Structural and Mechanistic Characterization of Type II-C Anti-CRISPR Proteins
Bibliographic record
Abstract
The CRISPR-Cas adaptive immune system provides bacteria with defence against phage predation.CRISPR-Cas systems use protein-RNA complexes to cleave and eliminate invading phage genomes in a sequence specific manner. Phages have evolved a countermeasure through the expression of small protein inhibitors against CRISPR-Cas called “anti-CRISPRs”. Anti-CRISPR proteins use incredibly diverse mechanisms to either bind directly to or enzymatically modify Cas proteins to prevent cleavage mediated by CRISPR-Cas. CRISPR-Cas enzymes, especially Cas9, have been repurposed in many biotechnologies including genome editing. Anti-CRISPRs have potential to be utilized as “off-switches” for CRISPRCas9 gene editing, addressing safety and ethical concerns for the technology. Since their discovery in 2013, over 90 protein families of anti-CRISPRs have been reported, with 37 being inhibitors of Cas9 specifically. However, much of the mechanistic details of the anti-CRISPR proteins are left incomplete. In this thesis work, I characterize the inhibitory mechanisms of two CRISPR-Cas9 inhibitors, AcrIIC4 and AcrIIC5, using structural biology and biochemical and biophysical assays. I demonstrate that AcrIIC4 inhibits the DNA cleavage activity of Cas9 by binding to the REC2 domain of Cas9 and preventing the conformational changes required for Cas9 to switch into the active state, while AcrIIC5 inhibits Cas9 from binding to target DNA using DNA mimicry. The inhibitory mechanisms elucidated in my thesis work can be applied to the potential development of AcrIIC4 and AcrIIC5 for biotechnological purposes. This work also expands our knowledge of the vast arsenal phages have evolved to circumvent CRISPRCas defence.
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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 | 0.001 | 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".