We have restarted our global Nucleic Acid Strcuture webinar series to bring the expiremental and computational communities together to discuss new developments in the field. Join us this Thursday for the next webinar. Sign up to our mailing list here: groups.google.com/g/casp-rna-sig

CASP16 did challenge predictors with very challenging large RNAs with no previous structures. While no predictors were able to tackle these problems, there are early indications that human experts and automated methods can extract tertiary information from MSAs if sufficiently deep!

Despite, sub-human performance, servers have seen a jump in performance in CASP16; for the first time servers are able to improve on best available structure template.

Throughout the history of NA structure prediction, accuracy has been dependent on template availability, a trend that unfortunately continues in CASP16. Protein structure prediction has shown this is a surmountable challenge.

Although the AF3-server can now predict nucleic acid structure, its performance on nucleic acid monomer structure prediction was average; 12 groups out-performed AF3.

As in CASP15, expert human groups ranked best across targets and categories. These groups are increasingly using deep-learning algorithms to sample structures, which they often then refine or select using physics based algorithms or expert knowledge.

Work by @alissahummer.com, Shujun He, Rongqing Yuan, Jing Zhang, Thomas Karagianes, Qian Cong, Andriy Kryshtafovych, Rhiju Das, and sizable participation of scientists across the world which provided 42 targets and predictions from 65 groups.

Curious about how ensembles have shaped our fundamental understanding of enzyme mechanisms? Check out this amazing work led by Siyuan Du, now published! Congrats all!

www.science.org/doi/10.1126/...

So, we went back to the cryo-EM data and analyzed the solvent shell density, independent of the model. There was excellent agreement between the two maps in the solvent shell indicating the cryo-EM data contained information about dynamic and complex water networks.

With Greg Pintilie, we updated SWIM, an algorithm which uses Segger to identify and model water and ions, to automatically model water and magnesium ions.

Check out our new study on water networks in high resolution cryo-EM RNA data!
@ Chiu Lab @ Das Lab

Additionally, using the same RNA preparation method we also can identify monomeric RNAs, such as the raiA motif, recently solved in parallel by the Bujnicki group, another triumph for the reproducibility of cryo-EM.

The complex formation is not an artifact of high concentrations in cryo-EM. We identify complex formation at sub-cellular concentrations using mass photometry and dynamic light scattering.

To stabilize these RNA complexes, each RNA forms many, interesting quaternary “bridges.” We found many of these bridges are conserved or covary in the RNA family.

Two of the most complex known RNA families, ROOL and GOLLD, are larger and more complex than thought, forming RNA nanocages. What might these microcompartments do in the cell?

The 6 helices of OLE form a bundle of pipes, whose loops weld together at a dimer interface. Known protein-binding motifs surround the outside of this bundle suggesting this RNA-protein complex may be RNA-scaffolded!

We discovered 3 RNA families that form homo-oligomeric RNA-only complexes! Studying nature continues to expand my wildest imagination. Read about it: doi.org/10.1101/2024...
Vivian Wu, Svetlana Shabalina, Hyunbin Lee, Grace Nye, Eugene Koonin, Alex Gao, Rhiju Das, Wah Chiu