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Prediction of base editor off-targets by deep learning

Medicine and Health

Prediction of base editor off-targets by deep learning

C. Zhang, Y. Yang, et al.

Discover how base editors can be optimized to minimize off-target mutations in gene editing! This innovative research by a team of experts, including Chengdong Zhang and Yuan Yang, establishes deep learning models that accurately predict off-target effects, making base editing safer and more effective.

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Playback language: English
Abstract
Base editors, fusion proteins comprising a catalytically impaired Cas9 nuclease and a nucleobase deaminase, frequently cause unwanted Cas9-dependent off-target mutations due to mismatch tolerance between gRNA and the target sequence. This study generated large datasets of off-target editing efficiencies for adenine base editors (ABEs) and cytosine base editors (CBEs) by stably integrating gRNA-off-target pairs into human cells. Deep learning models, ABEdeepoff and CBEdeepoff, were trained using these datasets (54,663 and 55,727 off-targets for ABEs and CBEs, respectively) to predict off-target sites. These models demonstrated strong predictive capabilities (Spearman correlation 0.710–0.859 for endogenous loci) and are accessible via an online web server (http://www.deephf.com/#/bedeep/bedeepoff) to facilitate minimizing off-target effects in base editing.
Publisher
Nature Communications
Published On
Sep 02, 2023
Authors
Chengdong Zhang, Yuan Yang, Tao Qi, Yuening Zhang, Linghui Hou, Jingjing Wei, Jingcheng Yang, Leming Shi, Sang-Ging Ong, Hongyan Wang, Hui Wang, Bo Yu, Yongming Wang
Tags
base editors
off-target mutations
deep learning
gene editing
adenine base editors
cytosine base editors
predictive models

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