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 (r ²) of 0.80 and a cross-validated variance (q ²) 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 (r ²) of 0.80 and a cross-validated variance (q ²) 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.     

Models of steroid binding based on the minimum deviation of structurally assigned 13C NMR spectra analysis (MiDSASA)

This paper develops a quantitative k-nearest neighbors modeling technique. The technique is used to demonstrate that a compound's biological binding activity to a receptor can be calculated from the minimum of the square root of the sum of squared deviations (SSSD) of a structurally assigned chemical shift on a template between the unknown compound to be predicted and a set of known compounds with known activities. When building models of biological activity, nonlinear relationships are built into the input training data. If a model is developed by selecting only compounds with minimum structurally assigned chemical shift deviations from the unknown compound, some of the nonlinear relationships can be removed. The smaller the total chemical shift deviation between a compound with known activity and another compound with unknown activity, the more likely it will have similar biological, chemical, and physical properties. This means that a model can be produced without rigorous statistics or neural networks. This technique is similar to structure-activity relationship (SAR) modeling, but instead of relying on substructure fragments to produce a model, this new model is based on minimum chemical shift differences on those substructure fragments. We refer to this method as minimum deviation of structurally assigned spectra analysis (MiDSASA) modeling. Modeling by the minimum deviation concept can be applied to other chemoinformatic data analyses such as metabolite concentrations in metabolic pathways for metabolomics research. A MiDSASA template model for 30 steroids binding the corticosterone binding globulin based on the activity factors of the two nearest compounds had a correlation of 0.88. A MiDSASA template model for 50 steroids binding the aromatse enzyme based on the average activity of the four nearest compounds had a correlation of 0.71.     

Multidimensional statistical analysis applied to electron ionization mass spectra to determine steroid stereochemistry

The differentiation of stereoisomers on the basis of their mass spectra only is usually a difficult challenge even when an informative ionization technique such as electron ionization is used; this is particularly the case for steroids. In this study, multivariate statistical techniques have been applied to the mass spectra of derivatized 5xi-androstane-3xi,17xi-diols (xi = alpha,beta) in order to investigate the possibility of discrimination among the different isomers. After collection of the data from the mass spectra (20 replicates for each of the 8 isomers), each ion was considered as a statistical variable and each mass spectrum as an observation. The more discriminative variables (42 out of the 160 initial ones) were selected using the analysis of variance technique (ANOVA). Thereafter, a linear discriminant analysis (LDA) allowed us to set up a predictive model for stereochemistry determination. The two-dimensional graphical display of the 160 observations on the basis of the canonical variables derived from LDA made it possible to separate the eight isomers. The discrimination of 5alpha and 5beta isomers as well as 3alpha and 3beta was unambiguous, whereas, the discrimination of 17alpha and 17beta epimers was less obvious. The robustness of the model was checked with 40 mass spectra recorded over a 6-month period on different quadrupole mass spectrometers and under different signal acquisition conditions. The percentage of correct assignment of these 'unknown' stereoisomers was 92%; only three 17alpha and 17beta epimers were not correctly plotted in the expected zone. Nevertheless, the performance score was better than those observed with traditional mass spectral libraries. Furthermore, this statistical approach allowed us to identify the main fragment ions involved in the discrimination between isomers: m/z 256 and 421 for isomers 5a-5b; m/z 241 and 331 for isomers 5alpha3alpha-5alpha3beta; m/z 143 and 162 for isomers 5beta3alpha-5beta3beta; and m/z 255 for epimers 17alpha-17beta.     

Deconvolution of fluorescence spectra: contribution to the structural analysis of complex molecules

Fluorescence spectroscopy is a sensitive analytical tool in the studies of both simple and complex molecular structures. In complex molecules, however, determining the number and position of components may give a specific insight into the structure, complementary to the other analytical techniques. We applied log–normal model to analyze fluorescence of simple monofluorophore molecule. In order to analyze spectra where both fluorophores and Raman emission bands were present, we developed a method obtained by combination of the symmetric, Gaussian, for Raman and asymmetric, log–normal model, for fluorescence, applicable to the molecules of different complexity. Technically, for each sample we varied excitation wavelength with 5 nm step and recorded the corresponding emission spectra. They were subsequently used for component analysis. Position of each component was plotted against the excitation wavelength. Applying this approach we could identify minimal number of components having stable positions, while their approximate probability density (APD) in a spectral series was correlated with the probable number of fluorophores in the molecule. The method was tested on molecules containing different number of fluorophores: monomers involved in the synthesis of plant polymer lignin—coniferyl alcohol (one fluorophore), ferulic acid (two fluorophores) and on lignin model compound produced from these monomers (many fluorophores). All investigated species belong to benzene-substituted class of compounds, and it is reasonable to assume that they have similar fluorescence band contour. We also report the results of environmental scanning electron microscopy (ESEM) studies showing multilayered dehydrogenative polymer (DHP) structure, in order to show complexity of the polymer. Our results present complementarity of these two approaches in the structural studies of the lignin model compound.     

