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 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…

• KO
  Knut och Alice Wallenbergs Stiftelse

  Award: 2022.0032
• NI
  National Institutes of Health

  Award: GM141109
• MR
  Medical Research Council

  Award: MC_UP_A025_1013