Fusions of catalytically inactive RusA to FokI nuclease coupled with PNA enable programable site-specific double-stranded DNA breaks
Bibliographic record
Abstract
ABSTRACT Programmable site-specific nucleases have revolutionized the genome editing. However, these systems still face challenges such as guide dependency, delivery issues, and off-target effects. Harnessing the natural functions of structure-guided nucleases offer promising alternatives for generating site-specific double-strand DNA breaks. Yet, structure-guided nucleases require precise reaction conditions and validation for in-vivo applicability. To address these limitations, we developed the P NA- C oupled F okI-(d) R us A ( PC-FIRA ) system. PC-FIRA combines the sequence-specific binding ability of peptide nucleic acids (PNAs) with the catalytic efficiency of FokI nuclease fused to a structurally-guided inactive RusA resolvase (FokI-(d)RusA). This system allows for precise double-strand DNA breaks without the constraints of existing site-specific nuclease and structure-guided nucleases. Through in vitro optimizations, we achieved high target specificity and cleavage efficiency. This included adjusting incubation temperature, buffer composition, ion concentration, and cleavage timing. Diverse DNA structures, such as Holliday Junctions, linear, and circular DNA, were tested demonstrating the potential activity on different target forms. Further investigation has revealed the PC-FIRA system capacity for facilitating the precise deletion of large DNA fragments. This can be useful in cloning, large-fragment DNA assembly, and genome engineering, with promising applications in biotechnology, medicine, agriculture, and synthetic biology.
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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) | 0.001 | 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".