Antje Chang

BRENDA, AMENDA and FRENDA the enzyme information system: new content and tools in 2009.

The BRENDA (BRaunschweig ENzyme DAtabase) (http://www.brenda-enzymes.org) represents the largest freely available information system containing a huge amount of biochemical and molecular information on all classified enzymes as well as software tools for querying the database and calculating molecular properties. The database covers information on classification and nomenclature, reaction and specificity, functional parameters, occurrence, enzyme structure and stability, mutants and enzyme engineering, preparation and isolation, the application of enzymes, and ligand-related data. The data in BRENDA are manually curated from more than 79 000 primary literature references. Each entry is clearly linked to a literature reference, the origin organism and, where available, to the protein sequence of the enzyme protein. A new search option provides the access to protein-specific data. FRENDA (Full Reference ENzyme DAta) and AMENDA (Automatic Mining of ENzyme DAta) are additional databases created by continuously improved text-mining procedures. These databases ought to provide a complete survey on enzyme data of the literature collection of PubMed. The web service via a SOAP (Simple Object Access Protocol) interface for access to the BRENDA data has been further enhanced.

BRENDA, the enzyme information system in 2011

The BRENDA (BRaunschweig ENzyme Database, http://www.brenda-enzymes.org) enzyme information system is the main collection of enzyme functional and property data for the scientific community. The majority of the data are manually extracted from the primary literature. The content covers information on function, structure, occurrence, preparation and application of enzymes as well as properties of mutants and engineered variants. The number of manually annotated references increased by 30% to more than 100u2009000, the number of ligand structures by 45% to almost 100u2009000. New query, analysis and data management tools were implemented to improve data processing, data presentation, data input and data access. BRENDA now provides new viewing options such as the display of the statistics of functional parameters and the 3D view of protein sequence and structure features. Furthermore a ligand summary shows comprehensive information on the BRENDA ligands. The enzymes are linked to their respective pathways and can be viewed in pathway maps. The disease text mining part is strongly enhanced. It is possible to submit new, not yet classified enzymes to BRENDA, which then are reviewed and classified by the International Union of Biochemistry and Molecular Biology. A new SBML output format of BRENDA kinetic data allows the construction of organism-specific metabolic models.

BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA

The BRENDA (BRaunschweig ENzyme DAtabase) enzyme portal (http://www.brenda-enzymes.org) is the main information system of functional biochemical and molecular enzyme data and provides access to seven interconnected databases. BRENDA contains 2.7 million manually annotated data on enzyme occurrence, function, kinetics and molecular properties. Each entry is connected to a reference and the source organism. Enzyme ligands are stored with their structures and can be accessed via their names, synonyms or via a structure search. FRENDA (Full Reference ENzyme DAta) and AMENDA (Automatic Mining of ENzyme DAta) are based on text mining methods and represent a complete survey of PubMed abstracts with information on enzymes in different organisms, tissues or organelles. The supplemental database DRENDA provides more than 910 000 new EC number–disease relations in more than 510 000 references from automatic search and a classification of enzyme–disease-related information. KENDA (Kinetic ENzyme DAta), a new amendment extracts and displays kinetic values from PubMed abstracts. The integration of the EnzymeDetector offers an automatic comparison, evaluation and prediction of enzyme function annotations for prokaryotic genomes. The biochemical reaction database BKM-react contains non-redundant enzyme-catalysed and spontaneous reactions and was developed to facilitate and accelerate the construction of biochemical models.

BRENDA in 2015: exciting developments in its 25th year of existence

The BRENDA enzyme information system (http://www.brenda-enzymes.org/) has developed into an elaborate system of enzyme and enzyme-ligand information obtained from different sources, combined with flexible query systems and evaluation tools. The information is obtained by manual extraction from primary literature, text and data mining, data integration, and prediction algorithms. Approximately 300 million data include enzyme function and molecular data from more than 30 000 organisms. The manually derived core contains 3 million data from 77 000 enzymes annotated from 135 000 literature references. Each entry is connected to the literature reference and the source organism. They are complemented by information on occurrence, enzyme/disease relationships from text mining, sequences and 3D structures from other databases, and predicted enzyme location and genome annotation. Functional and structural data of more than 190 000 enzyme ligands are stored in BRENDA. New features improving the functionality and analysis tools were implemented. The human anatomy atlas CAVEman is linked to the BRENDA Tissue Ontology terms providing a connection between anatomical and functional enzyme data. Word Maps for enzymes obtained from PubMed abstracts highlight application and scientific relevance of enzymes. The EnzymeDetector genome annotation tool and the reaction database BKM-react including reactions from BRENDA, KEGG and MetaCyc were improved. The website was redesigned providing new query options.

The BRENDA Tissue Ontology (BTO): the first all-integrating ontology of all organisms for enzyme sources

BTO, the BRENDA Tissue Ontology (http://www.BTO.brenda-enzymes.org) represents a comprehensive structured encyclopedia of tissue terms. The project started in 2003 to create a connection between the enzyme data collection of the BRENDA enzyme database and a structured network of source tissues and cell types. Currently, BTO contains more than 4600 different anatomical structures, tissues, cell types and cell lines, classified under generic categories corresponding to the rules and formats of the Gene Ontology Consortium and organized as a directed acyclic graph (DAG). Most of the terms are endowed with comments on their derivation or definitions. The content of the ontology is constantly curated with ∼1000 new terms each year. Four different types of relationships between the terms are implemented. A versatile web interface with several search and navigation functionalities allows convenient online access to the BTO and to the enzymes isolated from the tissues. Important areas of applications of the BTO terms are the detection of enzymes in tissues and the provision of a solid basis for text-mining approaches in this field. It is widely used by lab scientists, curators of genomic and biochemical databases and bioinformaticians. The BTO is freely available at http://www.obofoundry.org.

