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. 2016 Jan 4;44(D1):D380-4.
doi: 10.1093/nar/gkv1277. Epub 2015 Nov 20.

STITCH 5: Augmenting Protein-Chemical Interaction Networks With Tissue and Affinity Data

Free PMC article

STITCH 5: Augmenting Protein-Chemical Interaction Networks With Tissue and Affinity Data

Damian Szklarczyk et al. Nucleic Acids Res. .
Free PMC article


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


Figure 1.
Figure 1.
Display of binding affinities. The user interface of STITCH has been updated and the option to scale edge width of protein–chemical interactions according to binding affinity has been added. The shown network of multiple NSAIDs makes their different binding affinities clear: for example, aspirin has relatively low binding affinities, whereas rofecoxib is specifically binding PTGS2.
Figure 2.
Figure 2.
Filtering interaction networks according to tissue expression patterns. (A) The interaction network around diclofenac and PTGS1/2 is shown without filtering for tissue expression patterns. In this and the following panels, the top five interaction partners with the highest scores are shown. (B) Using the TISSUES resource, only proteins believed to be expressed in blood platelets (with medium confidence, i.e. three stars in TISSUES) become part of the interaction network. For these settings, PTGS2 is not expressed and is therefore shown in a lighter color. (C) Expression patterns according to RNA-seq data from the Human Protein Atlas are used to focus on genes expressed in smooth muscle. Confidence scores of interactions are scaled by the geometric mean of the binding partners’ expression percentiles. Due to the recomputed confidence scores, four interaction partners have been replaced by other proteins.

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