Rationally Designed Anti-CRISPR Nucleic Acid Inhibitors of CRISPR-Cas9
Bibliographic record
Abstract
Clustered regularly interspaced short palindromic repeat (CRISPR) RNAs and their associated effector (Cas) enzymes are being developed into promising therapeutics to treat disease. However, CRISPR-Cas enzymes might produce unwanted gene editing or dangerous side effects. Drug-like molecules that can inactivate CRISPR-Cas enzymes could help facilitate safer therapeutic development. Based on the requirement of guide RNA and target DNA interaction by Cas enzymes, we rationally designed small nucleic acid-based inhibitors (SNuBs) of Streptococcus pyogenes ( Sp ) Cas9. Inhibitors were initially designed as 2′- O -methyl-modified oligonucleotides that bound the CRISPR RNA guide sequence (anti-guide) or repeat sequence (anti-tracr), or DNA oligonucleotides that bound the protospacer adjacent motif (PAM)-interaction domain (anti-PAM) of Sp Cas9. Coupling anti-PAM and anti-tracr modules together was synergistic and resulted in high binding affinity and efficient inhibition of Cas9 DNA cleavage activity. Incorporating 2′F-RNA and locked nucleic acid nucleotides into the anti-tracr module resulted in greater inhibition as well as dose-dependent suppression of gene editing in human cells. CRISPR SNuBs provide a platform for rational design of CRISPR-Cas enzyme inhibitors that should translate to other CRISPR effector enzymes and enable better control over CRISPR-based applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".