For the purpose of optimizing the stereo-selective catalytic reduction of pro-chiral ketones we studied the flexibility of a biotinylated ligand in artificial metalloenzymes. J. Panek and A. Jezierska, both postdoctoral researchers in our laboratory, have contributed to this research during the course of the EU project IBAAC, T. Ward and M. Novič were the team leaders of two work-packages within this project.
COBISS.SI-ID: 4447002
In the field of traceability of food we published the results of research on the identification of geological origin of bottled mineral waters; the chemical analysis of (trace) elements were made available under the European project, TRACE. The model allows the classification samples of mineral water according to four different geological origins.
COBISS.SI-ID: 4072218
We have introduced novel distance matrix for graphs based on interpretation of columns of the adjacency matrix of a graph of n vertices as a set of points in n-dimensional space. Numerical values for the distances are based on the Euclidean distance between n points in n-dimensional space so we have combined the traditional representation of graphs (2D object of no fixed geometry) with their representation in n-dimensional space. The novel distance matrix, referred to as natural distance matrix, offers novel graph invariants as molecular descriptors for structure-property-activity studies.
COBISS.SI-ID: 516139289
Numerical characterization of proteome maps based on partial ordering of protein spots with respect to the mass and the charge is described. The partial ordering diagram is embedded directly over the 2-D map and the corresponding adjacency matrix is constructed. The adjacency matrix is augmented by the abundance of proteins in a gel as suitably scaled diagonal entries of the matrix. The approach is illustrated on proteome maps based on experimental results from liver cells of rats exposed to four peroxisome proliferators. The degree of similarity between proteome maps was determined.
COBISS.SI-ID: 4458010
In the review article, we summarized the results of our own research of in-silico models for evaluating compounds, endocrine disrupters, and the results were compared with other studies selected after a thorough literature overview. We also described an example of in-silico model (CAESAR) to predict reproductive toxicity, which is an important factor in the procedures of registration of chemicals.
COBISS.SI-ID: 4378138