Improved protein model quality assessment by integrating sequential and pairwise features using deep learning
Improved protein model quality assessment by integrating sequential and pairwise features using deep learning
复制标题
DOI：
10.1093/bioinformatics/btaa1037
复制
发表时间：
2020-12-01
期刊：
影响因子：
5.8
通讯作者：
Xu, Jinbo
中科院分区：
文献类型：
--
作者：
Jing, Xiaoyang;Xu, Jinbo
动机：在缺乏实验结构的情况下准确估计蛋白质模型的质量不仅对模型评估和选择很重要，而且对模型改进也很有用。通过引入新的特征和算法（特别是深度神经网络），已经取得了稳步进展，但质量评估（QA）的准确性仍然不是很令人满意，特别是对硬蛋白质targets.Results的局部QA：我们提出了一种新的基于单模型的QA方法ResNetQA的局部和全局质量评估。我们的方法通过使用由1D和2D卷积残差神经网络（ResNet）组成的深度神经网络集成顺序和成对特征来预测模型质量。2D ResNet模块从成对特征中提取有用的信息，例如模型衍生的距离图，协同进化信息和序列的预测距离潜力。1D ResNet用于从2D ResNet生成的序列特征和合并的成对信息预测局部（全局）模型质量。在CASP12和CASP13数据集上测试，我们的实验结果表明，我们的方法大大优于现有的最先进的方法。我们的消融研究表明，2D ResNet模块和成对特征在改善模型质量评估方面发挥着重要作用。
Motivation: Accurately estimating protein model quality in the absence of experimental structure is not only important for model evaluation and selection but also useful for model refinement. Progress has been steadily made by introducing new features and algorithms (especially deep neural networks), but the accuracy of quality assessment (QA) is still not very satisfactory, especially local QA on hard protein targets.Results: We propose a new single-model-based QA method ResNetQA for both local and global quality assessment. Our method predicts model quality by integrating sequential and pairwise features using a deep neural network composed of both 1D and 2D convolutional residual neural networks (ResNet). The 2D ResNet module extracts useful information from pairwise features such as model-derived distance maps, co-evolution information, and predicted distance potential from sequences. The 1D ResNet is used to predict local (global) model quality from sequential features and pooled pairwise information generated by 2D ResNet. Tested on the CASP12 and CASP13 datasets, our experimental results show that our method greatly outperforms existing state-of-the-art methods. Our ablation studies indicate that the 2D ResNet module and pairwise features play an important role in improving model quality assessment.