Prediction of editing efficiencies for diverse prime editing systems in multiple cell types

Applications of prime editing are often limited due to insufficient efficiencies, and it can require substantial time and resources to determine the most efficient pegRNAs and prime editors to generate a desired edit under various experimental conditions. Here we evaluated prime editing efficiencies for a total of 335,017 pairs of pegRNAs and target sequences in an error-free manner. These datasets enabled a systematic and extensive determination of factors affecting prime editing efficiencies. Then, we developed computational models, named DeepPrime and DeepPrime-FT, that can predict prime editing efficiencies for eight prime editing systems in seven cell types for all possible types of editing of up to 3 base pairs. We also extensively profiled the prime editing efficiencies at mismatched targets and developed a computational model predicting editing efficiencies at such targets. These computational models, together with our improved knowledge about prime editing efficiency determinants, will greatly facilitate prime editing applications.