A feel for the template: zinc finger protein transcription factors and chromatin
Bibliographic record
Abstract
Transcription factors and chromatin collaborate in bringing the eukaryotic genome to life. An important, and poorly understood, aspect of this collaboration involves targeting the regulators to correct binding sites in vivo. An implicit and insufficiently tested assumption in the field has been that chromatin simply obstructs most sites and leaves only a few functionally relevant ones accessible. The major class of transcription factors in all metazoa, zinc finger proteins (ZFPs), can bind to chromatin in vitro (as clearly shown for Spl, GATA-1 and -4, and the nuclear hormone receptors, for example). Data on the accessibility of DNA within heterochromatin to nonhistone regulators (E.A. Sekinger and D.S. Gross. 2001. Mol. Cell 105: 403-414; C. Jolly et al. 2002. J. Cell. Biol. 156: 775-781) and the ability of the basal transcription machinery to reside within highly condensed chromatin (most recently, R. Christova and T. Oelgeschlaeger. 2002. Nat. Cell Biol. 4: 79-82) further weaken the argument that chromatin acts as an across-the-board deterrent to ZFP binding. These proteins, however, do not bind promiscuously in vivo, and recent data on human cells (C.E. Horak et al. 2002. Proc. Natl. Acad. Sci. U.S.A. 99: 2924-2929) confirm earlier data on budding yeast (B. Ren et al. 2000. Science (Washington, D.C.), 290: 2306-2309) that primary DNA sequence, i.e., density of binding sites per unit DNA length, is not the primary determinant of where a ZFP transcription factor will bind in vivo. This article reviews these data and uses ZFP transcription factors as a model system to compare in vitro binding to chromatin by transcription factors with their in vivo behavior in gene regulation. DNA binding domain structure, nonrandom nucleoprotein organization of chromatin at target promoters, and cooperativity of regulator action may all contribute to target site selection in vivo.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.035 | 0.018 |
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".