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CoCoPRED: Coiled-Coil Protein Structural Feature Prediction from Amino Acid Sequence Using Deep Neural Networks.

Bioinformatics(2021)

Shanghai Jiao Tong Univ

Cited 7|Views26
Abstract
MOTIVATION:Coiled-coil is composed of two or more helices that are wound around each other. It widely exists in proteins and has been discovered to play a variety of critical roles in biology processes. Generally, there are three types of structural features in coiled-coil: coiled-coil domain (CCD), oligomeric state and register. However, most of the existing computational tools only focus on one of them.RESULTS:Here, we describe a new deep learning model, CoCoPRED, which is based on convolutional layers, bidirectional long short-term memory, and attention mechanism. It has three networks, i.e. CCD network, oligomeric state network, and register network, corresponding to the three types of structural features in coiled-coil. This means CoCoPRED has the ability of fulfilling comprehensive prediction for coiled-coil proteins. Through the 5-fold cross-validation experiment, we demonstrate that CoCoPRED can achieve better performance than the state-of-the-art models on both CCD prediction and oligomeric state prediction. Further analysis suggests the CCD prediction may be a performance indicator of the oligomeric state prediction in CoCoPRED. The attention heads in CoCoPRED indicate that registers a, b and e are more crucial for the oligomeric state prediction.AVAILABILITY AND IMPLEMENTATION:CoCoPRED is available at http://www.csbio.sjtu.edu.cn/bioinf/CoCoPRED. The datasets used in this research can also be downloaded from the website.SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
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要点】:论文提出了一种新型深度学习模型CoCoPRED,能够从氨基酸序列预测蛋白质卷曲螺旋结构的三种特征,即卷曲螺旋域(CCD)、寡聚状态和寄存器,实现了对卷曲螺旋蛋白质结构的全面预测,并优于现有模型。

方法】:作者采用卷积层、双向长短时记忆网络和注意力机制构建了CoCoPRED模型,包含三个子网络分别针对CCD、寡聚状态和寄存器三种结构特征。

实验】:通过5折交叉验证实验,使用公开数据集,证明了CoCoPRED在CCD预测和寡聚状态预测方面均优于现有先进模型。