Anti-CRISPR AcrIF9 functions by inducing the CRISPR–Cas complex to bind DNA non-specifically
Bibliographic record
Abstract
Phages and other mobile genetic elements express anti-CRISPR proteins (Acrs) to protect their genomes from destruction by CRISPR-Cas systems. Acrs usually block the ability of CRISPR-Cas systems to bind or cleave their nucleic acid substrates. Here, we investigate an unusual Acr, AcrIF9, that induces a gain-of-function to a type I-F CRISPR-Cas (Csy) complex, causing it to bind strongly to DNA that lacks both a PAM sequence and sequence complementarity. We show that specific and non-specific dsDNA compete for the same site on the Csy:AcrIF9 complex with rapid exchange, but specific ssDNA appears to still bind through complementarity to the CRISPR RNA. Induction of non-specific DNA-binding is a shared property of diverse AcrIF9 homologues. Substitution of a conserved positively charged surface on AcrIF9 abrogated non-specific dsDNA-binding of the Csy:AcrIF9 complex, but specific dsDNA binding was maintained. AcrIF9 mutants with impaired non-specific dsDNA binding activity in vitro displayed a reduced ability to inhibit CRISPR-Cas activity in vivo. We conclude that misdirecting the CRISPR-Cas complex to bind non-specific DNA is a key component of the inhibitory mechanism of AcrIF9. This inhibitory mechanism is distinct from a previously characterized anti-CRISPR, AcrIF1, that sterically blocks DNA-binding, even though AcrIF1and AcrIF9 bind to the same site on the Csy complex.
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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) | 0.000 | 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) | 0.002 | 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".