Antioxidants are important for maintaining the appropriate balance between oxidizing and reducing species in the body and thus preventing oxidative stress. Many natural compounds are being screened for their possible antioxidant activity. It was found that a mushroom pigment Norbadione A, which is a pulvinic acid derivative, shows an antioxidant activity; the same was found for other pulvinic acid derivatives and structurally related coumarines. Based on the results of in vitro studies performed on these compounds as a part of this study quantitative structure-activity relationship (QSAR) predictive models were constructed using multiple linear regression, counter-propagation artificial neural networks and support vector regression (SVR). The models have been developed in accordance with current QSAR guidelines, including the assessment of the models applicability domains. A new approach for the graphical evaluation of the applicability domain for SVR models is suggested. The developed models show sufficient predictive abilities for the screening of virtual libraries for new potential antioxidants.
COBISS.SI-ID: 5662490
This review article is, in a way, different from traditional review articles in that, besides being informative and reporting on the past published work in the area of graphical representation of complex biological data, it has an additional purpose in being provocative with respect to potentials of novelty in the future, rather than dwelling excessively on the accomplishments of the past. The review is factual; it is introductory and trying to be comprehensive with respect to its central topic—the graphical representation of proteins. Because it covers a topic, which may be viewed to be out of the mainstream avenue of comparative protein studies and an outline of events along a path not frequently trodden, we included sufficient introductory material on closely related topics of graphical representations of DNA and the secondary structure of RNA. It is not possible to study and understand proteins without studying and understanding DNA and RNA, so inclusion of material on DNA and RNA has been almost mandatory.
COBISS.SI-ID: 4616218
The transport activity of a membrane protein, bilitranslocase (T.C. # 2.A.65.1.1), which acts as a transporter of bilirubin from blood to liver cells, was experimentally determined for a large set of various endogenous compounds, drugs, purine and pyrimidine derivatives. On these grounds, the structure-activity models were developed following the OECD principles of QSAR models and their predictive ability for new chemicals was evaluated. The applicability domain of the models was estimated by Euclidean distances criteria according to the applied modeling method. The selection of the most influential structural variables was an important stage in the adopted modeling methodology. The interpretation of selected variables was performed in order to get an insight into the mechanism of transport through the cell membrane via bilitranslocase. Validation of the optimized models was performed by a previously determined validation set. The classification model was build to separate active from inactive compounds. The resulting accuracy, sensitivity, and specificity were 0.73, 0.89, and 0.64, respectively. Only active compounds were used to develop a predictive model for bilitranslocase inhibition constants. The model showed good predictive ability; Root Mean Squared error of the validation set, RMSV = 0.29 log units.
COBISS.SI-ID: 4723994
Alongside the validation, the concept of applicability domain (AD) is probably one of the most important aspects which determine the quality as well as reliability of the established quantitative structure-activity relationship (QSAR) models. To date, a variety of approaches for AD estimation have been devised which can be applied to particular type of QSAR models and their practical utilization is extensively elaborated in the literature. The present study introduces a novel, simple, and effective distance-based method for estimation of the AD in case of developed and validated predictive counter-propagation artificial neural network (CP ANN) models through a proficient exploitation of the Euclidean distance (ED) metric in the structure-representation vector space. The performance of the method was evaluated and explained in a case study by using a pre-built and validated CP ANN model for prediction of the transport activity of the transmembrane protein bilitranslocase for a diverse set of compounds. The method was tested on two more datasets in order to confirm its performance for evaluation of the applicability domain in CP ANN models. The chemical compounds determined aspotential outliers, i.e., outside of the CP ANN model AD, were confirmed in a comparative AD assessment by using the leverage approach. Moreover, the method offers a graphical depiction of the AD for fast and simple determination of the extreme points.
COBISS.SI-ID: 5132058
BPA was detected in 98% (n=146) of the samples with 0.1 ng/mL limit of detection. The geometric mean BPA concentration was 1.55 ng/mL. After the adjustment for potential confounders using linear regression models, an increase of natural logarithm (ln) transformed urinary BPA concentration was associated with lower lntransformed sperm count (b=0.241 95% confidence interval (CI) of 0.470 to 0.012), lntransformed sperm concentration (b=0.219, 95% CI of 0.436 to 0.003) and sperm vitality (b=2.660, 95% CI of 4.991 to 0.329). The embryo development parameters from oocyte fertilization to blastocyst formation stage were not affected by BPA exposure. Urinary BPA concentrations in male partners of subfertile couples may influence semen quality parameters, but do not affect embryo development up to the blastocyst stage after medically assisted reproduction.
COBISS.SI-ID: 4809023