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However, the intricate structure\u2013function 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\u2013RNA-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.<\/jats:p>","DOI":"10.1093\/bib\/bbaf419","type":"journal-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T11:53:23Z","timestamp":1754049203000},"source":"Crossref","is-referenced-by-count":11,"title":["Design of function-regulating RNA via deep learning and AlphaFold 3"],"prefix":"10.1093","volume":"26","author":[{"given":"Yan","family":"Xia","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, Aerospace Center Hospital, College of Life Science, Beijing Institute of Technology , No. 5 South Zhongguancun Street, Haidian District, Beijing, Beijing Municipality 100081 ,","place":["China"]},{"name":"Zhongguancun Artificial Intelligence Research Institute , Building 5, Yard 17, Daniu\u00adfang Second Ring Road, Haidian District, Beijing 100080 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