Machine learning reveals sequence and methylation determinants of SaCas9-PAM interactions in bacteria.

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$

SUBMITTER: Ham DT 

PROVIDER: S-EPMC12805903 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature