ArticleBriefings in bioinformatics2025
Design of function-regulating RNA via deep learning and AlphaFold 3.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- CsDRMH3 in tea plant (Camellia sinensis): functional study on mediating caffeine biosynthesis in relation to bud dormancy status.BMC plant biology · 2026Article
- Cat_Wiz: a stereochemistry-guided toolkit for locating, diagnosing, and annotating Mg2+ ions in RNA structures.Nucleic acids research · 2026Article
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Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
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.
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Registered trials
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