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Automated model building and protein identification in cryo-EM maps
Kiarash Jamali1, Lukas Käll2, Rui Zhang3
1MRC Laboratory of Molecular Biology, Cambridge, UK.
Biorxiv : the Preprint Server for Biology
|June 9, 2023
Summary
ModelAngelo is a new machine-learning tool that automates atomic model building in electron cryo-microscopy (cryo-EM) maps. It matches human expert quality for proteins and improves nucleotide backbone accuracy, speeding up structure determination.
Area of Science:
- Structural Biology
- Computational Biology
- Biophysics
Background:
- Interpreting electron cryo-microscopy (cryo-EM) maps requires significant expertise and manual effort.
- Accurate atomic model building is crucial for understanding protein function and biological mechanisms.
Approach:
- Developed ModelAngelo, a machine-learning approach utilizing graph neural networks.
- Integrated cryo-EM map data with protein sequence and structural information.
- Applied ModelAngelo to both protein and nucleotide components within cryo-EM maps.
Key Points:
- ModelAngelo builds atomic models for proteins with quality comparable to human experts.
- Achieved similar accuracy to human experts for nucleotide backbone construction.
- Outperformed human experts in identifying proteins with unknown sequences using predicted amino acid probabilities and hidden Markov model searches.
Conclusions:
- ModelAngelo automates and enhances atomic model building in cryo-EM.
- The tool reduces bottlenecks and increases objectivity in cryo-EM structure determination.
- Facilitates faster and more reliable structural biology research.
