Enhancement and rescue of target capture in Tn<i>10</i> transposition by site‐specific modifications in target DNA
Bibliographic record
Abstract
The bacterial transposon Tn10 inserts preferentially into specific target sequences. This insertion specificity appears to be linked to the ability of target sites to adopt symmetrically positioned DNA bends after binding the transposition machinery. Target DNA bending is thought to permit the transposase protein to make additional contacts with the target DNA, thereby stabilizing the target complex so that the joining of transposon and target DNA sequences can occur efficiently. In the current work, we have asked whether the introduction of a discontinuity in a target DNA strand, a modification that is expected to make it easier for a DNA molecule to bend, can enhance or rescue target capture under otherwise suboptimal reaction conditions. We show that either a nick or a missing phosphate specifically at the site of reaction chemistry increases the ability of various target DNAs to form the target capture complex. The result suggests that the bends in the target DNA are highly localized and include the scissile phosphates. This raises the possibility that strand transfer is mechanistically linked to target capture. We have also identified specific residues in the target DNA and in transposase that appear to play an important role in target DNA bending.
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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 | 0.001 |
| 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".