CFM-ID 4.0: More Accurate ESI-MS/MS Spectral Prediction and Compound Identification

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 EST-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 EST-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 ET-MS/MS spectral prediction and compound identification. CFM-ID 4.0 is available at http://cfmid4.wishartlab.com/ as a web server and docker images can be downloaded at https://hub.docker.com/r/wishartlab/cfmid.

Automated summary

Mass spectrometry (MS) is commonly used in metabolomics for identifying and annotating metabolites. CFM-ID is a computer program that accurately predicts EST-MS/MS spectrum for a given compound structure, improving compound-to-mass-spectrum prediction and in silico mass-spectrum-to-compound identification. This new version of CFM-ID (version 4.0) demonstrates significantly increased accuracy in both ET-MS/MS spectral prediction and compound identification.