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2 claims from 1 paper

1. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Improved protein structure prediction using predicted interresidue orientations

   Yang, Anishchenko, Park, Peng, Ovchinnikov and Baker · Proceedings of the National Academy of Sciences · 2020

   The authors built a deep network that predicts distances and orientations between amino acid pairs, then used it to guide Rosetta modelling of protein structures, reporting better benchmark results.

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   1. UncheckedOn CASP13- and CAMEO-derived benchmark sets, the authors' method outperforms all previously described protein structure-prediction methods.“In benchmark tests on 13th Community-Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13)- and Continuous Automated Model Evaluation (CAMEO)-derived sets, the method outperforms all previously described structure-prediction methods.”
   2. UncheckedA network trained only on natural proteins gives higher probability to de novo-designed proteins, picking out key fold-determining residues.“Although trained entirely on native proteins, the network consistently assigns higher probability to de novo-designed proteins, identifying the key fold-determining residues and providing an independent quantitative measure of the “ideality” of a protein structure.”