STITCH 4: Integration of Protein-chemical Interactions with User Data

Overview

Journal Nucleic Acids Res

Publisher Oxford University Press

Specialty Biochemistry

Date 2013 Dec 3

PMID 24293645

Citations 202

Authors

Michael Kuhn

Damian Szklarczyk

Sune Pletscher-Frankild

Thomas H Blicher

Christian von Mering

Lars J Jensen

Peer Bork

Affiliations

Soon will be listed here.

Abstract

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.

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References

Rognan D . Towards the Next Generation of Computational Chemogenomics Tools. Mol Inform. 2016; 32(11-12):1029-34. DOI: 10.1002/minf.201300054. View

Chen B, Sheridan R, Hornak V, Voigt J . Comparison of random forest and Pipeline Pilot Naïve Bayes in prospective QSAR predictions. J Chem Inf Model. 2012; 52(3):792-803. DOI: 10.1021/ci200615h. View

Caspi R, Altman T, Dale J, Dreher K, Fulcher C, Gilham F . The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 2009; 38(Database issue):D473-9. PMC: 2808959. DOI: 10.1093/nar/gkp875. View

Steinbeck C, Han Y, Kuhn S, Horlacher O, Luttmann E, Willighagen E . The Chemistry Development Kit (CDK): an open-source Java library for Chemo- and Bioinformatics. J Chem Inf Comput Sci. 2003; 43(2):493-500. PMC: 4901983. DOI: 10.1021/ci025584y. View

Wang Y, Xiao J, Suzek T, Zhang J, Wang J, Bryant S . PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009; 37(Web Server issue):W623-33. PMC: 2703903. DOI: 10.1093/nar/gkp456. View

Iskar M, Campillos M, Kuhn M, Jensen L, van Noort V, Bork P . Drug-induced regulation of target expression. PLoS Comput Biol. 2010; 6(9). PMC: 2936514. DOI: 10.1371/journal.pcbi.1000925. View

Kuhn M, Szklarczyk D, Franceschini A, Campillos M, von Mering C, Jensen L . STITCH 2: an interaction network database for small molecules and proteins. Nucleic Acids Res. 2009; 38(Database issue):D552-6. PMC: 2808890. DOI: 10.1093/nar/gkp937. View

Kuhn M, Campillos M, Letunic I, Jensen L, Bork P . A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol. 2010; 6:343. PMC: 2824526. DOI: 10.1038/msb.2009.98. View

Hobohm U, Scharf M, Schneider R, Sander C . Selection of representative protein data sets. Protein Sci. 1992; 1(3):409-17. PMC: 2142204. DOI: 10.1002/pro.5560010313. View

10.

Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A . DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res. 2010; 39(Database issue):D1035-41. PMC: 3013709. DOI: 10.1093/nar/gkq1126. View

11.

Heiskanen M, Aittokallio T . Predicting drug-target interactions through integrative analysis of chemogenetic assays in yeast. Mol Biosyst. 2013; 9(4):768-79. DOI: 10.1039/c3mb25591c. View

12.

Kuhn M, von Mering C, Campillos M, Jensen L, Bork P . STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res. 2007; 36(Database issue):D684-8. PMC: 2238848. DOI: 10.1093/nar/gkm795. View

13.

Basha O, Tirman S, Eluk A, Yeger-Lotem E . ResponseNet2.0: Revealing signaling and regulatory pathways connecting your proteins and genes--now with human data. Nucleic Acids Res. 2013; 41(Web Server issue):W198-203. PMC: 3692079. DOI: 10.1093/nar/gkt532. View

14.

Kuhn M, Szklarczyk D, Franceschini A, von Mering C, Jensen L, Bork P . STITCH 3: zooming in on protein-chemical interactions. Nucleic Acids Res. 2011; 40(Database issue):D876-80. PMC: 3245073. DOI: 10.1093/nar/gkr1011. View

15.

Davis A, Murphy C, Johnson R, Lay J, Lennon-Hopkins K, Saraceni-Richards C . The Comparative Toxicogenomics Database: update 2013. Nucleic Acids Res. 2012; 41(Database issue):D1104-14. PMC: 3531134. DOI: 10.1093/nar/gks994. View

16.

Barabasi A, Oltvai Z . Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004; 5(2):101-13. DOI: 10.1038/nrg1272. View

17.

Saric J, Jensen L, Ouzounova R, Rojas I, Bork P . Extraction of regulatory gene/protein networks from Medline. Bioinformatics. 2005; 22(6):645-50. DOI: 10.1093/bioinformatics/bti597. View

18.

Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M . KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2011; 40(Database issue):D109-14. PMC: 3245020. DOI: 10.1093/nar/gkr988. View

19.

Jensen L, Saric J, Bork P . Literature mining for the biologist: from information retrieval to biological discovery. Nat Rev Genet. 2006; 7(2):119-29. DOI: 10.1038/nrg1768. View

20.

Kuhn M, Al Banchaabouchi M, Campillos M, Jensen L, Gross C, Gavin A . Systematic identification of proteins that elicit drug side effects. Mol Syst Biol. 2013; 9:663. PMC: 3693830. DOI: 10.1038/msb.2013.10. View