An updated steroid benchmark set and its application in the discovery of novel nanomolar ligands of sex hormone-binding globulin.
ABSTRACT A benchmark data set of steroids with known affinity for sex hormone-binding globulin (SHBG) has been widely used to validate popular molecular field-based QSAR techniques. We have expanded the data set by adding a number of nonsteroidal SHBG ligands identified both from the literature and in our previous experimental studies. This updated molecular set has been used herein to develop 4D QSAR models based on "inductive" descriptors and to gain insight into the molecular basis of protein-ligand interactions. Molecular alignment was generated by means of docking active compounds into the active site of the SHBG. Surprisingly, the alignment of the benchmark steroids contradicted the classical ligand-based alignment utilized in previous CoMFA and CoMSIA models yet afforded models with higher statistical significance and predictive power. The resulting QSAR models combined with CoMFA and CoMSiA models as well as structure-based virtual screening allowed discovering several low-micromolar to nanomolar nonsteroidal inhibitors for human SHBG.
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ABSTRACT: The importance of reliable methods for representative sub-sampling in terms of experimental design and risk assessment within the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) system is crucial. We developed experimental design approaches, by utilising predicted properties and the 'distance to model' parameter, to estimate the benefits of certain compounds to the quality of a resulting model. A statistical evaluation of four regression data sets and one classification data set showed that the adaptive concept of iteratively refining the representation of the chemical space contributes to a more efficient and more reliable selection in comparison to traditional approaches. The evaluation of compounds with regard to the uncertainty and the correlation of prediction is beneficial, and in particular, for regression data sets of sufficient size, whereas the use of predicted properties to define the chemical space is beneficial for classification models.Alternatives to laboratory animals: ATLA 03/2013; 41(1):33-47. · 1.32 Impact Factor
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ABSTRACT: The circulating endogenous steroids are transported in the bloodstream. These are bound to a highly specific sex hormone-binding globulin (SHBG) and in lower affinity to proteins such as the corticosteroid-binding protein and albumin in vertebrates, including fish. It is generally believed that the glycoprotein SHBG protects these steroids from rapid metabolic degradation and thus intervenes in its availability at the target tissues. Endocrine disrupters binding to SHBG affect the normal activity of natural steroids. Since xenobiotics are primarily released in the aquatic environment, there is a need to evaluate the binding affinity of xenosteroid mimics on fish SHBG, especially in zebrafish (Danio rerio), a small freshwater fish originating in India and widely employed in ecotoxicology, toxicology, and genetics. In this context, a zebrafish SHBG (zfSHBG) homology model was developed using the human SHBG (hSHBG) receptor structure as template. It was shown that interactions with amino acids Ser-36, Asp-59 and Thr-54 were important for binding affinity. A ligand-based pharmacophore model was also developed for both zfSHBG and hSHBG inhibitors that differentiated binders from non-binders, but also demonstrated structural requirements for zfSHBG and hSHBG ligands. The study provides insights into the mechanism of action of endocrine disruptors in zebrafish as well as providing a useful tool for identifying anthropogenic compounds inhibiting zfSHBG.SAR and QSAR in Environmental Research 04/2014; 25(5). · 1.92 Impact Factor
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ABSTRACT: The Online Chemical Modeling Environment (OCHEM, http://ochem.eu) is a web-based platform that provides tools for automation of typical steps necessary to create a predictive QSAR/QSPR model. The platform consists of two major subsystems: a database of experimental measurements and a modeling framework. So far, OCHEM has been limited to the processing of individual compounds. In this work, we extended OCHEM with a new ability to store and model properties of binary non-additive mixtures. The developed system is publicly accessible, meaning that any user on the Web can store new data for binary mixtures and develop models to predict their non-additive properties.The database already contains almost 10,000 data points for the density, bubble point, and azeotropic behavior of binary mixtures. For these data, we developed models for both qualitative (azeotrope/zeotrope) and quantitative endpoints (density and bubble points) using different learning methods and specially developed descriptors for mixtures. The prediction performance of the models was similar to or more accurate than results reported in previous studies. Thus, we have developed and made publicly available a powerful system for modeling mixtures of chemical compounds on the Web.Journal of Cheminformatics 01/2013; 5(1):4. · 4.54 Impact Factor