance and 93.1% external predicted variance. The MIA-QSTR/GA-PLS performances were validated using various evaluation techniques such as cross-validation, applicability domain and Y-scrambling procedures, suggesting that the present methodology together with mechanistic interpretation may be useful to evaluate toxicity, safety and risk assessment of toxic environmental contaminants.

Accurate prediction of the toxicity of benzoic acid compounds in mice via oral without using any computer codes

Most of benzoic acid derivatives are toxic, which may cause serious public health and environmental problems. Two novel simple and reliable models are introduced for desk calculations of the toxicity of benzoic acid compounds in mice via oral LD(50) with more reliance on their answers as one could attach to the more complex outputs. They require only elemental composition and molecular fragments without using any computer codes. The first model is based on only the number of carbon and hydrogen atoms, which can be improved by several molecular fragments in the second model. For 57 benzoic compounds, where the computed results of quantitative structure-toxicity relationship (QSTR) were recently reported, the predicted results of two simple models of present method are more reliable than QSTR computations. The present simple method is also tested with further 324 benzoic acid compounds including complex molecular structures, which confirm good forecasting ability of the second model.

QSTR with extended topochemical atom (ETA) indices. 15. Development of predictive models for toxicity of organic chemicals against fathead minnow using second-generation ETA indices

Modern industrialisation has led to the production of millions of toxic chemicals having hazardous effects on the ecosystem. It is impracticable to determine the toxic potential of a large number of chemicals in animal models, making the use of quantitative structure-toxicity relationship (QSTR) models an alternative strategy for toxicity prediction. Recently we introduced a set of second-generation extended topochemical atom (ETA) indices for predictive modelling. Here we have developed predictive toxicity models on a large dataset of 459 diverse chemicals against fathead minnow (Pimephales promelas) using the second-generation ETA indices. These descriptors can be easily calculated from two-dimensional molecular representation without the need of time-consuming conformational analysis and alignment, making the developed models easily reproducible. Considering the importance of hydrophobicity for toxicity prediction, AlogP98 was used as an additional predictor in all the models, which were validated rigorously using multiple strategies. The ETA models were comparable in predictability to those involving various non-ETA topological parameters and those previously reported using various descriptors including computationally demanding quantum-chemical ones.

On some novel extended topochemical atom (ETA) parameters for effective encoding of chemical information and modelling of fundamental physicochemical properties

Extended topochemical atom (ETA) indices developed by our group have been extensively applied in our previous reports for toxicity and ecotoxicity modelling in the field of quantitative structure-activity relationships (QSARs). In the present study these indices have been further explored by defining additional novel parameters to model n-octanol-water partition coefficient (two data sets; n = 168 and 139), water solubility (n = 193), molar refractivity (n = 166), and aromatic substituent constants π, MR, σ (m), and σ (p) (n = 99). All the models developed in the present study have undergone rigorous internal and external validation tests and the models have high statistical significance and prediction potential. In terms of Q² and r² values the models developed for the datasets of whole molecules are better than those previously reported, with topochemically arrived unique (TAU) indices on the same datasets of chemicals. An attempt has also been made to develop models using non-ETA topological and information indices. Interestingly, ETA and non-ETA models have been found to have similar predictive capacity.