Comparative analysis of adaptive neuro-fuzzy inference system (ANFIS) and RSRM models to predict DBP (trihalomethanes) levels in the water treatment plant

Since disinfection by-products are a growing concern, it is important to know their quantities in water treatment plants before they are released to the public. As a result, there is a requirement for constant monitoring of disinfection by-products (DBPs), which can have major consequences for human health and productivity. Consequently, in previous studies, several models for predicting disinfection byproduct formation in drinking water have been developed which were either linear/log-linear, hybrid or neural network (radial basis function). In this study, an adaptive neuro-fuzzy inference system (ANFIS) is proposed for predicting trihalomethane levels in real distribution systems. To train and verify the proposed model, 24 sets of data were used, including THMs levels (TCM, BDCM, DBCM and T-THM levels) and five parameters (pH, temperature, UVA₂₅₄, residual chlorine, and dissolved organic carbon). As compared to response surface modeling (RSRM) coefficient of determination, R² is between 0.727 < R² < 0.886, average absolute deviation, AAD = 4.07–10.99 %), MAE = 0.01 – 0.978, and RMSE = 0.017 – 1.449. Further, ANFIS for THMs (T-THMs, TCM, BDCM, and DBCM) prediction consistently show higher regression coefficients between 0.956 < R² < 0.989, average absolute deviation, AAD = 0.350 – 1.977 %), MAE = 0.002 – 0.133, and RMSE = 0.007 – 0.401, Consequently, based on the statistical indices obtained, ANFIS, on the other hand, proved to be effective for predicting the formation of THMs, and thus allowed improved DBPs monitoring in water treatment systems.     

Cross-correlation analysis of Ge(Li) spectra

A sensitive technique is proposed for activation analysis using cross-correlation and improved spectral orthogonality achieved through use of a rectangular zero area digital filter.     

Human sex hormone binding globulin (SHBG): Isolation, characterization and determination

Conclusions SHBG was highly purified by a 4-step method. Molecular weight, isoelectric point and DHT-binding were determined. Antibodies were generated and the well known transferrin crossreactivity was eliminated by affinity chromatography. SHBG as standard was not suitable for the RIA because of insufficient stability and was replaced by pregnancy serum. The tracer had to be chromatographed before each use. SHBG levels measured with this RIA are in the same range as has been reported in literature.

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Isolierung, Charakterisierung und Bestimmung von menschlichem sexualhormonbindendem Globulin

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Supported by the Deutsche Forschungsgemeinschaft (Mu 585/1-1)     

Modeling zinc in biomolecules with the self consistent charge-density functional tight binding (SCC-DFTB) method: applications to structural and energetic analysis

Parameters for the zinc ion have been developed in the self-consistent charge density functional tight-binding (SCC-DFTB) framework. The approach was tested against B3LYP calculations for a range of systems, including small molecules that contain the typical coordination environment of zinc in biological systems (cysteine, histidine, glutamic/aspartic acids, and water) and active site models for a number of enzymes such as alcohol dehydrogenase, carbonic anhydrase, and aminopeptidase. The SCC-DFTB approach reproduces structural and energetic properties rather reliably (e.g., total and relative ligand binding energies and deprotonation energies of ligands and barriers for zinc-assisted proton transfers), as compared with B3LYP/6-311+G** or MP2/6-311+G** calculations.     

