Nuclear Receptor Coregulator (NRC): Mapping of the Dimerization Domain, Activation of p53 and STAT-2, and Identification of the Activation Domain AD2 Necessary for Nuclear Receptor Signaling
Bibliographic record
Abstract
Nuclear receptor coregulator (NRC) is a 250-kDa nuclear protein involved in transcriptional activation of nuclear hormone receptors, nuclear factor-kappaB, c-Jun, c-Fos, and cAMP response element-binding protein. NRC is organized into a modular structure consisting of two activation domains (AD1 and AD2), two nuclear hormone receptor-interacting motifs, LxxLL-1 and LxxLL-2, and a C-terminal regulatory region rich in serines, threonines, and leucines. The LxxLL-1 motif of NRC binds to a broad spectrum of nuclear hormone receptors with high affinity whereas LxxLL-2 interacts with a very limited number of receptors. In this study we present further evidence that NRC can act as a dimer and have identified a dimerization region of 146 amino acids including LxxLL-1. Mutation of the core LxxLL-1 motif, however, indicates that it is not involved in the dimerization of NRC. AD2, just C-terminal of LxxLL-1, was found to play a central role in ligand-dependent activation by nuclear receptors even though AD1 exhibits more potent intrinsic activity. Thus, a short region of approximately 300 amino acids including and flanking LxxLL-1 plays an important role in NRC dimerization and nuclear receptor binding and transcriptional activation. In addition, consistent with its role as a cointegrator for transcriptional activation, NRC also functions as a coactivator for signal transducer and activator of transcription 2 (STAT-2) and p53. Activation of p53 by NRC appears to involve a novel mechanism where NRC interacts indirectly with p53 through Trap80, a member of the mediator complex, which binds NRC interacting factor-1 (NIF-1), which interacts with and potentiates the effect of NRC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".