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Chemoinformatics-driven classification of Angiosperms using sulfur-containing compounds and machine learning

Muhammad-Redha Abdullah-Zawawi1,2, Nisha Govender1, Mohammad Bozlul Karim3

  • 1Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Malaysia.

Plant Methods
|November 6, 2022
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Summary
This summary is machine-generated.

Plant chemoinformatics using sulfur-containing compounds (SCCs) aids Angiosperm classification. Machine learning analysis of SCCs reveals evolutionary patterns and aids in classifying plant species based on their chemical profiles.

Keywords:
AngiospermsChemoinformaticsKNApSAck databaseMolecular fingerprintsMonocot-dicotSulfur-containing compounds

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Area of Science:

  • Plant chemoinformatics
  • Phytochemistry
  • Bioinformatics

Background:

  • Plant secondary metabolites, or phytochemicals, are crucial for adaptation and evolutionary patterns.
  • Plant chemoinformatics utilizes computational tools to analyze natural products for species differentiation and classification.
  • Metabolite diversity provides insights into plant phenetics and evolutionary history.

Purpose of the Study:

  • To classify Angiosperms (flowering plants) using information from sulfur-containing compounds (SCCs).
  • To explore the utility of chemoinformatics and machine learning in plant classification based on chemical data.

Main Methods:

  • Collected SCC data for 692 plant species from the KNApSAck database.
  • Calculated structural similarity scores (Tanimoto coefficient > 0.85) for metabolite pairs.
  • Employed machine learning algorithms for metabolite clustering and phylogenetic tree construction.

Main Results:

  • Metabolite clustering revealed associations between similar structures and content across species.
  • Phylogenetic analysis of Angiosperms resulted in three major clades: two eudicot-only clades and one mixed eudicot-monocot clade.
  • Eudicots in clades 1 and 2 were primarily characterized by glucosinolates, suggesting ancestral lineages, while clade 3 indicated chemical diversification during evolution.

Conclusions:

  • Sulfur-containing chemoinformatics, combined with machine learning, effectively informs Angiosperm classification.
  • This approach provides a novel perspective on plant evolutionary relationships and adaptations.