Estimate Metabolite Taxonomy and Structure with a Fragment-Centered Database and Fragment Network

TL;DR

This paper introduces MSFragDB, a fragment-centered database, and a fragment network approach to improve metabolite structure identification in mass spectrometry, addressing coverage gaps and spectrum variability.

Contribution

The work presents a novel fragment-based database and network model that enhance metabolite identification accuracy and taxonomy estimation, even with incomplete or contaminated spectra.

Findings

MSFragDB has higher hit ratio than existing databases.

The fragment network effectively estimates chemical structures.

The approach improves identification despite spectrum contamination.

Abstract

Metabolite structure identification has become the major bottleneck of the mass spectrometry based metabolomics research. Till now, number of mass spectra databases and search algorithms have been developed to address this issue. However, two critical problems still exist: the low chemical component record coverage in databases and significant MS/MS spectra variations related to experiment equipment and parameter settings. In this work, we considered the molecule fragment as basic building blocks of the metabolic components which had relatively consistent signatures in MS/MS spectra. And from a bottom-up point of view, we built a fragment centered database, MSFragDB, by reorganizing the data from the Human Metabolome Database (HMDB) and developed an intensity-free searching algorithm to search and rank the most relative metabolite according to the users' input. We also proposed the concept of fragment network, a graph structure that encoded the relationship between the molecule fragments to find close motif that indicated a specific chemical structure. Although based on the same dataset as the HMDB, validation results implied that the MSFragDB had a higher hit ratio and furthermore, estimated possible taxonomy that a query spectrum belongs to when the corresponding chemical component was missing in the database. Aid by the Fragment Network, the MSFragDB was also proved to be able to estimate the right structure while the MS/MS spectrum suffers from the precursor-contamination. The strategy proposed is general and can be adopted in existing databases. We believe MSFragDB and Fragment Network can improve the performance of structure identification with existing data. The beta version of the database is freely available at www.xrzhanglab.com/msfragdb/.