BitterMasS:从质谱中预测痛苦
Evgenii Ziaikin1, Edisson Tello2, Devin G Peterson2
1Food Science and Nutrition, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Institute of Biochemistry, Food and Nutrition, The Hebrew University of Jerusalem, 76100 Rehovot, Israel.
Journal of agricultural and food chemistry
|April 30, 2024
概括
BitterMasS使用质谱来预测苦味化合物,而不是化学结构. 这种机器学习方法有助于识别代谢组内的未知苦分子.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 代谢学 代谢学 代谢学
背景情况:
- 苦味化合物在天然产品和药品中普遍存在.
- 目前的机器学习模型根据化学结构预测苦味,但大多数代谢物缺乏指定的结构.
- 代谢组的很大一部分仍然没有表征,限制了苦味的预测.
研究的目的:
- 开发一个机器学习模型,BitterMasS,直接从实验性质谱中预测苦味.
- 与基于结构的方法相比,评估基于频谱的苦味预测策略的性能.
- 为了在没有先前的结构阐明的情况下,在复杂混合物中识别苦味化合物.
主要方法:
- 一个随机森林分类器 (BitterMasS) 在 5414 种苦和非苦化合物的实验质谱上受过训练.
- 使用内部和外部测试集,包括新获取的光谱数据来评估模型的性能.
- 频谱苦味预测与频谱结构苦味预测策略的比较.
主要成果:
- 在内部测试套件上,BitterMasS实现了高精度 (0.83) 和回忆 (0.90).
- 外部验证显示了良好的性能:67%的精度和93%的回忆对于文献频谱,58%的精度和99%的回忆对于新测量的频谱.
- 频谱苦度方法在有效性和复合覆盖率方面超过了基于结构的方法.
结论:
- BitterMasS有效地从质谱中预测痛苦,克服了基于结构的预测的局限性.
- 这种方法可以识别"黑暗"代谢中的苦味化合物.
- 应用包括代谢学,比较苦味分析和随着时间的推移监测苦味.
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