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Automated model building and protein identification in cryo-EM maps.

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.

SUBMITTER: Jamali K

PROVIDER: S-EPMC10245678 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Automated model building and protein identification in cryo-EM maps.

Jamali Kiarash K Käll Lukas L Zhang Rui R Brown Alan A Kimanius Dari D Scheres Sjors H W SHW

bioRxiv : the preprint server for biology 20231017

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 ...[more]

PMID: 37292681

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