AI-generated MLH1 small binder improves prime editing efficiency

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. RNA-seq profiling of Mock, PuroR, MLH1dn, SB, PE7, and PE7SB treated sample to investigate off-target effects on transcriptome level after gene editing