小柯机器人

瑞士苏黎世大学Gerald Schwank和Michael Krauthammer共同合作，最近取得重要工作进展。他们研究通过深度学习预测先导编辑的效率和编辑产物的纯度。相关研究成果2023年1月16日在线发表于《自然—生物技术》杂志上。

据介绍，先导编辑是一种通用的基因组编辑工具，但需要对先导编辑器的引导RNA（pegRNA）进行实验优化以实现较高的编辑效率。

研究人员进行了高通量筛选，以分析92423个pegRNA对13349个人类致病性突变（包括碱基替换、插入和缺失）的主要编辑结果。基于该数据集，研究人员识别出了影响先导编辑的序列背景特征，并训练了PRIDICT（先导编辑pegRNA预测工具），这是一个基于注意力的双向递归神经网络。PRIDICT可靠地预测了所有小规模遗传变化的编辑效率，对于靶标和非靶标的编辑，Spearman R分别为0.85和0.78。

总之，研究人员在内源性编辑位点和外部数据集上验证了PRIDICT，并显示PRIDICT评分高（>70）和低（<70）的pegRNA在体外不同细胞类型（12倍）和体内肝细胞（10倍）中编辑效率显著提高，突出了PRIDIT在基础和转化研究应用中的价值。

附：英文原文

Title: Predicting prime editing efficiency and product purity by deep learning

Author: Mathis, Nicolas, Allam, Ahmed, Kissling, Lucas, Marquart, Kim Fabiano, Schmidheini, Lukas, Solari, Cristina, Balzs, Zsolt, Krauthammer, Michael, Schwank, Gerald

Issue&Volume: 2023-01-16

Abstract: Prime editing is a versatile genome editing tool but requires experimental optimization of the prime editing guide RNA (pegRNA) to achieve high editing efficiency. Here we conducted a high-throughput screen to analyze prime editing outcomes of 92,423 pegRNAs on a highly diverse set of 13,349 human pathogenic mutations that include base substitutions, insertions and deletions. Based on this dataset, we identified sequence context features that influence prime editing and trained PRIDICT (prime editing guide prediction), an attention-based bidirectional recurrent neural network. PRIDICT reliably predicts editing rates for all small-sized genetic changes with a Spearman’s R of 0.85 and 0.78 for intended and unintended edits, respectively. We validated PRIDICT on endogenous editing sites as well as an external dataset and showed that pegRNAs with high (>70) versus low (<70) PRIDICT scores showed substantially increased prime editing efficiencies in different cell types in vitro (12-fold) and in hepatocytes in vivo (tenfold), highlighting the value of PRIDICT for basic and for translational research applications.

DOI: 10.1038/s41587-022-01613-7

Source: https://www.nature.com/articles/s41587-022-01613-7

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