Determining the structures of protein complexes can provide insights into their functions and inform the development of vaccines and therapies. Cryo-electron microscopy (cryo-EM) provides near-atomic resolution 3D maps of large molecules, but deriving all-atom structures of protein complexes from cryo-EM data has been challenging.
To accomplish this feat, the authors developed DeepTracer—a fast, accurate, fully automated software tool that leverages a tailored deep convolutional neural network.
The authors applied this technique to a set of 476 cryo-EM maps and compared its performance to that of Phenix—the state-of-the-art cryo-EM model determination method. Deposited model structures available in EMDataResource served as the ground truth.
DeepTracer outperformed Phenix on every metric calculated. DeepTracer created more complete protein structures, achieving 77% coverage of amino acid residues, whereas Phenix correctly determined only 46% of the residues.
DeepTracer also achieved greater spatial accuracy, predicted amino acid types with greater accuracy (50% versus 12%), and connected residues better. The authors obtained similar results when comparing the performance of the two methods at deriving models from 62 coronavirus-related cryo-EM maps.
DeepTracer also modeled large protein complexes exceptionally fast, tracing approximately 60,000 residues within two hours. According to the authors, DeepTracer can help to accelerate the scientific discovery process.
https://www.pnas.org/content/118/2/e2017525118
http://sciencemission.com/site/index.php?page=news&type=view&id=publications%2Fdeeptracer-for-fast-de&filter=22
Fast, fully automated software constructs accurate models of protein structure
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