Fuente: PubMed "rice" Natl Sci Rev. 2026 Feb 9;13(6):nwag090. doi: 10.1093/nsr/nwag090. eCollection 2026 Mar.ABSTRACTSince its inception, the CRISPR-Cas system, particularly Cas9, has demonstrated immense potential for life science applications, but expansion of the Cas9 toolkit is constrained by sequence-alignment-based strategies for mining and optimization. Here, we developed CasMiner-a deep-learning model for discovering and engineering novel Cas9 proteins. CasMiner achieved 99.63% accuracy in predicting Cas9s and identified VpCas9 from public databases. Experimental validation showed that VpCas9 exhibits robust double-strand cleavage activity. Combining CasMiner and evolutionary analysis, we engineered three mutants with markedly increased structural rigidity and positive charge. In vivo cleavage assays revealed that the mutant VPM2-3 achieved a higher average editing efficiency in rice callus and maize protoplasts than the wild-type VpCas9, the editing efficiency of which rivals that of SpCas9. This study thus establishes a comprehensive platform for mining and engineering Cas9 proteins, and provides VpCas9 and derivative nucleases as powerful tools that greatly broaden the horizon for genome-editing applications.PMID:41908308 | PMC:PMC13020425 | DOI:10.1093/nsr/nwag090