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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".