洞察HPLC-ESI-MS/MS中控制MS1反应的物理化学性质:一种深度学习方法
Naim Abdul-Khalek1, Reinhard Wimmer1, Michael Toft Overgaard1
1Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark.
Computational and structural biotechnology journal
|August 10, 2023
概括
一个新的深度学习模型仅使用氨基酸序列预测MS1强度,改善了无标签的定量蛋白质组学. 该方法确定了影响质谱反应的关键物理化学性质,减少了对昂贵标签标准的依赖.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 计算生物学 计算生物学
- 分析化学 分析化学
背景情况:
- 定量质谱法 (MS) 通常需要昂贵的标记标准来准确量化.
- 无标签的方法受到差异性电离效率的限制,需要了解影响MS反应的体物理化学性质.
研究的目的:
- 开发一种基于序列的深度学习模型来预测MS1的强度.
- 在质谱学中确定控制MS1反应的物理化学性质.
- 为了使无标签的定量蛋白质组学更容易获得.
主要方法:
- 使用了编码器-解码器深度学习模型,在等等池存储库数据上使用了注意力机制.
- 与氨基酸物理化学性质相关的模型注意力重量 (疏水性,电荷,结构倾向).
- 使用5倍交叉验证验证验证模型性能,并与随机森林和山脊回归进行比较.
主要成果:
- 深度学习模型准确地预测了日志转换MS1强度,平均误差为9.7 ± 0.5%.
- 确定了影响MS1反应的独特模式和分组的物理化学性质 (疏水性,电荷,结构).
- 该模型在预测MS1强度方面超过了传统的机器学习方法.
结论:
- 深度学习可以确定驱动质谱学中MS1反应的关键物理化学性质.
- 基于MS1强度的序列预测为无标签的定量蛋白质组学提供了一个强大的工具.
- 这种方法有可能对未来的定量蛋白质组学工作流产生重大影响,使其更高效和更具成本效益.
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