Nucleic Acids Research Advance Access published online on November 6, 2009
Nucleic Acids Research, doi:10.1093/nar/gkp937
© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
STITCH 2: an interaction network database for small molecules and proteins
Michael Kuhn1,
Damian Szklarczyk2,
Andrea Franceschini3,
Monica Campillos4,
Christian von Mering3,
Lars Juhl Jensen2,
Andreas Beyer1 and
Peer Bork4,5,*
1Biotechnology Center, TU Dresden, 01062 Dresden, Germany, 2Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3b, 2200 Copenhagen, Denmark, 3Institute of Molecular Biology and Swiss Institute of Bioinformatics, University of Zurich, Switzerland, 4European Molecular Biology Laboratory, Meyerhof street 1, 69117 Heidelberg and 5Max-Delbrück-Centre for Molecular Medicine, Robert-Rössle-Strasse 10, 13092 Berlin, Germany
*To whom correspondence should be addressed. Tel: +49 6221 387 8526; Fax: +49 6221 387 517; Email: bork{at}embl.de
Received September 15, 2009. Revised October 8, 2009. Accepted October 9, 2009.
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ABSTRACT
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Over the last years, the publicly available knowledge on interactions
between small molecules and proteins has been steadily increasing.
To create a network of interactions, STITCH aims to integrate
the data dispersed over the literature and various databases
of biological pathways, drug–target relationships and
binding affinities. In STITCH 2, the number of relevant interactions
is increased by incorporation of BindingDB, PharmGKB and the
Comparative Toxicogenomics Database. The resulting network can
be explored interactively or used as the basis for large-scale
analyses. To facilitate links to other chemical databases, we
adopt InChIKeys that allow identification of chemicals with
a short, checksum-like string. STITCH 2.0 connects proteins
from 630 organisms to over 74 000 different chemicals, including
2200 drugs. STITCH can be accessed at
http://stitch.embl.de/.
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INTRODUCTION
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The effects of small molecules on organisms have long been the
focus of biochemistry and pharmacology. Over the last years
there has been a considerable increase in the number of high-throughput
screens that have been performed using chemical libraries (
1–3).
At the same time, the molecular targets of individual chemicals
are being studied in ever greater detail (
4,
5). There also is
a great interest in chemical biology approaches, using small
molecules to perturb cellular functions (
6). For the design
and interpretation of these studies, the context of the chemicals
and proteins needs to be considered. For example, in the case
of high-content screening for specific cellular effects, it
is important to know whether the active chemicals already have
known activities that can explain the observed effects, or whether
novel mechanisms of actions might be present. Therefore, we
have developed a ‘search tool for interactions of chemicals’
(STITCH) both as a large-scale, downloadable database of interaction
data and as an interactive web tool for the exploration of interaction
networks (
Figure 1). Since its first release (
7), STITCH is
being accessed by over one hundred scientists each week and
has been used as a source of protein–chemical associations
e.g. by Prathipati
et al. (
8), who used the STITCH network to
automatically extract the targets of anti-tuberculosis compounds
in
Mycobacterium tuberculosis.
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Figure 1. Interaction network around aspirin. Human proteins predicted to interact with aspirin according to different sources of evidence are shown. Edges are colored according to the source of evidence (magenta: experimental information, cyan: manually curated databases, yellow: text-mining). Clicking on the node ‘aspirin’ will display a pop-up showing the structure and description.
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Here, we present the second version of STITCH. In addition to
the sources of protein–chemical interactions included
in the previous version—PDSP
Ki Database (
9), Protein
Data Bank (PDB) (
10), KEGG (
11), Reactome (
12), NCI-Nature Pathway
Interaction Database (
http://pid.nci.nih.gov), DrugBank (
13)
and MATADOR (
14)—we now further include interactions imported
from GLIDA (
15), PharmGKB (
16,
17), Comparative Toxicogenomics
Database (CTD) (
18) and BindingDB (
19). These added databases
mainly provide information on interactions between human proteins
and drugs or drug-like molecules.
