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.

Prime 编辑器 (PE) 是有前景的基因组编辑工具，但由于缺乏准确且广泛适用的方法，有效优化 Prime 编辑向导 RNA (pegRNA) 设计仍然是一个挑战。在这里，我们开发了优化的 Prime 编辑设计 (OPED)，这是一种可解释的核苷酸语言模型，利用迁移学习来提高其效率预测和 pegRNA 设计优化的准确性和通用性。对各种已发布数据集的全面验证证明了其在不同场景的效率预测中的广泛适用性。值得注意的是，具有高 OPED 分数的 pegRNA 始终表现出显着提高的编辑效率。此外，通过在 PE2、PE3/PE3b 和 ePE 编辑系统中使用优化的 pegRNA 有效安装各种 ClinVar 致病变异，证实了 OPED 在设计优化中的多功能性和功效。OPED 始终优于现有的最先进方法。我们构建了 OPEDVar 数据库，其中包含超过 20 亿所有致病变异候选物的优化设计，并为任何所需的编辑提供用户友好的 OPED Web 应用程序。