Design of function-regulating RNA via deep learning and AlphaFold 3

作者：Yan Xia, Zeyu Liang, Xiaowen Du, Dengtian Cao, Jing Li, Lichao Sun, Yi‐Xin Huo, Shuyuan Guo · 发表于：Briefings in Bioinformatics · 年份：2025 · DOI：10.1093/bib/bbaf419 · 被引用次数：12 · 研究领域：RNA and protein synthesis mechanisms、CRISPR and Genetic Engineering、RNA regulation and disease

RNAs are programmable macromolecules that play diverse regulatory roles in living organisms. However, the intricate structure-function relationships underlying their regulatory activities pose significant challenges for RNA design. Here, we introduce a computational framework that integrates deep learning and energy-based methods to enhance the sequence diversity of sgRNAs designs. Our approach demonstrates high editing efficiencies of up to 75% for gene knockouts, 100% for large fragment deletions, and 62.5% for multiplex gene editing using the designed sgRNAs. Molecular dynamic simulations suggested the stability of DNA-RNA-protein complex is essential to the functionality of designed RNAs. Moreover, we reveal that the confidence metrics of AlphaFold 3 can effectively distinguish functional sequences, enabling one-shot design of crRNAs. This work presents an efficient strategy for designing regulatory RNAs with complex interactions and establishes the potential of AlphaFold 3 in advancing RNA design.