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At least 919 records · Page 51Linked to original sources
PubMed1982

Steroid-protein interactions. Human corticosteroid-binding globulin: characterization of dimer and electrophoretic variants.

Human corticosteroid-binding globulin (CBG) forms a dimer that was isolated by gel filtration, has full binding affinity and capacity, and can be dissociated to the monomer. Monomeric CBG consists of two distinct molecular variants, which were detected by polyacrylamide gel electrophoresis in the presence and absence of sodium dodecyl sulfate. The two monomeric CBG species were separated by preparative gel electrophoresis and were found to bind cortisol, as well as progesterone, with equal affinity. They have one steroid binding site per CBG molecule. Amino acid and carbohydrate analyses are essentially the same for both of the CBG variants. Removal of sialic acid or 90% of the carbohydrate did not affect the existence of the two molecular forms. The two CBG species were isolated from each of the sera from five individual donors, indicating that the observed heterogeneity does not result from pooling genetic variants. The two species are immunologically identical. A possible explanation for the existence of the two electrophoretic variants is a difference in amidation.

Binding Sites↗
PubMed1979

Purification and characterization of the corticosteroid-binding globulin of pregnant guinea pig serum.

The corticosteroid-binding globulin from guinea pig pregnancy serum was purified by the sequential use of affinity chromatography, hydroxylapatite chromatography, and gel filtration chromatography at a cumulative yield of 80%. The protein was found to be homogeneous by analytical gel electrophoresis, equilibrium sedimentation ultracentrifugation, immunoelectrophoresis, and stoichiometry (1:1) of steroid binding. Guinea pig corticosteroid-binding globulin has a molecular weight of 43 300 and contains 29% carbohydrate. The intrinsic fluorescence of the corticosteroid-binding globulin is quenched by about 73% when 1 mol of cortisol is bound. The association constants (pH 7.4) at 4 and 37 degrees C are 2.5 X 10(7) and 1.5 X 10(6) M-1 for cortisol and 1.4 X 10(6) and 0.2 X 10(6) M-1 for progesterone, respectively.

Amino Acids↗
PubMedDate not supplied

Identification of active molecular sites using quantum-self-similarity measures.

A novel approach to construct theoretical QSAR models is proposed. This technique, based on the systematic use of quantum similarity measures as theoretical molecular descriptors, opens the possibility to localize and to identify the position of the bioactive part of drug molecules in situations, where the nature of the pharmacophore is not known. To test the reliability of this new approach, the method has been applied to the study of steroids binding to corticosteroid-binding human globulin. The studied molecules involved the set of 31 Cramer's steroids, often used as a benchmark set in QSAR studies. It has been shown that theoretical QSAR models based on the present procedure are superior to those derived from alternative existing approaches. In addition, a new method to measure the statistical significance of multiparameter QSAR models is also proposed.

Benzoates↗
PubMedDate not supplied

QSAR study of steroid benchmark and dipeptides based on MEDV-13.

A molecular electronegativity distance vector based on 13 atomic types, called MEDV-13, is a descriptor for predicting the biological activities of molecules based on the quantitative structure-activity relations (QSAR). The MEDV-13 uses a modified electrotopological state (E-state) index to substitute for the relative eletronegativity (q) of non-hydrogen atoms in the molecule of interest in the MEDV and a topological distance for the relative distance (d) in the MEDV. For an organic molecule containing several chemical elements such as C, H, O, N, S, F, Cl, Br, I, and P, the MEDV-13 includes at best 91 descriptors. Then it is essential to employ a principal component regression (PCR) technique to derive a QSAR model relating the biological activities to the MEDV-13. The MEDV-13 is used to study the QSAR of the corticosteroid-binding globulin (CBG) binding affinity of the steroids and the activity inhibiting angiotensin-converting enzyme (ACE) of dipeptides, and resulting models have a comparable quality to the current three-dimensional (3D) methods such as CoMFA though the MEDV-13 is a descriptor based on two-dimensional topological information.

Angiotensin-Converting Enzyme Inhibitors↗
PubMedDate not supplied

E-state modeling of corticosteroids binding affinity validation of model for small data set.

