The key innovation lies in introducing a deep learning approach for predicting RNA tertiary structure. While traditional methods relied on homologous modelling or physics-based folding simulations, ...

Deep learning is solving biology’s deepest secrets at breathtaking speed. Just a month ago, DeepMind cracked a 50-year-old grand challenge: protein folding. A week later, they produced a totally ...

NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation. Nature Communications, 2025; 16 (1) DOI: 10.1038/s41467-025-56261-7 ...

By modeling RNA's 3D structure, we can help bridge the gap created by the lack of experimentally determined structures, advancing research on RNA and its crucial roles in life and health.” ...

Acceptor Stem: The 3' end of the tRNA molecule, where the amino acid is attached. D-loop: A loop containing dihydrouridine, which contributes to the L-shaped tertiary structure. Anticodon Loop: ...

Magnesium Contact Ions Stabilize the Tertiary Structure of Transfer RNA: Electrostatics Mapped by Two-Dimensional Infrared Spectra and Theoretical Simulations. The Journal of Physical Chemistry B ...

Structural RNA has lower folding energy than random RNA of the same dinucleotide frequency. RNA, 11 (5):578-591 (2005). F. Ferre and P. Clote. Disulfide connectivity prediction using secondary ...

Currently, there are more than 30 million known RNA sequences in the RNA central database, but only less than 500 (or 0.0017 per cent) have experimentally solved structures.