通过深度学习解读邻近氨基酸对ESI-MS强度输出的影响
Naim Abdul-Khalek1, Reinhard Wimmer1, Michael Toft Overgaard1
1Department of Chemistry and Bioscience, Aalborg University, Fredrik Bajers Vej 7H, Aalborg 9220, Denmark.
Journal of proteomics
|September 28, 2024
概括
这项研究使用深度学习来将氨基酸序列与质谱信号强度联系起来. 特定的氨基酸对 (二次基因) 显著影响的反应,改善了蛋白质学中的定量分析.
科学领域:
- 蛋白质组学和质谱学
- 生物信息学和计算生物学
- 生物化学 生化学
背景情况:
- 质谱 (MS) 对于定量至关重要,但信号强度受到物理化学性质和氨基酸 (AA) 序列的影响,使直接定量变得复杂.
- 特定的AA序列和MS反应之间的关系仍然不太清楚,这阻碍了蛋白质组学中准确的无标签量化.
研究的目的:
- 探索局部氨基酸环境 (二次基因) 和MS1强度之间的关系.
- 开发一种深度学习模型,根据序列组成来预测MS1的强度.
- 为了确定在质谱学中显著影响体响应的特定二次基因.
主要方法:
- 开发了一个深度学习模型,一个带有注意力机制的编码解码器,用于分析序列.
- 该模型分析了含有近20万个独特的等分质池,以确定序列强度关系.
- 注意力权重与平均MS1强度相关,以精确确定有影响力的二元格 motif.
主要成果:
- 特定的二次基因,如体积大/芳香,其次是阴离子AA,以及连续的体积大/芳香/疏水性AA,被确定为MS1强度的重要预测因素.
- 含有Trp,His,Cys的基因与低反应有关,而含有Lys和体积大的疏水性AA的基因与高反应有关. Asn-Gly图案与低响应相关.
- 深度学习模型在预测MS1响应时实现了~11%的平均平均百分比误差和~0.64的皮尔森相关系数.
结论:
- 这项研究提供了关于局部氨基酸序列环境如何影响质谱学中的反应的宝贵见解.
- 这些发现表明深度学习模型在提高无标签量化的准确性方面的潜力.
- 识别依赖序列的响应变异对于推进定量蛋白质组学方法的发展至关重要.
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