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Automated model building and protein identification in cryo-EM maps
Open accessPreprint — published as 10.1038/s41586-024-07215-4 · Paper page
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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Self-citation
Metadata
- 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.
- Venue
- Nature
- Publisher
- openRxiv
- Published
- 2023-05-16
- Volume
- 628
- Issue
- 8007
- Pages
- 450-457
- ISSN
- 0028-0836 (ISSN-L)
- 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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