Cathepsin K is a major drug target for osteoporosis and related-bone disorders. Using a combination of virtual combinatorial chemistry, QSAR modeling, and molecular docking studies, a series of cathepsin K inhibitors based on N-(functionalized benzoyl)-homocycloleucyl-glycinonitrile scaffold was developed. In order to avoid previous problems of cathepsin K inhibitors associated with lysosomotropism of compounds with basic character that resulted in off-target effects, a weakly- to non-basic moiety was incorporated into the P3 position. Compounds 5, 6 and 9 were highly selective for cathepsin K when compared with cathepsins L and S with the Ki values in the 1030 nM range. The kinetic studies revealed that the new compounds exhibited reversible tight binding to cathepsin K, while the X-ray structural studies showed covalent and noncovalent binding between the nitrile group and the catalytic cysteine (Cys25) site.
COBISS.SI-ID: 5755162
Despite the efficiency of 6-fluoroquinolone (6-FQ) antibacterials in fighting tuberculosis (TB), the daily reports related to different forms of quinolonecaused “acquired resistance” in Mycobacterium tuberculosis are becoming rather frequent. Alongside the extensively reported mutations targeting predominantly the quinolone resistance-determining region (QRDR) of the gyrA subunit, some recent studies are pointing out the emergence of gyrB point mutations contributing to the M. tuberculosis resistance as well. To clarify the impact of gyrB alterations on 6-FQs resistance, in silico mutagenesis and structure-based methodology were proficiently employed. Three M. tuberculosis single point gyrB mutants (N473Tmod, T474Pmod, and E475Vmod) based on the recently available structural information were developed. The constructed mutant models were utilized as a starting point for performing molecular docking calculations on a set of 145 6-FQs with determined biological activity values, while their resistance profiles (identification of active/inactive 6-FQs) were evaluated relative to that of the wild-type model. This profiling methodology suggested the following order of resistance degree for our models (N473Tmod ) T474Pmod ) E475Vmod ) 3K9Fmod), which was additionally confirmed by molecular docking of a set of pre-selected 48 combinatoriallygenerated 6-FQ hits. Furthermore, we identified several attractive substructural fragments that could aid the development of novel 6-FQ antibacterials with possible enhanced anti-mycobacterial activity against diverse M. tuberculosis gyrB mutant strains.
COBISS.SI-ID: 5634074
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
The 2D mapping method based on Auto associative Neural Networks (ANN) (particularly, the feed-forward bottleneck neural network (FFBN NN)) was proposed to find the optimal setting of non linear processes. Application of 2D-neural network mapping technique (so-called the feed forward bottle neck neural network (FFBN NN)) together with traditional statistical method enable to establish certificate limits more affectively reaching to best quality and selecting the less cost processes. The represented FFBN NN mapping technique is simple in use, not time consuming and gives 2D visualization of multiple optima in studied technological processes.
COBISS.SI-ID: 5795610
Predicting the transmembrane regions is an important aspect of understanding the structures and architecture of different β-barrel membrane proteins. Despite significant efforts, currently available β- transmembrane region predictors are still limited in terms of prediction accuracy, especially in precision. Here, we describe PredβTM, a transmembrane region prediction algorithm for β-barrel proteins. Using amino acid pair frequency information in known β-transmembrane protein sequences, we have trained a support vector machine classifier to predict β-transmembrane segments. Position-specific amino acid preference data is incorporated in the final prediction. The predictor does not incorporate evolutionary profile information explicitly, but is based on sequence patterns generated implicitly by encoding the protein segments using amino acid adjacency matrix. With a benchmark set of 35 β- transmembrane proteins, PredβTM shows a sensitivity and precision of 83.71% and 72.98%, respectively. The segment overlap score is 82.19%. In comparison with other state-of-art methods, PredβTM provides a higher precision and segment overlap without compromising with sensitivity. Further, we applied PredβTM to analyze the β-barrel membrane proteins without defined transmembrane regions and the uncharacterized protein sequences in eight bacterial genomes and predict possible β- transmembrane proteins. PredβTM can be freely accessed on the web at http://transpred.ki.si/.
COBISS.SI-ID: 5840154