Structure-guided engineering of type I-F CASTs for targeted gene insertion in human cells
Bibliographic record
Abstract
Conventional genome editing tools rely on DNA double-strand breaks (DSBs) and host recombination proteins to achieve large insertions, resulting in heterogeneous mixtures of undesirable outcomes. We recently leveraged a type I-F CRISPR-associated transposase, PseCAST, for DSB-free DNA integration in human cells, albeit at low efficiencies; multiple lines of evidence suggest DNA binding may be a bottleneck for higher efficiencies. Here we report structural determinants of DNA recognition by the PseCAST QCascade complex using single-particle cryogenic electron microscopy (cryoEM), revealing subtype-specific interactions and RNA-DNA heteroduplex features. By combining structural data, library screens, and rationally engineered mutants, we uncover variants with increased integration efficiencies and modified PAM stringencies. We further leverage transpososome structural predictions to build hybrid CASTs that combine orthogonal DNA binding and integration modules. Our work provides unique structural insights into type I-F CASTs and showcases diverse strategies to investigate and engineer RNA-guided transposase architectures for human genome editing applications. CRISPR-associated transposases represent a promising tool for precise, DSB-free genome editing. Here, authors investigate a CAST system active in human cells, improve activity via structure-guided engineering, and develop novel chimeras for improved modularity.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".