Automated model building and protein identification in cryo-EM maps

Kiarash Jamali (), Lukas Käll, Rui Zhang, Alan Brown, Dari Kimanius () and Sjors H. W. Scheres ()
Additional contact information
Kiarash Jamali: MRC Laboratory of Molecular Biology
Lukas Käll: KTH Royal Institute of Technology
Rui Zhang: Washington University in St Louis
Alan Brown: Harvard Medical School
Dari Kimanius: MRC Laboratory of Molecular Biology
Sjors H. W. Scheres: MRC Laboratory of Molecular Biology

Nature, 2024, vol. 628, issue 8007, 450-457

Abstract: Abstract Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention in three-dimensional computer graphics programs1,2. Here we present ModelAngelo, a machine-learning approach for automated atomic model building in cryo-EM maps. By combining information from the cryo-EM map with information from protein sequence and structure in a single graph neural network, ModelAngelo builds atomic models for proteins that are of similar quality to those generated by human experts. For nucleotides, ModelAngelo builds backbones with similar accuracy to those built by humans. By using its predicted amino acid probabilities for each residue in hidden Markov model sequence searches, ModelAngelo outperforms human experts in the identification of proteins with unknown sequences. ModelAngelo will therefore remove bottlenecks and increase objectivity in cryo-EM structure determination.

Date: 2024
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.nature.com/articles/s41586-024-07215-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:628:y:2024:i:8007:d:10.1038_s41586-024-07215-4

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-024-07215-4

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().