BACKGROUND: RNA-guided nucleases such as CRISPR-Cas9 systems have revolutionized genome engineering by enabling programmable DNA modifications. Although structure-guided and evolution-derived high-fidelity Cas9 variants improve target specificity, they often compromise on-target activity or constrain guide RNA (gRNA) design.
METHODS: We performed head-to-head comparisons of OpenCRISPR-1 and Cas9 in human cells using amplicon sequencing, multiplex Digenome-seq, and off-target validation by targeted sequencing. Editing activity was assessed across 28 endogenous loci in HEK293T cells and further evaluated in human induced pluripotent stem cells (iPSCs) and MRC-5 fibroblasts. To test clinically relevant delivery, Cas9 and OpenCRISPR-1 ribonucleoproteins were delivered using engineered virus-like particles (eVLPs). We also generated OpenCRISPR-based prime editors, OpenCRISPR-PE2 and OpenCRISPR-PE7, and compared them with PE2max and PE7 using pegRNAs and engineered epegRNAs.
RESULTS: Here, we show that OpenCRISPR-1, an AI-designed, Cas9-like nuclease, retains Cas9-level editing efficiency across multiple genomic loci while significantly reducing off-target mutations. Using multiplex Digenome-seq and targeted deep sequencing, OpenCRISPR-1 exhibits up to a 553-fold reduction in off-target mutations compared to Cas9 and achieves off-target indices that match or surpass those of high-fidelity Cas9 variants. OpenCRISPR-1 also sustains robust editing across diverse gRNA formats (GX, gX, and gX), highlighting its enhanced versatility. Furthermore, converting OpenCRISPR-1 into a prime editor yields comparable editing efficiencies while lowering the relative specificity ratio by up to 97%. 19 19 20
CONCLUSIONS: These findings establish generative AI-guided protein design as a powerful strategy to overcome the specificity-efficiency trade-off, expanding the genome editing toolkit for both research and therapeutic use, and ushering in a new era of rational protein design.
