BRENDA (BRaunschweig ENzyme DAtabase) is the main repository of manually annotated enzyme data. The development of the database began in 1987 at the former German National Research GBF (now: Helmholtz Centre for Infection Research) in Braunschweig. Initially, the enzyme data were published as a series of books (Handbook of Enzymes, Springer, 1). The data were continuously curated and improved at the University of Cologne (Institute for Biochemistry) from 1996 to 2007. In this period, the database was transformed into a publicly available database and subsequently converted from a fulltext into a relational database system. Since 2007, BRENDA is maintained and curated at the Technische Universität Braunschweig, Institute for Bioinformatics & Systems Biology.
The website http://www.brenda-enzymes.org
is visited by more than 180
000 different users each month. All enzyme data are linked to a source organism and to a protein sequence if the sequence has been deposited. The data are manually annotated from the primary literature covering classification and nomenclature, reaction and specificity, functional parameters, occurrence, enzyme structure, application, mutant information and engineered variants, stability, disease, isolation and preparation.
An elaborate query engine provides access to all data stored in the tables of the relational database. The ‘Quick search’ option allows a simple search in one of the 56 data fields. More sophisticated queries can be performed using the ‘Advanced Search’ by combining up to 20 search categories for text or numerical data fields. The 2009 newly introduced ‘Protein Search’ offers a quick access to the protein-specific data in BRENDA. The ‘Fulltext Search’ option provides a search in all tables in BRENDA, including the ‘Commentary’ field. The ‘Substructure Search’ allows the display of enzyme–ligand interactions.
Furthermore a number of different tools afford access to the enzyme-related data, e.g. the ‘Ontology Explorer’ consisting of biologically and biochemically relevant ontologies, including the BTO (BRENDA Tissue Ontology), the ‘TaxTree Explorer’ to search for enzymes or organism in the taxonomic tree, the ‘EC Explorer’ to search and browse the hierarchical tree of classified enzymes, the ‘Genome Explorer’ which connects the enzymes with the corresponding genome sequence, and the ‘Sequence Search’ which is useful for enzymes with known protein sequences.
In addition to the manually annotated data, two databases FRENDA (Full Reference ENzyme DAta) and AMENDA (Automatic Mining of ENzyme DAta) are maintained based on text-mining procedures, which are continuously improved to increase the quality and to reduce the number of false-positive entries.
Since the last publication in 2009 (2
) the data content has increased substantially, and new tools and functionalities for the query and data analysis were implemented.
An essential part of BRENDA consists of information on metabolites and small molecules, which interact with enzymes as substrates and products, inhibitors, activating compounds, cofactors or bound metals. The ligands can be displayed as their 2D structures. In this context, new comprehensive ligand information, the ‘Ligand View’ was implemented to present a summarized view on each enzyme–ligand interaction.
BRENDA now provides a new visualization for the distribution of numeric functional parameters as histograms with the option to show special distribution diagrams for one of the six main Enzyme Commission numbers (EC-classes) or taxonomic groups such as Archaea, Bacteria or Eukaryota. In the future the statistical analysis options will be further developed.
Another new presentation is the display of protein 3D structures showing protein-specific sequence and structure features like active centres, secondary structures, binding sites, sites of post-translational modifications, etc., using the Jmol (http://www.jmol.org/
Since 2000, BRENDA provides enzyme disease-related information obtained from PubMed entries by text-mining procedures (3
). The text-mining approaches were reprogrammed and upgraded with a sub-classification of these results. This led to an improved quality of the automatic search for relevant literature.