Predicting Chemical Toxicity from Proteomics and Computational Chemistry
Final rept. Jul 2005-Jul 2008
MINNESOTA UNIV DULUTH NATURAL RESOURCES RESEARCH INST
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This project focuses on a two-pronged approach to modeling toxicity data. A standard structure-based QSAR approach using chemodescriptors descriptors based on the chemical structure of the toxicant has been coupled with the development of biodescriptors, a novel set of mathematical descriptors derived from 2-DE proteomic gel analyses. In the realm of chemodescriptors, the team has used them in a hierarchical manner, from topological and geometrical, to high-level quantum chemical indices such as vertical electron affinity which is believed to be mechanistically related to chemical toxicity. Biodescriptor development has focused on three main approaches global descriptors that characterize the entire proteomics map, local descriptors that characterize a subset of the proteins present in the gel, and spectrum-like descriptors. These efforts have been further enhanced through the use of robust statistical approaches and the development of new statistical techniques for analyzing the full set of proteins present in a proteomics map.
- Physical Chemistry