Showing papers in "Sar and Qsar in Environmental Research in 2012"

Simulation of chemical metabolism for fate and hazard assessment. V. Mammalian hazard assessment

Abstract: Animals and humans are exposed to a wide array of xenobiotics and have developed complex enzymatic mechanisms to detoxify these chemicals Detoxification pathways involve a number of biotransformations, such as oxidation, reduction, hydrolysis and conjugation reactions The intermediate substances created during the detoxification process can be extremely toxic compared with the original toxins, hence metabolism should be accounted for when hazard effects of chemicals are assessed Alternatively, metabolic transformations could detoxify chemicals that are toxic as parents The aim of the present paper is to describe specificity of eukaryotic metabolism and its simulation and incorporation in models for predicting skin sensitization, mutagenicity, chromosomal aberration, micronuclei formation and estrogen receptor binding affinity implemented in the TIMES software platform The current progress in model refinement, data used to parameterize models, logic of simulating metabolism, applicability domain and i

Assessing toxicological data quality: basic principles, existing schemes and current limitations.

Abstract: Existing toxicological data may be used for a variety of purposes such as hazard and risk assessment or toxicity prediction The potential use of such data is, in part, dependent upon their quality Consideration of data quality is of key importance with respect to the application of chemicals legislation such as REACH Whether data are being used to make regulatory decisions or build computational models, the quality of the output is reflected by the quality of the data employed Therefore, the need to assess data quality is an important requirement for making a decision or prediction with an appropriate level of confidence This study considers the biological and chemical factors that may impact upon toxicological data quality and discusses the assessment of data quality Four general quality criteria are introduced and existing data quality assessment schemes are discussed Two case study datasets of skin sensitization data are assessed for quality providing a comparison of existing assessment methods

In silico toxicity prediction by support vector machine and SMILES representation-based string kernel

Abstract: There is a great need to assess the harmful effects or toxicities of chemicals to which man is exposed. In the present paper, the simplified molecular input line entry specification (SMILES) representation-based string kernel, together with the state-of-the-art support vector machine (SVM) algorithm, were used to classify the toxicity of chemicals from the US Environmental Protection Agency Distributed Structure-Searchable Toxicity (DSSTox) database network. In this method, the molecular structure can be directly encoded by a series of SMILES substrings that represent the presence of some chemical elements and different kinds of chemical bonds (double, triple and stereochemistry) in the molecules. Thus, SMILES string kernel can accurately and directly measure the similarities of molecules by a series of local information hidden in the molecules. Two model validation approaches, five-fold cross-validation and independent validation set, were used for assessing the predictive capability of our developed mod...

Combined QSAR studies of inhibitor properties of O-phosphorylated oximes toward serine esterases involved in neurotoxicity, drug metabolism and Alzheimer's disease

Abstract: Oxime reactivation of serine esterases (EOHs) inhibited by organophosphorus (OP) compounds can produce O-phosphorylated oximes (POXs). Such oxime derivatives are of interest, because some of them can have greater anti-EOH potencies than the OP inhibitors from which they were derived. Accordingly, inhibitor properties of 58 POXs against four EOHs, along with pair-wise selectivities between them, have been analysed using different QSAR approaches. EOHs (with their abbreviations and consequences of inhibition in parentheses) comprised acetylcholinesterase (AChE: acute neurotoxicity; cognition enhancement), butyrylcholinesterase (BChE: inhibition of drug metabolism or stoichiometric scavenging of EOH inhibitors; cognition enhancement), carboxylesterase (CaE: inhibition of drug metabolism or stoichiometric scavenging of EOH inhibitors), and neuropathy target esterase (NTE: delayed neurotoxicity). QSAR techniques encompassed linear regression and backpropagation neural networks in conjunction with fragmental de...

