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Keyword: CASP13 ×Clear all

4 claims from 2 papers

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

   Improved protein structure prediction using potentials from deep learning

   Senior, Evans, Jumper et al. · Nature · 2020

   The authors trained a neural network to predict distances between amino acid residues, built a potential from them, and used it in AlphaFold, which performed well in the CASP13 blind assessment.

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   1. UncheckedA potential built from predicted residue distances can be optimised by simple gradient descent to produce protein structures, without complex sampling.“We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures.”
   2. UncheckedIn the CASP13 blind assessment, AlphaFold reached TM scores of 0.7 or higher on 24 of 43 free modelling domains; the next best method managed 14 of 43.“In the recent Critical Assessment of Protein Structure Prediction 5 (CASP13)-a blind assessment of the state of the field-AlphaFold created high-accuracy structures (with template modelling (TM) scores 6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which…”

2. 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.”