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At least 937 records · Page 52Linked to original sources
PubMed2003

Mapping property distributions of molecular surfaces: algorithm and evaluation of a novel 3D quantitative structure-activity relationship technique.

A novel molecular descriptor called MaP (mapping property distributions of molecular surfaces) is presented. It combines facile computation, translational and rotational invariance, and straightforward interpretability of the computed models. A three-step procedure is used to compute the MaP descriptor. First, an approximation to the molecular surface with equally distributed surface points is computed. Next, molecular properties are projected onto this surface. Finally, the distribution of surface properties is encoded into a translationally and rotationally invariant molecular descriptor that is based on radial distribution functions (distance-dependent count statistics). The calculated descriptor is correlated with biological data through chemometric regression techniques in combination with a variable selection. The latter is used to identify variables that are highly relevant for the model and hence for its interpretation. Three applications of the new descriptor are presented, each representing a different area of 3D-QSAR. For reasons of comparability, the new descriptor was tested on the steroid "benchmark" data set. Furthermore, a highly diverse data set with potentially eye-irritating compounds was studied, and third, a set of flexible structures with a modulating effect on the muscarinic M(2) receptor were studied. Not only were all models highly predictive but interpretation of the back-projected variables into the original molecular space led to biologically and chemically relevant conclusions.

Acetates↗
PubMed2006

Progressive docking: a hybrid QSAR/docking approach for accelerating in silico high throughput screening.

A combination of protein-ligand docking and ligand-based QSAR approaches has been elaborated, aiming to speed-up the process of virtual screening. In particular, this approach utilizes docking scores generated for already processed compounds to build predictive QSAR models that, in turn, assess hypothetical target binding affinities for yet undocked entries. The "progressive docking" has been tested on drug-like substances from the NCI database that have been docked into several unrelated targets, including human sex hormone binding globulin (SHBG), carbonic anhydrase, corticosteroid-binding globulin, SARS 3C-like protease, and HIV1 reverse transcriptase. We demonstrate that progressive docking can reduce the amount of computations 1.2- to 2.6-fold (when compared to traditional docking), while maintaining 80-99% hit recovery rates. This progressive-docking procedure, therefore, substantially accelerates high throughput screening, especially when using high accuracy (slower) docking approaches and large-sized datasets, and has allowed us to identify several novel potent nonsteroidal SHBG ligands.

Binding Sites↗
PubMed1997

Validation of EGSITE2, a mixed integer program for deducing objective site models for experimental binding data.

EGSITE2 represents a substantial advance in a long series of methods for calculating receptor site models given only specific binding data. Compared to our most recently reported technique, EGSITE [Schnitker et al. J. Comput.-Aided Mol. Des. 1997, 11, 93-110] the user no longer has to simplify the structures of the molecules in the training set by clustering the atoms into a few superatoms. The only remaining source of subjectivity is the user's choice of compounds for the training set, which can be surprisingly few in number. Then EGSITE2 automatically produces typically several different models that explain the observed binding without outliers. The models are remarkably simple but have substantial predictive power for any sort of test compound, with an estimation of the uncertainty of the prediction. Validation of the method is reported for four standard test cases: triazines and pyrimidines binding to dihydrofolate reductase, steroids binding to corticosteroid-binding globulin and to testosterone-binding globulin, and peptides binding to angiotensin-converting enzyme.

Algorithms↗
PubMed1997

Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 1. Method and validations.

The utility of genetic neural network (GNN) to obtain quantitative structure-activity relationships (QSAR) from molecular similarity matrices is described. In this application, the corticosteroid-binding globulin (CBG) binding affinity of the well-known steroid data set is examined. Excellent predictivity can be obtained through the use of either electrostatic or shape properties alone. Statistical validation using a standard randomization test indicates that the results are not due to chance correlations. Application of GNN on the combined electrostatic and shape matrix produces a six-descriptor model with a cross-validated r2 value of 0.94. The model is superior to those obtained from partial least-squares and genetic regressions, and it also compares favorably with the results for the same data set from other established 3D QSAR methods. The theoretical basis for the use of molecular similarity in QSAR is discussed.

