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

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 for virtual screening of ligand databases. In the case of P450 1A1, the best model was obtained with artificial neural network analysis and gave an r (2) of 0.66 and an external prediction [Formula: see text] of 0.73. Concerning P450 1B1, artificial neural network analysis gave a much more robust model, associated with an r (2) value of 0.73 and an external prediction [Formula: see text] of 0.59.

Homology modelling of the Apis mellifera nicotinic acetylcholine receptor (nAChR) and docking of imidacloprid and fipronil insecticides and their metabolites.

Five homology models for honeybee (Apis mellifera) nicotinic acetylcholine receptor (nAChR) alpha1/beta1, alpha3/beta2, alpha4/beta2, alpha6/beta2 and alpha9/alpha9 subtypes were built from the Torpedo marmorata nAChR X-ray structure. Then, imidacloprid, fipronil and their metabolites were docked into the ligand binding domain (LBD) of these receptors and the corresponding scoring functions were calculated. The binding modes of the docked compounds were carefully analysed. Finally, multivariate analyses were used for deriving structure-activity relationships based on hydrogen bond number and scoring functions between the insecticides and the nAChR models.

Decision trees versus support vector machine for classification of androgen receptor ligands.

With the current concern of limiting experimental assays, increased interest now focuses on in silico models able to predict toxicity of chemicals. Endocrine disruptors cover a large number of environmental and industrial chemicals which may affect the functions of natural hormones in humans and wildlife. Structure-activity models are now increasingly used for predicting the endocrine disruption potential of chemicals. In this study, a large set of about 200 chemicals covering a broad range of structural classes was considered in order to categorize their relative binding affinity (RBA) to the androgen receptor. Classification of chemicals into four activity groups, with respect to their log RBA value, was carried out in a cascade of recursive partitioning trees, with descriptors calculated from CODESSA software and encoding topological, geometrical and quantum chemical properties. The hydrophobicity parameter (log P), Balaban index, and descriptors relying on charge distribution (maximum partial charge, nucleophilic index on oxygen atoms, charged surface area, etc.) appear to play a major role in the chemical partitioning. Separation of strongly active compounds was rather straightforward. Similarly, about 90% of the inactive compounds were identified. More intricate was the separation of active compounds into subsets of moderate and weak binders, the task requiring a more complex tree. A comparison was made with support vector machine yielding similar results.

Endocrine disruption profile analysis of 11,416 chemicals from chemometrical tools.

A number of chemicals released into the environment have the potential to disturb the normal functioning of the endocrine system. These chemicals termed endocrine disruptors (EDs) act by mimicking or antagonizing the normal functions of natural hormones and may pose serious threats to the reproductive capability and development of living species. Batteries of laboratory bioassays exist for detecting these chemicals. However, due to time and cost limitations, they cannot be used for all the chemicals which can be found in the ecosystems. SAR and QSAR models are particularly suited to overcome this problem but they only deal with specific targets/endpoints. The interest to account for profiles of endocrine activities instead of unique endpoints to better gauge the complexity of endocrine disruption is discussed through a SAR study performed on 11,416 chemicals retrieved from the US-NCI database and for which 13 different PASS (Prediction of Activity Spectra for Substances) endocrine activities were available. Various multivariate analyses and graphical displays were used for deriving structure-activity relationships based on specific structural features.

SAR and QSAR modeling of endocrine disruptors.

A number of xenobiotics by mimicking natural hormones can disrupt crucial functions in wildlife and humans. These chemicals termed endocrine disruptors are able to exert adverse effects through a variety of mechanisms. Fortunately, there is a growing interest in the study of these structurally diverse chemicals mainly through research programs based on in vitro and in vivo experimentations but also by means of SAR and QSAR models. The goal of our study was to retrieve from the literature all the papers dealing with structure-activity models on endocrine disruptor xenobiotics. A critical analysis of these models was made focusing our attention on the quality of the biological data, the significance of the molecular descriptors and the validity of the statistical tools used for deriving the models. The predictive power and domain of application of these models were also discussed.

Structural e-bioinformatics and drug design.

Nowadays the in silico scenario for drug design is totally dependent on structural biology and structural bioinformatics. A myriad of free bioinformatics applications and services have been posted on the web. This mini-review mentions web sites that are useful in structure-based drug design. The information is given in a logical manner, following the drug design process i.e. characterization of a protein target, modelling the protein using sequence homology, optimization of the protein structure and finally docking of small ligands into the active site.

Homology model of the rainbow trout estrogen receptor (rtERalpha) and docking of endocrine disrupting chemicals (EDCs).

A model for rainbow trout (Oncorhynchus mykiss) estrogen receptor (rtERa) was built by homology with the human estrogen receptor (hERa). A high level of sequence conservation between the two receptors was found with 64% and 80% of identity and similarity, respectively. Selected endocrine disrupting chemicals were docked into the ligand binding domain (LBD) of rtERa and the corresponding free binding energies Delta(DeltaG(bind)) values were calculated. A Quantitative Structure-Activity Relationship (QSAR) model between the relative binding affinity data and the Delta(DeltaG(bind)) values was derived in order to predict which further organic pollutants are likely to bind to rtERa.

e-Quantum chemistry free resources.

Among computational chemistry methods, quantum mechanics calculates geometries and electronic structures with accuracy especially for systems with electronic delocalization. The use of a multiconfigurational approach is able to treat highly degenerated states such as those occurring at the transition state in some chemical reactions. Moreover, an accurate description of potential energy surfaces can be obtained with the evaluation of the dynamic electron correlation effects by this approach. Molecular properties range from simple dipole moments, vibrational frequencies or IR intensities to frequency dependent hyperpolarizabilities. Quantum chemical calculations are thus an attractive source of molecular descriptors which can be used in QSAR/QSPR studies and which can express all electronic and geometric properties of molecules. A survey and a comparison of the performance of free e-resources for semi-empirical and ab initio calculations is provided.

e-molecular shapes and properties.

Due to recent computer technology advances, shape analysis has gained importance in all domains. In drug design and proteomics, molecular surfaces (van der Waals surface, solvent accessible surface, solvent excluded surface, polar surface area, electron density surface, separating surface, etc.), buried surfaces (gap, cleft, cavity, etc.) as well as shape properties of these surfaces, can be easily computed and visualized via the Internet. Freely available resources from the Internet for academic use, are reviewed.

An ab initio investigation of 2-amino-2-imidazoline: a key moiety in chemical and biochemical processes.

The 2-amino-2-imidazoline moiety is currently used not only in drugs, but also in insecticides, and fungicides. Ab initio calculations are performed to evaluate the molecular properties of the two tautomeric forms and the protonated form with extended basis sets ranging from 6-31G* to 6-311++G** at Hartree-Fock and density functional (BLYP and B3LYP) levels. Møller-Plesset perturbation is tested at the MP2/6-31G* level only. Optimized geometry structures, energies and thermochemical properties are generated. Basis set and correlation effects on geometries, tautomer equilibrium constant and protonation enthalpy are carefully analysed. Although observed for the isolated molecule, these results may be extrapolated to chemical and biochemical systems of interest.