Biomedical subjects

Marjana Novic

Publications and source records attributed to Marjana Novic.

8 recordsLinked to original sources

PubMed2006

Properties of flavonoids influencing the binding to bilitranslocase investigated by neural network modelling.

Bilitranslocase is a plasma membrane carrier firstly identified on the sinusoidal (vascular) domain of liver cells and later on also in the gastric epithelium. It transports diverse organic anions, such as bilirubin, some phthaleins and many dietary anthocyanins, suggesting that it could play a role both in the absorption of flavonoids from dietary sources and in their hepatic metabolism. This work was aimed at characterising the interaction of bilitranslocase with flavonols, a flavonoid sub-class. The results obtained show that, contrary to anthocyanins, flavonol glycosides do not interact with the carrier, whereas just some of the corresponding aglycones act as relatively poor ligands to bilitranslocase. These data point to a clear-cut discrimination between anthocyanins and flavonols occurring at the level of the bilitranslocase transport site. A quantitative structure-activity relationship based on counter propagation artificial neural network modelling was undertaken in order to shed light on the nature of flavonoid interaction with bilitranslocase. It was found that binding relies on the ability to establish hydrogen bonds, ruling out the involvement of charge interactions. This requisite might be at the basis of the discrimination between anthocyanins and flavonols by bilitranslocase and could lie behind some aspects of the distinct pharmacokinetic properties of anthocyanins and flavonols in mammals.

Animals↗

PubMed2006

Artificial transfer hydrogenases based on the biotin-(strept)avidin technology: fine tuning the selectivity by saturation mutagenesis of the host protein.

Incorporation of biotinylated racemic three-legged d6-piano stool complexes in streptavidin yields enantioselective transfer hydrogenation artificial metalloenzymes for the reduction of ketones. Having identified the most promising organometallic catalyst precursors in the presence of wild-type streptavidin, fine-tuning of the selectivity is achieved by saturation mutagenesis at position S112. This choice for the genetic optimization site is suggested by docking studies which reveal that this position lies closest to the biotinylated metal upon incorporation into streptavidin. For aromatic ketones, the reaction proceeds smoothly to afford the corresponding enantioenriched alcohols in up to 97% ee (R) or 70% (S). On the basis of these results, we suggest that the enantioselection is mostly dictated by CH/pi interactions between the substrate and the eta6-bound arene. However, these enantiodiscriminating interactions can be outweighed in the presence of cationic residues at position S112 to afford the opposite enantiomers of the product.

Biotin↗

PubMed2006

(Strept)avidin as host for biotinylated coordination complexes: stability, chiral discrimination, and cooperativity.

Incorporation of a biotinylated ruthenium tris(bipyridine) [Ru(bpy)(2)(Biot-bpy)](2+) (1) in either avidin or streptavidin-(strept)avidin-can be conveniently followed by circular dichroism spectroscopy. To determine the stepwise association constants, cooperativity, and chiral discrimination properties, diastereopure (Lambda and Delta)-1 species were synthesized and incorporated in tetrameric (strept)avidin to afford (Delta-[Ru(bpy)(2)(Biot-bpy)](2+))(x)() subsetavidin, (Lambda-[Ru(bpy)(2)(Biot-bpy)](2+))(x)() subsetavidin, (Delta-[Ru(bpy)(2)(Biot-bpy)](2+))(x)() subsetstreptavidin, and (Lambda-[Ru(bpy)(2)(Biot-bpy)](2+))(x)() subsetstreptavidin (x = 1-4) For these four systems, the overall stability constants are log beta(4) = 28.6, 30.3, 36.2, and 36.4, respectively. Critical analysis of the CD titrations data suggests a strong cooperativity between the first and the second binding event (x = 1, 2) and a pronounced difference in affinity between avidin and streptavidin for the dicationic guest 1 as well as modest enantiodiscrimination properties with avidin as host.

2,2'-Dipyridyl↗

PubMed2004

Quantitative structure-activity relationships of alpha1 adrenergic antagonists.

A quantitative structure-activity relationship study with respect to selectivity for alpha1 adrenoreceptor subtypes (alpha1a, alpha1b and alpha1d) of a wide series of structurally heterogeneous alpha1 adrenoreceptor antagonists has been performed. A large variety of molecular descriptors have been calculated and then analyzed by a heuristic method. The orthogonalization of the descriptors has been applied to build the QSAR equations. Ad hoc defined shape descriptors calculated by the Connolly algorithm with respect to reference supermolecules have also been considered in the rationalization of the mechanism of the activity of the ligands acting as antagonists on all three subtypes of alpha1 adrenoreceptors.

Adrenergic alpha-1 Receptor Antagonists↗

PubMedDate not supplied

Classification of potential endocrine disrupters on the basis of molecular structure using a nonlinear modeling method.