Data for 31 steroids binding to the corticosteroid binding globulin (CBG) were modeled using E-state molecular structure descriptors and a kappa shape index. Both E-state and hydrogen E-state descriptors appear in the model in atom-level and atom-type descriptors. A four-variable model is obtained that is statistically satisfactory: r (2) = 0.81, s = 0.51; r (2)(press) = 0.72; s(press) = 0.62. Structure interpretation is given for each variable in the model. A leave-group-out (LGO) approach to model-validation is presented in which each observation is removed from the data set three times in random groups of 20% of the whole data set. The average of the resulting predicted values constitutes consensus predictions for these data for which r (2)(LOO) = 0.70. These collective results support the claim that the E-state model may be useful for prediction of pK binding values for new compounds.

Adrenal Cortex Hormones↗
PubMedDate not supplied

Molecular quantum similarity-based QSARs for binding affinities of several steroid sets.

The application of Molecular Quantum Similarity Measures (MQSM) to correlate biological activities for three different sets of steroids is reported. A general protocol for the generation of descriptors is detailed, thus covering molecular superposition, electronic density fitting, and quantum similarity calculation issues. Satisfactory Quantitative Structure-Activity Relationship (QSAR) models (r(2) in [0.69,0.94] and q(2) in [0.59,0.73]), comparable to previous studies, are obtained in all cases, where steroid binding affinities to different enzymes are studied. In this work, MQSM, properly scaled using Carbó Index, are related to activity using a Partial Least Squares routine.

Aromatase↗
PubMedDate not supplied

Comparative structural connectivity spectra analysis (CoSCoSA) models of steroid binding to the corticosteroid binding globulin.

A three-dimensional quantitative spectrometric data-activity relationship (3D-QSDAR) model was developed that is built by combining NMR spectral information with structural information in a 3D-connectivity matrix. The 3D-connectivity matrix is built by displaying all possible carbon-to-carbon connections with their assigned carbon NMR chemical shifts and distances between the carbons. Selected 2D (13)C-(13)C COrrelation SpectroscopY (COSY) (through-bond nearest neighbors) and selected theoretical 2D (13)C-(13)C distance connectivity spectral slices from the 3D-connectivity matrix to produce a relationship among the spectral patterns for 30 steroids binding to corticosteroid binding globulin. We call this technique a comparative structural connectivity spectra analysis (CoSCoSA) modeling. A CoSCoSA principal component linear regression model based on the combination of (13)C-(13)C COSY and (13)C-(13)C distance spectra principal components (PCs) had an r(2) of 0.96 and a leave-one-out (LOO) cross-validation q(2) of 0.92. A CoSCoSA parallel distributed artificial neural network (PD-ANN) model based on the combination of (13)C-(13)C COSY and (13)C-(13)C distance spectra had an r(2) of 0.96, a leave-three-out q(3)(2) of 0.78, and a leave-ten-out q(10)(2) of 0.73. CoSCoSA modeling attempts to uniquely combine the quantum mechanics information from the NMR chemical shifts with internal molecular atom-to-atom distances into an accurate modeling technique. The CoSCoSA modeling technique has the flexibility and accuracy to outperform the cross-validated variance q(2) of previously published quantitative structure-activity relationship (QSAR), quantitative spectral data-activity relationship (QSDAR), self-organizing map (SOM), and electrotopological state (E-state) models.

Binding Sites↗
PubMedDate not supplied

A steroids QSAR approach based on approximate similarity measurements.

A new QSAR method based on approximate similarity measurements is described in this paper. Approximate similarity is calculated using both the classical similarity based on the graph isomorphism and a distance computation between nonisomorphic subgraphs. The latter is carried out through a parametric function where different topological invariants can be considered. After optimizing the contribution of nonisomorphic distance to the new graph similarity, predictive models built with approximate similarity matrixes show higher predictive ability than those using traditional similarity matrixes. The new method has been applied to the prediction of steroids binding to the corticosteroid globulin receptor. The proposed model allows us to obtain valuable external predictions (r=0.82 and SEP=0.30) after training the model by cross-validation (Q2=0.84 and SECV=0.47). Slope and bias parameters are also given.

Least-Squares Analysis↗
PubMedDate not supplied

The receptor-like neural network for modeling corticosteroid and testosterone binding globulins.