The imported sources of information are scored separately and then combined with information from text-mining (7). Databases which contain manually annotated interactions receive high scores, while interactions based on experimental information are scored by the confidence or relevance of the reported interaction. The number of high-confidence (score 
0.7) human chemical–protein interactions increased from 51 000 to 85 000. For these high-confidence interactions, the number of interacting human proteins increases from 5300 to 7400 (as STITCH is locus-based, only one gene product is counted per gene).
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INCREASING THE NUMBER OF SPECIFIED ACTIONS
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The STITCH network is created by mapping interactions from the
sources mentioned above and from text-mining onto a consolidated
set of chemicals that has been derived from PubChem, assigning
a confidence score for each interaction (
7). The newly-derived
protein–chemical and chemical–chemical associations
are then complemented with protein–protein interactions
from the STRING database (
20). In the previous version of STITCH
(
7), we began to import ‘actions’ derived from natural
language processing (NLP), pathway and interaction databases.
These actions specify the nature of the interaction independent
of the source of interaction information. For example, a ‘binding’
action could be derived from a binding affinity database and
an ‘inhibition’ action could be imported from NLP.
We have greatly extended the set of available actions by further
importing action types from GLIDA (
15), PharmGKB (
16,
17), CTD
(
18), BindingDB (
19) and a manually annotated set of interactions.
This set of interactions has been curated from DrugBank (
13)
records, results from NLP analysis of PubMed abstracts, Medical
Subject Headings (MeSH) pharmacological actions, Anatomical
Therapeutic Chemical classification (ATC) entries and a review
paper on drugs and their targets (
21). An action has been assigned
to 81% of the high-confidence human chemical–protein interactions.
The number of available edges with a high-confidence action
annotation increased from 44 000 to 65 000 human chemical–protein
interactions.
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HANDLING OF CHEMICAL STRUCTURES
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As described previously (
7), STITCH creates a consolidated set
of chemicals from PubChem (
22) by merging stereo isomers and
salt forms of the same molecule into one compound. This is done
to ensure that all information about the same biologically active
entity is merged. While this works very well for drugs that
can be supplied in different formulations (e.g. different salt
forms), it also has limitations, especially regarding carbohydrates.
It is our long-term goal to associate interactions both with
the individual isomer and the merged structure. For now, we
have taken the step to explicitly display all the different
compounds that have been deemed biologically equivalent (
Figure 2).
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Figure 2. Different structural scaffolds corresponding to aspirin. For the drug aspirin, a link to PubChem and a short description is shown. Different salts of aspirin that will have the same bioactivity have been consolidated and merged with the main, uncharged form. Below each chemical structure, the first part of the InChIKey is shown, corresponding to an encoded (hashed) description of the structure. This short string can be used to search for more information about the compound on the Internet.
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Recently, the International Union of Pure and Applied Chemistry
(IUPAC) has standardized an open format for chemical structures,
namely the IUPAC International Chemical Identifier (InChI).
In addition to the existing capability to search chemical structures
using SMILES string, we have now also implemented a search for
InChIs. We use the tool Open Babel to convert InChIs to SMILES
strings, which are in turn searched against our chemical database
by using hashed fingerprints as implemented in the open-source
Chemical Development Kit (
23). Furthermore, we have implemented
a search for InChIKeys, which are short strings that represent
an encoded (hashed) form of the chemical structure. InChIKeys
consist of two parts, the first of which is based on the chemical
connectivity, whereas the second part contains information about
stereochemistry, tautomers and other structural variations.
As STITCH currently considers structures with the same connectivity
to be equivalent (thus merging stereo isomers), only the first
part of the InChIKey is queried against our chemical database.
We also use this part of the InChIKey to provide links to Google
and ChemSpider.
