Reaction graph kernels predict EC numbers of unknown enzymatic reactions in plant secondary metabolism

Hiroto Saigo, Masahiro Hattori, Hisashi Kashima, Koji Tsuda

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Background: Understanding of secondary metabolic pathway in plant is essential for finding druggable candidate enzymes. However, there are many enzymes whose functions are not yet discovered in organism-specific metabolic pathways. Towards identifying the functions of those enzymes, assignment of EC numbers to the enzymatic reactions they catalyze plays a key role, since EC numbers represent the categorization of enzymes on one hand, and the categorization of enzymatic reactions on the other hand.Results: We propose reaction graph kernels for automatically assigning EC numbers to unknown enzymatic reactions in a metabolic network. Reaction graph kernels compute similarity between two chemical reactions considering the similarity of chemical compounds in reaction and their relationships. In computational experiments based on the KEGG/REACTION database, our method successfully predicted the first three digits of the EC number with 83% accuracy. We also exhaustively predicted missing EC numbers in plant's secondary metabolism pathway. The prediction results of reaction graph kernels on 36 unknown enzymatic reactions are compared with an expert's knowledge. Using the same data for evaluation, we compared our method with E-zyme, and showed its ability to assign more number of accurate EC numbers.Conclusion: Reaction graph kernels are a new metric for comparing enzymatic reactions.

Original languageEnglish
Article numberS31
JournalBMC bioinformatics
Issue numberSUPPLL.1
Publication statusPublished - Jan 18 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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