QSTR modelling of the acute toxicity of pharmaceuticals to fish

Abstract: Extensive use of pharmaceuticals as human and veterinary medication raises concerns for their adverse effects on non-target organisms. The purpose of this study was to employ multiple linear regression (MLR) to predict the toxicities of a diverse set of pharmaceuticals to fish. The descriptor pool consisted of about 1500 descriptors calculated using Dragon 5.4, Spartan 06 and Codessa 2.2 software. Descriptor selection was made by the heuristic method available in Codessa 2.2. The data set was divided into training and test sets using Kohonen networks. The training set contained approximately 65% of the compounds of the full data set (99 compounds). The training set model contained eight descriptors from all dimensions, all of which were obtained from Dragon 5.4. The statistical parameters of the model for the training set are R(2 )= 0.664, F = 13.588, and R(cv)(2) (LOO) = 0.542 while it achieves R(2 )= 0.605 for the test set. The training, test and external sets have no response outliers considering the standardized residual greater than three. The external validation of the model was made with a set of pharmaceuticals obtained from several databases. The R(pred)(2) is 0.777, reflecting a relatively good predictive power for the external set.

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.

Abstract: 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 rigo...

QSAR classification models for the screening of the endocrine-disrupting activity of perfluorinated compounds.

Abstract: Perfluorinated compounds (PFCs) are a class of emerging pollutants still widely used in different materials as non-adhesives, waterproof fabrics, fire-fighting foams, etc. Their toxic effects include potential for endocrine-disrupting activity, but the amount of experimental data available for these pollutants is limited. The use of predictive strategies such as quantitative structure-activity relationships (QSARs) is recommended under the REACH regulation, to fill data gaps and to screen and prioritize chemicals for further experimentation, with a consequent reduction of costs and number of tested animals. In this study, local classification models for PFCs were developed to predict their T4-TTR (thyroxin-transthyretin) competing potency. The best models were selected by maximizing the sensitivity and external predictive ability. These models, characterized by robustness, good predictive power and a defined applicability domain, were applied to predict the activity of 33 other PFCs of environmental concern. Finally, classification models recently published by our research group for T4-TTR binding of brominated flame retardants and for estrogenic and anti-androgenic activity were applied to the studied perfluorinated chemicals to compare results and to further evaluate the potential for these PFCs to cause endocrine disruption.

The impact of network biology in pharmacology and toxicology

Abstract: With the need to investigate alternative approaches and emerging technologies in order to increase drug efficacy and reduce adverse drug effects, network biology offers a novel way of approaching drug discovery by considering the effect of a molecule and protein's function in a global physiological environment. By studying drug action across multiple scales of complexity, from molecular to cellular and tissue level, network-based computational methods have the potential to improve our understanding of the impact of chemicals in human health. In this review we present the available large-scale databases and tools that allow integration and analysis of such information for understanding the properties of small molecules in the context of cellular networks. With the recent advances in the omics area, global integrative approaches are necessary to cope with the massive amounts of data, and biomedical researchers are urged to implement new types of analyses that can lead to new therapeutic interventions with increased safety and efficacy compared with existing medications.

Role of in silico genotoxicity tools in the regulatory assessment of pharmaceutical impurities

Abstract: The toxicological assessment of genotoxic impurities is important in the regulatory framework for pharmaceuticals. In this context, the application of promising computational methods (e.g. Quantitative Structure–Activity Relationships (QSARs), Structure–Activity Relationships (SARs) and/or expert systems) for the evaluation of genotoxicity is needed, especially when very limited information on impurities is available. To gain an overview of how computational methods are used internationally in the regulatory assessment of pharmaceutical impurities, the current regulatory documents were reviewed. The software recommended in the guidelines (e.g. MCASE, MC4PC, Derek for Windows) or used practically by various regulatory agencies (e.g. US Food and Drug Administration, US and Danish Environmental Protection Agencies), as well as other existing programs were analysed. Both statistically based and knowledge-based (expert system) tools were analysed. The overall conclusions on the available in silico tools for ge...

Simulation of chemical metabolism for fate and hazard assessment. III. New developments of the bioconcentration factor base-line model

Abstract: The new development of the bioconcentration factor (BCF) base-line model of Dimitrov et al. [SAR QSAR Environ. Res. 6 (2005), pp. 531–554] is presented. The model applicability domain was expanded by enlarging the training set of the model up to 705 chemicals. The list of chemical-dependent mitigating factors was expanded by including water solubility of chemicals. The original empirical term for estimating ionization of chemicals was mechanistically analysed using two different approaches. In the first one, the ionization potential of chemicals was estimated based on the acid dissociation constant (pKa ). This term was found to be less adequate for inclusion in the ultimate BCF model, due to overestimating ionization of chemicals. The second approach, estimating the ionization as a ratio between distribution and partition coefficients (log P and log D), was found to be more successful. The new ionization term allows modelling of chemicals with both acidic and basic functionalities and chemicals undergoin...

