Automated Pipeline for De Novo Metabolite Identification Using Mass-Spectrometry-Based Metabolomics

Abstract

Metabolite identification is one of the biggest bottlenecks in metabolomics. Identifying human metabolites poses experimental, analytical, and computational challenges. Here we present a pipeline of previously developed cheminformatic tools and demonstrate how it facilitates metabolite identification using solely LC/MS(n) data. These tools process, annotate, and compare MS(n) data, and propose candidate structures for unknown metabolites either by identity assignment of identical mass spectral trees or by de novo identification using substructures of similar trees. The working and performance of this metabolite identification pipeline is demonstrated by applying it to LC/MS(n) data of urine samples. From human urine, 30 MS(n) trees of unknown metabolites were acquired, processed, and compared to a reference database containing MS(n) data of known metabolites. From these 30 unknowns, we could assign a putative identity for 10 unknowns by finding identical fragmentation trees. For 11 unknowns no similar fragmentation trees were found in the reference database. On the basis of elemental composition only, a large number of candidate structures/identities were possible, so these unknowns remained unidentified. The other 9 unknowns were also not found in the database, but metabolites with similar fragmentation trees were retrieved. Computer assisted structure elucidation was performed for these 9 unknowns: for 4 of them we could perform de novo identification and propose a limited number of candidate structures, and for the other 5 the structure generation process could not be constrained far enough to yield a small list of candidates. The novelty of this work is that it allows de novo identification of metabolites that are not present in a database by using MS(n) data and computational tools. We expect this pipeline to be the basis for the computer-assisted identification of new metabolites in future metabolomics studies, and foresee that further additions will allow the identification of even a larger fraction of the unknown metabolites.