The evolutionary entanglement of flipons with zinc fingers and retroelements has engendered a large family of Z-DNA and G-quadruplex binding proteins
Bibliographic record
Abstract
Sequences called flipons can adopt discrete, alternative nucleic acid conformations, such as the left-handed Z-DNA and Z-RNA double helices (referred to collectively as ZNA), and the four-stranded RNA and DNA G-quadruplexes. Each flipon conformation encodes different information. For example, the base-specific interactions of proteins with B-DNA enable sequence-specific recognition. In contrast, the higher energy Z-DNA and G-quadruplexes facilitate the speedy scanning of chromosomes to locate active regions of the genome. Results synthesized from small-scale benchside and large-scale computational experimental approaches provide compelling evidence that zinc-finger protein domains (ZFDs) not only engage in base-specific recognition of B-DNA, but also bind directly to Z-DNA and G-quadruplexes. The findings address the long-standing speed-stability paradox of how high-affinity ZFPs with multiple zinc fingers can rapidly localize to a specific binding site. The energy gap between different DNA interaction modes enables fast off-rates during the scanning of Z-DNA for cognate binding sites, and a slow off-rate following engagement of the B-DNA conformer. ZFPs represent the most prominent human transcription factor family with 804 annotated members. The coevolution of flipons and ZFP enhances suppression of retroelements and enables rapid, context-specific responses. ZNA and GQ binding proteins are consequently more frequent in the proteome than currently conceded.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".