AI Article Synopsis
- Base editors allow for precise changes in genetic material without causing double-strand breaks, making them useful for disease modeling and gene therapy, but their use is limited due to the need for specific PAM sequences.
- Researchers developed four near-PAMless base editor versions that broaden the range of target sequences and found that both the PAM and surrounding sequences influence editing efficiency.
- The team created BEguider, a deep learning model that predicts editing results for these new editors and provided experimental editing data on 20,541 ClinVar sites, showing that previously difficult-to-edit variants can now be addressed accurately; the predictive tool and data are available for public use.
Article Abstract
Base editors enable the direct conversion of target bases without inducing double-strand breaks, showing great potential for disease modeling and gene therapy. Yet, their applicability has been constrained by the necessity for specific protospacer adjacent motif (PAM). We generate four versions of near-PAMless base editors and systematically evaluate their editing patterns and efficiencies using an sgRNA-target library of 45,747 sequences. Near-PAMless base editors significantly expanded the targeting scope, with both PAM and target flanking sequences as determinants for editing outcomes. We develop BEguider, a deep learning model, to accurately predict editing results for near-PAMless base editors. We also provide experimentally measured editing outcomes of 20,541 ClinVar sites, demonstrating that variants previously inaccessible by NGG PAM base editors can now be precisely generated or corrected. We make our predictive tool and data available online to facilitate development and application of near-PAMless base editors in both research and clinical settings.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11511846 | PMC |
| http://dx.doi.org/10.1038/s42003-024-07078-5 | DOI Listing |
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