Affinity Capillary Electrophoresis
We continue to develop affinity CE (ACE). ACE is a widely used technique to examine receptor-ligand interactions to the extent that it is now a complementary technique to standard assays. Much of our work in ACE has been in examining glycopeptide antibiotics as the model system. Vancomycin, for example, from Streptomyces orientalis, is a parenteral glycopeptide antibiotic that has historically killed bacterial cells by inhibiting peptidoglycan biosynthesis. It functions by binding to the terminal D-Ala-D-Ala dipeptide of bacterial cell wall precursors, thereby, impeding further processing of these intermediates into peptidoglycan. Recent bacterial mutations have posed the need for increasing study in glycopeptide research. Hence, there is a great need for techniques that can easily screen large numbers of potential drug targets. We have pioneered the development of several variations in ACE including multiple injection ACE (MIACE), partial-filling ACE (PFACE) and voltage gradient PFACE techniques to determine binding constants between receptors and ligands. These techniques utilize less material than in standard ACE techniques.
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CE Microreactors
We are developing on-column methodologies in the derivatization of molecules either via enzymatic or non-enzymatic means. In these techniques, samples are injected separately onto the capillary column and electrophoresed. Differential transport velocities permit the separate zones of sample to penetrate each other under an applied field, thereby facilitating reaction. These techniques provide for both simultaneous reaction and analysis of the reaction.
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Chemometrics
We are using chemometrics in CE to optimize experimental conditions in a collaboration with Dr. Grady Hanrahan (California Lutheran University). We have used chemometric response surface methodology (RSM) in optimizing conditions for ACE and electrophoretically mediated microanalyisis (EMMA) and are also exploring artificial neural networks (ANN). Specifically, a Box-Behnken design was implemented. Unlike univariate approaches to optimization, chemometric experimental design and optimization tools can be used to systematically evaluate and better understand the factors that influence a particular system (in terms of a response) by means of statistical approaches. RSMs are multivariate techniques that mathematically fit the experimental domain studied in the theoretical design through a response function.
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