Prediction of protein secondary structure based on an improved channel attention and multiscale convolution module.
Prediction of protein secondary structure based on an improved channel attention and multiscale convolution module.
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基于改进的通道注意力和多尺度卷积模块的蛋白质二级结构预测
DOI：
10.3389/fbioe.2022.901018
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发表时间：
2022
影响因子：
5.7
通讯作者：
Yao, Shaowen
中科院分区：
文献类型：
--
作者：
Jin, Xin;Guo, Lin;Jiang, Qian;Wu, Nan;Yao, Shaowen
关键词：
蛋白质二级结构预测是蛋白质科学中的一个关键问题。蛋白质二级结构预测（PSSP）的目的是构建一个函数，将氨基酸序列映射到二级结构中，从而根据氨基酸序列得到蛋白质的二级结构。在深度学习的驱动下，近年来蛋白质二级结构的预测精度得到了很大的提高。为了探索一种新的PSSP技术，本研究将对抗博弈的概念引入到二级结构预测中，提出了一种基于条件生成对抗网络（GAN）的预测模型。我们引入了一个新的多尺度卷积模块和一个改进的通道注意力（伊卡）模块来生成二级结构，然后设计了一个与生成器冲突的学习器来学习蛋白质的复杂特征。然后，我们提出了一个PSSP方法的基础上提出的多尺度卷积模块和伊卡模块。实验结果表明，基于条件GAN的蛋白质二级结构预测（CGAN-PSSP）模型具有较强的对抗性特征学习能力，是可行的，值得进一步研究。
Prediction of the protein secondary structure is a key issue in protein science. Protein secondary structure prediction (PSSP) aims to construct a function that can map the amino acid sequence into the secondary structure so that the protein secondary structure can be obtained according to the amino acid sequence. Driven by deep learning, the prediction accuracy of the protein secondary structure has been greatly improved in recent years. To explore a new technique of PSSP, this study introduces the concept of an adversarial game into the prediction of the secondary structure, and a conditional generative adversarial network (GAN)-based prediction model is proposed. We introduce a new multiscale convolution module and an improved channel attention (ICA) module into the generator to generate the secondary structure, and then a discriminator is designed to conflict with the generator to learn the complicated features of proteins. Then, we propose a PSSP method based on the proposed multiscale convolution module and ICA module. The experimental results indicate that the conditional GAN-based protein secondary structure prediction (CGAN-PSSP) model is workable and worthy of further study because of the strong feature-learning ability of adversarial learning.
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