Design of prime-editing guide RNAs with deep transfer learning

Abstract

Prime editors (PEs) are promising genome-editing tools, but effective optimization of prime-editing guide RNA (pegRNA) design remains a challenge owing to the lack of accurate and broadly applicable approaches. Here we develop Optimized Prime Editing Design (OPED), an interpretable nucleotide language model that leverages transfer learning to improve its accuracy and generalizability for the efficiency prediction and design optimization of pegRNAs. Comprehensive validations on various published datasets demonstrate its broad applicability in efficiency prediction across diverse scenarios. Notably, pegRNAs with high OPED scores consistently show significantly increased editing efficiencies. Furthermore, the versatility and efficacy of OPED in design optimization are confirmed by efficiently installing various ClinVar pathogenic variants using optimized pegRNAs in the PE2, PE3/PE3b and ePE editing systems. OPED consistently outperforms existing state-of-the-art approaches. We construct the OPEDVar database of optimized designs from over two billion candidates for all pathogenic variants and provide a user-friendly web application of OPED for any desired edit.