QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks
QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks
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DOI：
10.1093/bioinformatics/btaa455
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发表时间：
2020-07-01
期刊：
影响因子：
5.8
通讯作者：
Bhattacharya, Debswapna
中科院分区：
文献类型：
--
作者：
Shuvo, Md Hossain;Bhattacharya, Sutanu;Bhattacharya, Debswapna
动机：蛋白质模型质量估计在许多方面为蛋白质结构预测提供了信息。尽管它们是紧密耦合的，但现有的模型质量估计方法并没有利用残基间距离信息或深度学习的最新技术突破，而深度学习最近已经彻底改变了蛋白质结构prediction.Results：我们提出了一种新的基于距离的单模型质量估计方法，称为QDeep，它利用了堆栈深度残差神经网络（ResNets）的功能。我们的方法首先采用堆叠的深度ResNets在多个预定义的错误阈值下执行残差级集成错误分类，然后结合来自各个错误分类器的预测来估计蛋白质结构模型的质量。实验结果表明，我们的方法在多个独立的测试数据集中，在广泛的准确性测量中，始终优于现有的最先进的方法，包括ProQ 2，ProQ 3，ProQ 3D，ProQ 4，3DCNN，MESHI和VoroMQA;并且预测的距离信息显着有助于提高QDeep的性能。
Motivation: Protein model quality estimation, in many ways, informs protein structure prediction. Despite their tight coupling, existing model quality estimation methods do not leverage inter-residue distance information or the latest technological breakthrough in deep learning that has recently revolutionized protein structure prediction.Results: We present a new distance-based single-model quality estimation method called QDeep by harnessing the power of stacked deep residual neural networks (ResNets). Our method first employs stacked deep ResNets to perform residue-level ensemble error classifications at multiple predefined error thresholds, and then combines the predictions from the individual error classifiers for estimating the quality of a protein structural model. Experimental results show that our method consistently outperforms existing state-of-the-art methods including ProQ2, ProQ3, ProQ3D, ProQ4, 3DCNN, MESHI, and VoroMQA in multiple independent test datasets across a wide-range of accuracy measures; and that predicted distance information significantly contributes to the improved performance of QDeep.