CFM-ID 4.0: More Accurate ESI-MS/MS Spectral Prediction and Compound Identification.
CFM-ID 4.0: More Accurate ESI-MS/MS Spectral Prediction and Compound Identification.
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DOI：
10.1021/acs.analchem.1c01465
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发表时间：
2021-08-31
影响因子：
7.4
通讯作者：
Wishart, David S.
中科院分区：
文献类型：
--
作者：
Wang, Fei;Liigand, Jaanus;Tian, Siyang;Arndt, David;Greiner, Russell;Wishart, David S.
在代谢组学领域，质谱（MS）是最常用于鉴定和注释代谢物的方法。由于这通常涉及将给定的MS光谱与实验获得的参考光谱库进行匹配，因此该方法受到这种库的覆盖范围和大小（通常以千计）的限制。这些实验库可以通过预测已知化学结构的MS光谱（数量以百万计）来创建计算参考光谱库来大大扩展。为了便于生成预测的光谱参考库，我们开发了CFM-ID，这是一种可以准确预测给定化合物结构的ESI-MS/MS光谱的计算机程序。CFM-ID是化合物-质谱预测的最佳方法之一，也是计算机质谱-化合物鉴定的顶级工具之一。这项工作通过以下方式提高了CFM-ID预测化合物ESI-MS/MS光谱的能力：（1）从基于分子拓扑结构的特征学习参数，（2）增加一种新的环裂解方法，该方法将这种裂解建模为简单化学键解离的序列，以及（3）扩展其手写的基于规则的预测器以覆盖更多的化学类别，包括酰基肉毒碱、酰基胆碱、黄酮醇、黄酮，黄烷酮和黄酮苷。我们证明，这个新版本的CFM-ID（版本4.0）是显着更准确的比以前的CFM-ID版本，在EI-MS/MS光谱预测和化合物鉴定。CFM-ID 4.0可在http://cfmid4.wishartlab.com/作为Web服务获得，Docker镜像可在https://hub.docker.com/r/wishartlab/cfmid下载
In the field of metabolomics, mass spectrometry (MS) is the method most commonly used for identifying and annotating metabolites. As this typically involves matching a given MS spectrum against an experimentally acquired reference spectral library, this approach is limited by the coverage and size of such libraries (which typically number in the thousands). These experimental libraries can be greatly extended by predicting the MS spectra of known chemical structures (which number in the millions) to create computational reference spectral libraries. To facilitate the generation of predicted spectral reference libraries we developed CFM-ID, a computer program that can accurately predict ESI-MS/MS spectrum for a given compound structure. CFM-ID is one of the best-performing methods for compound-to-mass-spectrum prediction, and also one of the top tools for in silico mass-spectrum-to-compound identification. This work improves CFM-ID’s ability to predict ESI-MS/MS spectra from compounds by: (1) learning parameters from features based on the molecular topology, (2) adding a new approach to ring cleavage that models such cleavage as a sequence of simple chemical bond dissociations and (3) expanding its hand-written rule-based predictor to cover more chemical classes, including acylcarnitines, acylcholines, flavonols, flavones, flavanones, and flavonoid glycosides. We demonstrate that this new version of CFM-ID (version 4.0) is significantly more accurate than previous CFM-ID versions, in terms of both EI-MS/MS spectral prediction and compound identification. CFM-ID 4.0 is available at http://cfmid4.wishartlab.com/ as a webservice and docker images can be downloaded at https://hub.docker.com/r/wishartlab/cfmid
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影响因子：
18.2
作者：
Gómez-Bombarelli R;Wei JN;Duvenaud D;Hernández-Lobato JM;Sánchez-Lengeling B;Sheberla D;Aguilera-Iparraguirre J;Hirzel TD;Adams RP;Aspuru-Guzik A
通讯作者：
Aspuru-Guzik A
影响因子：
8.6
作者：
Djoumbou Feunang Y;Eisner R;Knox C;Chepelev L;Hastings J;Owen G;Fahy E;Steinbeck C;Subramanian S;Bolton E;Greiner R;Wishart DS
通讯作者：
Wishart DS
影响因子：
13.1
作者：
Dunn, WB;Ellis, DI
通讯作者：
Ellis, DI
影响因子：
7.4
作者：
Kind, Tobias;Wohlgemuth, Gert;Lee, Do Yup;Lu, Yun;Palazoglu, Mine;Shahbaz, Sevini;Fiehn, Oliver
通讯作者：
Fiehn, Oliver
DOI：
10.1093/bioinformatics/bty245
发表时间：
2018-07-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Ludwig M;Dührkop K;Böcker S
通讯作者：
Böcker S