Frequently Asked Questions

0. Related websites
1. What kind of information can I find inside this database?
2. How was the data in the database created?
3. How can I search for drugs?
4. How can I search for Targets?
5. What can the KEGG-maps be used for?
6. What does Ontology mean and how can it be used?
7. What are the fulltext search and basket good for?
8. How are target sequence similarities calculated?

0. Related websites

  1. BindingDB
  3. PubChem
  4. UniProt
  5. KEGG
  6. DrugBank
  7. SuperCyp
  8. SIDER
  9. ConsensusPathDB

1. What kind of information can I find inside this database?

SUPERTARGET is a database developed in the first place to collect informations about drug-target relations. It consist mainly of three different types of entities:

  1. DRUGS
Beside this, informations about pathways and ontologies can be gained. Separate section is dedicated to a special soubgroup of the the targets - the cytochromes (CYPs) P450. All these entities are connected between each other through drug-protein, protein-protein and drug-side-effect relations and include rich annotation about the source, ID's, physical properties, references and much more.
Proteins are retrieved from UniProt and are displayed with synonyms and organism information. Also information about target-target interactions and sequenc similarities between the targets are involved. Additional information as 3D-structures from PDB and EC-numbers are given if available. Drugs gained from SuperDrug were mapped with BindingDB, integrated to SUPERTARGET and assigned metadata as ATC-codes (Anatomical Therapeutic Chemical), structure information and binding affinities. The side-effects contained in the database were fetched from SIDER and related to the drugs.

2. How was the data in the database created?

The aim of SUPERTARGET is to offer an at most comprehensive datasets. Here for drug-target relations from different well-known databases as DrugBank, BindingDB and SuperCyp were integrated. To enhance the completeness of the dataset additional newly explored relations were incorporated, too. The new information was obtained in two steps:

  1. Firstly, text-mining algorithms were applied to sort all PubMed listed papers by their relevance for drug-target relations.
  2. In a second step, the 7,000 papers with the highest rank were manually revised.

Protein-protein interaction data was obtained from ConsensusPathDB which integrates physical protein-protein interactions, metabolic and signaling reactions and gene regulatory interactions. Information on complex composition comes from Corum a protein complex database. Detailed overview about sources and relations between the entities presented by SUPERTARGET is summarized in following figure :

3. How can I search for drugs?

There are several search options to find the desired drug(s). As we have a great list of almost 200,000 synonyms the search via name is promising. If you already know IDs like PubChem ID or an appropriate ATC-code according to the Anatomical Therapeutic Chemical Classification System, you can simply use the text fields shown below. Also a name of a side-effect can be used for the search. After the first several letters are typed in the related search field a table filled with relevant terms appears as a hint. Dependant of the search field used fot the query, other text fields are grayed out, if those comibantion would not make a sense. It is possible to choose whether only drugs with known targets or all drugs are shown by putting a checkmark on this option.

The query results are shown in a table with their name and PubChem id as shown on the next image. Please notice, that you have sorting options via clicking on the column haedings. To get additional information on the drug, click on its name, you will be redirected to a drug detail site. Synonyms, ATC-Codes and Targets are available on-click as well. To see in which pathways the targets addressed by the drug are involved click on the pathway icon .

4. How can I search for Targets?

If you already know accession IDs it will be easy to identify the target you are looking for using the search fields below. Furthermore, we have implemented a direct target-search via synonyms (50,000). This will be helpful option, probably requiring further narrowing of the results.

The query results are shown in a table with their name, Uniprot name, Uniprot Accession number, organism and if available EC-number.
To get additional information on the target, click on its name, you will be redirected to a target detail site. Synonyms and drugs acting on the target are available on-click as well. To see in which pathways the targets is involved click on the pathway icon .

5. What can the KEGG-maps be used for?

To put the drug target relations contained in the database into a cellular context, the information was mapped onto KEGG Pathway maps. The Pathways can be picked by different species. All KEGG-Pathways to the selected organism are afterwards included in the corresponding selector box. A simple pathway-selection forwards the pathway to the lower result list. For a complete view click the Display selected pathway button. Targets for which drug information is available are marked in yellow. Targets are displayed in different colors depending on their characteristics as shown below.

Target present in the chosen organism, without drug interaction data.
Target with one or more mapped drugs.
Target chosen in the query step before.
Targets addressed by the last chosen drug.
To get additional information about the targeting drugs place the mouse pointer on the target. Drugs are shown with their name and structure. An additional table summarizing the Drug-Target relations is shown below the pathway-graph. Like this the user can retrieve information on drug target relations in a pathway and organism specific way. The knowledge about on which pathways a drug acts can be very helpful in drug repositioning.

6. What does Ontology mean and how can it be used?

The GO project provides ontologies to describe attributes of gene products in three non-overlapping domains of molecular biology. Within each ontology, terms have free text definitions and stable unique identifiers. Molecular Function (MF) describes activities, such as catalytic or binding activities, at the molecular level. GO molecular function terms represent activities rather than the entities (molecules or complexes) that perform the actions, and do not specify where, when or in what context the action takes place. Examples of individual molecular function terms are the broad concept kinase activity and the more specific 6-phosphofructokinase activity, which represents a subtype of kinase activity. Cellular Component (CC) describes locations, at the levels of subcellular structures and macromolecular complexes. Examples of cellular components include nuclear inner membrane, with the synonym inner envelope, and the ubiquitin ligase complex, with several subtypes of these complexes represented. Biological Process (BP) describes biological goals accomplished by one or more ordered assemblies of molecular functions. High-level processes such as cell death can have both subtypes, such as apoptosis, and subprocesses, such as apoptotic chromosome condensation.
Type in a term in the assigned input field of the ontology tab (e.g. transmembrane in 'Molecular function') and press the 'search' button. The result list displays all ontology terms that contain the search term. For further analysis forward the term to basket by clicking the assigned symbol on the right side in every row of the results.

7. What are the fulltext search and basket good for?

The fulltext search, situated on the home-page, is our general search interface. It makes it possible to have a quick start into SUPERTARGET and to search for drugs, targets and interactions at once. Results are displayed ordered into the categories target, drug and interaction. Found objects are linked to the related detail sites. The fulltext search is provided by our own search engine developped for the SUPERTARGET project - hence the name
The basket search allows more detailed and comprehensive search queries. At the end of every line in the table of results, as well as on the top of the pages with the detailed information 'add to basket' icons are placed. By clicking on the symbol, the related entities are stored for further search. The actual count of the entities placed in the basket is shovn beside the basket symbol in the header of every page. By cliking on the symbol or the Adv.search tab you get to the advanced search section. The entities stored in the basket can be taken and put tohether in the relations using logical operators as AND or OR. Dependent on whether you search for drugs or targets different combination of search querys are allowed and at most 4 additional criterions as molecular weight or binding affinities can be add for further search. It is also possible to generally search for drugs or targets by their properties using the combinations of those criterions, without any other itmes from the basket.

8. How are target sequence similarities calculated?

As of November 10th 2011, we changed the way how target similarities are calculated. SuperTarget comprises pre-calculated target sequence similarities and sequence identities. Pairwise global alignments were calculated between all sequences of target proteins using the Needleman-Wunsch implementation Needle which is part of the package EMBOSS. For further information refer to: EMBOSS alignment formats and 'EMBOSS: The European Molecular Biology Open Software Suite (2000) Rice,P. Longden,I. and Bleasby,A. Trends in Genetics 16, (6) pp276--277'

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Robert Preissner (robert.preissner@charite.de)

Charite   Institute for Physiology
Structural Bioinformatics Group
Lindenberger Weg 80
13125 Berlin
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