Algorithms↗
PubMed1998

Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices.

The program SEAL is suited to describe the electrostatic, steric, hydrophobic, and hydrogen bond donor and acceptor similarity of different molecules in a quantitative manner. Similarity scores AF can be calculated for pairs of molecules, using either a certain molecular property or a sum of weighted properties. Alternatively, their mutual similarity can be derived from distances d or covariances c between SEAL-based property fields that are calculated in a regular grid. For a set of N chemically related molecules, such values form an N x N similarity matrix which can be correlated with biological activities, using either regression analysis and an appropriate variable selection procedure or partial least-squares (PLS) analysis. For the Cramer steroid data set, the test set predictivities (r2pred = 0.53-0.84) of different PLS models, based on a weighted sum of molecular properties, are superior to published results of CoMFA and CoMSIA studies (r2pred = 0.31-0.40), regardless of whether a common alignment or individual, pairwise alignments of all molecules are used in the calculation of the similarity matrices. Training and test set selections have a significant influence on the external predictivities of the models. Although the SEAL similarity score between two molecules is a single number, its value is based on the 3D properties of both molecules. The term 3D quantitative similarity-activity analyses (3D QSiAR) is proposed for approaches which correlate 3D structure-derived similarity matrices with biological activities.

Drug Design↗
PubMed1999

Self-organizing molecular field analysis: a tool for structure-activity studies.

Self-organizing molecular field analysis (SOMFA) is a novel technique for three-dimensional quantitative structure-activity relations (3D-QSAR). It is simple and intuitive in concept and avoids the complex statistical tools and variable selection procedures favored by other methods. Our calculations show the method to be as predictive as the best 3D-QSAR methods available. Importantly, steric and electrostatic maps can be produced to aid the molecular design process by highlighting important molecular features. The simplicity of the technique leaves scope for further development, particularly with regard to handling molecular alignment and conformation selection. Here, the method has been used to predict the corticosteroid-binding globulin binding affinity of the "benchmark" steroids, expanded from the usual 31 compounds to 43 compounds. Test predictions have also been performed on a set of sulfonamide endothelin inhibitors.

Drug Design↗
PubMed1997

Objective models for steroid binding sites of human globulins.

We report the application of a recently developed alignment-free 3D QSAR method [Crippen, G.M., J. Comput. Chem., 16 (1995) 486] to a benchmark-type problem. The test system involves the binding of 31 steroid compounds to two kinds of human carrier protein. The method used not only allows for arbitrary binding modes, but also avoids the problems of traditional least-squares techniques with regard to the implicit neglect of informative outlying data points. It is seen that models of considerable predictive power can be obtained even with a very vague binding site description. Underlining a systematic, but usually ignored, problem of the QSAR approach, there is not one unique type of model but, rather, an entire manifold of distinctly different models that are all compatible with the experimental information. For a given model, there is also a considerable variation in the found binding modes, illustrating the problems that are inherent in the need for 'correct' molecular alignment in conventional 3D QSAR methods.

Binding Sites↗
PubMed1999

Facet diagrams for quantum similarity data.

The objective of this work is to demonstrate that an appropriate treatment of quantum similarity matrices can reveal hidden data grouping related to relevant structural features and even to biological properties of interest. Classical scaling is used here to extract the information contained in the similarity relationships between the elements of a molecular set. Facet theory is invoked to relate, in a qualitative way, the spatial regions to structural characteristics as well as to properties of interest. Two application examples are discussed: the Cramer steroid set and a benzene, toluene and xylene derivatives set.

Benzene↗
PubMed1998

QSAR modeling with the electrotopological state indices: corticosteroids.

A structure-activity analysis of a series of steroids binding to corticosteroid-binding globulin was made using the electrotopological state index for each atom in the molecule. Two indices were found to correlate well with the binding affinity. The indices encode structural characteristics in the A and the D rings of the steroids in the study. One of the indices was formulated as the difference between two indices in the A ring. The two were not intercorrelated, suggesting that the composite index signals the influence of structure changes in or near the A ring that can be monitored by the composite index. This is a new observation using this structure-activity method. It is suggested that this model makes some contributions towards detection of the pharmacophore.