Docking and QSAR comparative studies of polycyclic aromatic hydrocarbons and other procarcinogen interactions with cytochromes P450 1A1 and 1B1

Abstract: To obtain chemical clues on the process of bioactivation by cytochromes P450 1A1 and 1B1, some QSAR studies were carried out based on cellular experiments of the metabolic activation of polycyclic aromatic hydrocarbons and heterocyclic aromatic compounds by those enzymes. Firstly, the 3D structures of cytochromes 1A1 and 1B1 were built using homology modelling with a cytochrome 1A2 template. Using these structures, 32 ligands including heterocyclic aromatic compounds, polycyclic aromatic hydrocarbons and corresponding diols, were docked with LigandFit and CDOCKER algorithms. Binding mode analysis highlighted the importance of hydrophobic interactions and the hydrogen bonding network between cytochrome amino acids and docked molecules. Finally, for each enzyme, multilinear regression and artificial neural network QSAR models were developed and compared. These statistical models highlighted the importance of electronic, structural and energetic descriptors in metabolic activation process, and could be used ...

Development predictive QSAR models for artemisinin analogues by various feature selection methods: A comparative study

Abstract: Quantitative structure-activity relationship (QSAR) models were derived for 179 analogues of artemisinin, a potent antimalarial agent. The activities of these compounds were investigated by means of multiple linear regression (MLR). To select relevant descriptors, several methods including stepwise selection, successive projection algorithm and an ant colony optimization algorithm (called memorized_ACS) were employed. A wide variety of molecular descriptors belonging to various structural properties were calculated for each molecule. Two matrixes (D1 and D2) of molecular properties were built. The D1 matrix included the calculated descriptors and the D2 matrix contained the first to third orders of the calculated descriptors and the logarithm of absolute values of the calculated descriptors. For both data matrixes, significant QSAR models were obtained by the memorized_ACS algorithm. The reactive and PEOE (partial equalization of orbital electronegativity) descriptors represented the highest impact on the antimalarial activity. The PEOE descriptors belong to partial charge descriptors and the reactive descriptor is an indicator of the presence of the reactive groups in the molecule. The best MLR model has a training error of 0.71 log RA units (r (2 )= 0.81) and a prediction error of 0.48 log RA units (r (2) = 0.88).

Rank-based ant system method for non-linear QSPR analysis: QSPR studies of the solubility parameter

Abstract: The solubility parameter (δ) plays a unique role in the development of stable pharmaceutical formulations for assessing phase segregation during product synthesis. Understanding this parameter helps to determine how a drug substance will behave when processed or when dosed in vivo. The aim of this work was to develop a novel comprehensive yet rapid and accurate Quantitative Structure–Property Relationship (QSPR) method based on the rank-based ant system feature selection. The method was coupled with the multiple linear regression and support vector regression and applied to the assessment of solubility parameters for a diverse dataset of 1804 chemical compounds. The models were validated by solubility prediction of 360 test set compounds which were not used in building models. The developed models have high prediction power characterized by r 2 values 0.75 and 0.82, and RMSE values 1.96 and 1.65 (J/(cm3))0.5 for the external test set. Various validation techniques and comparison results with the novel opt...

