Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 117, Issue 3, Pages 1496-1503Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1914677117
Keywords
protein structure prediction; deep learning; protein contact prediction
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Funding
- National Natural Science Foundation of China [NSFC 11871290, 61873185]
- Fok Ying-Tong Education Foundation [161003]
- Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin
- Thousand Youth Talents Plan of China
- China Scholarship Council
- Fundamental Research Funds for the Central Universities
- National Institute of General Medical Sciences [R01-GM092802-07]
- National Institute of Allergy and Infectious Diseases [HHSN272201700059C]
- Schmidt Family Foundation
- Office of the Director of the National Institutes of Health [DP5OD026389]
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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.
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