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.

Fibroblast growth factors (FGF) and their receptors play an important role in cell proliferation, angiogenesis and embryonal development. In this study, we show that expression of the FGF receptor-2 (FGFR-2) protein is induced in the mid-to-late G1 phase of the cell cycle in serum-starved mouse NIH3T3 cells released from starvation. Transcription of mouse FGFR-2 was activated by E2F-1. Analysis of various mouse FGFR-2 promoter mutant constructs showed that a sequence located þ 57/ þ 64 downstream of the transcriptional initiation site, related to the consensus E2F-responsive sequence, is necessary for the activation. The promoter activity of the mouse FGFR-2 gene is also positively regulated by E2F-2 and E2F-3, but not by E2F-4 and E2F-5. Moreover, the E2F-1-induced activation of mouse FGFR-2 gene transcription is suppressed by pRB. Taken together, the results demonstrate that FGFR-2 is a new class of targets for E2F, and expression of mouse FGFR-2 in mid-to-late G1 phase would be mediated, at least in part, by the activation of a pRB/E2F pathway.

BackgroundT-helper cell type 1 (Th1) polarization in chronic immune thrombocytopenia (cITP) has been reported at the protein and mRNA levels. We evaluated the impact of Th1/Th2 cytokine and cytokine receptor functional polymorphisms on both susceptibility to, and severity of, cITP. We analysed IFN-γ + 874 T/A, IFN-γR -611G/A, IL-4 -590C/T, and IL-4Rα Q576R polymorphisms in 126 cITP patients (male/female: 34/92; median age: 47.7 years) and 202 healthy control donors. Genotyping was determined by PCR and direct sequencing. The Th1/Th2 ratio was detected in peripheral blood mononuclear cells via flow cytometry.ResultscITP patients had a higher frequency of the IL-4Rα 576 non-QQ genotype compared to healthy subjects (P = 0.04). cITP patients with the IFN-γ +874 non-AA genotype (high expression type) showed more severe thrombocytopenia than those with the AA genotype (P < 0.05). cITP patients had a significantly higher Th1/Th2 ratio than control patients (P < 0.01); this ratio was inversely correlated with platelet counts. Furthermore, patients with both IFN-γ +874 non-AA genotype (high expression type) and IFN-γR −611 non-AA genotype (high-function type) had a significantly higher Th1/Th2 ratio (P < 0.05).ConclusionsThe cytokine polymorphisms affecting Th1/Th2 increase the susceptibility to, and severity of, chronic ITP.

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