Prime editing enables the introduction of virtually any small-sized genetic change without requiring donor DNA or double-strand breaks. However, evaluation of prime editing efficiency requires time-consuming experiments, and the factors that affect efficiency have not been extensively investigated. In this study, we performed high-throughput evaluation of prime editor 2 (PE2) activities in human cells using 54,836 pairs of prime editing guide RNAs (pegRNAs) and their target sequences. The resulting data sets allowed us to identify factors affecting PE2 efficiency and to develop three computational models to predict pegRNA efficiency. For a given target sequence, the computational models predict efficiencies of pegRNAs with different lengths of primer binding sites and reverse transcriptase templates for edits of various types and positions. Testing the accuracy of the predictions using test data sets that were not used for training, we found Spearman’s correlations between 0.47 and 0.81. Our computational models and information about factors affecting PE2 efficiency will facilitate practical application of prime editing.

Prime 编辑几乎可以引入任何小规模的基因变化，而不需要供体 DNA 或双链断裂。然而，评估prime编辑效率需要耗时的实验，并且影响效率的因素尚未得到广泛研究。在这项研究中，我们使用 54,836 对引物编辑引导 RNA (pegRNA) 及其靶序列对人体细胞中的引物编辑器 2 (PE2) 活性进行了高通量评估。由此产生的数据集使我们能够确定影响 PE2 效率的因素并开发三种计算模型来预测 pegRNA 效率。对于给定的靶序列，计算模型预测具有不同长度的引物结合位点和逆转录酶模板的 pegRNA 对各种类型和位置的编辑的效率。使用未用于训练的测试数据集测试预测的准确性，我们发现 Spearman 相关性在 0.47 和 0.81 之间。我们的计算模型和有关影响 PE2 效率的因素的信息将促进 Prime 编辑的实际应用。