ChEBI: a database and ontology for chemical entities of biological interest.

Chemical Entities of Biological Interest (ChEBI) is a freely available dictionary of molecular entities focused on 'small' chemical compounds. The molecular entities in question are either natural products or synthetic products used to intervene in the processes of living organisms. Genome-encoded macromolecules (nucleic acids, proteins and peptides derived from proteins by cleavage) are not as a rule included in ChEBI. In addition to molecular entities, ChEBI contains groups (parts of molecular entities) and classes of entities. ChEBI includes an ontological classification, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified. ChEBI is available online at http://www.ebi.ac.uk/chebi/

IntEnz, the integrated relational enzyme database.

IntEnz is the name for the Integrated relational Enzyme database and is the official version of the Enzyme Nomenclature. The Enzyme Nomenclature comprises recommendations of the Nomenclature Committee of the International Union of Bio chemistry and Molecular Biology (NC-IUBMB) on the nomenclature and classification of enzyme-catalysed reactions. IntEnz is supported by NC-IUBMB and contains enzyme data curated and approved by this committee. The database IntEnz is available at http://www.ebi.ac.uk/intenz.

COMe: the ontology of bioinorganic proteins.

BACKGROUND: Many characterised proteins contain metal ions, small organic molecules or modified residues. In contrast, the huge amount of data generated by genome projects consists exclusively of sequences with almost no annotation. One of the goals of the structural genomics initiative is to provide representative three-dimensional (3-D) structures for as many protein/domain folds as possible to allow successful homology modelling. However, important functional features such as metal co-ordination or a type of prosthetic group are not always conserved in homologous proteins. So far, the problem of correct annotation of bioinorganic proteins has been largely ignored by the bioinformatics community and information on bioinorganic centres obtained by methods other than crystallography or NMR is only available in literature databases. RESULTS: COMe (Co-Ordination of Metals) represents the ontology for bioinorganic and other small molecule centres in complex proteins. COMe consists of three types of entities: 'bioinorganic motif' (BIM), 'molecule' (MOL), and 'complex proteins' (PRX), with each entity being assigned a unique identifier. A BIM consists of at least one centre (metal atom, inorganic cluster, organic molecule) and two or more endogenous and/or exogenous ligands. BIMs are represented as one-dimensional (1-D) strings and 2-D diagrams. A MOL entity represents a 'small molecule' which, when in complex with one or more polypeptides, forms a functional protein. The PRX entities refer to the functional proteins as well as to separate protein domains and subunits. The complex proteins in COMe are subdivided into three categories: (i) metalloproteins, (ii) organic prosthetic group proteins and (iii) modified amino acid proteins. The data are currently stored in both XML format and a relational database and are available at http://www.ebi.ac.uk/come/. CONCLUSION: COMe provides the classification of proteins according to their 'bioinorganic' features and thus is orthogonal to other classification schemes, such as those based on sequence similarity, 3-D fold, enzyme activity, or biological process. The hierarchical organisation of the controlled vocabulary allows both for annotation and querying at different levels of granularity.

ChEBI: an open bioinformatics and cheminformatics resource.

Chemical Entities of Biological Interest (ChEBI) is a freely available dictionary of molecular entities focused on "small" chemical compounds. This unit provides a detailed guide to browsing, searching, downloading, and programmatic access to the ChEBI database.

"Good annotation practice" for chemical data in biology.

A structural diagram, in the form of a two-dimensional (2-D) sketch, remains the most effective portrait of a "small molecule" or chemical reaction. However, such structural diagrams, as for any other core data, cannot be used in speech (and should not be used in free text). "Good annotation practice" for biological databases is to use either consistent and widely recognised terminology or unique identifiers from a dedicated database to refer to the molecule of interest. Ideally, scientists should use terminology that is both pronounceable and meaningful. Thus, a viable solution for a bioinformatician is to use a definitive controlled vocabulary of biochemical compounds and reactions, which contains both systematic and common names. In addition, chemical ontologies provide a means for placing entities of interest into wider chemical, biological or medical contexts. We present some challenges and achievements in the standardisation of chemical language in biological databases, with emphasis on three aspects of annotation: 1. good drawing practice: how to draw unambiguous 2-D diagrams; 2. good naming practice: how to give most appropriate names; and 3. good ontology practice: how to link the entity of interest by defined logical relationships to other entities.