Drug target identification using side-effect similarity.
Abstract + PDF
Targets for drugs have so far been predicted on the basis of molecular or cellular features, for example, by exploiting similarity in chemical structure or in activity across cell lines. We used phenotypic side-effect similarities to infer whether two drugs share a target. Applied to 746 marketed drugs, a network of 1018 side effect-driven drug-drug relations became apparent, 261 of which are formed by chemically dissimilar drugs from different therapeutic indications. We experimentally tested 20 of these unexpected drug-drug relations and validated 13 implied drug-target relations by in vitro binding assays, of which 11 reveal inhibition constants equal to less than 10 micromolar. Nine of these were tested and confirmed in cell assays, documenting the feasibility of using phenotypic information to infer molecular interactions and hinting at new uses of marketed drugs.
SuperTarget and Matador: resources for exploring drug-target relationships.
Günther S, Kuhn M
, Dunkel M, Campillos M
, Senger C, Petsalaki E, Ahmed J, Urdiales EG, Gewiess A, Jensen LJ
, Schneider R, Skoblo R, Russell RB, Bourne PE, Bork P
, Preissner R
2008 Jan 16; 36: D919-22. Epub 2007 Oct 16; PubMed: 17942422.
Abstract + PDF
The molecular basis of drug action is often not well understood. This is partly because the very abundant and diverse information generated in the past decades on drugs is hidden in millions of medical articles or textbooks. Therefore, we developed a one-stop data warehouse, SuperTarget that integrates drug-related information about medical indication areas, adverse drug effects, drug metabolization, pathways and Gene Ontology terms of the target proteins. An easy-to-use query interface enables the user to pose complex queries, for example to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target the same protein but are metabolized by different enzymes. Furthermore, we provide tools for 2D drug screening and sequence comparison of the targets. The database contains more than 2500 target proteins, which are annotated with about 7300 relations to 1500 drugs; the vast majority of entries have pointers to the respective literature source. A subset of these drugs has been annotated with additional binding information and indirect interactions and is available as a separate resource called Matador. SuperTarget and Matador are available at http://insilico.charite.de/supertarget and http://matador.embl.de.
Large-scale prediction of drug-target relationships.
2008 Apr 9; 582(8): 1283-90. Epub 2008 Feb 20; PubMed: 18291108.
Abstract + PDF
The rapidly increasing amount of publicly available knowledge in biology and chemistry enables scientists to revisit many open problems by the systematic integration and analysis of heterogeneous novel data. The integration of relevant data does not only allow analyses at the network level, but also provides a more global view on drug-target relations. Here we review recent attempts to apply large-scale computational analyses to predict novel interactions of drugs and targets from molecular and cellular features. In this context, we quantify the family-dependent probability of two proteins to bind the same ligand as function of their sequence similarity. We finally discuss how phenotypic data could help to expand our understanding of the complex mechanisms of drug action.
STITCH: interaction networks of chemicals and proteins.
2008 Jan 15; 36: D684-8. Epub 2007 Dec 15; PubMed: 18084021.
Abstract + PDF
The knowledge about interactions between proteins and small molecules is essential for the understanding of molecular and cellular functions. However, information on such interactions is widely dispersed across numerous databases and the literature. To facilitate access to this data, STITCH ('search tool for interactions of chemicals') integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug-target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. Our database contains interaction information for over 68 000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING database. STITCH is available at http://stitch.embl.de/