Structural and functional characterization of an atypical activation domain in erythroid Krüppel-like factor (EKLF)
Bibliographic record
Abstract
Erythroid Krüppel-like factor (EKLF) plays an important role in erythroid development by stimulating β-globin gene expression. We have examined the details by which the minimal transactivation domain (TAD) of EKLF (EKLFTAD) interacts with several transcriptional regulatory factors. We report that EKLFTAD displays homology to the p53TAD and, like the p53TAD, can be divided into two functional subdomains (EKLFTAD1 and EKLFTAD2). Based on sequence analysis, we found that EKLFTAD2 is conserved in KLF2, KLF4, KLF5, and KLF15. In addition, we demonstrate that EKLFTAD2 binds the amino-terminal PH domain of the Tfb1/p62 subunit of TFIIH (Tfb1PH/p62PH) and four domains of CREB-binding protein/p300. The solution structure of the EKLFTAD2/Tfb1PH complex indicates that EKLFTAD2 binds Tfb1PH in an extended conformation, which is in contrast to the α-helical conformation seen for p53TAD2 in complex with Tfb1PH. These studies provide detailed mechanistic information into EKLFTAD functions as well as insights into potential interactions of the TADs of other KLF proteins. In addition, they suggest that not only have acidic TADs evolved so that they bind using different conformations on a common target, but that transitioning from a disordered to a more ordered state is not a requirement for their ability to bind multiple partners.
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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".