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Updated: May 31, 2025

Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry CCMS

Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry CCMS

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DLBWE-Cys:一种基于深度学习的工具,用于识别使用二进制重量编码的氨酸S-碳酸乙烯化位点.

Zhengtao Luo^1,2,3, Qingyong Wang^1,2,3, Yingchun Xia^1,2,3

¹School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, China.

Frontiers in genetics

|January 23, 2025

概括

研究人员开发了DLBWE-Cys,这是一种深度学习模型,可以准确预测囊素S-碳氧乙化位点. 这一进步有助于了解自身免疫性疾病,如结脊髓炎.

关键词:

通过S-碳氧乙基化处理.巴哈达纳乌注意力机制二进制重量编码二进制重量编码深度学习是一种深度学习.后翻译修改后的修改

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Last Updated: May 31, 2025

Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry CCMS

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科学领域:

生物化学生物化学
计算生物学计算生物学
免疫学免疫学免疫学

背景情况:

半氨酸S-碳氧乙基化是一种新型的翻译后修饰 (PTM),涉及自身免疫性疾病的发病,如性脊髓炎.
准确识别S-碳氧乙基化位点对于理解其功能作用至关重要.
现有的计算工具缺乏预测这些修改站点所需的准确性,阻碍了研究进展.

研究的目的:

开发一种新的深度学习模型,用于准确预测囊S-碳氧乙基化修饰站点.
解决目前用于识别这些PTM的计算工具的局限性.
为研究自身免疫性疾病的研究人员提供宝贵的资源.

主要方法:

开发了DLBWE-Cys,这是一个集成CNN,BiLSTM,Bahdanau attention和FNN的深度学习模型.
利用专门为预测氨酸S-碳氧乙化位点而设计的二元重量编码.
通过5倍交叉验证和独立测试评估模型性能,包括t-SNE可视化.

主要成果:

与现有的机器学习和深度学习模型相比,DLBWE-Cys表现出卓越的性能.
功能比较实验证实了二进制重量编码对其他方法的有效性.
t-SNE可视化验证了该模型强大的分类能力.

结论:

开发的DLBWE-Cys模型提供了一个非常准确的方法,用于预测蛋白质序列中的氨酸S-碳氧乙基化位点.
预计该工具将大大推进对疾病中S-碳氧乙基化功能的机制的研究.
该模型和数据是公开可用的,这有助于进一步的研究和开发.