The article explores advancements in genome editing involving CRISPR-Cas9 technologies, focusing on the integration of machine learning and high-throughput protein engineering to create specialized Cas9 enzymes. By generating approximately 1,000 engineered variants through innovative methods such as saturation mutagenesis and bacterial selection, the research team effectively characterized the specific protospacer-adjacent motif (PAM) requirements. This enables better-targeted genome editing applications while addressing concerns around off-target effects, ultimately enhancing the functionality and precision of CRISPR tools.
By integrating high-throughput protein engineering with machine learning, we developed customized Cas9 enzymes aimed at enhancing the efficacy of genome editing while minimizing off-target effects.
Our approach leverages structure/function-informed saturation mutagenesis and bacterial selections, culminating in nearly 1,000 engineered SpCas9 variants, tailored for specific genomic targets.
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