AI-generated small binder improves prime editing

Abstract

The prime editing 2 (PE2) system comprises a nickase Cas9 fused to a reverse transcriptase utilizing a prime editing guide RNA (pegRNA) to introduce desired mutations at target genomic sites. However, the PE efficiency is limited by mismatch repair (MMR) that excises the DNA strand containing desired edits. Thus, inhibiting key components of MMR complex through transient expression of a dominant negative MLH1 (MLH1dn) exhibited approximately 7.7-fold increase in PE efficiency over PE2, generating PE4. Herein, by utilizing a generative artificial intelligence (AI) technologies, RFdiffusion and AlphaFold 3, we ultimately generated a de novo MLH1 small binder (named MLH1-SB), which bind to the dimeric interface of MLH1 and PMS2 to disrupt the formation of key MMR components. MLH1-SBs small size (82 amino acids) allowed it to be integrated into pre-existing PE architectures via the 2A system, creating a novel PE-SB platform. Resultantly, by incorporating MLH1-SB into PE7, we have developed an improved PE architecture called PE7-SB, which demonstrates the highest PE efficiency to date (29.4-fold over PE2 and 2.4-fold over PE7 in HeLa cells), providing an insight that generative AI technologies will boost up the improvement of genome editing tools.