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DeepNphos:一种用于预测N-酸化位点的深度学习架构
Xulin Chang1, Yafei Zhu1, Yu Chen1
1College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China.
DeepNphos是第一个能够预测N-酸化位点的模型,包括酸 (pK),酸 (pR) 和酸 (pH). 这个计算工具使用卷积神经网络和深度转移学习来提高预测这些关键细胞修饰的准确性.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 蛋白质组学是指蛋白质组学.
背景情况:
- 酸化是一种关键的翻译后修饰,调节细胞功能.
- O-酸化部位的预测已经很成熟,但N-酸化部位的预测是不发达的.
- N-酸化包括-氨酸 (pK),-氨酸 (pR) 和-氨酸 (pH).
研究的目的:
- 开发第一个用于预测N-酸化位点的计算模型.
- 分析和预测-氨酸,-氨酸和-氨酸的位置.
- 为预测N-酸化位点建立一个基准.
主要方法:
- 开发了一个名为DeepNphos.phos的集成模型.
- 使用卷积神经网络 (CNN) 采用一热编码.
- 应用深度转移学习 (DTL) 策略用于基于已知的 lysine 修饰的 pK 预测.
- 分析了数千个经过实验识别的pK,pR和pH位点.
主要成果:
- 通过十倍交叉验证,DeepNphos获得了pK的AUC值为0.856,pR为0.805,pH为0.802.
- 使用One-Hot编码的CNN显示了pK,pR和pH预测的良好表现.
- DTL增强了pK预测,但对pR预测的影响很小.
- 由于希斯蒂丁修饰数据有限,DTL并未应用于pH预测.
结论:
- DeepNphos 是第一个用于预测N-酸化位点的分类器.
- 该模型为研究N-酸化提供了有价值的工具.
- 这些发现突显了深度学习在预测翻译后修改方面的潜力.
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