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Automated model building and protein identification in cryo-EM maps

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Jamali, Kiarash, Käll, Lukas, Zhang, Rui, Brown, Alan, Kimanius, Dari, Scheres, Sjors H.W.·2023·Nature

DOI 10.1101/2023.05.16.541002PMC PMC11006616

Preprint — published as 10.1038/s41586-024-07215-4 · Paper page

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Abstract

Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention. 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 as those generated by human experts. For nucleotides, ModelAngelo builds backbones with similar accuracy as 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 thus remove bottlenecks and increase objectivity in cryo-EM structure determination.

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14. Lin, Z., et al. Language models of protein sequences at the scale of evolution enable accurate structure prediction. bioRxiv (2022). URL https://www.biorxiv.org/content/early/2022/07/21/2022.07.20.500902. https://www.biorxiv.org/content/early/2022/07/21/2022.07.20.500902.full.pdf.
Jamali, K. , Kimanius, D. & Scheres, S. H . A graph neural network approach to automated model building in cryo-EM maps. In The Eleventh International Conference on Learning Representations (2023). URL https://openreview.net/forum?id=65XDF_nwI61.
2019, Macromolecular structure determination using x-rays, neutrons and electrons: recent developments in phenix, Acta Crystallographica Section D: Structural Biology, 75, 861, 10.1107/S2059798319011471 · DOI · Paper page

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Authors: Jamali, K., Käll, L., Zhang, R., Brown, A., Kimanius, D., & Scheres, S. H. W.
Affiliations: MRC Laboratory of Molecular Biology (GB)
Science for Life Laboratory (SE)
Washington University in St. Louis (US)
Harvard University (US)
Corresponding author: Jamali, Kiarash
Kimanius, Dari
Scheres, Sjors H.W.
ORCID: Jamali, Kiarash
Käll, Lukas
Zhang, Rui
Brown, Alan
Kimanius, Dari
Scheres, Sjors H.W.
Venue: Nature
Publisher: openRxiv
Published: 2023-05-16
Volume: 628
Issue: 8007
Pages: 450-457
ISSN: 0028-0836 (ISSN-L)
DOI: 10.1101/2023.05.16.541002
PMC: PMC11006616
OpenAlex: W4392155415
Cited by: 686
References: 72
Citations by year: 2026: 259 · 2025: 264 · 2024: 134 · 2023: 28
FWCI: 120.08
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
Locations: Nature (publishedVersion, cc-by)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Apollo (University of Cambridge) (acceptedVersion, cc-by)
PubMed Central (submittedVersion, cc-by)
Digital Commons@Becker (Washington University School of Medicine) (submittedVersion, cc-by)
RePEc: Research Papers in Economics (submittedVersion)
Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
Funders: European Commission (HORIZON2020, 895412)
Knut och Alice Wallenbergs Stiftelse (2022.0032)
National Institutes of Health
Medical Research Council (MC_UP_A025_1013)
National Institute of General Medical Sciences (R01GM138854, R01GM141109)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Advanced Electron Microscopy Techniques and Applications
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
Keywords: cryo-EM density maps, protein identification, graph neural networks, protein structure determination, machine learning

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