Analysis of van der Waals surface area properties for human ether-a-go-go-related gene blocking activity: computational study on structurally diverse compounds

Abstract: In the present investigation, a computational analysis was performed on a data set comprised of human ether-a-go-go-related gene (hERG) blockers (triethanolamine, 1,3-thiazol-2-yl and tetrasubstituted imidazoline derivatives) in order to investigate the structural features required to reduce the hERG-induced cardiotoxicity problems in an early stage of drug discovery. The results derived from the quantitative structure–activity relationship (QSAR) analysis showed that the volume, surface area and shape descriptors (vsurf_) contributed significantly in all the models. This reveals that the hydrogen-bonding and hydrophilicity properties (vsurf_HB1, vsurf_CW4 and a_acc) on the van der Waals (vdW) surface of the molecule is negatively contributed for the hERG blocking activity and the hydrophobic property (vsurf_D6) and the total polar volume (vsurf_Wp2) on the vdW surface of the molecule are favourable for the activity. Further, the pharmacophore analysis also shows that the Aro/Hyd/Acc contour is one of the...

Evaluation of the OECD (Q)SAR Application Toolbox for the profiling of estrogen receptor binding affinities

Abstract: The determination of binding affinities for the estrogen receptor (ER) is used extensively to assess potential hazards to human health and the environment arising from chemicals that can interfere with natural hormone homeostasis. Given the great number of chemicals to which humans and wildlife are exposed, (quantitative) structure–activity relationship (Q)SAR models for the characterization of ER disruptors represent a fast and cost-efficient alternative to experimental testing. In this toxicological context, the freely available Organisation for Economic Co-operation and Development (OECD) (Q)SAR Application Toolbox provides a profiler for the categorical profiling of chemicals according to their ER binding propensities. The aim of this study was to evaluate the predictive performances of this profiler. To achieve such a purpose, prediction results with the ER-profiler were compared with experimental binding affinities relative to two large datasets of chemicals (rat and human). The resulting Cooper sta...

Docking studies of novel pyrazinopyridoindoles class of antihistamines with the homology modelled H(1)-receptor.

Abstract: Histamine is an important neurotransmitter as it controls a multitude of physiological functions by activating specific receptors on target cells. It exerts its effects by binding to four different histamine receptors (H(1)-H(4)), which all belong to the large family of G protein-coupled receptors (GPCRs). Research and development of H(1) ligand has largely focused on antagonists which are used for their anti-allergy effects in the periphery. Recent understanding of the clinical importance of H(1) receptors in brain, however, suggests the pharmacotherapeutic potential of H(1) agonists in neurodegenerative and neuropsychiatric disorders. Despite the therapeutic importance of the H(1) receptor, for many years the molecular features of the H(1) receptor protein had been unknown. In view of the recently reported crystal structure of human H(1) receptor and in continuation of our work on 3D-pharmacophore on antihistamine H(1) and homology model of histamine H(1) receptor, docking studies have been carried out on some promising pyrazinopyridoindole class of antihistamine H(1), including two outliers, to validate our earlier reported models/hypotheses on H(1)-receptor, where a good explanation between estimated and observed activities has been obtained. In addition, the docking study also provided insights about the optimal activity of the outliers, for which no explanation was reported previously.

Comparison of Tetrahymena pyriformis toxicity based on hydrophobicity, polarity, ionization and reactivity of class-based compounds

Abstract: A toxicity data set containing the toxicities of 970 hydrophobic, polar and ionizable, nitro substituted and α,β-unsaturated compounds to Tetrahymena pyriformis was classified into different groups based on the structure and substituted functional groups. Polar, ionizable and reactive compounds exhibit greater toxicity as compared with the non-polar hydrophobic compounds. Step-by-step analysis was carried out between the toxicity and descriptors representing hydrophobicity, polarity/polarizability, ionization and reactivity of compounds. Significant relationships were developed between the toxicity and these descriptors for the compounds. The models developed are simple, interpretable and transparent, using a small number of descriptors that may reflect the interactions of chemicals with the biological macromolecules at the target sites. Hydrophobic parameter log P reflects bio-uptake process compounds. Polarity/polarizability descriptor S reflects the interaction of hydrophilic residues of polar chemical...

The definition of the applicability domain relevant to skin sensitization for the aromatic nucleophilic substitution mechanism.

