SingleFrag:一种深度学习工具,用于MS/MS碎片和光谱预测以及代谢物注释
Maribel Pérez-Ribera1, Muhammad Faizan-Khan2, Roger Giné2
1Department of Chemical Engineering, Universitat Rovira i Virgili, 43007 Tarragona, Catalonia.
Briefings in bioinformatics
|July 11, 2025
概括
使用双重质谱法 (MS/MS) 准确地识别代谢物是具有挑战性的,因为光谱库有限. 新的深度学习工具SingleFrag可以预测单个分子碎片,改善未知的MS/MS光谱的注释.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 生物化学 生物化学
背景情况:
- 代谢物和小分子的识别依赖于与参考库匹配实验串联质谱 (MS/MS) 光谱.
- 综合光谱库的稀缺性阻碍了精确的化合物识别.
- 准确的in silico碎片化预测对于产生缺乏经验数据的化合物的MS/MS光谱至关重要.
研究的目的:
- 开发和验证一个新的深度学习工具,SingleFrag,用于预测MS/MS频谱.
- 通过预测单个碎片来解决当前in silico碎片工具的局限性.
- 为了增强未知的MS/MS光谱的注释.
主要方法:
- 开发SingleFrag,这是一个深度学习模型,可以预测单个分子碎片.
- 评估SingleFrag的性能与最先进的片碎片工具相比.
- 应用SingleFrag用于注释人类样本中未知的MS/MS化合物的光谱.
主要成果:
- 与现有的最先进的片碎片工具相比,SingleFrag显示出更高的性能.
- 该工具准确地预测单个碎片,有助于更可靠的光谱预测.
- 成功标注了人类样本中常见的三种以前未知的化合物.
结论:
- SingleFrag提供了一种强大而准确的方法,用于in silicoMS/MS光谱预测.
- 这种工具可以显著帮助注释未知的MS/MS光谱,特别是对于没有现有参考光谱的化合物.
- 单片提升了代谢物识别能力,在各种生物和化学分析中具有潜在的应用.
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