Machine learning reveals sequence and methylation determinants of SaCas9–PAM interactions in bacteria
Bibliographic record
Abstract
Cas9 nucleases defend bacteria against invading DNA and can be used with single guide RNAs (sgRNAs) as antimicrobials and genome-editing tools. However, bacterial applications are limited by incomplete knowledge of Cas9-target interactions. Here, we generated large-scale Staphylococcus aureus Cas9 (SaCas9)/sgRNA activity datasets in bacteria and trained a machine learning model (crispr macHine trAnsfer Learning) to predict SaCas9 activity. Incorporating downstream sequences flanking the canonical NNGRRN protospacer adjacent motif (PAM) at positions [+1] and [+2] improved predictive performance, with T-rich dinucleotides at these positions correlating with higher in vivo activity. Crucially, SaCas9 showed $\sim$10-fold reduced activity at sites containing a 5$^{\prime}$-NNGGAT[C]-3$^\prime$ PAM [+1] sequence in pooled sgRNA experiments in Escherichia coli and Citrobacter rodentium. Plasmid cleavage assays in DNA adenine methyltransferase (DAM)-deficient E. coli confirmed that adenine methylation at GATC motifs inhibited SaCas9 activity. Removal of a DAM site within a PAM sequence enhanced cleavage, while introduction of a site reduced activity, directly linking adenine methylation to SaCas9 activity. These findings demonstrate that machine learning can uncover biologically relevant determinants of Cas9 activity. Avoidance of methylated PAMs may reflect an evolutionary adaptation by SaCas9 to discriminate self from nonself or to counter methylation as a phage and plasmid antirestriction strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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".