Structure-based discovery of human L-xylulose reductase inhibitors from database screening and molecular docking

Human L-xylulose reductase (XR) is an enzyme of the glucuronic acid/uronate cycle of glucose metabolism and is a possible target for treatment of the long-term complications of diabetes. In this study we utilised the molecular modelling program DOCK to analyse the 249,071 compounds of the National Cancer Institute Database and retrieved those compounds with high predicted affinity for XR. Several carboxylic acid-based compounds were tested and shown to inhibit XR. These included nicotinic acid (IC50=100 microM), benzoic acid (IC50=29 microM) and their derivatives. These results extend and improve upon the activities of known, commercially available inhibitors of XR such as the aliphatic fatty acid n-butyric acid (IC50=64 microM). To optimise the interaction between the inhibitor and the holoenzyme, the program GRID was used to design de novo compounds based on the inhibitor benzoic acid. The inclusion of a hydroxy-phenyl group and a phosphate to the benzoic acid molecule increased the net binding energy by 1.3- and 2.4-fold, respectively. The resultant compounds may produce inhibitors with improved specificity for XR.