CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics.
CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics.
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DOI：
10.1021/acs.analchem.2c03491
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发表时间：
2022-12-20
影响因子：
7.4
通讯作者：
Fernandez, Facundo M.
中科院分区：
文献类型：
--
作者：
Rainey, Markace A.;Watson, Chandler A.;Asef, Carter K.;Foster, Makayla R.;Baker, Erin S.;Fernandez, Facundo M.
代谢物注释仍然是非靶向代谢组学工作流程中广泛接受的瓶颈。代谢物的注释通常依赖于高分辨率质谱 (MS) 与母体和串联测量、同位素簇评估和肯德里克质量缺陷 (KMD) 分析的组合。与标准品相匹配的色谱保留时间通常用于该过程的后期阶段，随后还可以利用核磁共振 (NMR) 光谱进行代谢物分离和结构确认。通过离子淌度 (IM) 光谱法测量气相碰撞截面 (CCS) 值还通过生成可用于过滤不太可能的结构的附加分子参数，为该工作流程增加了一个重要维度。 IM 光谱测定的毫秒时间尺度可以快速测量 CCS 值，并可以轻松与现有 MS 工作流程配对。在这里，我们以开源 Jupyter Notebook 格式报告了一种高度准确的机器学习算法 (CCSP 2.0)，用于基于线性支持向量回归模型预测 CCS 值。该工具允许根据用户的需求定制训练集，从而能够为新的加合物或以前未探索的分子类别生成模型。 CCSP 生成的预测精度等于或高于现有机器学习方法（例如 CCSbase、DeepCCS 和 AllCCS），同时更好地符合 FAIR（可查找、可访问、可互操作和可重用）数据原则。 CCSP 2.0 的另一个独特之处是它通过 Mordred Python 包包含了一个包含 1613 个分子描述符的大型库，进一步编码了异构分子结构的精细方面。使用 McLean CCS 纲要中的 CCS 值测试 CCS 预测准确性，测试的 170 [M − H]−、155 [M + H]+ 和 138 [M + Na]+ 加合物的中值相对误差分别为 1.25、1.73 和 1.87%。对于超类匹配的数据集，通过 CCSP 进行的 CCS 预测允许过滤 36.1% 的不正确结构，同时使用 2.8% 的 ΔCCS 阈值和 10 ppm 的质量误差保留总共 100% 的正确注释。
Metabolite annotation continues to be the widely accepted bottleneck in nontargeted metabolomics workflows. Annotation of metabolites typically relies on a combination of high-resolution mass spectrometry (MS) with parent and tandem measurements, isotope cluster evaluations, and Kendrick mass defect (KMD) analysis. Chromatographic retention time matching with standards is often used at the later stages of the process, which can also be followed by metabolite isolation and structure confirmation utilizing nuclear magnetic resonance (NMR) spectroscopy. The measurement of gas-phase collision cross-section (CCS) values by ion mobility (IM) spectrometry also adds an important dimension to this workflow by generating an additional molecular parameter that can be used for filtering unlikely structures. The millisecond timescale of IM spectrometry allows the rapid measurement of CCS values and allows easy pairing with existing MS workflows. Here, we report on a highly accurate machine learning algorithm (CCSP 2.0) in an open-source Jupyter Notebook format to predict CCS values based on linear support vector regression models. This tool allows customization of the training set to the needs of the user, enabling the production of models for new adducts or previously unexplored molecular classes. CCSP produces predictions with accuracy equal to or greater than existing machine learning approaches such as CCSbase, DeepCCS, and AllCCS, while being better aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Another unique aspect of CCSP 2.0 is its inclusion of a large library of 1613 molecular descriptors via the Mordred Python package, further encoding the fine aspects of isomeric molecular structures. CCS prediction accuracy was tested using CCS values in the McLean CCS Compendium with median relative errors of 1.25, 1.73, and 1.87% for the 170 [M − H]−, 155 [M + H]+, and 138 [M + Na]+ adducts tested. For superclass-matched data sets, CCS predictions via CCSP allowed filtering of 36.1% of incorrect structures while retaining a total of 100% of the correct annotations using a ΔCCS threshold of 2.8% and a mass error of 10 ppm.
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影响因子：
7.4
作者：
Picache, Jaqueline A.;May, Jody C.;McLean, John A.
通讯作者：
McLean, John A.
影响因子：
7.4
作者：
Paglia, Giuseppe;Angel, Peggi;Williams, Jonathan P.;Richardson, Keith;Olivos, Hernando J.;Thompson, J. Will;Menikarachchi, Lochana;Lai, Steven;Walsh, Callee;Moseley, Arthur;Plumb, Robert S.;Grant, David F.;Palsson, Bernhard O.;Langridge, James;Geromanos, Scott;Astarite, Giuseppe
通讯作者：
Astarite, Giuseppe
影响因子：
1.7
作者：
Hollas, B
通讯作者：
Hollas, B
影响因子：
1.8
作者：
Bleiholder, Christian;Wyttenbach, Thomas;Bowers, Michael T.
通讯作者：
Bowers, Michael T.
影响因子：
14.9
作者：
Wishart DS;Jewison T;Guo AC;Wilson M;Knox C;Liu Y;Djoumbou Y;Mandal R;Aziat F;Dong E;Bouatra S;Sinelnikov I;Arndt D;Xia J;Liu P;Yallou F;Bjorndahl T;Perez-Pineiro R;Eisner R;Allen F;Neveu V;Greiner R;Scalbert A
通讯作者：
Scalbert A