Structural basis of CBP/p300 recruitment by the microphthalmia-associated transcription factor
Bibliographic record
Abstract
ABSTRACT The microphthalmia-associated transcription factor (MITF) is a master regulator of the melanocyte cell lineage. Aberrant MITF activity can lead to multiple malignancies including skin cancer, where it modulates the proliferation and invasiveness of melanoma. MITF-dependent gene expression requires recruitment of transcriptional co-activators such as CBP/p300, but details of this process are not fully defined. Here, we investigate the structural and functional interaction between the MITF N-terminal transactivation domain (MITF TAD ) and CBP/p300. A combination of pulldown assays and nuclear magnetic resonance spectroscopy determined that MITF binds both TAZ1 and TAZ2 domains of CBP/p300 with high affinity. The solution-state structure of the MITF TAD :TAZ2 complex reveals that MITF interacts with a hydrophobic surface of TAZ2, while remaining relatively dynamic. Peptide array and mutagenesis experiments determined that an acidic motif is integral to the MITF TAD :TAZ2 interaction and is necessary for transcriptional activity of MITF. Peptides that bind to the same surface of TAZ2 as MITF TAD , such as the adenoviral protein E1A, are capable of displacing MITF from TAZ2 and inhibiting transactivation. These results provide mechanistic insight into co-activator recruitment by MITF that are fundamental to our understanding of MITF targeted gene regulation and melanoma biology.
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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 | 0.000 |
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".