1.
BRENDA
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BRENDA is an information system representing one of the most comprehensive enzyme repositories. It is a resource that comprises molecular and biochemical information on enzymes that have been classified by the IUBMB. Every classified enzyme is characterized with respect to its catalyzed biochemical reaction, kinetic properties of the corresponding reactants are described in detail. BRENDA contains enzyme-specific data manually extracted from scientific literature and additional data derived from automatic information retrieval methods such as text mining. It provides a user interface that allows a convenient and sophisticated access to the data. BRENDA was founded in 1987 at the former German National Research Centre for Biotechnology in Braunschweig and was published as a series of books. Its name was originally an acronym for the Braunschweig Enzyme Database, from 1996 to 2007, BRENDA was located at the University of Cologne. There, BRENDA developed into a publicly accessible enzyme information system, in 2007, BRENDA returned to Braunschweig. Currently, BRENDA is maintained and further developed at the Department of Bioinformatics, a major update of the data in BRENDA is performed twice a year. Besides the upgrade of its content, improvements of the interface are also incorporated into the BRENDA database. The latest update was performed in January 2015, Database, The database contains more than 40 data fields with enzyme-specific information on more than 7000 EC numbers that are classified according to the IUBMB. Currently, BRENDA contains manually annotated data from over 140,000 different scientific articles, each enzyme entry is clearly linked to at least one literature reference, to its source organism, and, where available, to the protein sequence of the enzyme. An important part of BRENDA represent the more than 110,000 enzyme ligands, the term ligand is used in this context to all low molecular weight compounds which interact with enzymes. These include not only metabolites of primary metabolism, co-substrates or cofactors, the origin of these molecules ranges from naturally occurring antibiotics to synthetic compounds that have been synthesized for the development of drugs or pesticides. Furthermore, cross-references to external resources such as sequence and 3D-structure databases. Extensions, Since 2006, the data in BRENDA is supplemented with information extracted from the literature by a co-occurrence based text mining approach. For this purpose, four text-mining repositories FRENDA, AMENDA, DRENDA and KENDA were introduced and these text-mining results were derived from the titles and abstracts of all articles in the literature database PubMed. Data access, There are several tools to access to the data in BRENDA
2.
MetaCyc
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The MetaCyc database contains extensive information on metabolic pathways and enzymes from many organisms. MetaCyc is also used in engineering and metabolomics research. MetaCyc contains extensive data on individual enzymes, describing their subunit structure, cofactors, activators and inhibitors, substrate specificity, MetaCyc data on reactions includes predicted atom mappings that describe the correspondence between atoms in the reactant compounds and the product compounds. It also provides enzyme mini-reviews and literature references, MetaCyc data on metabolites includes chemical structures, predicted Gibbs free energies of formation, and links to external databases
3.
Protein Data Bank
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The Protein Data Bank is a crystallographic database for the three-dimensional structural data of large biological molecules, such as proteins and nucleic acids. The PDB is overseen by a called the Worldwide Protein Data Bank. The PDB is a key resource in areas of structural biology, most major scientific journals, and some funding agencies, now require scientists to submit their structure data to the PDB. Many other databases use protein structures deposited in the PDB, for example, SCOP and CATH classify protein structures, while PDBsum provides a graphic overview of PDB entries using information from other sources, such as Gene ontology. By 1971, one of Meyers programs, SEARCH, enabled researchers to access information from the database to study protein structures offline. SEARCH was instrumental in enabling networking, thus marking the beginning of the PDB. Upon Hamiltons death in 1973, Tom Koeztle took over direction of the PDB for the subsequent 20 years, in January 1994, Joel Sussman of Israels Weizmann Institute of Science was appointed head of the PDB. In October 1998, the PDB was transferred to the Research Collaboratory for Structural Bioinformatics, the new director was Helen M. Berman of Rutgers University. In 2003, with the formation of the wwPDB, the PDB became an international organization, the founding members are PDBe, RCSB, and PDBj. Each of the four members of wwPDB can act as deposition, data processing, the data processing refers to the fact that wwPDB staff review and annotate each submitted entry. The data are automatically checked for plausibility. The PDB database is updated weekly, likewise, the PDB holdings list is also updated weekly. As of 14 March 2017, the breakdown of current holdings is as follows,103,514 structures in the PDB have a structure factor file,9,057 structures have an NMR restraint file. 2,826 structures in the PDB have a chemical shifts file, therefore, the final conformation of the protein is obtained, in the latter case, by solving a distance geometry problem. A few proteins are determined by cryo-electron microscopy, the significance of the structure factor files, mentioned above, is that, for PDB structures determined by X-ray diffraction that have a structure file, the electron density map may be viewed. The data of such structures is stored on the electron density server, however, since 2007, the rate of accumulation of new protein structures appears to have plateaued. The file format used by the PDB was called the PDB file format. This original format was restricted by the width of computer punch cards to 80 characters per line, around 1996, the macromolecular Crystallographic Information file format, mmCIF, which is an extension of the CIF format started to be phased in
4.
PubMed
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PubMed is a free search engine accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. The United States National Library of Medicine at the National Institutes of Health maintains the database as part of the Entrez system of information retrieval, from 1971 to 1997, MEDLINE online access to the MEDLARS Online computerized database primarily had been through institutional facilities, such as university libraries. PubMed, first released in January 1996, ushered in the era of private, free, home-, the PubMed system was offered free to the public in June 1997, when MEDLINE searches via the Web were demonstrated, in a ceremony, by Vice President Al Gore. Information about the journals indexed in MEDLINE, and available through PubMed, is found in the NLM Catalog. As of 5 January 2017, PubMed has more than 26.8 million records going back to 1966, selectively to the year 1865, and very selectively to 1809, about 500,000 new records are added each year. As of the date,13.1 million of PubMeds records are listed with their abstracts. In 2016, NLM changed the system so that publishers will be able to directly correct typos. Simple searches on PubMed can be carried out by entering key aspects of a subject into PubMeds search window, when a journal article is indexed, numerous article parameters are extracted and stored as structured information. Such parameters are, Article Type, Secondary identifiers, Language, publication type parameter enables many special features. As these clinical girish can generate small sets of robust studies with considerable precision, since July 2005, the MEDLINE article indexing process extracts important identifiers from the article abstract and puts those in a field called Secondary Identifier. The secondary identifier field is to store numbers to various databases of molecular sequence data, gene expression or chemical compounds. For clinical trials, PubMed extracts trial IDs for the two largest trial registries, ClinicalTrials. gov and the International Standard Randomized Controlled Trial Number Register, a reference which is judged particularly relevant can be marked and related articles can be identified. If relevant, several studies can be selected and related articles to all of them can be generated using the Find related data option, the related articles are then listed in order of relatedness. To create these lists of related articles, PubMed compares words from the title and abstract of each citation, as well as the MeSH headings assigned, using a powerful word-weighted algorithm. The related articles function has been judged to be so precise that some researchers suggest it can be used instead of a full search, a strong feature of PubMed is its ability to automatically link to MeSH terms and subheadings. Examples would be, bad breath links to halitosis, heart attack to myocardial infarction, where appropriate, these MeSH terms are automatically expanded, that is, include more specific terms. Terms like nursing are automatically linked to Nursing or Nursing and this important feature makes PubMed searches automatically more sensitive and avoids false-negative hits by compensating for the diversity of medical terminology. The My NCBI area can be accessed from any computer with web-access, an earlier version of My NCBI was called PubMed Cubby
