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A generative artificial-intelligence tool has designed a synthetic CRISPR system that successfully edits human DNA and sharply reduces off-target effects.

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  1. Pascal Notin
    1. Pascal Notin is in the Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA.

Despite emerging clinical successes, current genome editors suffer from off-target effects and can trigger unwanted responses from the immune system, limiting their broader therapeutic applications. Writing in Nature, Ruffolo et al.1 present OpenCRISPR-1, the first AI-generated CRISPR–Cas protein to edit human DNA successfully. The work demonstrates how machine learning can be used to engineer functional biological systems that extend beyond those found in nature.

doi: https://doi.org/10.1038/d41586-025-02135-3

References

  1. Ruffolo, J. A. et al. Nature https://doi.org/10.1038/s41586-025-09298-z (2025).

    Article  Google Scholar 

  2. Ishino, Y., Shinagawa, H., Makino, K., Amemura, M. & Nakata, A. J. Bacteriol. 169, 5429–5433 (1987).

    Article  PubMed  Google Scholar 

  3. Jinek, M. et al. Science 337, 816–821 (2012).

    Article  PubMed  Google Scholar 

  4. Cong, L. et al. Science 339, 819–823 (2013).

    Article  PubMed  Google Scholar 

  5. Frangoul, H. et al. N. Engl. J. Med. 384, 252–260 (2021).

    Article  PubMed  Google Scholar 

  6. Fu, Y. et al. Nature Biotechnol. 31, 822–826 (2013).

    Article  PubMed  Google Scholar 

  7. Charlesworth, C. T. et al. Nature Med. 25, 249–254 (2019).

    Article  PubMed  Google Scholar 

  8. Walton, R. T., Christie, K. A., Whittaker, M. N. & Kleinstiver, B. P. Science 368, 290–296 (2020).

    Article  PubMed  Google Scholar 

  9. Kleinstiver, B. P. et al. Nature 529, 490–495 (2016).

    Article  PubMed  Google Scholar 

  10. Lee, J. K. et al. Nature Commun. 9, 3048 (2018).

    Article  PubMed  Google Scholar 

  11. Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N. & Madani, A. Cell Syst. 14, 968–978 (2023).

    Article  PubMed  Google Scholar 

  12. Radford, A., Narasimhan, K., Salimans, T. & Sutskever, I. Improving language understanding by generative pre-training (OpenAI, 2018).

    Google Scholar 

  13. Blalock, N. et al. Preprint at bioRxiv https://doi.org/10.1101/2025.05.02.651993 (2025).

  14. Stocco, F. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2412.12979 (2024).

  15. Silverstein, R. A. et al. Nature 643, 539–550 (2025).

    Article  PubMed  Google Scholar 

  16. Vince, O. et al. Preprint at bioRxiv https://doi.org/10.1101/2025.06.11.658620 (2025).

  17. Li, M. et al. In Proc. 37th Conf. Neural Inf. Process. Syst. 35700–35726 (2024).

  18. Zhang, Z. et al. In Proc. 37th Conf. Neural Inf. Process. Syst. 101456–101473 (2024).

  19. Weitzman, R. et al. Poster 45825 in Proc. 42nd Int. Conf. Mach. Learn. (2025).

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Competing Interests

The author declares no competing interests.

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