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Improved protein structure prediction using predicted interresidue orientations
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DOI：10.1073/pnas.1914677117.png)
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摘要
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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.
Keyword:
protein structure prediction
deep learning
protein contact prediction
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期刊
P
IF:
9.1
论文数:
10.8W
被引数:
73.5W
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