bims-lances 2024-11-03 papers

on Landscapes from Cryo-EM and Simulations

Issue of 2024–11–03
two papers selected by
James M. Krieger, National Centre for Biotechnology

CryoSTAR: leveraging structural priors and constraints for cryo-EM heterogeneous reconstruction.
Automated detection and de novo structure modeling of nucleic acids from cryo-EM maps.

Nat Methods. 2024 Oct 29.

CryoSTAR: leveraging structural priors and constraints for cryo-EM heterogeneous reconstruction.

Yilai Li, Yi Zhou, Jing Yuan, Fei Ye, Quanquan Gu.

Resolving conformational heterogeneity in cryogenic electron microscopy datasets remains an important challenge in structural biology. Previous methods have often been restricted to working exclusively on volumetric densities, neglecting the potential of incorporating any preexisting structural knowledge as prior or constraints. Here we present cryoSTAR, which harnesses atomic model information as structural regularization to elucidate such heterogeneity. Our method uniquely outputs both coarse-grained models and density maps, showcasing the molecular conformational changes at different levels. Validated against four diverse experimental datasets, spanning large complexes, a membrane protein and a small single-chain protein, our results consistently demonstrate an efficient and effective solution to conformational heterogeneity with minimal human bias. By integrating atomic model insights with cryogenic electron microscopy data, cryoSTAR represents a meaningful step forward, paving the way for a deeper understanding of dynamic biological processes.

DOI: https://doi.org/10.1038/s41592-024-02486-1
Nat Commun. 2024 Oct 30. 15(1): 9367

Automated detection and de novo structure modeling of nucleic acids from cryo-EM maps.

Tao Li, Hong Cao, Jiahua He, Sheng-You Huang.

Cryo-electron microscopy (cryo-EM) is one of the most powerful experimental methods for macromolecular structure determination. However, accurate DNA/RNA structure modeling from cryo-EM maps is still challenging especially for protein-DNA/RNA or multi-chain DNA/RNA complexes. Here we propose a deep learning-based method for accurate de novo structure determination of DNA/RNA from cryo-EM maps at <5 Å resolutions, which is referred to as EM2NA. EM2NA is extensively evaluated on a diverse test set of 50 experimental maps at 2.0-5.0 Å resolutions, and compared with state-of-the-art methods including CryoREAD, ModelAngelo, and phenix.map_to_model. On average, EM2NA achieves a residue coverage of 83.15%, C4' RMSD of 1.06 Å, and sequence recall of 46.86%, which outperforms the existing methods. Moreover, EM2NA is applied to build the DNA/RNA structures with 10 to 5347 nt from an EMDB-wide data set of 263 unmodeled raw maps, demonstrating its ability in the blind model building of DNA/RNA from cryo-EM maps. EM2NA is fast and can normally build a DNA/RNA structure of <500 nt within 10 minutes.

DOI: https://doi.org/10.1038/s41467-024-53721-4