Combining NMR spectral and structural data to form models of polychlorinated dibenzodioxins,dibenzofurans, and biphenyls binding to the AhR

A three-dimensional quantitative spectrometric data-activity relationship (3D-QSDAR) modeling technique which uses NMR spectral and structural information that is combined in a 3D-connectivity matrix has been developed. A 3D-connectivity matrix was built by displaying all possible assigned carbon NMR chemical shifts, carbon-to-carbon connections, and distances between the carbons. Two-dimensional ¹³C-¹³C COSY and 2D slices from the distance dimension of the 3D-connectivity matrix were used to produce a relationship among the 2D spectral patterns for polychlorinated dibenzofurans, dibenzodioxins, and biphenyls (PCDFs, PCDDs, and PCBs respectively) binding to the aryl hydrocarbon receptor (AhR). We refer to this technique as comparative structural connectivity spectral analysis (CoSCoSA) modeling. All CoSCoSA models were developed using forward multiple linear regression analysis of the predicted ¹³C NMR structure-connectivity spectral bins. A CoSCoSA model for 26 PCDFs had an explained variance (r²) of 0.93 and an average leave-four-out cross-validated variance (q₄ ²) of 0.89. A CoSCoSA model for 14 PCDDs produced an r² of 0.90 and an average leave-two-out cross-validated variance (q₂ ²) of 0.79. One CoSCoSA model for 12 PCBs gave an r² of 0.91 and an average q₂ ² of 0.80. Another CoSCoSA model for all 52 compounds had an r² of 0.85 and an average q₄ ² of 0.52. Major benefits of CoSCoSA modeling include ease of development since the technique does not use molecular docking routines.     

Comparative studies of electron-impact (EI) and field ionization (FI) mass spectra of pentaerythritol derivatives

The EI Mass spectra of the polyfunctional pentaerythritol derivatives show that the molecular ions [M]⁺· exhibit extensive ion fragmentation. No Information about [M]⁺·. can be obtained. In Contrast to this, the FI Mass spectra of these compounds show intense [M]⁺· and/or [M + 1]⁺, and a characteristic ion at m/e 31, which is assumed to be the oxonium ion \documentclass{article}\pagestyle{empty}\begin{document}$ {\rm CH}_2 = \mathop {\rm O}\limits^ + {\rm H} $\end{document}. Because of surface adsorption and field attraction, FI mass spectrometry presents a serious problem in quantitative analysis of a mixture containing compounds with quite different degrees of polarization.     

Application of simulated standard spectra for the analysis of complex sample spectra from NaI(Tl) detectors

The most accurate method for the analysis of complex gamma ray spectra from scintillation detectors is least squares method. The major requirement of this method is individual standard spectra of all nuclides expected in the complex spectrum which is not possible and feasible for some nuclides. In the present work, an approach of using simulated standard spectrum of the radionuclides for the least squares analysis is studied. The paper describes the methodology used for the generation of simulated spectrum which is the main objective, and validation of results using standard sources in the Sodium Iodide (NaI(Tl)) based gamma ray spectrometer.     

(13)C NMR quantitative spectrometric data-activity relationship (QSDAR) models of steroids binding the aromatase enzyme

Five quantitative spectroscopic data-activity relationships (QSDAR) models for 50 steroidal inhibitors binding to aromatase enzyme have been developed based on simulated (13)C nuclear magnetic resonance (NMR) data. Three of the models were based on comparative spectral analysis (CoSA), and the two other models were based on comparative structurally assigned spectral analysis (CoSASA). A CoSA QSDAR model based on five principal components had an explained variance (r(2)) of 0.78 and a leave-one-out (LOO) cross-validated variance (q(2)) of 0.71. A CoSASA model that used the assigned (13)C NMR chemical shifts from a steroidal backbone at five selected positions gave an r(2) of 0.75 and a q(2) of 0.66. The (13)C NMR chemical shifts from atoms in the steroid template position 9, 6, 3, and 7 each had correlations greater than 0.6 with the relative binding activity to the aromatase enzyme. All five QSDAR models had explained and cross-validated variances that were better than the explained and cross-validated variances from a five structural parameter quantitative structure-activity relationship (QSAR) model of the same compounds. QSAR modeling suffers from errors introduced by the assumptions and approximations used in partial charges, dielectric constants, and the molecular alignment process of one structural conformation. One postulated reason that the variances of QSDAR models are better than the QSAR models is that (13)C NMR spectral data, based on quantum mechanical principles, are more reflective of binding than the QSAR model's calculated electrostatic potentials and molecular alignment process. The QSDAR models provide a rapid, simple way to model the steroid inhibitor activity in relation to the aromatase enzyme.