Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.     

Effects of Suanzaorentang on behavior changes and central monoamines

Previously, it was found that the ancient Chinese remedy of Suanzaorentang could be a promising anxiolytic drug (Chen and Hsieh, 1985a, Chen and Hsieh, 1985b). To understand the mechanism of the action of Suanzaorentang, the effects of Suanzaorentang on behavior changes and central monoamines and their metabolites were studied in rats. It was found that Suanzaorentang significantly (1) prolonged the period from the onset of clonic to tonic convulsions induced by pentylenetetrazol or picrotoxin, (2) prolonged the sleep duration induced by hexobarbital, (3) reduced locomotor activity, (4) enhanced the hypomotility induced by alpha-MT, (5) reduced the locomotor stimulation produced by levodopa plus benserazide, and (6) reduced central HVA, VMA, and 5-HIAA, but had no significant effects on central DA, NA, and 5-HT. These facts implied that Suanzaorentang decreased the turnover rate of central monoamines and central catecholaminergic activity.