A neural-net method for simulation of corticosteroid and testosterone binding globulin (CBG, TBG)-ligand interactions is presented. Molecular modeling provides the geometry and partial atomic charges of 31 steroid molecules. The atomic coordinates within the molecule of the compound of the highest affinity are then used to train a self-organizing map (SOM) that forms a template for the comparison to other molecules. Comparison is done using a series of normalized patterns produced by the SOM. The template SOM, after overlaying on the set of random vectors, mimics the topology of the receptor site and is used to train unsupervisedly a neuron capable of recognizing the degree of similarity between the reference and tested patterns. A good correlation is observed for signals generated by the neuron plotted against the experimental CBG affinities. For TBG affinity modeling a modified procedure is designed which is capable of separating electrostatic and shape effects. The high predictive power of the model is achieved by keeping close analogy to the processes taking place at the real receptor sites.

Binding Sites↗
PubMedDate not supplied

Three-dimensional quantitative structure-activity relationships from tuned molecular quantum similarity measures: prediction of the corticosteroid-binding globulin binding affinity for a steroid family.

Predictive models based on tuned molecular quantum similarity measures and their application to obtain quantitative structure-activity relationships (QSAR) are described. In the present paper, the corticosteroid-binding globulin binding affinity of a 31 steroid family is studied by means of a multilinear regression using molecular descriptors derived from mixed steric-electrostatic quantum similarity matrixes as parameters, obtaining satisfactory predictions. A systematic procedure to treat outliers by using triple-density quantum similarity measures is also presented. This method depicts an alternative to the grid-based QSAR techniques, providing a consistent approach that avoids problematic result dependency on the grid parameters.

In Vitro Techniques↗
PubMed1994

Compass: predicting biological activities from molecular surface properties. Performance comparisons on a steroid benchmark.

We describe a new method, Compass, for predicting the biological activities of molecules based on the activities and three-dimensional structures of other molecules. The method improves on previous techniques by representing only the surface of molecules, by incorporating a nonlinear statistical method, and by automatically choosing conformations and alignments of molecules. We use a benchmark problem of steroid binding affinity prediction to compare the performance of the method with that of two previous systems: CoMFA and a molecular similarity method. Compass predicts steroid affinities substantially more accurately than the others, which represent the state of the art. We present experiments showing that the improved performance depends on each of the technical innovations.

Algorithms↗
PubMed1993

Structure-activity relationships from molecular similarity matrices.

An alternative method for determining structure-activity correlations is presented. Ligand molecules are described using data matrices derived from the results of N by N (each molecule compared to every other) molecular similarity calculations. The matrices were analyzed using a neural network pattern recognition technique and partial least squares statistics, with the results obtained compared to those achieved using comparative molecular field analysis (CoMFA). The molecular series used in the study comprised 31 steroids. The resultant pattern recognition analysis showed clustering of compounds with high, intermediate, and low affinity into separate regions of the neuron output plots. The cross-validated correlation coefficients obtained from statistical analyses of the matrices against steroid binding data compared well with those achieved using CoMFA. These results show that data matrices derived from molecular similarity calculations can provide the basis for rapid elucidation of both qualitative and quantitative structure-activity relationships.

Computer Simulation↗
PubMed2000

GRid-INdependent descriptors (GRIND): a novel class of alignment-independent three-dimensional molecular descriptors.

Traditional methods for performing 3D-QSAR rely upon an alignment step that is often time-consuming and can introduce user bias, the resultant model being dependent upon and sensitive to the alignment used. There are several methods which overcome this problem, but in general the necessary transformations prevent a simple interpretation of the resultant models in the original descriptor space (i.e. 3D molecular coordinates). Here we present a novel class of molecular descriptors which we have termed GRid-INdependent Descriptors (GRIND). They are derived in such a way as to be highly relevant for describing biological properties of compounds while being alignment-independent, chemically interpretable, and easy to compute. GRIND are obtained starting from a set of molecular interaction fields, computed by the program GRID or by other programs. The procedure for computing the descriptors involves a first step, in which the fields are simplified, and a second step, in which the results are encoded into alignment-independent variables using a particular type of autocorrelation transform. The molecular descriptors so obtained can be used to obtain graphical diagrams called "correlograms" and can be used in different chemometric analyses, such as principal component analysis or partial least-squares. An important feature of GRIND is that, with the use of appropriate software, the original descriptors (molecular interaction fields) can be regenerated from the autocorrelation transform and, thus, the results of the analysis represented graphically, together with the original molecular structures, in 3D plots. In this respect, the article introduces the program ALMOND, a software package developed in our group for the computation, analysis, and interpretation of GRIND. The use of the methodology is illustrated using some examples from the field of 3D-QSAR. Highly predictive and interpretable models are obtained showing the promising potential of the novel descriptors in drug design.

Butyrophenones↗