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Standardized Identification of Compound Structure in Tibetan Medicine Using Ion Trap Mass Spectrometry and Multiple-Stage Fragmentation Analysis
Published on: March 17, 2023
Substructure-based annotation of high-resolution multistage MS(n) spectral trees.
Lars Ridder1, Justin J J van der Hooft, Stefan Verhoeven
1Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam, The Netherlands. lars.ridder@wur.nl
Rapid Communications in Mass Spectrometry : RCM
|September 15, 2012
Summary
This study introduces an improved in silico method for analyzing multistage mass spectrometry (MS(n)) data. The approach enhances compound identification in metabolomics by utilizing hierarchical fragmentation patterns, reducing expert analysis time.
Area of Science:
- Analytical Chemistry
- Computational Chemistry
- Metabolomics
Background:
- High-resolution multistage MS(n) data offers rich structural information for metabolomics.
- Current in silico methods for MS(n) data analysis are limited to single-level fragmentation.
- Manual analysis of complex MS(n) data is time-consuming for experts.
Purpose of the Study:
- To develop an extended substructure-based approach for annotating hierarchical spectral trees from MS(n) experiments.
- To improve the accuracy and efficiency of compound identification in metabolomics using MS(n) data.
Main Methods:
- An extended substructure-based algorithm was developed to analyze hierarchical spectral trees.
- The algorithm generates a hierarchical tree of molecular substructures to explain MS(n) fragmentation.
- A matching score quantifies the agreement between candidate structures and observed fragmentation patterns.
Main Results:
- The method was successfully applied to MS(n) spectral trees of various metabolomic compounds.
- Correct molecules were prioritized from the PubChem database based on the calculated matching score.
- The approach demonstrated effectiveness in identifying compounds across diverse chemical classes.
Conclusions:
- Incorporating deeper fragmentation levels in MS(n) data analysis significantly improves compound identification.
- Hierarchical information in MS(n) spectral trees enhances identification, particularly with lower mass accuracy.
- This computational method can substantially decrease the time needed for MS(n) data analysis by experts.
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