我们能否提高N-甘氨基识别的信心? 智能碰撞能源选择,考虑到结构和搜索引擎
Helga Hevér1, Andrea Xue1, Kinga Nagy1,2
1MS Proteomics Research Group, HUN-REN Research Centre for Natural Sciences, Magyar Tudósok körútja 2., Budapest H-1117, Hungary.
Journal of the American Society for Mass Spectrometry
|January 29, 2024
概括
在质谱学中优化碰撞能量可以提高N-糖的识别可靠性和可重现性. 结构特征和搜索引擎选择显著影响N-糖分析的最佳能量设置.
科学领域:
- 分析化学 分析化学
- 生物化学 生物化学
- 蛋白质组学是指蛋白质组学.
背景情况:
- 从底部向上对N-糖的质谱识别面临着信心和可重复性方面的挑战.
- 在MS/MS和数据库搜索引擎中的碰撞能量是准确识别N-糖的关键因素.
研究的目的:
- 为了研究N-glycopeptide结构特征和搜索引擎选择如何影响高可靠性识别的最佳碰撞能量.
- 开发一个改进的MS/MS工作流程,用于N-糖分析.
主要方法:
- 在不同的N-糖上,LC-MS/MS测量具有不同的碰撞能量.
- 对Byonic,pGlyco和GlycoQuest搜索引擎性能进行评估.
- 统计和机器学习分析以确定有影响力的参数.
主要成果:
- 搜索引擎的行为有所不同 (以为中心与以糖为中心),影响最佳碰撞能量依赖于m/z.
- 根据搜索引擎,的疏水性,甘氨酸/质量和移动质子数量显著影响结果.
- 一个使用疏水性和糖质量用于智能碰撞能量选择的拟议工作流.
结论:
- 开发的工作流可以显著提高N-glycopeptide识别的可靠性 (高达100%),特别是在低得分的命中.
- 在N-glycopeptide分析中可以获得更高的信心和可重复性.
- 这种方法有可能提高草甘研究的可靠性.
关键词:
在N-glycosylation的过程中.从底部向上的蛋白质组学.碰撞能量优化 碰撞能量优化一般线性模型一般线性模型甘氨酸结构 甘氨酸结构识别分数是指识别分数的分数.拉索回归是一种回归方式.搜索引擎搜索引擎是什么双联质谱质量测量技术更多相关视频
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