Monomeric site-specific nucleases for genome editing
Bibliographic record
Abstract
Targeted manipulation of complex genomes often requires the introduction of a double-strand break at defined locations by site-specific DNA endonucleases. Here, we describe a monomeric nuclease domain derived from GIY-YIG homing endonucleases for genome-editing applications. Fusion of the GIY-YIG nuclease domain to three-member zinc-finger DNA binding domains generated chimeric GIY-zinc finger endonucleases (GIY-ZFEs). Significantly, the I-TevI-derived fusions (Tev-ZFEs) function in vitro as monomers to introduce a double-strand break, and discriminate in vitro and in bacterial and yeast assays against substrates lacking a preferred 5'-CNNNG-3' cleavage motif. The Tev-ZFEs function to induce recombination in a yeast-based assay with activity on par with a homodimeric Zif268 zinc-finger nuclease. We also fused the I-TevI nuclease domain to a catalytically inactive LADGLIDADG homing endonuclease (LHE) scaffold. The monomeric Tev-LHEs are active in vivo and similarly discriminate against substrates lacking the 5'-CNNNG-3' motif. The monomeric Tev-ZFEs and Tev-LHEs are distinct from the FokI-derived zinc-finger nuclease and TAL effector nuclease platforms as the GIY-YIG domain alleviates the requirement to design two nuclease fusions to target a given sequence, highlighting the diversity of nuclease domains with distinctive biochemical properties suitable for genome-editing applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".