Yuan Wu

This paper demonstrates that deep learning can accurately design RNA pseudoknots, achieving results comparable to human experts in a competition.

The paper uses cryo-EM to study a ribozyme's structure, revealing how it evolved to polymerize nucleotides and how mutations can enhance its function.

The paper identifies a structured RNA region, MLS, necessary and sufficient for Mbp mRNA transport to oligodendrocyte projections and shows it interacts with hnRNP-F, suggesting a structural switch model for mRNA transport to translation.

Structure providers for 18 CASP16 nucleic acid targets find blind predictions capture secondary structure but miss non-canonical interactions, backbone kinks, ligand sites, and intermolecular interfaces.

Cryo-EM at 2.9-3.1 Å shows bacterial OLE, ROOL and GOLLD RNAs self-assemble without protein into a dimer, an 8-chain nanocage and a 14-chain nanocage.

The paper introduces RibonanzaNet, a deep learning model trained on crowdsourced RNA chemical mapping data to predict RNA structure and related properties.