Abstract
In line with our studies on propafenone-type inhibitors of P-glycoprotein (P-gp), we applied several methods to approach virtual screening tools for identification of new P-gp inhibitors on one hand and the molecular basis of ligand-protein interaction on the other hand. For virtual screening, a combination of autocorrelation vectors and selforganising artificial neural networks proved extremely valuable in identifying P-gp inhibitors with structurally new scaffolds. For a closer view on the binding region for propafenone-type ligands we applied a combination of pharmacophore-driven photoaffinity labeling and protein homology modeling. On LmrA, a bacterial homologue of P-gp, we were able to identify distinct regions on transmembrane helices 3, 5 and 6 which show significant changes in the labeling pattern during different steps of the catalytic cycle.
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