通过MS2Tox对水分析和基于毒性的特征优先级的非目标质谱数据采集策略的评估
Pilleriin Peets1,2, May Britt Rian3, Jonathan W Martin3,4
1Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, SE-106 91, Stockholm, Sweden.
Environmental science & technology
|September 19, 2024
概括
机器学习工具MS2Tox从分子MS2光谱中预测鱼类的死亡率. 与环境水样中的数据独立采集 (DIA) 相比,数据依赖采集 (DDA) 改善了结构注释和毒性预测的准确性.
科学领域:
- 环境化学环境化学
- 分析化学 分析化学
- 计算毒理学计算毒理学
背景情况:
- 非定位质谱 (MS) 对于识别未知的环境污染物至关重要.
- 优先考虑危险分子需要在结构识别之前准确的毒性预测.
- MS2Tox是一种机器学习工具,可以从MS2光谱中预测鱼类的急性死亡率.
研究的目的:
- 评估分子覆盖,MS2光谱质量和毒性预测信心如何受到样本复杂性和MS2数据采集策略的影响.
- 为了比较数据依赖获取 (DDA) 和数据独立获取 (DIA) 的结构注释和MS2Tox毒性预测.
- 为MS2Tox预测引入一个信心评分.
主要方法:
- 使用液体染色学高分辨率质谱法 (LC-HRMS) 进行DDA和DIA策略的比较.
- 使用SIRIUS+CSI:FingerID对191种参考化学物质的结构注释准确性进行评估,这些化学物质在各种水矩阵 (水,地下水,地表水,废水) 中增加.
- 评估了MS2Tox毒性预测准确度和MS2检测率,用于两个采购策略.
主要成果:
- 在大多数矩阵中,DDA的结构注释率比DIA (19-50%) 高 (19-62%).
- 与DDA (37-82%) 相比,DIA实现了更高的MS2检测率 (59-84%).
- DDA表现出优越的MS2Tox毒性预测准确度 (RMSE 0.62 log-mM) 与DIA (RMSE 0.71 log-mM) 相比.
结论:
- 使用MS2Tox.DDA更有效地准确地进行环境污染物的结构注释和毒性预测.
- DIA提供更广泛的分子覆盖范围,但预测准确性较低.
- 开发的"CombinedConfidence"评分提高了环境水域潜在生态毒性特征的优先级.
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