Abstract: This study outlines how a glutathione reactivity assay (so-called in chemico data) can be used to define the applicability domain for the nucleophilic aromatic substitution (SNAr) reaction for benzenes. This reaction is one of the six mechanistic domains that have been shown to be important in toxicological endpoints in which the ability to bind covalently to a protein is a key molecular initiating event. This study has analysed the experimental data, allowing a clear and interpretable structure–activity relationship to be developed for the SNAr domain. The applicability domain has resulted in a series of structural alerts. The definition of the applicability domain for the SNAr reaction and the resulting structural alerts are likely to be beneficial in the development of computational tools for category formation and read-across. The study concludes with how this information can be used in the development of adverse outcome pathways.

Linear and non-linear QSAR modelling of juvenile hormone esterase inhibitors.

Abstract: A tight control of juvenile hormone (JH) titre is crucial during the life cycle of a holometabolous insect. JH metabolism is made through the action of enzymes, particularly the juvenile hormone esterase (JHE). Trifluoromethylketones (TFKs) are able to inhibit this enzyme to disrupt the endocrine function of the targeted insect. In this context, a set of 96 TFKs, tested on Trichoplusia ni for their JHE inhibition, was split into a training set (n = 77) and a test set (n = 19) to derive a QSAR model. TFKs were initially described by 42 CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) descriptors, but a feature selection process allowed us to consider only five descriptors encoding the structural characteristics of the TFKs and their reactivity. A classical and spline regression analysis, a three-layer perceptron, a radial basis function network and a support vector regression were experienced as statistical tools. The best results were obtained with the support vector regression ...

Pharmacophore-based database mining for probing fragmental drug-likeness of diketo acid analogues.

Abstract: A number of the structurally diverse chemical compounds with functional diketo acid (DKA) subunit(s) have been revealed by combined online and MoStBiodat 3D pharmacophore-guided ZINC and PubChem database screening. We used the structural data available from such screening to analyse the similarities of the compounds containing the DKA fragment. Generally, the analysis by principal component analysis and self-organizing neural network approaches reveals four families of compounds complying with the chemical constitution (aromatic, aliphatic) of the compounds. From a practical point of view, similar studies may reveal potential bioisosteres of known drugs, e.g. raltegravir/elvitegravir. In this context, it seems that mono-halogenated aryl substructures with para group show the closest similarity to these compounds, in contrast to structures where the aromatic ring is halogenated in both ortho- and para-locations.

Quantitative structure–activity relationship analysis of human neutrophil elastase inhibitors using shuffling classification and regression trees and adaptive neuro-fuzzy inference systems

Abstract: The purpose of this study was to develop quantitative structure–activity relationship models for N-benzoylindazole derivatives as inhibitors of human neutrophil elastase. These models were developed with the aid of classification and regression trees (CART) and an adaptive neuro-fuzzy inference system (ANFIS) combined with a shuffling cross-validation technique using interpretable descriptors. More than one hundred meaningful descriptors, representing various structural characteristics for all 51 N-benzoylindazole derivatives in the data set, were calculated and used as the original variables for shuffling CART modelling. Five descriptors of average Wiener index, Kier benzene-likeliness index, subpolarity parameter, average shape profile index of order 2 and folding degree index selected by the shuffling CART technique have been used as inputs of the ANFIS for prediction of inhibition behaviour of N-benzoylindazole derivatives. The results of the developed shuffling CART-ANFIS model compared to other tech...

4D-QSAR study of HEPT derivatives by electron conformational-genetic algorithm method.

Abstract: In this work, the EC-GA method, a hybrid 4D-QSAR approach that combines the electron conformational (EC) and genetic algorithm optimization (GA) methods, was applied in order to explain pharmacophore (Pha) and predict anti-HIV-1 activity by studying 115 compounds in the class of 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio) thymine (HEPT) derivatives as non-nucleoside reverse transcriptase inhibitors (NNRTIs). The series of NNRTIs were partitioned into four training and test sets from which corresponding quantitative structure-activity relationship (QSAR) models were constructed. Analysis of the four QSAR models suggests that the three models generated from the training and test sets used in previous works yielded comparable results with those of previous studies. Model 4, the data set of which was partitioned randomly into two training and test sets with 11 descriptors, including electronical and geometrical parameters, showed good statistics both in the regression (r2(training) )= 0.867, r2test = 0.923) and cross-validation (q (2) = 0.811, q2(ext1) = 0.909, q2(ext2) = 0.909) for the training set of 80 compounds and the test set of 27 compounds. The prediction of the anti-HIV-1 activity of HEPT compounds by means of the EC-GA method allowed for a quantitatively consistent QSAR model. In addition, eight novel compounds never tested experimentally have been designed theoretically using model 4.

