GPMass模拟器:一种基于图形的方法用于糖MS/MS光谱预测.
Yihui Ren1,2, Dongbo Bu1, Bo Duan3
1Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
Analytical chemistry
|December 9, 2025
概括
深度学习框架GPMassSimulator准确地预测N-糖的光谱和保留时间. 这通过改善复杂糖的识别,甚至区分类似结构来推进糖蛋白组学.
科学领域:
- 生物化学 生化学
- 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:
- 蛋白质糖化是一种重要的翻译后修饰,在生物过程和疾病中起着重要的作用.
- 使用质谱学的糖蛋白组学分析面临的挑战是由于糖的结构复杂性和异质性.
- 目前的葡萄糖鉴定工具往往未充分利用光谱强度数据,限制它们区分类似的葡萄糖的能力.
研究的目的:
- 开发一个创新的深度学习框架,GPMassSimulator,用于准确预测完整的N-glycopeptide双重质谱 (MS/MS) 光谱和保留时间.
- 通过整合序和甘氨酸结构信息来增强糖标识工具的区分能力.
- 在复杂的生物样本中提高糖标识的灵敏度和准确性.
主要方法:
- 开发了GPMassSimulator,这是一个使用GpepFormer模块来表示和整合序列和甘氨酸结构的深度学习框架.
- 在GPMassSimulator中使用预测模块来生成MS/MS的理论光谱和糖的保留时间.
- 在基准数据集上验证了模型的性能,包括区分类似的甘氨酸化合物和异构结构的实验.
主要成果:
- GPMassSimulator在区分类似的甘氨酸组合物方面实现了97.1%的识别准确度.
- 与现有方法相比,该框架证明了与现有方法相比,对异构结构的Top-1识别更准确.
- 在pGlyco3数据的恢复实验中,GPMassSimulator对糖标识的灵敏度显著提高.
结论:
- GPMassSimulator提供了一种强大的深度学习方法,用于准确预测N-glycopeptideMS/MS光谱和保留时间.
- 该框架有效地捕捉了序列和甘氨酸结构之间的复杂依赖关系,增强了甘氨酸的识别.
- GPMassSimulator显示出在疾病状态分析中推进甘氨酸蛋白质组学研究和应用方面的重大前景.
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