Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:1.5.1.3 (dihydrofolate reductase)
 5,819 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

p27Kip1 is an inhibitor of the cyclin-dependent kinases and it plays an inhibitory role in the progression of cell cycle through G1 phase. To investigate the mechanism of cell cycle inhibition by p27Kip1, we constructed a cell line that inducibly expresses p27Kip1 upon addition of isopropyl-1-thio-beta-D-galactopyranoside in the culture medium. Isopropyl-1-thio-beta-D-galactopyranoside-induced expression of p27Kip1 in these cells causes a specific reduction in the expression of the E2F-regulated genes such as cyclin E, cyclin A, and dihydrofolate reductase. The reduction in the expression of these genes correlates with the p27Kip1-induced accumulation of the repressor complexes of the E2F family of factors (E2Fs). Our previous studies indicated that p21WAF1 could disrupt the interaction between cyclin/cyclin-dependent kinase 2 (cdk2) and the E2F repressor complexes E2F-p130 and E2F-p107. We show that p27Kip1, like p21WAF1, disrupts cyclin/cdk2-containing complexes of E2F-p130 leading to the accumulation of the E2F-p130 complexes, which is found in growth-arrested cells. In transient transfection assays, expression of p27Kip1 specifically inhibits transcription of a promoter containing E2F-binding sites. Mutants of p27Kip1 harboring changes in the cyclin- and cdk2-binding motifs are deficient in inhibiting transcription from the E2F sites containing reporter gene. Moreover, these mutants of p27Kip1 are also impaired in disrupting the interaction between cyclin/cdk2 and the repressor complexes of E2Fs. Taken together, these observations suggest that p27Kip1 reduces expression of the E2F-regulated genes by generating repressor complexes of E2Fs. Furthermore, the results also demonstrate that p27Kip1 inhibits expression of cyclin A and cyclin E, which are critical for progression through the G1-S phases.
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PMID:p27Kip1 induces an accumulation of the repressor complexes of E2F and inhibits expression of the E2F-regulated genes. 930 76

The ability to predict ligand binding modes without the aid of wet-lab experiments may accelerate and reduce the cost of drug discovery research. Despite significant recent progress, virtual screening has not yet eliminated the need for wet-lab experiments. For example, after a lead compound has been identified, the precise binding mode is still typically determined by experimental structural biology. This structural knowledge is then employed to guide lead optimization. We present a step toward improving protein-ligand binding mode prediction for a set of ligands known to interact with a common protein. There is thus an important distinction between this work and traditional virtual screening algorithms. Whereas traditional approaches attempt to identify binding ligands from a large database of available compounds, our approach aims to more accurately predict the binding mode for a set of ligands which are already known to bind the target protein. The approach is based on the hypothesis that each active site contains a set of interaction points which binding ligands tend to exploit. In a more traditional context, these interaction points make up a pharmacophoric map. Our algorithm first performs traditional protein-ligand docking for each known binder. The ranked lists of candidate binding modes are then evaluated to identify a set of poses maximally self-consistent with respect to a pharmacophoric map generated from the same poses. We have extensively demonstrated the application of the algorithm to four protein systems (thrombin, cyclin-dependent kinase 2, dihydrofolate reductase, and HIV-1 protease) and attained predictions with an average RMSD < 2.5 A for all tested systems. This represents a typical improvement of 0.5-1.0 A (up to 25%) RMSD over the naive virtual docking predictions. Our algorithm is independent of the docking method and may significantly improve binding mode prediction of virtual docking experiments.
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PMID:Predicting multiple ligand binding modes using self-consistent pharmacophore hypotheses. 1971 52