A methodology for the classification of endocrine disruption chemicals is proposed. It is based on a data set of 106 substances extracted from the list of 553 chemicals that were inspected by the European Union Commission for the scientific evidence of their endocrine disruption activity. The substances belong to different categories defined in the EU Commission report: (i) literature evidence for certainly active as endocrine disrupters, (ii) for potentially active, (iii) for less probable active--lacking evidence, and (iv) for certainty nonactive. 3D molecular coordinates were calculated using the AM1or the PM3 optimization method. From 3D coordinates an extensive set of molecular descriptors was calculated. The classification model based on the counterpropagation neural network was constructed and evaluated. This is the first time that the counterpropagation neural network is applied for the classification of compounds regarding their literature evidence for the endocrine disruption activity. The developed classification model is proposed as a tool for a preliminary assessment of potential endocrine disrupters, which would help the assessors to make the priority list for a large amount of chemicals that have to be tested with more expensive in vitro and in vivo methods.

Journal Article↗

PubMedDate not supplied

Novel characterization of proteomics maps by sequential neighborhoods of protein spots.

We consider a characterization of proteomics maps based on an alternative kind of neighborhood graphs for the protein spots on 2-D gel. The novel approach considers for every protein spot only the nearest neighborhood consisting of protein spots of higher abundance. The approach has the simplicity and advantages of the recently introduced characterization of proteome maps based on considering the nearest neighborhoods of protein spots, but it also has important additional desirable computational features. The characterization of the nearest neighborhood graphs of 2-D gel proteomics maps is sensitive to the number of spots considered and may lead to changes in the degree of similarity of different maps when the number of points has been changed, thus imposing restrictions on the protocol used for comparison of maps. The novel approach presented in this work is less sensitive to the number of points used in the analysis because graphs are constructed in a stepwise process in which the role of more distant neighbors has been diminished by linking a new spot to the nearest spot that has been already part of the neighborhood graph. In this way a graph with N + 1 spots is obtained from the graph on N spots by adding a single new link, while in the case of the nearest neighborhood graphs adding a new spot introduces novel neighborhoods and generally results in a graph that may differ significantly from the neighborhood graph on N points.

Animals↗

PubMedDate not supplied

Variable selection and interpretation in structure-affinity correlation modeling of estrogen receptor binders.

A computational approach for the identification and investigation of correlations between a chemical structure and a selected biological property is described. It is based on a set of 132 compounds of known chemical structures, which were tested for their binding affinities to the estrogen receptor. Different multivariate modeling methods, i.e., partial least-squares regression, counterpropagation neural network, and error-back-propagation neural network, were applied, and the prediction ability of each model was tested in order to compare the results of the obtained models. To reduce the extensive set of calculated structural descriptors, two types of variable selection methods were applied, depending on the modeling approach used. In particular, the final partial least-squares regression model was built using the "variable importance in projection" variable selection method, while genetic algorithms were applied in neural network modeling to select the optimal set of descriptors. A thorough statistical study of the variables selected by genetic algorithms is shown. The results were assessed with the aim to get insight to the mechanisms involved in the binding of estrogenic compounds to the receptor. The variable selection on the basis of genetic algorithm was controlled with the test set of compounds, extracted from the data set available. To compare the predictive ability of all the optimized models, a leave-one-out cross-validation procedure was applied, the best model being the nonlinear neural network model based on error back-propagation algorithm, which resulted in R2= 92.2% and Q2= 70.8%.

Algorithms↗

PubMedDate not supplied

On characterization of dose variations of 2-D proteomics maps by matrix invariants.

We explore the characterization of 2-D electrophoresis proteomics maps by certain structural invariants derived from matrixes constructed by considering for all pairs of spots in a proteomics maps the shortest (Euclidean) distances and distances measured along zigzag lines connecting protein spots of the neighboring abundance. This paper is a sequel to previous papers in which we outlined the idea of characterizing 2-D proteomics maps by graph-theoretical descriptors. To illustrate the approach, we selected data of Anderson et al. (Anderson, N. L.; Esquer-Blasco, R.; Richardson, F.; Foxworthy, P.; Eacho, P. The effects of peroxisome proliferators on protein abundances in mouse liver. Toxicol. Appl. Pharmacol. 1996, 137, 75-89) on protein abundance in mouse liver under a series of dose of peroxisome proliferator LY1711883. We found strong linear correlation between the experimentally applied doses and the leading eigenvalue of a D/D-type matrix (Randić, M.; Kleiner, A. F.; DeAlba, L. M. Distance/ distance matrices. J. Chem. lnf. Comput. Sci. 1994, 34, 277-286) constructed for the experimental proteomics maps.

Acetophenones↗