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High-resolution Tandem Mass Spectrometry for Studying Chemical Constituents of Gynura bicolor DC
Published on: February 2, 2024
RIKEN tandem mass spectral database (ReSpect) for phytochemicals: a plant-specific MS/MS-based data resource and
Yuji Sawada1, Ryo Nakabayashi, Yutaka Yamada
1RIKEN Plant Science Center, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, Japan.
Phytochemistry
|August 8, 2012
Summary
A new plant metabolite database, ReSpect, aids phytochemical structural identification using tandem mass spectrometry (MS/MS) fragment patterns. This resource successfully identified unknown metabolites in Lotus japonicus, advancing plant science research.
Area of Science:
- Metabolomics
- Plant Science
- Bioinformatics
Background:
- Tandem mass spectrometry (MS/MS) fragment pattern analysis is crucial for metabolite structural characterization.
- A dedicated plant-specific MS/MS data resource is needed to identify complex phytochemical structures.
Purpose of the Study:
- To develop a web-based database of MS/MS data for phytochemicals.
- To establish a fragment search function for structural identification.
- To improve the accuracy of metabolite annotation using fragmentation rules.
Main Methods:
- Developed the ReSpect (RIKEN tandem mass spectral database) web application.
- Compiled MS/MS data from literature and authentic standards.
- Implemented a fragment search based on m/z values.
- Utilized MS/MS fragmentation association rules for confidence scoring.
- Conducted a case study on untargeted MS/MS data from Lotus japonicus.
Main Results:
- The ReSpect database contains 3595 metabolites, with 76% from literature and 24% from standards.
- A functional fragment search application was established.
- The MS/MS fragmentation association rule algorithm was integrated for annotation confidence.
- Unknown metabolites in Lotus japonicus were successfully narrowed down to putative structures.
Conclusions:
- The ReSpect database and its search function are valuable tools for phytochemical structural elucidation.
- This resource facilitates the identification of unknown metabolites in plant species.
- The developed methods enhance the accuracy and efficiency of metabolomic data analysis.
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