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

TY - JOUR

T1 - Design of function-regulating RNA via deep learning and AlphaFold 3

AU - Xia, Yan

AU - Liang, Zeyu

AU - Du, Xiaowen

AU - Cao, Dengtian

AU - Li, Jing

AU - Sun, Lichao

AU - Huo, Yi Xin

AU - Guo, Shuyuan

N1 - Publisher Copyright: © The Author(s) 2025. Published by Oxford University Press.

PY - 2025/7/1

Y1 - 2025/7/1

N2 - 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.

AB - 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.

KW - AlphaFold3

KW - CRISPR

KW - RNA design

KW - deep learning

KW - gene editing

UR - https://www.scopus.com/pages/publications/105013524345

U2 - 10.1093/bib/bbaf419

DO - 10.1093/bib/bbaf419

M3 - Article

C2 - 40817912

AN - SCOPUS:105013524345

SN - 1467-5463

VL - 26

JO - Briefings in Bioinformatics

JF - Briefings in Bioinformatics

IS - 4

M1 - bbaf419

ER -