De novo engineered guide RNA-directed transposition with TnpB-family proteins reveals features of naturally evolved systems
Bibliographic record
Abstract
Programmable DNA integration using CRISPR-associated transposons (CASTs) offers powerful capabilities for genome engineering. The single effector Cas12k CAST examples evolved from a fixed guide TnpB nuclease protein. Here, we engineer de novo RNA-guided transposition systems, where the single guide RNA effector components are repurposed nuclease-dead TnpB-family proteins. These compact systems mediate high-efficiency guide RNA-directed DNA insertion with preserved orientation control and target immunity, reduced off-site targeting, release of a host factor requirement, and can be paired with an exonuclease domain to mediate cut-and-paste transposition. In this engineered context, the TnpB derivatives show features not predicted from the original enzymes suggesting untapped avenues for improvement. In parallel, we show that mutations at the TniQ-TnsC interface in the Cas12k CAST system selectively attenuate off-site insertions while enhancing on-site activity. These results establish how Cas12 proteins and antecedent TnpB proteins can be engineered for high performance and specificity with guide RNA directed 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) | 0.000 | 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 | 0.001 |
| 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 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".