No abstract

Predictions of interactions between target proteins and potential leads are of great benefit in the drug discovery process. We present a comprehensively applicable statistical prediction method for interactions between any proteins and chemical compounds, which requires only protein sequence data and chemical structure data and utilizes the statistical learning method of support vector machines. In order to realize reasonable comprehensive predictions which can involve many false positives, we propose two approaches for reduction of false positives: (i) efficient use of multiple statistical prediction models in the framework of two-layer SVM and (ii) reasonable design of the negative data to construct statistical prediction models. In two-layer SVM, outputs produced by the first-layer SVM models, which are constructed with different negative samples and reflect different aspects of classifications, are utilized as inputs to the second-layer SVM. In order to design negative data which produce fewer false positive predictions, we iteratively construct SVM models or classification boundaries from positive and tentative negative samples and select additional negative sample candidates according to pre-determined rules. Moreover, in order to fully utilize the advantages of statistical learning methods, we propose a strategy to effectively feedback experimental results to computational predictions with consideration of biological effects of interest. We show the usefulness of our approach in predicting potential ligands binding to human androgen receptors from more than 19 million chemical compounds and verifying these predictions by in vitro binding. Moreover, we utilize this experimental validation as feedback to enhance subsequent computational predictions, and experimentally validate these predictions again. This efficient procedure of the iteration of the in silico prediction and in vitro or in vivo experimental verifications with the sufficient feedback enabled us to identify novel ligand candidates which were distant from known ligands in the chemical space.

Background:The spatiotemporal regulation of Rac1 controls cell migration. Results: EGF induced two waves of Rac1 activation in the process of cell migration. Conclusion: 14-3-3 proteins regulate the second EGF-induced wave of Rac1 activation by interacting with RacGEF. Significance: The second wave of Rac1 activation might be required for EGF-induced cell migration.

Cell migration is an essential step for tumor metastasis. The small GTPase Rac1 plays an important role in cell migration. Previously, we reported that epidermal growth factor (EGF) induced two waves of Rac1 activation; namely, at 5 min and 12 h after stimulation. A second wave of EGF-induced Rac1 activation was required for EGF-induced cell migration, however, the spatiotemporal regulation of the second wave of EGF-induced Rac1 activation remains largely unclear. In this study, we found that 5-lipoxygenase (5-LOX) is activated in the process of EGF-induced cell migration, and that leukotriene C4 (LTC4) produced by 5-LOX mediated the second wave of Rac1 activation, as well as cell migration. Furthermore, these effects caused by LTC4 were found to be blocked in the presence of the antagonist of cysteinyl leukotriene receptor 1 (CysLT1). This blockage indicates that LTC4-mediated CysLT1 signaling regulates the second EGF-induced wave of Rac1 activation. We also found that 5-LOX inhibitors, CysLT1 antagonists and the knockdown of CysLT1 inhibited EGF-induced T cell lymphoma invasion and metastasis-inducing protein 1 (Tiam1) expression. Tiam1 expression is required for the second wave of EGF-induced Rac1 activation in A431 cells. Therefore, our results indicate that the 5-LOX/LTC4/CysLT1 signaling pathway regulates EGF-induced cell migration by increasing Tiam1 expression, leading to a second wave of Rac1 activation. Thus, CysLT1 may serve as a new molecular target for antimetastatic therapy. In addition, the CysLT1 antagonist, montelukast, which is used clinically for allergy treatment, might have great potential as a novel type of antimetastatic agent.

In the course of screening for a new type of androgen receptor (AR) antagonist, we isolated a novel compound, arabilin, with two structural isomers, spectinabilin and SNF4435C, produced by Streptomyces sp. MK756-CF1. Structure elucidation on the basis of the spectroscopic properties showed that arabilin is a novel polypropionate-derived metabolite with a p-nitrophenyl group and a substituted c-pyrone ring. Arabilin competitively blocked the binding of androgen to the ligand-binding domain of AR in vitro. In addition, arabilin inhibited androgen-induced prostate-specific antigen mRNA expression in prostate cancer LNCaP cells.