Mao Yufeng, Chu Guangyun, Liang Qingling, Liu Ye, Yang Yi, Liao Xiaoping, Wang Meng
Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.
Sheng Wu Gong Cheng Xue Bao. 2025 Mar 25;41(3):949-967. doi: 10.13345/j.cjb.240865.
With the rapid advancement of synthetic biology, CRISPR-Cas systems have emerged as a powerful tool for gene editing, demonstrating significant potential in various fields, including medicine, agriculture, and industrial biotechnology. This review comprehensively summarizes the significant progress in applying artificial intelligence (AI) technologies to the design, mining, and modification of CRISPR-Cas systems. AI technologies, especially machine learning, have revolutionized sgRNA design by analyzing high-throughput sequencing data, thereby improving the editing efficiency and predicting off-target effects with high accuracy. Furthermore, this paper explores the role of AI in sgRNA design and evaluation, highlighting its contributions to the annotation and mining of CRISPR arrays and Cas proteins, as well as its potential for modifying key proteins involved in gene editing. These advancements have not only improved the efficiency and precision of gene editing but also expanded the horizons of genome engineering, paving the way for intelligent and precise genome editing.
随着合成生物学的迅速发展，CRISPR-Cas系统已成为一种强大的基因编辑工具，在医学、农业和工业生物技术等各个领域展现出巨大潜力。本综述全面总结了将人工智能（AI）技术应用于CRISPR-Cas系统的设计、挖掘和改造方面取得的重大进展。人工智能技术，尤其是机器学习，通过分析高通量测序数据彻底改变了sgRNA设计，从而提高了编辑效率并高精度预测脱靶效应。此外，本文探讨了人工智能在sgRNA设计和评估中的作用，强调了其对CRISPR阵列和Cas蛋白的注释与挖掘的贡献，以及其对参与基因编辑的关键蛋白进行改造的潜力。这些进展不仅提高了基因编辑的效率和精度，还拓展了基因组工程的视野，为智能和精确的基因组编辑铺平了道路。
