STITCH 4: integration of protein–chemical interactions with user data

By Michael Kuhn, Damian Szklarczyk, Sune Frankild, Thomas H. Blicher, C. von Mering, Lars Juhl Jensen, Peer Bork. 2013

457 citations recorded by the source corpus.

STITCH is a database of protein–chemical interactions that integrates many sources of experimental and manually curated evidence with text-mining information and interaction predictions. Available at http://stitch.embl.de, the resulting interaction network includes 390 000 chemicals and 3.6 million proteins from 1133 organisms. Compared with the previous version, the number of high-confidence protein–chemical interactions in human has increased by 45%, to 367 000. In this version, we added features for users to upload their own data to STITCH in the form of internal identifiers, chemical structures or quantitative data. For example, a user can now upload a spreadsheet with screening hits to easily check which interactions are already known. To increase the coverage of STITCH, we expanded the text mining to include full-text articles and added a prediction method based on chemical structures. We further changed our scheme for transferring interactions between species to rely on orthology rather than protein similarity. This improves the performance within protein families, where scores are now transferred only to orthologous proteins, but not to paralogous proteins. STITCH can be accessed with a web-interface, an API and downloadable files.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions (2016, background): “Consequently, public databases for human-specific molecular interaction data have been undergoing a rapid growth within the past decade, such as BIND [18], DIP [19], STITCH [20], HPRD [21], TTD [22], DrugBank [23], ChEMBL [24], KEGG [25], BindingDB [26], SuperTarget and Matador [27], to name a few.”
DISEASES: Text mining and data integration of disease–gene associations (2014, methodology): “In addition to using it for disease–gene associations as described here, we have since applied the same scoring scheme to extract information on protein–small molecule associations in the STITCH database [48], protein subcellular localization in the COMPARTMENTS database [40], and tissue distributio”
Open Targets: a platform for therapeutic target identification and validation (2016, background): “Several resources such as DrugBank (35), the Therapeutic Target Database (TTD,(36)), STITCH (37), PharmaGKB (38), SuperTarget (39) have been developed to provide comprehensive information on drug targets by integrating information from multiple sources.”
STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data (2015, methodology): “Also, its chemical space grew by a quarter compared to the previous version (18), from 340 000 to 430 000 compounds (not including different stereoisomers).”
eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences (2015, unknown): “This step is coordinated with the STRING (27) and STITCH (28) databases so that the underlying set of protein sequences and names is shared among all three resources.”
LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases (2018, unknown): “The probability disjunction formula has been widely employed to measure combined scores in the case that multiple pieces of evidence exist (22,23).”
STRING v10: protein–protein interaction networks, integrated over the tree of life (2014, background): “Users can also arrive via a number of external websites (29– 32) that maintain cross-links with STRING, including the partner resources Search Tool for Interactions of Chem-icals (STITCH; 33) and eggNOG (34)––the latter both share protein sequences, annotations and name-spaces with STRING.”
ChemProt-3.0: a global chemical biology diseases mapping (2016, unknown): “We updated all the chemical protein interactions data from the open source databases ChEMBL (version 19) (4), BindingDB (17), PDSP Ki database (18), DrugBank (version 4) (19), PharmGKB (20), IUPHAR-DB database (21) and STITCH (version 4) (22).”