Hierarchical virtual screening: identification of potential high-affinity and selective β(3)-adrenergic receptor agonists.

Abstract: The hierarchical virtual screening (HVS) study, consisting of pharmacophore modelling, docking and VS of the generated focussed virtual library, has been carried out to identify novel high-affinity and selective β(3)-adrenergic receptor (β-AR) agonists. The best pharmacophore model, comprising one H-bond donor, two hydrophobes, one positive ionizable and one negative ionizable feature, was developed based on a training set of 51 β(3)-AR agonists using the pharmacophore generation protocol implemented in Discovery Studio. The model was further validated with the test set, external set and ability of the pharmacophoric features to complement the active site amino acids of the homology modelled β(3)-AR developed using MODELLER software. The focussed virtual library was generated using the structure-based insights gained from our earlier reported comprehensive study focussing on the structural basis of β-AR subtype selectivity of representative agonists and antagonists. The HVS with the sequential use of the best pharmacophore model and homology modelled β(3)-AR in the screening of the generated focussed library has led to the identification of potential virtual leads as novel high-affinity and selective β(3)-AR agonists.

Simulation of chemical metabolism for fate and hazard assessment. IV. Computer-based derivation of metabolic simulators from documented metabolism maps

Abstract: Computer simulation of xenobiotic metabolism and degradation is usually performed proceeding from a set of expert-developed rules modelling the actual enzyme-driven chemical reactions. With the accumulation of extensive metabolic pathway data, the analysis required to derive such chemical reaction patterns has become more objective, but also more convoluted and demanding. Herein we report on our computer-based approach for the analysis of metabolic maps, leading to the construction of reaction rules statistically suitable for simulation purposes. It is based on the set of so-called bare transformations which encompass all unique reaction patterns as obtained by a heuristically enhanced maximum common subgraph algorithm. The bare transformations guarantee that no existing metabolite is missed in simulation at the expense of an enormous amount of false positive predictions. They are rendered more selective by correlating the generated true and false positives to the locations of typical chemical functional ...

yaInChI: Modified InChI string scheme for line notation of chemical structures

Abstract: A modified InChI (International Chemical Identifier) string scheme, yaInChI (yet another InChI), is suggested as a method for including the structural information of a given molecule, making it straightforward and more easily readable. The yaInChI theme is applicable for checking the structural identity with higher sensitivity and generating three-dimensional (3-D) structures from the one-dimensional (1-D) string with less ambiguity than the general InChI method. The modifications to yaInChI provide non-rotatable single bonds, stereochemistry of organometallic compounds, allene and cumulene, and parity of atoms with a lone pair. Additionally, yaInChI better preserves the original information of the given input file (SDF) using the protonation information, hydrogen count +1, and original bond type, which are not considered or restrictively considered in InChI and SMILES. When yaInChI is used to perform a duplication check on a 3D chemical structure database, Ligand.Info, it shows more discriminating power ...

In Silico fragment-based drug design using a PASS approach

Abstract: Fragment-based drug design integrates different methods to create novel ligands using fragment libraries focused on particular biological activities. Experimental approaches to the preparation of fragment libraries have some drawbacks caused by the need for target crystallization (X-ray and nuclear magnetic resonance) and careful immobilization (surface plasmon resonance). Molecular modelling (docking) requires accurate data on protein-ligand interactions, which are difficult to obtain for some proteins. The main drawbacks of QSAR application are associated with the need to collect large homogeneous datasets of chemical structures with experimentally determined self-consistent quantitative values (potency). We propose a ligand-based approach to the selection of fragments with positive contribution to biological activity, developed on the basis of the PASS algorithm. The robustness of the PASS algorithm for heterogeneous datasets has been shown earlier. PASS estimates qualitative (yes/no) prediction of biological activity spectra for over 4000 biological activities and, therefore, provides the basis for the preparation of a fragment library corresponding to multiple criteria. The algorithm for fragment selection has been validated using the fractions of intermolecular interactions calculated for known inhibitors of nine enzymes extracted from the Protein Data Bank database. The statistical significance of differences between fractions of intermolecular interactions corresponds, for several enzymes, to the estimated positive and negative contribution of fragments in enzyme inhibition.

