测绘质谱预测的边缘:机器学习的评估EIMS预测Xeno氨基酸的预测
Sean M Brown1, Evan Allgair1, Robin Kryštůfek2
1Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland 21250, United States.
NEIMS光谱预测算法在训练数据之外显示氨基酸的不准确性. 这凸显了需要改进机器学习和初步方法来扩大用于未知生物分子检测的光谱数据库的需求.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 天体生物学 天体生物学
背景情况:
- 质谱测量 (MS) 对于通过将观察到的光谱与数据库进行比较来识别未知的化合物至关重要.
- 目前的识别方法受限于实验确定频谱的可用性.
- 在MS光谱预测算法的进步旨在克服这些局限性.
研究的目的:
- 为了评估NEIMS光谱预测算法的准确性.
- 评估单替代α-氨基酸的MS光谱预测的性能.
- 为了告知研究人员使用预测的光谱来检测未知的生物分子.
主要方法:
- 分析NEIMS光谱预测算法的准确性.
- 专注于与天体生物学,合成生物学和生物医学应用相关的单替代α-氨基酸.
- 将预测的光谱与实验数据进行比较.
主要成果:
- NEIMS算法在未包括在其训练数据集中的氨基酸中显示了不准确的结果.
- 这些不准确不归因于物理化学差异或衍生状态.
- 当前的预测算法可能无法可靠地识别新型或复杂的生物分子.
结论:
- 现有的基于机器学习的MS光谱预测算法需要改进.
- 优化初始光谱预测是扩展数据库所必需的.
- 增强的算法对于检测更广泛的分子,包括理论分子至关重要.
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