小柯机器人

韩国延世大学医学院Hyongbum Henry Kim团队，近期取得重要工作进展。他们开发利用深度学习模型预测不同碱基编辑器的编辑效率和结果。相关研究成果2023年5月15日在线发表于《自然—生物技术》杂志上。

据介绍，碱基编辑器的应用经常受到对原间隔区相邻基序（PAM）的要求限制，并且为给定的靶标选择最佳碱基编辑器（BE）和成对的sgRNA比较困难。

为了在没有大量实验工作的情况下选择BE和sgRNA，研究人员系统地比较了七种BE的编辑窗口、结果和首选基序，包括两种胞嘧啶BE、两种腺嘌呤BE和三种C•G到G•C BE，它们位于数千个靶序列上。研究人员还评估了九种识别不同PAM序列的Cas9变体，并开发了一个深度学习模型DeepCas9变体，用于预测哪些变体在具有给定靶序列的位点上最有效地发挥作用。然后，研究人员开发了一个计算模型DeepBE，该模型预测了63个BE的编辑效率和结果，这些BE是通过将9个Cas9变体作为缺口酶结构域合并到7个BE变体中而产生的。

总之，基于DeepBE设计的BE的预测中值效率比合理设计的含SpCas9的BE高2.9至20倍。

附：英文原文

Title: Deep learning models to predict the editing efficiencies and outcomes of diverse base editors

Author: Kim, Nahye, Choi, Sungchul, Kim, Sungjae, Song, Myungjae, Seo, Jung Hwa, Min, Seonwoo, Park, Jinman, Cho, Sung-Rae, Kim, Hyongbum Henry

Issue&Volume: 2023-05-15

Abstract: Applications of base editing are frequently restricted by the requirement for a protospacer adjacent motif (PAM), and selecting the optimal base editor (BE) and single-guide RNA pair (sgRNA) for a given target can be difficult. To select for BEs and sgRNAs without extensive experimental work, we systematically compared the editing windows, outcomes and preferred motifs for seven BEs, including two cytosine BEs, two adenine BEs and three CG to GC BEs at thousands of target sequences. We also evaluated nine Cas9 variants that recognize different PAM sequences and developed a deep learning model, DeepCas9variants, for predicting which variants function most efficiently at sites with a given target sequence. We then develop a computational model, DeepBE, that predicts editing efficiencies and outcomes of 63 BEs that were generated by incorporating nine Cas9 variants as nickase domains into the seven BE variants. The predicted median efficiencies of BEs with DeepBE-based design were 2.9- to 20-fold higher than those of rationally designed SpCas9-containing BEs.

DOI: 10.1038/s41587-023-01792-x

Source: https://www.nature.com/articles/s41587-023-01792-x

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex