Toward the Virtual Screening of α-Glucosidase Inhibitors with the Homology-Modeled Protein Structure

Discovery of <TEX>α</TEX>-glucosidase inhibitors has been actively pursued with the aim to develop therapeutics for the treatment of diabetes and the other carbohydrate mediated diseases. As a method for the discovery of new novel inhibitors of <TEX>α</TEX>-glucosidase, we have addressed the performance of the computer-aided drug design protocol involving the homology modeling of <TEX>α</TEX>-glucosidase and the structure-based virtual screening with the two docking tools: FlexX and the automated and improved AutoDock implementing the effects of ligand solvation in the scoring function. The homology modeling of <TEX>α</TEX>-glucosidase from baker’s yeast provides a high-quality 3-D structure enabling the structure-based inhibitor design. Of the two docking programs under consideration, AutoDock is found to be more accurate than FlexX in terms of scoring putative ligands to the extent of 5-fold enhancement of hit rate in database screening when 1% of database coverage is used as a cutoff. A detailed binding mode analysis of the known inhibitors shows that they can be stabilized in the active site of <TEX>α</TEX>- glucosidase through the simultaneous establishment of the multiple hydrogen bond and hydrophobic interactions. The present study demonstrates the usefulness of the automated AutoDock program with the improved scoring function as a docking tool for virtual screening of new <TEX>α</TEX>-glucosidase inhibitors as well as for binding mode analysis to elucidate the activities of known inhibitors.