We updated the set of chemicals ( 6), and find interactions with 390 000 distinct chemicals. We also for the first time use the evidence transfer algorithm described for the STRING 9.1 database ( 5) to improve the performance for protein families.Ĭompared with STITCH 3, we use the same underlying set of proteins, containing 1133 species. New in this version is the possibility to upload spreadsheets with chemical descriptors and experimental data that can be directly added to the network, as described later in text. Already in the previous versions, it has been possible to query STITCH using protein or chemical names, InChIKeys and SMILES strings. Here, we present recent improvements to the database and user interface of STITCH. The underlying STITCH database can be accessed in multiple ways: via an intuitive web interface, via download files (for large-scale analysis) and via an API (enabling automated access on a small to medium scale). At the same time, links to the original sources are retained, making it possible to trace the provenance of the data. This network abstracts the complexity of the underlying data sources, making large-scale studies possible. The concept of STITCH (‘search tool for interacting chemicals’) was from the beginning to combine sources of protein–chemical interactions from experimental databases, pathway databases, drug–target databases, text mining and drug–target predictions into a unified network ( 2–4). By treating proteins and chemicals as nodes of a graph, which are linked by edges if they have been found to interact ( 1), we can adopt a network view that enables us to integrate many different sources. For such research, it is important to have, as complete as possible, data on protein–chemical interactions. A large collection of such interactions can, therefore, be used to study a variety of cellular functions and the impact of drug treatment on the cell. Protein–chemical interactions are essential for any biological system for example, they drive the metabolism of the cell or initiate many signaling cascades and most pharmaceutical interventions. STITCH can be accessed with a web-interface, an API and downloadable files. This improves the performance within protein families, where scores are now transferred only to orthologous proteins, but not to paralogous proteins. We further changed our scheme for transferring interactions between species to rely on orthology rather than protein similarity. 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. For example, a user can now upload a spreadsheet with screening hits to easily check which interactions are already known. 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. Compared with the previous version, the number of high-confidence protein–chemical interactions in human has increased by 45%, to 367 000. ![]() ![]() Available at, the resulting interaction network includes 390 000 chemicals and 3.6 million proteins from 1133 organisms. 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.
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