102 Genome engineering nucleases derived from GIY-YIG homing endonucleases
Bibliographic record
Abstract
Efficient targeted manipulation of complex genomes requires highly specific endonucleases to generate double-strand breaks at defined locations (Bibikova et al., 2003; Bogdanove and Voytas, 2011). The predominantly engineered nucleases, zinc-finger nucleases (ZFNs), and TAL effector nucleases (TALENs) use the catalytic domain of FokI as the nuclease portion. This domain, however, functions as a dimer to nonspecifically cleave DNA meaning that ZFNs and TALENs must be designed in head-to-head pairs to target a desired sequence. To overcome this limitation and expand the toolbox of genome editing reagents, we used the N-terminal catalytic domain and interdomain linker of the monomeric GIY-YIG homing endonuclease I-TevI to create I-TevI-zinc-fingers (Tev-ZFEs), and I-TevI-TAL effectors (Tev-TALs) (Kleinstiver et al. 2012). We also made I-TevI fusions to LAGLIDADGs homing endonucleases (I-Tev-LHEs). All the three fusions showed activity on model substrates on par with ZFNs and TALENs in yeast-based recombination assays. These proof-of-concept experiments demonstrate that the catalytic domain of GIY-YIG homing endonucleases can be targeted to relevant loci by fusing the domain to characterize DNA-binding platforms. Recent efforts have focused on improving the Tev-TAL platform by (1) understanding the spacing requirements between the nuclease cleavage site and the DNA binding site, (2) probing the DNA binding requirements of the I-TevI linker domain, and (3) demonstrating activity in mammalian systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".