Automated detection and de novo structure modeling of nucleic acids from cryo-EM maps.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
30 Oct 2024
30 Oct 2024
Historique:
received:
09
05
2024
accepted:
18
10
2024
medline:
31
10
2024
pubmed:
31
10
2024
entrez:
31
10
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 39477926
doi: 10.1038/s41467-024-53721-4
pii: 10.1038/s41467-024-53721-4
doi:
Substances chimiques
RNA
63231-63-0
DNA
9007-49-2
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
9367Subventions
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 32161133002
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 62072199
Informations de copyright
© 2024. The Author(s).
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