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
Published: October 9, 2021
Citation
Wang F., J. Liigand, S. Tian, D. Arndt, R. Greiner, and D. Wishart. 2021.CFM-ID 4.0: More Accurate ESI MS/MS Spectral Prediction and Compound Identification.Analytical Chemistry 93, no. 34:11692–11700.PNNL-SA-160966.doi:10.1021/acs.analchem.1c01465