Least Absolute Shrinkage and Selection Operator-based Prediction of Collision Cross Section Values for Ion Mobility Mass Spectrometric Analysis of Lipids
Least Absolute Shrinkage and Selection Operator-based Prediction of Collision Cross Section Values for Ion Mobility Mass Spectrometric Analysis of Lipids
复制标题
用于脂质离子淌度质谱分析的碰撞截面值的最小绝对收缩和基于选择算子的预测
DOI：
10.1039/d1an02161c
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Gui-Zhong Xin
中科院分区：
文献类型：
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作者：
Jianying Wang;Yinghao Yin;Jia-Yi Zheng;Lifang Liu;Zhong-Ping Yao;Gui-Zhong Xin
离子淌度质谱 (IM-MS) 生成的碰撞截面 (CCS) 值通常用于促进脂质识别。然而，这受到可用脂质标准有限的阻碍。近年来，人们通过计算机计算来预测CCS值，但预测精度普遍较差，且脂质异构体的CCS预测值几乎相同。为了应对这一挑战，本研究开发了基于最小绝对收缩和选择算子 (LASSO) 的预测方法来预测脂质的 CCS 值。在该方法中，筛选和优化了一系列分子描述符，以反映不同脂质异构体之间结构的细微差异。分子描述符与大量脂质标准 CCS 值（总共 365 个）的使用显着提高了 LASSO 模型的准确性和精确度。使用独立数据集对其准确性进行了外部验证，中值相对误差 (MRE) <1.1%。该方法被证明可以区分顺式/反式和顺式位置异构体。结果还表明，基于 LASSO 的预测方法实际上可以减少基于 IM-MS 的脂质组学中的假阳性识别。
Collision cross section (CCS) values generated from ion mobility mass spectrometry (IM-MS) have commonly been employed to facilitate lipid identification. However, this is hindered by the limited available lipid standards. Recently, CCS values were predicted by means of computational calculations, though the prediction precision was generally not good and the predicted CCS values of the lipid isomers were almost identical. To address this challenge, a least absolute shrinkage and selection operator (LASSO)-based prediction method was developed for the prediction of lipids’ CCS values in this study. In this method, an array of molecular descriptors were screened and optimized to reflect the subtle differences in structures among the different lipid isomers. The use of molecular descriptors together with a wealth of standard CCS values for the lipids (365 in total) significantly improved the accuracy and precision of the LASSO model. Its accuracy was externally validated with median relative errors (MREs) of <1.1% using an independent data set. This approach was demonstrated to allow differentiation of cis/trans and sn-positional isomers. The results also indicated that the LASSO-based prediction method could practically reduce false-positive identifications in IM-MS-based lipidomics.