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USER INTERFACE IMPROVEMENTS
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Many proteins, especially drug targets, have a large number
of high-scoring interactions with small molecules in the STITCH
network. In this case, a network centered on such a protein
will only show chemicals unless very many interaction partners
are requested to be shown (
Figure 3a). In order to allow the
user to see more of the context of the query protein, we now
offer the option to show a network in which proteins and chemicals
each make up more than a third of the nodes (
Figure 3b). When
this option is selected, only a limited number of the highest-scoring
chemicals are displayed. Further chemicals are omitted in favor
of proteins (or vice versa) and their number is shown to the
user. If the network consists of only chemicals, but no proteins
are available at the current settings (e.g. due to a minimum
score limit), then the option to show more context is not shown.
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Figure 3. Interactions of prostaglandin-endoperoxide synthase 1 (PTGS1). (a) The highest-scoring interaction partners of PTGS1 are non-steroidal anti-inflammatory drugs (NSAIDs). As the confidence scores for these interactions are very high, no interacting proteins are shown. (b) The user may ask STITCH to display more of the interaction context and to let at least one-third of the interaction partners be proteins. In this case, STITCH is skipping 19 high-scoring chemicals in order to include four interacting proteins. In both networks, the color of the edge corresponds to the type of connected nodes (e.g. green: chemical–protein interaction) and the width of the edge correlates with the confidence score.
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Previously, STITCH required the user to select an organism when
searching for interactions with a chemical. Now, this is not
required anymore. When no organism is selected, the organism
with the highest-scoring interaction partners is selected. In
case of multiple organisms with equal scores, human and several
model organisms are preferentially selected. (Human is one of
the highest-ranking species for 60% of the chemicals with protein–chemical
interactions.) For example, the binding between the antipsychotic
agent fluspiperone and the 5–hydroxytryptamine (serotonin)
receptor 7 has only been studied in mouse and rat. Consequently,
a user searching for this compound would be directed to the
protein–chemical interaction network in mouse. It is also
possible to restrict the search to different levels of the NCBI
taxonomy (
24), e.g. bacteria, fungi or rodents.
While central repositories of gene annotations exist, no such information is available in a centralized manner for chemicals. To be able to display text annotation for chemicals, we have imported information from the following databases: DrugBank (13), National Cancer Institute (NCI) thesaurus (25), MeSH descriptors and qualifiers. Using STITCH’s dictionary of chemical synonyms we mapped compounds from these databases to STITCH identifiers. In case where descriptions are available for different forms of the same compound (e.g. different salt forms, which have been merged in STITCH), we have automatically assigned the description of the main compound. Any remaining inconsistencies were manually resolved. For each chemical we have assigned the text annotation from only one source, prioritizing sources as follows: NCI (descriptions), DrugBank (descriptions), DrugBank (pharmacology), DrugBank (drug category), MeSH (pharmacological action), NCI (tags) and MeSH (scope note). Descriptions are available for 33 352 chemicals, covering 33% of the chemicals with interactions.
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USE CASES
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The STITCH homepage offers several short tutorials to introduce
the different query options (e.g. searching for a single identifier
or multiple chemical structures). A search for ‘aspirin’
on the homepage will lead to the interaction network shown in
Figure 1. Here, the main interactors of the drug are shown in
human (which is selected automatically as described above).
The known main targets, PTGS1 and PTGS2, are connected by very
high scores. While most interaction partners are backed up by
evidence from manually curated databases and are therefore very
reliable, one interaction is derived only from text-mining:
COX1 is actually a false positive arising from an ambiguous
synonym.
Taken together, STITCH 2 offers an enlarged set of protein–chemical interactions, extended inter-database operability, increased query options and an improved user interface. STITCH can be accessed at http://stitch.embl.de/. Users can explore the interaction network interactively or download the complete set of interactions. In addition, we provide an application programming interface (API) to let scripts resolve identifiers and retrieve interaction networks either as an image or in standard network formats (20).
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FUNDING
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Klaus Tschira Foundation (to M.K. and A.B.). Novo Nordisk Foundation
Center for Protein Research (partial). Funding for open access
charge: European Molecular Biology Laboratory.
Conflict of interest statement. None declared.
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ABSTRACT
INTRODUCTION