Adrenal Cortex Hormones↗
PubMed1997

MS-WHIM, new 3D theoretical descriptors derived from molecular surface properties: a comparative 3D QSAR study in a series of steroids.

The recently proposed WHIM (Weighted Holistic Invariant Molecular) approach [Todeschini, R., Lasagni, M. and Marengo, E., J. Chemometrics, 8 (1994) 263] has been applied to molecular surfaces to derive new 3D theoretical descriptors, called MS-WHIM. To test their reliability, a 3D QSAR study has been performed on a series of steroids, comparing the MS-WHIM description to both the original WHIM indices and CoMFA fields. The analysis of the statistical models obtained shows that MS-WHIM descriptors provide meaningful quantitative structure-activity correlations. Thus, the results obtained agree well with those achieved using CoMFA fields. The concise number of indices, the ease of their calculation and their invariance to the coordinate system make MS-WHIM an attractive tool for 3D QSAR studies.

Computer Simulation↗
PubMed2000

Evaluation of the EVA descriptor for QSAR studies: 3. The use of a genetic algorithm to search for models with enhanced predictive properties (EVA_GA).

The EVA structural descriptor, based upon calculated fundamental molecular vibrational frequencies, has proved to be an effective descriptor for both QSAR and database similarity calculations. The descriptor is sensitive to 3D structure but has an advantage over field-based 3D-QSAR methods inasmuch as structural superposition is not required. The original technique involves a standardisation method wherein uniform Gaussians of fixed standard deviation (sigma) are used to smear out frequencies projected onto a linear scale. The smearing function permits the overlap of proximal frequencies and thence the extraction of a fixed dimensional descriptor regardless of the number and precise values of the frequencies. It is proposed here that there exist optimal localised values of sigma in different spectral regions; that is, the overlap of frequencies using uniform Gaussians may, at certain points in the spectrum, either be insufficient to pick up relationships where they exist or mix up information to such an extent that significant correlations are obscured by noise. A genetic algorithm is used to search for optimal localised sigma values using crossvalidated PLS regression scores as the fitness score to be optimised. The resultant models were then validated against a previously unseen test set of compounds and through data scrambling. The performance of EVA_GA is compared to that of EVA and analogous CoMFA studies; in the latter case a brief evaluation is made of the effect of grid resolution upon the stability of CoMFA PLS scores particularly in relation to test set predictions.

Algorithms↗
PubMed2001

Developing 13C NMR quantitative spectrometric data-activity relationship (QSDAR) models of steroid binding to the corticosteroid binding globulin.

We have developed four quantitative spectrometric data-activity relationship (QSDAR) models for 30 steroids binding to corticosteroid binding globulin, based on comparative spectral analysis (CoSA) of simulated 13C nuclear magnetic resonance (NMR) data. A QSDAR model based on 3 spectral bins had an explained variance (r2) of 0.80 and a cross-validated variance (q2) of 0.78. Another QSDAR model using the 3 atoms from the comparative structurally assigned spectral analysis (CoSASA) of simulated 13C NMR on a steroid backbone template gave an explained variance (r2) of 0.80 and a cross-validated variance (q2) of 0.73. Positions 3 and 14 from the steroid backbone template have correlations with the relative binding activity to corticosteroid binding globulin that are greater than 0.52. The explained correlation and cross-validated correlation of these QSDAR models are as good as previously published quantitative structure-activity relationship (QSAR), self-organizing map (SOM) and electrotopological state (E-state) models. One reason that the cross-validated variance of QSDAR models were as good as the other models is that simulated 13C NMR spectral data are more accurate than the errors introduced by the assumptions and approximations used in calculated electrostatic potentials, E-states, HE-states, and the molecular alignment process of QSAR modeling. The QSDAR models developed provide a rapid, simple way to predict the binding activity of a steroid to corticosteroid binding globulin.

Computer Simulation↗
PubMed2001

Corticosteroid-binding globulin synthesis and distribution in rat white adipose tissue.

