A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation

Large-scale metabolite annotation is a bottleneck in
untargeted
metabolomics. Here, we present a structure-guided molecular network
strategy (SGMNS) for deep annotation of untargeted ultra-performance
liquid chromatography-high resolution mass spectrometry (MS) metabolomics
data. Different from the current network-based metabolite annotation
method, SGMNS is based on a global connectivity molecular network
(GCMN), which was constructed by molecular fingerprint similarity
of chemical structures in metabolome databases. Neighbor metabolites
with similar structures in GCMN are expected to produce similar spectra.
Network annotation propagation of SGMNS is performed using known metabolites
as seeds. The experimental MS/MS spectra of seeds are assigned to
corresponding neighbor metabolites in GCMN as their “pseudo”
spectra; the propagation is done by searching predicted retention
times, MS1, and “pseudo” spectra against
metabolite features in untargeted metabolomics data. Then, the annotated
metabolite features were used as new seeds for annotation propagation
again. Performance evaluation of SGMNS showed its unique advantages
for metabolome annotation. The developed method was applied to annotate
six typical biological samples; a total of 701, 1557, 1147, 1095,
1237, and 2041 metabolites were annotated from the cell, feces, plasma
(NIST SRM 1950), tissue, urine, and their pooled sample, respectively,
and the annotation accuracy was >83% with RSD <2%. The results
show that SGMNS fully exploits the chemical space of the existing
metabolomes for metabolite deep annotation and overcomes the shortcoming
of insufficient reference MS/MS spectra.