一个组成数据模型,以预测平均的同位素分布,使用一个组成的支线模型
Annelies Agten1, Frédérique Vilenne1,2, Piotr Prostko1
1Data Science Institute, Hasselt University, Diepenbeek, Belgium.
Proteomics
|December 3, 2023
概括
我们开发了一种新方法,使用质谱数据近似估计同位素分布. 光谱精度,而不是我们的模型,是预测组成的错误的主要来源.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 计算化学计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 准确地近似同位素分布对于基于质谱的蛋白质组学至关重要.
- 现有的方法可能无法充分考虑硫含量和光谱变异性等因素.
研究的目的:
- 为近似同位素分布提出一个更新的计算方法.
- 从观察到的同位素分布中开发估计硫原子数量的方法.
- 评估与实验数据对比的拟议模型的性能.
主要方法:
- 在UNIPROT数据库的in-silico裂变中生成理论数据集.
- 构成数据建模的应用,加法逻辑比转换和处罚线回归.
- 针对不同硫原子计数 (0-5) 的单独模型的开发.
- 关于从观察到的同位素分布中估计硫原子的三种方法的建议.
- 在UPS2数据上使用平均二次误差和修改的Pearson's chi-square适合度的评估.
主要成果:
- 评估了拟议的线条模型和硫预测方法.
- 发现光谱精度 (MS1扫描) 的变化比理论同位素分布近似值更大,导致错误.
- 预测硫原子的准确性受到质谱数据的测量准确度的限制.
结论:
- 开发的计算方法为近似同位素分布提供了一种精细的方法.
- 光谱精度是影响质谱学中标识和量化可靠性的关键因素.
- 需要进一步提高质谱测量精度,以提高对元素组成,特别是硫含量的预测.
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