Abstract:
:The prediction of interresidue contacts and distances from coevolutionary data using deep learning has considerably advanced protein structure prediction. Here, we build on these advances by developing a deep residual network for predicting interresidue orientations, in addition to distances, and a Rosetta-constrained energy-minimization protocol for rapidly and accurately generating structure models guided by these restraints. 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. 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. The method promises to be useful for a broad range of protein structure prediction and design problems.
journal_name
Proc Natl Acad Sci U S Aauthors
Yang J,Anishchenko I,Park H,Peng Z,Ovchinnikov S,Baker Ddoi
10.1073/pnas.1914677117subject
Has Abstractpub_date
2020-01-21 00:00:00pages
1496-1503issue
3eissn
0027-8424issn
1091-6490pii
1914677117journal_volume
117pub_type
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