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BiGRUD-SA:基于BiGRU和自我注意力的蛋白质S-硫化位点预测
Tingting Zhang1, Jihua Jia2, Cheng Chen3
1College of Computer Science and Technology, Shandong University, Qingdao, 266237, China; College of Information Science and Technology, School of Data Science, Qingdao University of Science and Technology, Qingdao, 266061, China.
Computers in biology and medicine
|June 19, 2023
概括
预测蛋白质S-硫化位点对于理解细胞过程至关重要. 一种新的计算方法,BiGRUD-SA,利用双向封闭的反复单位和自我注意力来准确识别S-硫化位点.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- S-硫化是一种关键的蛋白质转化后修饰 (PTM),参与氧化还原反应,信号转导和蛋白质功能调节.
- 对S-硫化位点的实验识别面临重大局限性,需要计算方法来进行有效的研究.
研究的目的:
- 开发和验证一种用于准确预测蛋白质S-硫化位点的计算方法.
- 通过改进S-硫化位预测,增强对蛋白质功能和生物机制的理解.
主要方法:
- 使用AAC,BLOSUM62,AAindex,EAAC和GAAC进行特征提取,然后进行特征融合.
- 使用SMOTE-Tomek方法进行数据平衡.
- 一个深度学习模型 (BiGRUD-SA) 集成双向封闭的循环单元 (BiGRU) 和自我注意机制,其次是用于预测的深度神经网络 (DNN).
主要成果:
- 在训练组中达到96.66%的高预测准确率,在独立测试组中达到95.91%.
- 通过成功预测Arabidopsis thaliana中的S-硫化位点,表现出强大的概括能力.
结论:
- BiGRUD-SA方法提供了一个强大而准确的计算工具,用于识别蛋白质S-硫化位点.
- 这种方法促进了对S-硫化在各种生物系统中的功能影响的进一步研究.
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