Importance of the substrate-binding loop region of human monomeric carbonyl reductases in catalysis and coenzyme binding

Identification of a functional antioxidant responsive element in the promoter of the Chinese hamster carbonyl reductase 3 (Chcr3) gene

CHCR3, a member of the short-chain dehydrogenase/reductase superfamily, is a carbonyl reductase 3 enzyme in Chinese hamsters. Carbonyl reductase 3 in humans has been believed to involve the metabolism and/or pharmacokinetics of anthracycline drugs, and the mechanism underlying the gene regulation has been investigated. In this study, the nucleotide sequence of the Chcr3 promoter was originally determined, and its promoter activity was characterised. The proximal promoter region is TATA-less and GC-rich, similar to the promoter region of human carbonyl reductase 3. Cobalt stimulated the transcriptional activity of the Chcr3 gene. The results of a luciferase gene reporter assay demonstrated that cobalt-induced stimulation required an antioxidant responsive element. Forced expression of Nrf2, the transcription factor that binds to antioxidant responsive elements, enhanced the transcriptional activity of the Chcr3 gene. These results suggest that cobalt induces the expression of the Chcr3 gene via the Nrf2-antioxidant responsive element pathway.

Regulation of human carbonyl reductase 1 (CBR1, SDR21C1) gene by transcription factor Nrf2

Monomeric carbonyl reductase 1 (CBR1, SDR21C1) is a member of the short-chain dehydrogenase/reductase superfamily and is involved in the metabolism of anthracycline anti-cancer drugs, prostaglandins, and isatin, which is an endogenous inhibitor of monoamine oxidases. Additionally, cancer progression may be partly regulated by CBR1. In the present study, we screened more than 10 drugs for the induction of the human CBR1 gene to investigate its regulation. Of the drugs, butylated hydroxyanisole (BHA) was found to be an inducer. BHA induced the mRNA and protein expression of CBR1 in hepatoma HepG2 cells. In a luciferase reporter gene assay, the promoter region between -2062 bp and the transcription start site of CBR1 was also activated by BHA. The transcription factor Nrf2 is known to be activated by BHA. There are 2 anti-oxidant responsive elements (ARE) that are bound by Nrf2 in this region. Mutation analyses revealed that one of the AREs participates in the gene regulation of CBR1 by Nrf2. Electrophoretic mobility shift assay revealed that Nrf2 binds the site. Moreover, to determine whether the functional ARE of CBR1 is conserved with the promoter region of homologues in other species, the nucleotide sequences of the functional AREs of the Chcr1 and Chcr2 genes, which are the Chinese hamster homologues of CBR1, were determined. The region has 2 AREs, and these genes were also induced by the forced expression of Nrf2 (cotransfection of pNrf2) in the luciferase reporter gene assay. In conclusion, Nrf2 is a novel transcriptional regulator of CBR1 genes in humans and the Chinese hamster. Because the regulation of CBR1 appears to be important for diseases, the induction of CBR1 by Nrf2 may be a therapeutic target.

Predicting flexible loop regions that interact with ligands: the challenge of accurate scoring

Flexible loop regions play a critical role in the biological function of many proteins and have been shown to be involved in ligand binding. In the context of structure-based drug design, using or predicting an incorrect loop configuration can be detrimental to the study if the loop is capable of interacting with the ligand. Three protein systems, each with at least one flexible loop region in close proximity to the known binding site, were selected for loop prediction using the CorLps program; a six residue loop region from phosphoribosylglycinamide formyltransferase (GART), two nine residue loop regions from cytochrome P450 (CYP) 119, and an 11 residue loop region from enolase were selected for loop prediction. The results of this study indicate that the statistically based DFIRE scoring function implemented in the CorLps program did not accurately rank native-like predicted loop configurations in any protein system. In an attempt to improve the ranking of the native-like predicted loop configurations, the MM/GBSA and the optimized MM/GBSA-dsr scoring functions were used to re-rank the predicted loops with and without bound ligand. In general, single snapshot MM/GBSA scoring provided the best ranking of native-like loop configurations. Based on the scoring function analyses presented, the optimal ranking of native-like loop configurations is still a difficult challenge and the choice of the "best" scoring function appears to be system dependent.

New computational method for prediction of interacting protein loop regions

Flexible loop regions of proteins play a crucial role in many biological functions such as protein-ligand recognition, enzymatic catalysis, and protein-protein association. To date, most computational methods that predict the conformational states of loops only focus on individual loop regions. However, loop regions are often spatially in close proximity to one another and their mutual interactions stabilize their conformations. We have developed a new method, titled CorLps, capable of simultaneously predicting such interacting loop regions. First, an ensemble of individual loop conformations is generated for each loop region. The members of the individual ensembles are combined and are accepted or rejected based on a steric clash filter. After a subsequent side-chain optimization step, the resulting conformations of the interacting loops are ranked by the statistical scoring function DFIRE that originated from protein structure prediction. Our results show that predicting interacting loops with CorLps is superior to sequential prediction of the two interacting loop regions, and our method is comparable in accuracy to single loop predictions. Furthermore, improved predictive accuracy of the top-ranked solution is achieved for 12-residue length loop regions by diversifying the initial pool of individual loop conformations using a quality threshold clustering algorithm.