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

BMC Bioinformatics

Research

Koji Tsuda¹  Masahiro Hattori²  Hisashi Kashima³  Hiroto Saigo⁴ 

[1] AIST Computational Biology Research Center, 2-42 Aomi, 135-0064, Koto-ku, Tokyo, Japan;Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Kyoto, Japan;Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, 113-8656, Bunkyo-ku,, Tokyo, Japan;Max Planck Institute for Informatics\, Campus E1 4, 66123, Saarbrucken, Germany;

关键词: Random Walk;    Label Sequence;    Graph Kernel;    Loganin;    Secologanin;   

DOI  :  10.1186/1471-2105-11-S1-S31

来源: Springer

【 摘 要 】

BackgroundUnderstanding 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.ResultsWe 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.ConclusionReaction graph kernels are a new metric for comparing enzymatic reactions.

【 授权许可】

Unknown   
© Saigo et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.