A buffer-tuning strategy to profile domain-specific activity of chimeric I-TevI/CRISPR gene editors in vitro
Bibliographic record
Abstract
Protein-DNA interactions can be manipulated in vitro by changing buffer conditions. Here, we develop a methodology to map the cleavage preferences of chimeric gene editors that are fusions of the I-TevI nuclease domain to CRIPSR nucleases by manipulating in vitro salt concentrations. We found that DNA cleavage by the I-TevI (Tev) nuclease domain at CNNNG sites was de-coupled from the gRNA-targeted site in low salt buffers. For TevCas12a, this non-targeted cleavage activity was enriched at Tev CNNNG cleavage motifs optimally positioned within a 30-bp window upstream of a Cas12a TTTV PAM site. Non-targeted cleavage did not require Cas12a nuclease activity or specific Cas12a gRNA targeting. Similar non-targeted products were observed in low salt buffer conditions for TevSaCas9, Tev-meganuclease and Tev-zinc finger editors. Cas12a and SaCas9 activity at gRNA-directed sites and sites with multiple mismatches were also sensitive to buffer salt concentration. Oxford Nanopore sequencing revealed a remarkably similar Tev CNNNG cleavage preference at different salt concentrations and in different fusion contexts, emphasizing the robustness and specificity of Tev activity. More generally, our work highlights the sensitivity of gene editors to in vitro reaction conditions and how these conditions can be leveraged to functionally dissect the activity of individual domains of chimeric gene editors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".