Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics

Abstract

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.

Keywords: compound identification; high resolution mass spectrometry; in silico fragmentation; library search; metabolomics; tandem mass spectrometry.

Figure 1

Computational metabolomics approaches help to unravel the complexity of the metabolome and especially shed light on unknown metabolites. This includes technologies across different disciplines, including quantum chemistry, machine learning, heuristic approaches and reaction chemistry-based methods.

Figure 2

In silico fragmentation tools such as MS-Finder, CFM-ID, CSI:FingerID and Metfrag utilized known compounds from structure databases to calculate fragments compare those theoretical fragmentations against experimental spectra. When combined with MS/MS database search and utilizing additional metadata annotation rates can be increased tremendously.

Figure 3

Ion mobility can be used as an additional orthogonal approach to resolve complex mixtures. The experimental collision cross-section values (CCS) can be further utilized to train machine learning models to further enrich compound databases with CCS information.