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Updated: May 11, 2026

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Published on: April 4, 2018

3DCOOR-Kace:一种用于识别 lysine 乙化位点的3D空间坐标表示方法

Lichao Zhang¹, Xue Wang², Liang Kong³

¹School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao, PR China; Hebei Innovation Center for Smart Perception and Applied Technology of Agricultural Data, Qinhuangdao, PR China.

Analytical biochemistry

|October 18, 2025

概括

这项研究引入了一种新的3D空间坐标方法来表示氨基酸位置,改善了 lysine 乙化 (Kace) 位点的识别. 3DCOOR-Kace模型有效地整合了序列和结构数据,以提高预测准确度.

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:

素乙化 (Kace) 是一个关键的翻译后修饰.
蛋白质结构的实验性确定是耗时和资源密集的,限制了大规模分析.
整合结构信息对于增强凯斯站点预测的计算模型至关重要.

研究的目的:

为氨基酸开发一种新的空间坐标表示,以克服实验限制.
为了构建复杂的网络,使用这些坐标来提取特征.
提出一个先进的计算模型,3DCOOR-Kace,通过融合序列和结构信息来改进Kace站点识别.

主要方法:

一个新的3D空间坐标表示,使用基于定义的属性序列的2阶张量.
从空间坐标构建最佳复杂网络,以导出基于网络的特征.
开发3DCOOR-Kace模型,使用DenseNet和Squeeze-and-Excitation层集成序列和结构数据.

主要成果:

拟议的空间坐标有效地捕获全球生物信息,优于传统的蛋白质接触网络 (PCN).
3DCOOR-Kace模型在一个独立的测试集上实现了0.7358的马修斯相关系数 (MCC).
与MusiteDeep和TransPTM等现有方法相比,3DCOOR-Kace表现出卓越的性能,MCC的改善分别为0.4261和0.1660.

结论:

三维空间坐标表示提供直接的位置信息,绕过实验限制.
3DCOOR-Kace模型有效地整合了序列和结构数据,以准确地预测Kace站点.
这种方法通过提供易于访问的位置数据来促进计算方法和生物功能研究.

关键词:

密集卷积神经网络是一个密集的卷积神经网络.lysine 的乙化作用.网络衍生结构特征网络衍生结构特征蛋白质网络是一种蛋白质网络.

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Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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