The I-TevI Nuclease and Linker Domains Contribute to the Specificity of Monomeric TALENs
Bibliographic record
Abstract
Precise genome editing in complex genomes is enabled by engineered nucleases that can be programmed to cleave in a site-specific manner. Here, we fused the small, sequence-tolerant monomeric nuclease domain from the homing endonuclease I-TevI to transcription-like activator effectors (TALEs) to create monomeric Tev-TALE nucleases (Tev-mTALENs). Using the PthXo1 TALE scaffold to optimize the Tev-mTALEN architecture, we found that choice of the N-terminal fusion point on the TALE greatly influenced activity in yeast-based assays, and that the length of the linker used affected the optimal spacing of the TALE binding site from the I-TevI cleavage site, specified by the motif 5'-CNNNG-3'. By assaying activity on all 64 possible sequence variants of this motif, we discovered that in the Tev-mTALEN context, I-TevI prefers A/T-rich triplets over G/C-rich ones at the cleavage site. Profiling of nucleotide requirements in the DNA spacer that separates the CNNNG motif from the TALE binding site revealed substantial, but not complete, tolerance to sequence variation. Tev-mTALENs showed robust mutagenic activity on an episomal target in HEK 293T cells consistent with specific cleavage followed by nonhomologous end-joining repair. Our data substantiate the applicability of Tev-mTALENs as genome-editing tools but highlight DNA spacer and cleavage site nucleotide preferences that, while enhancing specificity, do confer moderate targeting constraints.
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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".