The Structure of the Nuclear Factor-κB Protein-DNA Complex Varies with DNA-binding Site Sequence
Bibliographic record
Abstract
Transcriptional regulation of many immune responsive genes is under the control of the transcription factor NF-kappaB. This factor is found in cells as a dimer which can contain any two members of the Rel family of proteins (p50, p65, p52, c-Rel, and RelB). The different dimers show distinct preferences for DNA-binding site sequences. To understand the relationship between the DNA binding properties of the dimer forms and transcriptional activation, the physical properties of the complexes of p50 and p65 with DNA have been analyzed. Comparison of apparent DNA binding affinity showed differences in selectivity of DNA-binding site sequence. The ionic strength dependence of apparent binding affinity has shown that the number of ionic interactions in the protein-DNA complex depends on the DNA-binding site sequence and the dimer form, which are consistent with changes in the structure of the protein-DNA complex. Using a fluorescent technique to measure DNA structure changes, protein binding does not appear to alter the structure of the DNA-binding site within the limits of detection. These results are consistent with a change in protein structure that may result in activation differences due to alternative interactions with other transcription proteins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".