BRITE database

KEGG BRITE is a collection of hierarchies and binary relations with two inter-related objectives corresponding to the two types of graphs: to automate functional interpretations associated with the KEGG pathway reconstruction and to assist discovery of empirical rules involving genome-environment interactions. Currently, we focus on hierarchical structuring of our knowledge on functional aspects of the genomic and chemical spaces (Table 2), including the KEGG orthology (KO) system for ortholog/paralog gene groups, the reaction classification (RC) system for biochemical reactions, and other classifications for compounds and drugs tentatively called chemical ontology as shown in Figure 1. We plan to extend the KO system to include the definition of functional modules in the KEGG pathways and to develop ontologies for computational inference of higher-order functions.

Table 2

Functional hierarchies in KEGG BRITE

Network hierarchy

KO

Protein families

Enzymes

Transcription factors

Ribosome

Translation factors

ABC transporters

G-protein-coupled receptors

Ion channels

Cytokines

Cytokine receptors

Cell adhesion molecules (CAMs)

CAM ligands

CD molecules

Bacterial motility proteins

Compounds

Compounds with biological roles

Lipids

Phytochemical compounds

Compound interactions

Ion channel agonists/antagonists

Cytochrome P450 substrates

Drugs

Therapeutic category of drugs

Drug classification

Diseases

Disease genes, genomes and pathways

Organisms

KEGG organisms

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As on September 12, 2005.

PATHWAY database

The KEGG PATHWAY database is a collection of manually drawn pathway maps for metabolism, genetic information processing, environmental information processing such as signal transduction, various other cellular processes and human diseases. During the past 2 years we have significantly increased the number of pathway maps for regulatory pathways including signal transduction, ligand–receptor interaction and cell communication, all based on extensive survey of published literature. For metabolic pathways we created two new sections, ‘Glycan Biosynthesis and Metabolism’ and ‘Biosynthesis of Polyketides and Nonribosomal Peptides’. The XML version of the pathway maps is available for both metabolic and regulatory pathways. These KEGG Markup Language (KGML) files provide graph information that can be used to computationally reproduce and manipulate KEGG pathway maps.

GENES database

The KEGG GENES database is a collection of gene catalogs for all complete genomes and some partial genomes (31 eukaryotes, 235 bacteriaand 23 archaea as of September 12, 2005), generated from publicly available resources, mostly NCBI RefSeq (3). All genomes in KEGG GENES are subject to SSDB computation and given manual KO assignments as described below. There are auxiliary collections of gene catalogs: DGENES for draft genomes (21 eukaryotes) and EGENES for expressed sequence tag consensus contigs (25 plants). These are meant to supplement the repertoire of KEGG organisms, and all are given automatic KO assignments using GENES as a reference dataset. Each GENES entry contains cross-reference information to outside databases, including NCBI gi numbers, Entrez Gene IDs and UniProt accession numbers. Starting with KEGG release 37.0 (January 2006) automatic ID conversion is implemented enabling use of such outside identifiers to access KEGG GENES and then the other KEGG databases.

KEGG orthology

There is a total of over one million genes in KEGG GENES, representing a tiny, but well-characterized part of the genomic space that makes up the biological world. From this part we organize knowledge about orthologous genes and paralogous genes, which, we hope, can be generalized for understanding the entire genomic space. This knowledge is stored in the KO system, a pathway-based classification of orthologous genes, including orthologous relationships of paralogous gene groups. The KO identifier, or the K number, is a common identifier for linking genomic information in the GENES database with network information in the PATHWAY database. The pathway nodes represented by rectangles in the KEGG reference pathway maps are given KO identifiers, so that organism-specific pathways can be computationally generated once each genome is annotated with KO's. This annotation or the KO assignment is done manually for KEGG GENES with the help of the GFIT tool using best-hit relations in pairwise genome comparisons stored in the SSDB database (4).

Because the number of ortholog groups that can be linked to pathways is limited, we have introduced two additional ways to define KO's. One is to use COG (5) to cover a broad-range of possible ortholog groups. The other is to rely on experts' classifications of protein families, which tend to be more functionally oriented resulting in narrowly defined KO's. A growing number of protein families are being added to the KO system, and they are shown in separate hierarchies different from the KEGG network hierarchy. The KO system can be best viewed from the KEGG BRITE database (Table 2).

LIGAND database

Originally, the LIGAND database consisted of just two components: ENZYME for enzyme nomenclature and COMPOUND for chemical compound structures (6). It later successively included additional components: REACTION for chemical reaction formulas, GLYCAN for glycan structures, RPAIR for reactant pair transformation patterns and DRUG for drug information. This expansion of the LIGAND collection represents our expanded efforts for understanding the chemical space that is part of the biological world.

The KEGG DRUG database is a new addition from KEGG release 36.1 (December 2005). It contains chemical structures and additional information such as therapeutic categories and target molecules. A most unique feature of KEGG DRUG is a collection of drug structure maps, which graphically illustrate, in a manner similar to KEGG pathway maps, our knowledge on groups of chemical structural patterns, therapeutic categories, their relationships and the chronology of drug development if known.

Reaction classification

The RC system in the chemical space is a counterpart of the KO system in the genomic space (Figure 1). It represents our attempt to organize knowledge on chemical reactions by categorizing chemical structure transformation patterns. The REACTION database contains individual reaction formulas taken from the ENZYME database. Each reaction formula is split into a set of substrate-product pairs, and the chemical structure comparison program SIMCOMP is applied to obtain an optimal alignment. This comparison is based on atom typing, which is the conversion of regular atomic (C, N, O, S, P and so on) representation to what we call KCF representation that consists of 68 atom types distinguishing functional groups and atomic environments (7). The chemical structure alignment generated by SIMCOMP is used to define the R atom for the reaction center, the D atom(s) for adjacent atom(s) in the mismatched region and the M atom(s) for adjacent atom(s) in the matched region (8). This is first done computationally and is followed by extensive manual curation.

The RPAIR database is still under development, but it is the basis for the RC system categorizing curated RDM patterns. Since an enzymatic reaction usually involves multiple substrates and products, one EC number corresponds to a combination of RDM patterns. The RC system has enabled automatic assignment of EC numbers from a set of substrate and product structures (8) and will further enable exploration of unknown reactions by generating plausible combinations of RDM patterns, which may then be related to possible paralogs of enzyme genes.

The KEGG project is supported by the Institute for Bioinformatics Research and Development of the Japan Science and Technology Agency, the 21st Century COE program ‘Genome Science’, and a grant-in-aid for scientific research on the priority area from the Ministry of Education, Culture, Sports, Science and Technology of Japan. The computational resources were provided by the Bioinformatics Center, Institute for Chemical Research, Kyoto University. Funding to pay the Open Access publication charges for this article was provided by the grant-in-aid for scientific research.

Conflict of interest statement. None declared.