Journal Article

Automated model building and protein identification in cryo-EM maps

Scheres, Sjors H. W., Kimanius, Dari, Zhang, Rui, Jamali, Kiarash, Brown, Alan, Käll, Lukas

2024

Overview

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

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 101/28 / 631/114/1305 / 631/535/1258/1259 / Amino Acid Sequence / Amino acids / Automation / Computer Graphics / Cryoelectron Microscopy - methods / Cryoelectron Microscopy - standards / Graph neural networks / Humanities and Social Sciences / Identification / Machine Learning / Markov Chains / Microscopy / Models, Molecular / multidisciplinary / Neural networks / Neural Networks, Computer / Nucleotides / Protein Conformation / Protein structure / Proteins / Proteins - chemistry / Proteins - ultrastructure / Science / Science (multidisciplinary)