2025年6月10日  星期二
位置:首页 > 科研成果 > 论文  
 

Discovery of Deaminase Functions by Structure-Based Protein Clustering

Jiaying Huang, Qiupeng Lin, Hongyuan Fei, Zixin He, Hu Xu, Yunjia Li, Kunli Qu, Peng Han, Qiang Gao, Boshu Li, Guanwen Liu, Lixiao Zhang, Jiacheng Hu, Rui Zhang, Erwei Zuo, Yonglun Luo, Yidong Ran, Jin-Long Qiu, Kevin Tianmeng Zhao, and Caixia Gao


Cell
DOI:10.1016/j.cell.2023.05.041


Abstract

The elucidation of protein function and its exploitation in bioengineering have greatly advanced the life sciences. Protein mining efforts generally rely on amino acid sequences rather than protein structures. We describe here the use of AlphaFold2 to predict and subsequently cluster an entire protein family based on predicted structure similarities. We selected deaminase proteins to analyze and identified many previously unknown properties. We were surprised to find that most proteins in the DddA-like clade were not double-stranded DNA deaminases. We engineered the smallest single-strand specific cytidine deaminase, enabling efficient cytosine base editor (CBE) to be packaged into a single adeno-associated virus (AAV). Importantly, we profiled a deaminase from this clade that edits robustly in soybean plants, which previously was inaccessible to CBEs. These discovered deaminases based on AI-assisted structural predictions, greatly expand the utility of base editors for therapeutic and agricultural applications.