MDDeep-Ace：基于多域适应的物种特异性乙酰化位点预测
MDDeep-Ace: species-specific acetylation site prediction based on multi-domain adaptation.
作者信息
Liu Yu, Ye Chaofan, Lin Can, Mao Kangkang, Zhu Ming
机构信息
School of Integrated Circuits, Anhui University, Hefei City, Anhui, China.
出版信息
PeerJ. 2025 Jul 3;13:e19649. doi: 10.7717/peerj.19649. eCollection 2025.
BACKGROUND
Lysine post-translational modification (PTM) is pivotal in regulating diverse cellular processes, profoundly impacting protein structure and function. Over recent decades, numerous experimental techniques have advanced PTM site identification, significantly contributing to research progress. However, these methods are time-intensive and labor-intensive. Deep learning technologies have shown promise in predicting PTM sites, yet current approaches struggle with species-specific PTM site prediction.
METHODS
We introduce MDDeep-Ace, a novel deep learning method based on multi-domain adaptation for predicting lysine acetylation sites. By integrating data from multiple species, MDDeep-Ace enhances the generalization of species-specific prediction models, improving predictive performance.
RESULTS
Experimental findings illustrate that our proposed multi-domain adaptation approach significantly enhances prediction accuracy across multiple species, surpassing existing lysine acetylation site prediction tools.
背景
赖氨酸翻译后修饰（PTM）在调节多种细胞过程中起着关键作用，对蛋白质结构和功能有深远影响。近几十年来，众多实验技术推动了PTM位点鉴定的发展，为研究进展做出了重大贡献。然而，这些方法耗时且费力。深度学习技术在预测PTM位点方面显示出了潜力，但目前的方法在物种特异性PTM位点预测方面存在困难。
方法
我们引入了MDDeep-Ace，这是一种基于多域适应的新型深度学习方法，用于预测赖氨酸乙酰化位点。通过整合来自多个物种的数据，MDDeep-Ace增强了物种特异性预测模型的泛化能力，提高了预测性能。
结果
实验结果表明，我们提出的多域适应方法显著提高了多个物种的预测准确性，超过了现有的赖氨酸乙酰化位点预测工具。
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