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AI-generated MLH1 small binder improves prime editing efficiency

GSE293633 Homo sapiens Expression profiling by high throughput sequencing 6 samples Submitted 2025/04/07 Platform GPL34281
Summary
The prime editing (PE) system consists of a Cas9 nickase fused to a reverse transcriptase, which introduces precise edits into the target genomic region guided by a prime editing guide RNA. However, PE efficiency is limited by mismatch repair. To overcome this limitation, transient expression of a dominant-negative MLH1 (MLH1dn) has been used to inhibit key components of mismatch repair. Here, we designed a de novo MLH1 small binder (MLH1-SB) that binds to the dimeric interface of MLH1 and PMS2 using RFdiffusion and AlphaFold 3. The compact size of MLH1-SB enabled its integration into existing PE architectures via 2A systems, creating a novel PE-SB platform. The PE7-SB system significantly improved PE efficiency, achieving an 18.8-fold increase over PEmax and a 2.5-fold increase over PE7 in HeLa cells, as well as a 3.4-fold increase over PE7 in mice. This study highlights the potential of generative AI in advancing genome editing technology.
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Direct links to NCBI, no account and no request form: the whole study as GSE293633_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 6 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1229090 and SRA study SRP566612. Searching any of these in the dataset finder brings you back here.

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