Rachael C. Kretsch

The paper describes a Kaggle competition for RNA 3D structure prediction, showing that template-based methods can outperform deep learning approaches.

This paper evaluates blind predictions of dynamic water and ion ensembles around RNA using cryo-EM densities from CASP16, finding that molecular dynamics approaches perform best.

This paper evaluates progress in predicting protein complex structures in CASP16, finding moderate improvements but ongoing challenges in model ranking, stoichiometry prediction, and handling difficult targets.

Across 42 CASP16 nucleic acid targets, accuracy stayed tied to template availability; human experts (Vfold first) led servers and AlphaFold 3, though servers now improve on templates on average.

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.

Two Tetrahymena ribozyme cryo-EM maps (2.2 and 2.3 Å) reveal cross-validated ordered waters and Mg2+ ions, plus reproducible diffuse solvent density resembling molecular dynamics water networks.

Ensemble-function analyses provide a quantitative model for serine protease catalysis, reveal previously unknown conformational features that contribute to their catalysis, and identify a structural motif that underlie these features and has evolved in >100 different enzymes from 32 protein folds.

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

The paper evaluates CASP15 cryoEM predictions using density maps, showing high accuracy but challenges in side-chain positioning and loop modeling.