AI-driven strategies for Cas12 protein design and genome editing optimization
Abstract
The rise of the CRISPR-CAS system has greatly promoted the development of gene editing technology. Cas12, with its unique molecular characteristics, has become an important editing tool following Cas9. In recent years, artificial intelligence (AI) has been widely applied in Cas12-related research, ranging from protein structure optimization, target design to editing efficiency prediction and off-target risk assessment. AI has demonstrated great potential in enhancing system performance and discovering new mutants. Most of these models were trained and benchmarked in human cell lines, E. coli, or yeast, whereas only a subset of engineering strategies has been validated directly in plants, including soybean hairy roots, rice, and Populus. This mini-review summarizes the latest research progress of AI in the field of Cas12 protein optimization and functional prediction, discusses the opportunities and challenges in its application in plants, and provides new ideas for future AI-driven plant gene editing.
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BibTeX
@article{li2026aidriven,
title = {{AI-driven strategies for Cas12 protein design and genome editing optimization}},
author = {Yang Li and Chi Zhang and Fanli Meng},
journal = {Frontiers in Plant Science},
year = {2026},
month = nov,
doi = {10.3389/fpls.2026.1997664},
url = {https://www.frontiersin.org/articles/10.3389/fpls.2026.1997664},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - AI-driven strategies for Cas12 protein design and genome editing optimization
AU - Yang Li
AU - Chi Zhang
AU - Fanli Meng
PY - 2026
DA - 2026/11/09
JO - Frontiers in Plant Science
DO - 10.3389/fpls.2026.1997664
UR - https://www.frontiersin.org/articles/10.3389/fpls.2026.1997664
AB - The rise of the CRISPR-CAS system has greatly promoted the development of gene editing technology. Cas12, with its unique molecular characteristics, has become an important editing tool following Cas9. In recent years, artificial intelligence (AI) has been widely applied in Cas12-related research, ranging from protein structure optimization, target design to editing efficiency prediction and off-target risk assessment. AI has demonstrated great potential in enhancing system performance and discovering new mutants. Most of these models were trained and benchmarked in human cell lines, E. coli, or yeast, whereas only a subset of engineering strategies has been validated directly in plants, including soybean hairy roots, rice, and Populus. This mini-review summarizes the latest research progress of AI in the field of Cas12 protein optimization and functional prediction, discusses the opportunities and challenges in its application in plants, and provides new ideas for future AI-driven plant gene editing.
SN - 1664-462X
ER -