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Summary

This study introduces a new computational method using a multilayer perceptron (MLP) network to rapidly identify glucosinolates (GSLs) and their derivatives in radish seeds, accelerating plant chemical analysis.

Keywords:
UPLC-QE-MS/MSautomated compound screeningchemical structural characterizationmass data processing

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Area of Science:

  • Plant biochemistry
  • Metabolomics
  • Computational chemistry

Background:

  • Glucosinolates (GSLs) are key secondary metabolites in Brassicaceae plants.
  • Multilayer Perceptron (MLP) networks offer advanced capabilities for analyzing complex plant metabolomes.
  • Radish seeds serve as a model system for studying GSLs.

Purpose of the Study:

  • To develop an efficient computational workflow for the identification and annotation of GSLs and related compounds in radish seeds.
  • To leverage deep learning and network analysis for accelerating the discovery of novel plant metabolites.

Main Methods:

  • Utilized an MLP network for deep learning-based filtering of GSL precursor ions.
  • Implemented an automated screening system with a scoring mechanism for compound prioritization.
  • Constructed a tracer molecular network for accurate compound labeling and pathway elucidation.
  • Employed a mathematical tool for de novo prediction of GSL structures.

Main Results:

  • Annotated 195 GSL-related compounds in radish seeds, including regular GSLs, malonyl products, sinapoyl compounds, and diglycosides.
  • Confirmed eight compounds using authentic standards and tentatively identified others via mass spectrometry, network analysis, and database matching.
  • Identified 36 putatively novel GSL derivatives requiring further structural validation.

Conclusions:

  • The developed computational approach significantly reduces the time and effort required for chemical profiling of complex plant samples.
  • This methodology is applicable to diverse plant species, including other vegetables and medicinal herbs.
  • Enhances our understanding of plant chemical diversity and facilitates deeper insights into plant metabolomics.