Search Pathways for Secondary Metabolites Based on
Genome Analysis and Chemical Knowledge onMetabolism
Takaaki Nishioka 1nishioka@scl.kyoto-u.ac.jp
1 Graduate

SchoolofAgriculturalKimito Funatsu 2

funatsu@tutkie.tut.ac.jp

Sciences,

Kyoto

University,

Oiwake-cho,

Ki-tashirakawa, Kyoto 606-8502, Japan

2 Department of Knowledge-Based Information Engineering, Toyohashi University ofTechnology, 1-1 Tempaku, Toyohashi, Aichi 441-8580, Japan

1 Introduction
To estimate the whole aspects of metabolic activity of a biological species, the rst step is to reconstruct all the possible metabolic pathways from the enzymes coded by the genes on the genome.
For the basic metabolites that are common products in various organisms, pathways for their mutual
chemical conversion are well studied and have been collected as the PATHWAY and REACTION
databases in Kyoto Encyclopedia of Genes and Genomes (KEGG) [2, 3]. On the other, those remain
unknown for the secondary metabolites that are synthesized from basic metabolites via species speci cpathways. Prediction of biosynthesis pathways for them might not only reveal the enzymes catalyzing the reactions and the metabolic intermediates, but also be applicable to design novel bioactive
compounds through gene engineering of microorganisms.
Pathcomp developed and implemented on KEGG by Goto [2, 6] is the system that is now available
for practical use of searching pathways. In their system, every pair of two metabolites that are mutuallyconverted by one enzymatic reaction is extracted as a binary relation and registered in the REACTION
database. When two compounds that are mutually converted via unknown pathway are given, it
generates all possible pathways by connecting binary relations available. Their system, however, is
not applicable to most secondary metabolites because starting basic metabolites are unknown. Thisis the reason why we develop another system that searches both the pathway and the starting basic
metabolite for a given secondary metabolite.

2 Concept of System
Two procedures must be included in the system. The one is to list all the possible reaction sites
in the chemical structure of a given compound. Reaction sites are usually on the functional groups,
heteroatoms, and chemical bonds connecting them. The other is to rank the relative reactivity for thereaction sites listed. In this procedure, knowledge of metabolic reactions evaluates the reactivity ateach site by comparing the structure of the given compound with the substrate speci city of enzymes.
For this purpose, knowledge database is designed to analyze and to accumulate the reaction rules on
the substrate speci city and reaction type for well-characterized enzyme reactions.
In the eld of organic synthesis, a method of neural network developed by Kohonen [5] has beensuccessfully applied for learning the reaction rules of various organic reactions [7]. This method has
never been applied to enzyme catalyzed reactions.
In the present study, we apply Kohonen self-organizing network simulator TUT-SOM [1], which isoriginally devised for data classi cation of organic reactions, to enzyme catalyzed reactions. Learning312

in TUT-SOM repeats the following three steps; selection of a reference vector from a set of inputvectors, matching of weight vectors to input vectors, and modi cation of weight vectors. TUT-SOM
also supports counterpropagation method developed by Hecht-Nielsen [4].

3 Results and Discussions
In the application of Kohonen self-organizing network to the enzyme catalyzed reactions, the following
problems are revealed.
(1) In organic synthetic reactions, two reactants locally interact with each other only at theirreaction sites. Input parameters are quantitative physicochemical parameters such as electron density,ionization potential, and electron anity of the reaction sites of reactants and products. In enzyme
catalyzed reactions, two reactants rst form a complex with enzyme molecule, then they interactwith each other. Critical step is the complex formation, in which the enzyme strictly recognizes thewhole structures of the reactants. Input parameters must include global structural features of thereactant molecules as well as local ones near the reaction sites. It is dicult to nd proper parametersdescribing the global structural features.
(2) To extract reaction rules for one enzyme reaction, data should be collected for the reactions withthe same enzyme and more substrates with di erent structures than the number of input parameters.
In the databases, only limited number of data are collected for each enzyme; reactions for one or two
di erent substrate analogues because of strict substrate speci city. This makes it dicult to extract
reaction rules.

Acknowledgments
This work is supported in part by a Grant-in-Aid (10168101) for Scienti c Research on Priority Areas
from The Ministry of Education, Science, Sports and Culture in Japan.

References[1] Funatsu, K., Manual for TUT-SOM, 1997.
[2] Goto, S., Bono, H., Ogata, H., Fujibuchi, W., Nishioka, T., Sato, K., and Kanehisa, M., Organizing and Computing Metabolic Pathway Data in terms of Binary Relations. Paci c Symposium
on Biocomputing '97, (Ed. by Altman, R. B. and Dunker, A. K. and Hunter, L. and Klein, T.
E.), World Scienti c, 175{186, 1997.
[3] Goto, S., Nishioka, T. and Kanehisa, M., LIGAND Database for Enzymes, Compounds, andReactions, Nucleic Acids Research, in press.
[4] Hecht-Nielsen, R., Counterpropagation Networks, Appl. Optics., 26:4979{4984, 1987.
[5] Kohonen, T., Self-Organized Formation of Topologically Correct Feature Maps, Biol. Cybern.,
43:59{69, 1982.
[6] Ogata, H., Goto, S., Fujibuchi, W. and Kanehisa, M., Computation with the KEGG pathway
database, Biosystem, 47:119{128, 1998.
[7] Satoh, H., Sacher, O., Nakata, T., Chen, L., Gasteiger, J. and Funatsu, K., Classi cation of Organic Reactions: Similarity of Reactions Based on Changes in the Electronic Features of Oxygen
Atoms at the Reaction Sites, J. Chem. Inf. Comput. Sci., 38:210{219, 1998.

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