STITCH 2: An interaction network database for small molecules and proteins

Abstract

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/.

Full text

STITCH 2: an interaction network database for
small molecules and proteins
Michael Kuhn
1
, Damian Szklarczyk
2
, Andrea Franceschini
3
, Monica Campillos
4
,
Christian von Mering
3
, Lars Juhl Jensen
2
, Andreas Beyer
1
and Peer Bork
4,5,
*
1
Biotechnology Center, TU Dresden, 01062 Dresden, Germany,
2
Novo Nordisk Foundation Center for Protein
Research, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3b, 2200 Copenhagen,
Denmark,
3
Institute of Molecular Biology and Swiss Institute of Bioinformatics, University of Zurich, Switzerland,
4
European Molecular Biology Laboratory, Meyerhof street 1, 69117 Heidelberg and
5
Max-Delbru
¨ck-Centre
for Molecular Medicine, Robert-Ro
¨ssle-Strasse 10, 13092 Berlin, Germany
Received September 15, 2009; Revised October 8, 2009; Accepted October 9, 2009
ABSTRACT
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/.
INTRODUCTION
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 per-
formed 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 interac-
tion data and as an interactive web tool for the explora-
tion 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.
Here, we present the second version of STITCH. In
addition to the sources of protein–chemical interactions
included in the previous version—PDSP K
i
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 sepa-
rately 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
*To whom correspondence should be addressed. Tel: +49 6221 387 8526; Fax: +49 6221 387 517; Email: bork@embl.de
D552–D556 Nucleic Acids Research, 2010, Vol. 38, Database issue Published online 6 November 2009
doi:10.1093/nar/gkp937
ßThe 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.

human proteins increases from 5300 to 7400 (as STITCH
is locus-based, only one gene product is counted per gene).
INCREASING THE NUMBER OF SPECIFIED
ACTIONS
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 interac-
tion 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.
HANDLING OF CHEMICAL STRUCTURES
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. differ-
ent 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
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.
Nucleic Acids Research, 2010, Vol. 38, Database issue D553

merged structure. For now, we have taken the step to
explicitly display all the different compounds that have
been deemed biologically equivalent (Figure 2).
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.
USER INTERFACE IMPROVEMENTS
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
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.
D554 Nucleic Acids Research, 2010, Vol. 38, Database issue

settings (e.g. due to a minimum score limit), then the
option to show more context is not shown.
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 differ-
ent 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 follow-
ing 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 com-
pound. 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.
USE CASES
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 automat-
ically 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 ambigu-
ous 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
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.
Nucleic Acids Research, 2010, Vol. 38, Database issue D555

retrieve interaction networks either as an image or in
standard network formats (20).
FUNDING
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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