Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, USA.
Department of Biological Sciences, Auburn University, Auburn, AL 36849, USA.
Bioinformatics. 2020 Jul 1;36(Suppl_1):i285-i291. doi: 10.1093/bioinformatics/btaa455.
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.
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.
https://github.com/Bhattacharya-Lab/QDeep.
Supplementary data are available at Bioinformatics online.
蛋白质模型质量评估在很多方面为蛋白质结构预测提供信息。尽管它们紧密耦合，但现有的模型质量评估方法并没有利用残基间距离信息，也没有利用深度学习的最新技术突破，这些突破最近彻底改变了蛋白质结构预测。
我们通过利用堆叠深度残差神经网络（ResNets）的强大功能，提出了一种新的基于距离的单模型质量评估方法 QDeep。我们的方法首先使用堆叠深度 ResNets 在多个预定义的误差阈值下执行残基级别的整体误差分类，然后结合来自各个误差分类器的预测值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上，在多个准确性度量标准中始终优于现有的最先进方法，包括 ProQ2、ProQ3、ProQ3D、ProQ4、3DCNN、MESHI 和 VoroMQA；并且预测的距离信息显著有助于提高 QDeep 的性能。
https://github.com/Bhattacharya-Lab/QDeep。
补充数据可在 Bioinformatics 在线获得。
