{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ham DT"],"funding":["CIHR"],"pagination":["gkaf1520"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12805903"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["54(2)"],"pubmed_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$"],"journal":["Nucleic acids research"],"pubmed_title":["Machine learning reveals sequence and methylation determinants of SaCas9-PAM interactions in bacteria."],"pmcid":["PMC12805903"],"funding_grant_id":["PJT 191939","PJT 159708"],"pubmed_authors":["Gloor GB","Uruthirapathy AS","Zhang CQ","Edgell DR","Foo GW","Ham DT","Browne TS"],"additional_accession":[]},"is_claimable":false,"name":"Machine learning reveals sequence and methylation determinants of SaCas9-PAM interactions in bacteria.","description":"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$","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-05-31T03:16:38.16Z","creation":"2026-05-31T03:10:18.008Z"},"accession":"S-EPMC12805903","cross_references":{"pubmed":["41538317"],"doi":["10.1093/nar/gkaf1520"]}}