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Improved protein structure prediction using predicted interresidue orientations.
Proceedings of the National Academy of Sciences of the United States of America ( IF 9.4 ) Pub Date : 2020-01-02 , DOI: 10.1073/pnas.1914677117
Jianyi Yang 1 , Ivan Anishchenko 2, 3 , Hahnbeom Park 2, 3 , Zhenling Peng 4 , Sergey Ovchinnikov 5 , David Baker 3, 6, 7
Affiliation  

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.

中文翻译:

使用预测的残基间方向改进的蛋白质结构预测。

使用深度学习预测残基间接触和与进化数据的距离,具有相当先进的蛋白质结构预测。在这里,我们通过开发深度的残差网络(用于预测距离之间的残基间方向)以及罗塞塔约束的能量最小化协议来快速,准确地生成受这些约束指导的结构模型,从而在这些进步的基础上进一步发展。在第13个社区范围内对蛋白质结构预测技术(CASP13)和连续自动模型评估(CAMEO)的技术进行关键性评估的实验的基准测试中,该方法的性能优于先前描述的所有结构预测方法。尽管网络完全是针对天然蛋白质进行训练的,但网络始终为从头设计的蛋白质分配更高的概率,鉴定决定关键折叠的残基并提供蛋白质结构“理想性”的独立定量度量。该方法有望用于广泛的蛋白质结构预测和设计问题。
更新日期:2020-01-22
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