Automated model building and protein identification in cryo-EM maps

• 成果类型: Article
• 署名作者: Jamali, Kiarash; Kaell, Lukas; Zhang, Rui; Brown, Alan; Kimanius, Dari; Scheres, Sjors H. W.
• 署名单位: MRC Laboratory Molecular Biology; Royal Institute of Technology; Washington University (WUSTL); Harvard University; Harvard Medical School
• 刊物名称: Nature
• ISSN/ISSBN: 0028-5509
• DOI: 10.1038/s41586-024-07215-4
• 发表日期: 2024-04-11
• 关键词: biology; SPACE

摘要

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. ModelAngelo builds atomic models and identifies proteins with unknown sequences in cryo-EM maps.