BRENDA in 2017: new perspectives and new tools in BRENDA

The BRENDA enzyme database (www.brenda-enzymes.org) has developed into the main enzyme and enzyme-ligand information system in its 30 years of existence. The information is manually extracted from primary literature and extended by text mining procedures, integration of external data and prediction algorithms. Approximately 3 million data from 83 000 enzymes and 137 000 literature references constitute the manually annotated core. Text mining procedures extend these data with information on occurrence, enzyme-disease relationships and kinetic data. Prediction algorithms contribute locations and genome annotations. External data and links complete the data with sequences and 3D structures. A total of 206 000 enzyme ligands provide functional and structural data. BRENDA offers a complex query tool engine allowing the users an efficient access to the data via different search methods and explorers. The new design of the BRENDA entry page and the enzyme summary pages improves the user access and the performance. New interactive and intuitive BRENDA pathway maps give an overview on biochemical processes and facilitate the visualization of enzyme, ligand and organism information in the biochemical context. SCOPe and CATH, databases for protein structure classification, are included. New online and video tutorials provide online training for the users. BRENDA is freely available for academic users.

Solubilization and characterization of a senecionine N-oxygenase from Crotalaria scassellatii seedlings

Abstract Seeds of Crotalaria scassellatii (Fabaceae) store pyrrolizidine alkaloids as tertiary amines. During the beginning of seed germination the tertiary alkaloids are rapidly converted into the respective alkaloid N -oxides which are the ultimate forms of alkaloid transport, metabolism and storage in vegetative plant organs. The enzyme catalyzing the N -oxidation was isolated from 2-day old seedling, partially purified and characterized. It is a membrane-bound, but not microsomal enzyme sedimenting in the 39u2008000 g fraction. The particulate enzyme was solubilized in the presence of 0.4% CHAPS and 0.4xa0M NaCl. The solubilized enzyme was partially purified (228-fold) by means of 70% ammonium sulfate precipitation and monocrotaline affinity chromatography. Inhibitor experiments, temperature sensitivity and lack of a carboxy ferrocytochrome absorption maximum at 450xa0nm strongly indicate SNO to be a flavin dependent enzyme. It is a mixed function monooxygenase that specifically N -oxidizes a number of structurally related pyrrolizidine alkaloids including the alkaloids of Crotalaria . A great variety of related alkaloids and xenobiotics were tested as substrates, none was accepted. The apparent K m values of senecionine, monocrotaline and heliotrine representing the three major types of pyrrolizidine alkaloids, are 12.4, 40.1 and 370.9xa0 μ M, respectively. Senecionine is the best substrate, consequently the enzyme was named senecionine N -oxygenase (SNO). The substrate specificity of SNO is almost identical with that of a soluble insect SNO recently characterized from the haemolymph of arctiid larvae [Lindigkeit, R., Biller, A., Buch, M., Schiebel, H.-M., Boppre, M. and Hartmann, T., European Journal of Biochemistry , 1997, 245 , 626].

The BRENDA enzyme information system–From a database to an expert system

Enzymes, representing the largest and by far most complex group of proteins, play an essential role in all processes of life, including metabolism, gene expression, cell division, the immune system, and others. Their function, also connected to most diseases or stress control makes them interesting targets for research and applications in biotechnology, medical treatments, or diagnosis. Their functional parameters and other properties are collected, integrated, and made available to the scientific community in the BRaunschweig ENzyme DAtabase (BRENDA). In the last 30 years BRENDA has developed into one of the most highly used biological databases worldwide. The data contents, the process of data acquisition, data integration and control, the ways to access the data, and visualizations provided by the website are described and discussed.

Development of a classification scheme for disease-related enzyme information

BackgroundBRENDA (BR aunschweig EN zyme DA tabase, http://www.brenda-enzymes.org) is a major resource for enzyme related information. First and foremost, it provides data which are manually curated from the primary literature. DRENDA (D isease RE lated EN zyme information DA tabase) complements BRENDA with a focus on the automatic search and categorization of enzyme and disease related information from title and abstracts of primary publications. In a two-step procedure DRENDA makes use of text mining and machine learning methods.ResultsCurrently enzyme and disease related references are biannually updated as part of the standard BRENDA update. 910,897 relations of EC-numbers and diseases were extracted from titles or abstracts and are included in the second release in 2010. The enzyme and disease entity recognition has been successfully enhanced by a further relation classification via machine learning. The classification step has been evaluated by a 5-fold cross validation and achieves an F1 score between 0.802 ± 0.032 and 0.738 ± 0.033 depending on the categories and pre-processing procedures. In the eventual DRENDA content every category reaches a classification specificity of at least 96.7% and a precision that ranges from 86-98% in the highest confidence level, and 64-83% for the smallest confidence level associated with higher recall.ConclusionsThe DRENDA processing chain analyses PubMed, locates references with disease-related information on enzymes and categorises their focus according to the categories causal interaction, therapeutic application, diagnostic usage and ongoing research. The categorisation gives an impression on the focus of the located references. Thus, the relation categorisation can facilitate orientation within the rapidly growing number of references with impact on diseases and enzymes. The DRENDA information is available as additional information in BRENDA.