Insight into eukaryotic topoisomerase II-inhibiting fused heterocyclic compounds in human cancer cell lines by molecular docking

Abstract: Etoposide is effective as an anti-tumour drug by inhibiting eukaryotic DNA topoisomerase II via establishing a covalent complex with DNA. Unfortunately, its wide therapeutic application is often hindered by multidrug resistance (MDR), low water solubility and toxicity. In our previous study, new derivatives of benzoxazoles, benzimidazoles and related fused heterocyclic compounds, which exhibited significant eukaryotic DNA topoisomerase II inhibitory activity, were synthesized and exhibited better inhibitory activity compared with the drug etoposide itself. To expose the binding interactions between the eukaryotic topoisomerase II and the active heterocyclic compounds, docking studies were performed, using the software Discovery Studio 2.1, based on the crystal structure of the Topo IIA-bound G-segment DNA (PDB ID: 2RGR). The research was conducted on a selected set of 31 fused heterocyclic compounds with variation in structure and activity. The structural analyses indicate coordinate and hydrogen bonding interactions, van der Waals interactions and hydrophobic interactions between ligands and the protein, as Topo IIA-bound G-segment DNA are responsible for the preference of inhibition and potency. Collectively, the results demonstrate that the compounds 1a, 1c, 3b, 3c, 3e and 4a are significant anti-tumour drug candidates that should be further studied.

3D-QSAR studies of quinoline ring derivatives as HIV-1 integrase inhibitors

Abstract: In the process of HIV-1 virus replication, integrase plays a quite important role. Integrase inhibitors of quinoline ring derivatives were analysed by the Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Induces Analysis (CoMSIA) and Topomer CoMFA methods. Firstly, 77 compounds were selected to form the training and test sets. Secondly, predictive models were constructed with the CoMFA, CoMSIA and Topomer CoMFA methods. The CoMFA model yielded the best model with q 2 of 0.76 and of 0.99, the CoMSIA model has q 2 = 0.70 and of 0.99, while the Topomer CoMFA model has q 2 of 0.66 and of 0.97. These results provide a helpful contribution to the design of novel highly active HIV-1 integrase inhibitors.

Consideration of reactivity to acute fish toxicity of α,β-unsaturated carbonyl ketones and aldehydes

Abstract: To understand the key factor for fish toxicity of 11 α,β-unsaturated carbonyl aldehydes and ketones, we used quantum chemical calculations to investigate their Michael reactions with methanethiol or glutathione. We used two reaction schemes, with and without an explicit water molecule (Scheme-1wat and Scheme-0wat, respectively), to account for the effects of a catalytic water molecule on the reaction pathway. We determined the energies of the reactants, transition states (TS), and products, as well as the activation energies of the reactions. The acute fish toxicities of nine of the carbonyl compounds were evaluated to correlate with their hydrophobicities; no correlation was observed for acrolein and crotonaldehyde. The most toxic compound, acrolein, had the lowest activation energy. The activation energy of the reaction could be estimated with Scheme-1wat but not with Scheme-0wat. The complexity of the reaction pathways of the compounds was reflected in the difficulty of the TS structure searches when S...

On graphical representation of trans-membrane proteins.

Abstract: In the first part of this paper, we present a novel graphical representation of proteins, which starts with constructing a map of a protein that is obtained from a matrix, the elements of which record the adjacencies of pairs of amino acids in the primary structure of a protein. Starting with the novel protein map, one interprets its matrix elements as vertices of a graph, which are labelled in sequential order as in the protein sequence. The nearest vertices are connected to the nearest neighbour which has a smaller label. In the second part of this paper, we describe the construction of protein binary codes that can serve as protein descriptors. This novel graphical representation of proteins is illustrated on segments of trans-membrane proteins, which are embedded in the membrane.