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It is shown that intricate RNA structures can be generated with current deep learning tools through accurate de novo design of pseudoknot secondary structures, suggesting that some difficult RNA design tasks may be tractable without first solving RNA 3D structure prediction.
Key dynamics directly linked to biological function, including enzyme catalysis and ligand binding, are particularly well predicted by Dyna-1, which parallels the findings that residues experiencing µs-ms exchange are more conserved.
Nuclear magnetic resonance (NMR) spectroscopy yields rich residue-level information on biomolecular dynamics and chemical environments, two frontiers for quantitative predictive methods in biochemistry. Decades of data are publicly archived in the Biological Magnetic Resonance Data Bank (BMRB)1, yet in practice, this i...
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