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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 programs 1,2 . 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.

Titel:	Automated model building and protein identification in cryo-EM maps.
Autor/in / Beteiligte Person:	Jamali, K ; Käll, L ; Zhang, R ; Brown, A ; Kimanius, D ; Scheres, SHW
Zeitschrift:	Nature, Jg. 628 (2024-04-01), Heft 8007, S. 450-457
Veröffentlichung:	Basingstoke : Nature Publishing Group ; <i>Original Publication</i>: London, Macmillan Journals ltd., 2024
Medientyp:	academicJournal
ISSN:	1476-4687 (electronic)
DOI:	10.1038/s41586-024-07215-4
Schlagwort:	Amino Acid Sequence Markov Chains Neural Networks, Computer Protein Conformation Computer Graphics Cryoelectron Microscopy methods Cryoelectron Microscopy standards Machine Learning Models, Molecular Proteins chemistry Proteins ultrastructure
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Comparative Study; Journal Article Language: English [Nature] 2024 Apr; Vol. 628 (8007), pp. 450-457. <i>Date of Electronic Publication: </i>2024 Feb 26. MeSH Terms: Cryoelectron Microscopy* / methods ; Cryoelectron Microscopy* / standards ; Machine Learning* ; Models, Molecular* ; Proteins* / chemistry ; Proteins* / ultrastructure ; Amino Acid Sequence ; Markov Chains ; Neural Networks, Computer ; Protein Conformation ; Computer Graphics Comments: Update of: bioRxiv. 2023 Oct 17;:. (PMID: 37292681) References: Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of coot. Acta Crystallogr. D 66, 486–501 (2010). (PMID: 20383002285231310.1107/S0907444910007493) ; Croll, T. I. Isolde: a physically realistic environment for model building into low-resolution electron-density maps. Acta Crystallogr. D 74, 519–530 (2018). (PMID: 10.1107/S2059798318002425) ; Nakane, T. et al. 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Automated model building and protein identification in cryo-EM maps.