STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data

By Damian Szklarczyk, Alberto Santos, C. von Mering, Lars Juhl Jensen, Peer Bork, Michael Kuhn. 2015

1,484 citations recorded by the source corpus.

Interactions between proteins and small molecules are an integral part of biological processes in living organisms. Information on these interactions is dispersed over many databases, texts and prediction methods, which makes it difficult to get a comprehensive overview of the available evidence. To address this, we have developed STITCH (‘Search Tool for Interacting Chemicals’) that integrates these disparate data sources for 430 000 chemicals into a single, easy-to-use resource. In addition to the increased scope of the database, we have implemented a new network view that gives the user the ability to view binding affinities of chemicals in the interaction network. This enables the user to get a quick overview of the potential effects of the chemical on its interaction partners. For each organism, STITCH provides a global network; however, not all proteins have the same pattern of spatial expression. Therefore, only a certain subset of interactions can occur simultaneously. In the new, fifth release of STITCH, we have implemented functionality to filter out the proteins and chemicals not associated with a given tissue. The STITCH database can be downloaded in full, accessed programmatically via an extensive API, or searched via a redesigned web interface at http://stitch.embl.de.

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Citations in the corpus

Open Graph Benchmark: Datasets for Machine Learning on Graphs (2020, methodology): “This may result in techniques specialised for sparse link inference that are not generalizable to domains with dense graphs, such as the protein-protein association graphs and drug-drug interaction networks typically found in biology and medicine (Szklarczyk et al., 2019; Wishart et al., 2018; Davis”
The BioGRID interaction database: 2019 update (2018, background): “Comparatively few data resources combine chemical– protein interaction data with relevant protein interactions but include STITCH (56), ConsensusPathDB (57), SuperTarget (58) and IntAct (59).”
Embedding Logical Queries on Knowledge Graphs (2018, methodology): “We obtained relationships between proteins and drugs from the STITCH database, which integrates various chemical and protein networks [39].”
TCMID 2.0: a comprehensive resource for TCM (2017, background): “Moreover, we redesigned the target table in ingredient page which convey more valuable information about each target including the confidence score from STITCH, association mode, detailed action between ingredients and targets, ENSP ID and UniProt ID.”
Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, background): “In addition, STITCH (http:// stitch. embl. de/) is another text mining-driven data-base, which contains information on interactions between proteins and chemicals/small molecules [92].”
Graph convolutional networks for computational drug development and discovery (2019, background): “Search Tool for Interacting Chemicals (STITCH) [99] is a database that integrates data sources for 430 000 chemicals and more than 9 600 000 proteins into a single resource.”
STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets (2018, background): “STRING shares its genome, protein-and name spaces with a number of sister projects, dedicated to orthology (eggNOG (32)), small molecules (STITCH (33)), protein abundances (PaxDB (34)), tissue expression (TISSUES (35)) and viruses (Viruses.”