SIM: Discovery of novel RNA-targeting argonautes by self-iterative learning from scarce data

Acta Pharmaceutica Sinica B 2026 Open access

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Authors: Shuze Peng, FeiMing Huang, Nuolan Li, Karl Luigi Loza Vidaurre, Luer Chen, Yu Yang, Jiaying Hu, Yanyu Kou, Wei He, Shiwei Wang, Lei Shi, Kehao Tao
+10 more Bo Sun, Xiaoxuan Song, Hao Yang, Hainan Zhang, Lin Yang, Zixin Deng, Yanqiang Han, Yan Feng, Qian Liu, Jinjin Li
DOI: 10.1016/j.apsb.2026.01.009
Cited by: 0
References: 49

Abstract

Ago) confirmed its RNA cleavage activity and a distinctive RNA modification-sensing capability. We leveraged this latter finding to develop a rapid, cost-effective method for quantifying modified RNAs. Our study not only expands the repertoire of RNA-targeting tools but also establishes SIM as a generalizable framework for protein function prediction under data-scarce conditions. This work has broad implications for both RNA biotechnology and the application of machine learning in data-limited fields.

Fields of Study & Subjects

CRISPR and Genetic Engineering RNA regulation and disease RNA modifications and cancer

Publication details

Journal / source
Acta Pharmaceutica Sinica B
Volume
16
Issue
4
Pages
2282–2298
Publication date
13 January 2026
Language
EN