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

Abstract

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 web server and docker images can be downloaded at https://hub.docker.com/r/wishartlab/cfmid.

Figure 1.

A fragmentation graph for the acetic acid [M+H]+ ion. Here each node denotes an ion fragment and each edge denotes the transition between a pair of nodes.

Figure 2.

(a) An example of how earlier versions of CFM-ID model a fragmentation that only involves one chemical bond (highlighted in green). (b) An example of how earlier versions of CFM-ID model a fragmentation that involves a chemical ring structure. Note that the order of FV⁰ and FV¹, is arbitrary. (c) An example of how the new version of CFM-ID models a fragmentation that involves the same chemical ring structure. Note that this method does not need the “double” feature vector design used by earlier versions of CFM-ID.

Figure 3.

(a) An ion fragment with its root atom highlighted by the blue circle. (b) Extracting a graph G from the ion fragment. (c) Labelling every vertex in the graph G. (d) Indexing each vertex is determined based on its label. (e) Selecting a subgraph SG from graph G. (f) Computing adjacency matrix from graph SG. (g) Creating two tensors from the adjacency matrix. (h) Flattening two tensors into vectors. (i) Joining two vectors into the output feature vector.