Evaluation of an Artificial Neural Network Retention Index Model for Chemical Structure Identification in Nontargeted Metabolomics.
Evaluation of an Artificial Neural Network Retention Index Model for Chemical Structure Identification in Nontargeted Metabolomics.
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DOI：
10.1021/acs.analchem.8b03118
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发表时间：
2018-11-06
影响因子：
7.4
通讯作者：
Grant DF
中科院分区：
文献类型：
--
作者：
Samaraweera MA;Hall LM;Hill DW;Grant DF
液相色谱-电喷雾串联质谱（LC-ESI-MS/MS）是一种用于非靶向鉴定生物体液中代谢物的主要分析技术。通常，在基于LC-ESI-MS/MS的数据库辅助结构解析流水线中，未知化合物的确切质量用于挖掘化学结构数据库以获得可能候选物的初始集合。随后匹配的碰撞诱导解离（CID）光谱的未知的候选结构的CID光谱有助于识别。然而，由于大量的潜在候选者（即，假阳性），CID光谱不可用。为了克服这个问题，已经开发了CID片段化预测程序，但是如果在候选集合中存在大量具有相似CID光谱的异构体，则这些程序也具有有限的成功。在这项研究中，我们研究了使用保留指数（RI）预测模型作为正交方法，以帮助提高识别率。该模型被用来消除候选结构的预测RI值显着不同的未知化合物的实验确定的RI值。我们使用一组91种内源性代谢物和四种计算机CID片段化算法测试了这种方法：CFM-ID、CSI：FingerID、Mass Frontier和MetFrag。对从PubChem和人类代谢物数据库（HMDB）获得的候选集进行了排序，使用和不使用RI过滤，然后进行计算机光谱匹配。在RI过滤后，91种代谢物中的12种从它们各自的候选集合中被消除，即，都被错误地评为阴性对于其余的79种化合物，我们发现RI过滤平均消除了PubChem候选集的58%。这导致使用CFM-ID，Mass Frontier和MetFrag时平均排名提高了约2倍。此外，RI过滤略微增加了所有4种碎片化算法排名第一的发生率。然而，RI过滤并没有显着提高平均排名时，HMDB被用作候选数据库，也没有显着提高平均排名时，使用CSI：FingerID。总的来说，我们表明，目前的RI模型错误地消除了更多的真阳性（12）比预期（4-5）的基础上的过滤方法。然而，当使用CFM-ID、Mass Frontier和MetFrag时，它稍微提高了正确的第一名排名的数量，并提高了总体平均排名。
Liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) is a major analytical technique used for nontargeted identification of metabolites in biological fluids. Typically, in LC-ESI-MS/MS based database assisted structure elucidation pipelines, the exact mass of an unknown compound is used to mine a chemical structure database to acquire an initial set of possible candidates. Subsequent matching of the collision induced dissociation (CID) spectrum of the unknown to the CID spectra of candidate structures facilitates identification. However, this approach often fails because of the large numbers of potential candidates (i.e., false positives) for which CID spectra are not available. To overcome this problem, CID fragmentation predication programs have been developed, but these also have limited success if large numbers of isomers with similar CID spectra are present in the candidate set. In this study, we investigated the use of a retention index (RI) predictive model as an orthogonal method to help improve identification rates. The model was used to eliminate candidate structures whose predicted RI values differed significantly from the experimentally determined RI value of the unknown compound. We tested this approach using a set of ninety-one endogenous metabolites and four in silico CID fragmentation algorithms: CFM-ID, CSI:FingerID, Mass Frontier, and MetFrag. Candidate sets obtained from PubChem and the Human Metabolite Database (HMDB) were ranked with and without RI filtering followed by in silico spectral matching. Upon RI filtering, 12 of the ninety-one metabolites were eliminated from their respective candidate sets, i.e., were scored incorrectly as negatives. For the remaining seventy-nine compounds, we show that RI filtering eliminated an average of 58% from PubChem candidate sets. This resulted in an approximately 2-fold improvement in average rankings when using CFM-ID, Mass Frontier, and MetFrag. In addition, RI filtering slightly increased the occurrence of number one rankings for all 4 fragmentation algorithms. However, RI filtering did not significantly improve average rankings when HMDB was used as the candidate database, nor did it significantly improve average rankings when using CSI:FingerID. Overall, we show that the current RI model incorrectly eliminated more true positives (12) than were expected (4–5) on the basis of the filtering method. However, it slightly improved the number of correct first place rankings and improved overall average rankings when using CFM-ID, Mass Frontier, and MetFrag.
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影响因子：
14.9
作者：
Allen F;Pon A;Wilson M;Greiner R;Wishart D
通讯作者：
Wishart D
影响因子：
1.8
作者：
Hall, L. Mark;Hill, Dennis W.;Grant, David F.
通讯作者：
Grant, David F.
影响因子：
7.4
作者：
Menikarachchi, Lochana C.;Cawley, Shannon;Grant, David F.
通讯作者：
Grant, David F.
影响因子：
5.7
作者：
Johnson SR;Lange BM
通讯作者：
Lange BM
DOI：
10.5702/massspectrometry.s0039
发表时间：
2014-01-01
期刊：
Mass spectrometry (Tokyo, Japan)
影响因子：
--
作者：
Nishioka, Takaaki;Kasama, Takeshi;Yamamoto, Atsushi
通讯作者：
Yamamoto, Atsushi