Retrieving and Utilizing Hypothetical Neutral Losses from Tandem Mass Spectra for Spectral Similarity Analysis and Unknown Metabolite Annotation.

Spectral similarity comparison through tandem mass spectrometry (MS2) is a powerful approach to annotate known and unknown metabolic features in mass spectrometry (MS)-based untargeted metabolomics. In this work, we proposed the concept of hypothetical neutral loss (HNL), which is the mass difference between a pair of fragment ions in a MS2 spectrum. We demonstrated that HNL values contain core structural information that can be used to accurately assess the structural similarity between two MS2 spectra. We then developed the Core Structure-based Search (CSS) algorithm based on HNL values. CSS was validated with sets of hundreds of randomly selected metabolites and their reference MS2 spectra, showing significantly improved correlation between spectral and structural similarities. Compared to state-of-the-art spectral similarity algorithms, CSS generates better ranking of structurally relevant chemicals among false positives. Combining CSS, HNL library, and biotransformation database, we further developed Metabolite core structure-based Search (McSearch), a novel computational solution to facilitate the annotation of unknown metabolites using the reference MS2 spectra of their structural analogs. McSearch generates better results in the Critical Assessment of Small Molecule Identification (CASMI) 2017 data set than conventional unknown feature annotation programs. McSearch was also tested in experimental MS2 data of xenobiotic metabolite derivatives belonging to three different metabolic pathways. Our results confirmed that McSearch can better capture the underlying structural similarity between MS2 spectra. Overall, this work provides a novel direction for metabolite annotation via HNL values, paving the way for annotating metabolites using their structurally similar compounds.