Corticosterone binding (CB) capacity was determined in visceral and subcutaneous white adipose tissue (WAT), as well as in plasma of lean Zucker rats. Perfusion of rats with saline eliminated most liver and kidney corticosterone binding but did not affect CB in WAT. The cytosol extracts of isolated cells, however, did not bind corticosterone in detectable amounts. By means of a RT-PCR procedure it was found that corticosterone-binding globulin (CBG) was expressed in WAT. By immunohistochemical detection in WAT sections, CBG was seen in a thin layer surrounding the cells near the plasma membrane. These data suggest that the CBG layer surrounding the cells may act as a protective barrier limiting the access of glucocorticoids to adipocytes.

Adipose Tissue↗
PubMed2002

Expression and function of 3beta hydroxisteroid dehydrogenase (3beta HSD) type II and corticosteroid binding globulin (CBG) in granulosa cells from ovaries of women with and without endometriosis.

PURPOSE: To investigate the secretion of progesterone (P4) and corticosteroid binding globulin (CBG) by granulosa luteal cells (GC) as well as the mRNA levels of CBG and 3beta hydroxisteroid dehydrogenase (3beta HSD), in women with and without endometriosis in vivo and in vitro. METHODS: Prospective study in a private, university-affiliated assisted reproduction unit, including women with severe endometriosis (n = 14) or without the disease (n = 20) undergoing in vitro fertilization/intracytoplasmic sperm injection and embryo transfer. GC were obtained from each follicle aspirated, pooled for each patient, and follicular and blood contaminating leukocytes depleted through immunomagnetic purification. Secreted P4 and CBG, and mRNA for both 3beta HSD and CBG were determined in vivo and in vitro using RIA and reverse transcription followed by competitive polymerase chain reaction (cRT-PCR). RESULTS: The pattern of expression of 3beta HSD and CBG mRNAs in vivo and in vitro was similar in both groups. Also, GC from patients with endometriosis produced equal amounts of P4 and CBG than controls without the disease, either in freshly isolated cells or in 24-h cultures. CONCLUSIONS: The GC function in terms of 3beta HSD and CBG mRNA expression and P4/CBG secretion does not seem to be altered in patients with endometriosis in comparison with those without this condition.

3-Hydroxysteroid Dehydrogenases↗
PubMed1995

Effect of corticosteroid binding globulin on the pharmacokinetics of prednisolone in rats.

PURPOSE: The effect of exogenous corticosteroid binding globulin (CBG) on the pharmacokinetics of intravenous prednisolone was determined in rats to test the "free hormone hypothesis." METHODS: A dose of CBG to yield 95% binding with 1000 ng/ml of prednisolone in vitro in rat plasma or saline was administered before dosing 2 mg/kg of prednisolone hemisuccinate or methylprednisolone intravenously. Drug concentrations in plasma samples were assayed by HPLC. RESULTS: Single administration of CBG decreased apparent prednisolone clearance by 56% (155 to 66 ml/min/kg) and reduced apparent VSS by 35% (4.1 to 2.7 L/kg) (p < 0.001). Methylprednisolone pharmacokinetics, studied as a negative control because the drug does not bind to CBG, did not change. CONCLUSIONS: The corticosteroid bound to CBG does not appear to be available for removal by clearance organs.

Animals↗
PubMed2004

Quantitative structure-activity relationships from optimised ab initio bond lengths: steroid binding affinity and antibacterial activity of nitrofuran derivatives.

The present day abundance of cheap computing power enables the use of quantum chemical ab initio data in Quantitative Structure-Activity Relationships (QSARs). Optimised bond lengths are a new such class of descriptors, which we have successfully used previously in representing electronic effects in medicinal and ecological QSARs (enzyme inhibitory activity, hydrolysis rate constants and pKas). Here we use AM1 and HF/3-21G* bond lengths in conjunction with Partial Least Squares (PLS) and a Genetic Algorithm (GA) to predict the Corticosteroid-Binding Globulin (CBG) binding activity of the classic steroid data set, and the antibacterial activity of nitrofuran derivatives. The current procedure, which does not require molecular alignment, produces good r2 and q2 values. Moreover, it highlights regions in the common steroid skeleton deemed relevant to the active regions of the steroids and nitrofuran derivatives.

Adrenal Cortex Hormones↗