Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks
Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks
复制标题
残差二维双向长短期记忆与卷积神经网络耦合准确预测蛋白质接触图
DOI：
10.1093/bioinformatics/bty481
复制
发表时间：
2018-12-01
期刊：
影响因子：
5.8
通讯作者：
Zhou, Yaoqi
中科院分区：
文献类型：
--
作者：
Hanson, Jack;Peliwal, Kuldip;Zhou, Yaoqi
动机
蛋白质接触图的准确预测在很大程度上取决于从靶残基对的周围残基捕获尽可能多的上下文信息。最近，在最新的结构预测技术的关键评估（CASP 12）中，发现超深残差卷积网络是最先进的，用于蛋白质接触图预测，试图在每个残基对处提供蛋白质范围的背景。循环神经网络在最近的蛋白质残基分类问题中取得了巨大成功，因为它们能够通过长蛋白质序列（尤其是长短期记忆（LSTM）细胞）传播信息。在这里，我们提出了一种新的蛋白质接触图预测方法，通过将残差卷积网络与二维残差双向递归LSTM网络堆叠，并使用一维基于序列和二维基于进化耦合的信息。
结果
我们表明，所提出的方法在验证和独立测试集上实现了鲁棒性能，在所有测试中，受试者工作特征曲线（AUC）下的面积> 0.95。当与用于独立测试228种蛋白质的几种最先进的方法相比时，该方法产生的AUC值为0.958，而次佳方法获得的AUC为0.909。更重要的是，改善是在所有序列位置分离的接触。具体而言，分别观察到最高L scin 10预测的精确度增加了8.95%，5.65%和2.84%，而不是短距离，中距离和长距离接触的下一个最佳预测。这证实了ResNets聚集短程关系和2D-BRLSTM在整个蛋白质接触图“图像”中传播长程依赖关系的有用性。
可用性和实施
SPOT-联系服务器URL：http://sparks-lab.org/jack/server/SPOT-Contact/。
补充资料
补充数据可在Bioinformatics在线获得。
Motivation
Accurate prediction of a protein contact map depends greatly on capturing as much contextual information as possible from surrounding residues for a target residue pair. Recently, ultra-deep residual convolutional networks were found to be state-of-the-art in the latest Critical Assessment of Structure Prediction techniques (CASP12) for protein contact map prediction by attempting to provide a protein-wide context at each residue pair. Recurrent neural networks have seen great success in recent protein residue classification problems due to their ability to propagate information through long protein sequences, especially Long Short-Term Memory (LSTM) cells. Here, we propose a novel protein contact map prediction method by stacking residual convolutional networks with two-dimensional residual bidirectional recurrent LSTM networks, and using both one-dimensional sequence-based and two-dimensional evolutionary coupling-based information.
Results
We show that the proposed method achieves a robust performance over validation and independent test sets with the Area Under the receiver operating characteristic Curve (AUC) > 0.95 in all tests. When compared to several state-of-the-art methods for independent testing of 228 proteins, the method yields an AUC value of 0.958, whereas the next-best method obtains an AUC of 0.909. More importantly, the improvement is over contacts at all sequence-position separations. Specifically, a 8.95%, 5.65% and 2.84% increase in precision were observed for the top L∕10 predictions over the next best for short, medium and long-range contacts, respectively. This confirms the usefulness of ResNets to congregate the short-range relations and 2D-BRLSTM to propagate the long-range dependencies throughout the entire protein contact map 'image'.
Availability and implementation
SPOT-Contact server url: http://sparks-lab.org/jack/server/SPOT-Contact/.
Supplementary information
Supplementary data are available at Bioinformatics online.