STITCH 3: zooming in on protein–chemical interactions

By Michael Kuhn, Damian Szklarczyk, Andrea Franceschini, C. von Mering, Lars Juhl Jensen, Peer Bork. Nucleic Acids Res., 2011

303 citations recorded by the source corpus.

To facilitate the study of interactions between proteins and chemicals, we have created STITCH, an aggregated database of interactions connecting over 300 000 chemicals and 2.6 million proteins from 1133 organisms. Compared to the previous version, the number of chemicals with interactions and the number of high-confidence interactions both increase 4-fold. The database can be accessed interactively through a web interface, displaying interactions in an integrated network view. It is also available for computational studies through downloadable files and an API. As an extension in the current version, we offer the option to switch between two levels of detail, namely whether stereoisomers of a given compound are shown as a merged entity or as separate entities. Separate display of stereoisomers is necessary, for example, for carbohydrates and chiral drugs. Combining the isomers increases the coverage, as interaction databases and publications found through text mining will often refer to compounds without specifying the stereoisomer. The database is accessible at http://stitch.embl.de/.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2278ac58c577f33093c9ed7d45366f8f4347b24c

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Citations in the corpus

Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “uk/databases/credo; Schreyer & Blundell, 2009) and the extensive protein-ligand databases, STITCH (http://stitch.embl.de; Kuhn et al., 2012) and BindingDB (http://bindingdb.”
Systematic prediction of drug combinations based on clinical side-effects (2014, methodology): “The outline of this study is demonstrated in Figure 1. different naming issues in different data sources, DDCs were represented by their two components whose names were mapped to STITCH ID 22 for comparison.”
Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014, unknown): “We manually checked whether these top five interactions can be found in the latest online version of STITCH [65].”
Analysis of chemical and biological features yields mechanistic insights into drug side effects. (2013, background): “Protein Interactors The STITCH database (Kuhn et al., 2012) is an aggregate repository that captures as much as possible of the publicly available knowledge on proteinchemical interactions.”
STITCH 4: integration of protein–chemical interactions with user data (2013, unknown): “In human, high-confidence interactions for 172 000 compounds are available in STITCH 4 (Figure 1), compared with 110 000 in STITCH 3 (4).”
DrugComboRanker: drug combination discovery based on target network analysis (2014, background): “First, dysfunctional signaling networks of diseases are complex and stable, and effective drug combinations can inhibit major modules of the networks simultaneously.”