Target deconvolution strategies in drug discovery

Terstappen, Georg C.; Schlüpen, Christina; Raggiaschi, Roberto; Gaviraghi, G.

doi:10.1038/nrd2410

Cited by 387 publications

(342 citation statements)

References 72 publications

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“…Developing new methods for drug target identification is an area of intense interest, and both experimental and computational approaches have been developed (28,29). Previous methods for drug target identification have had substantial success, but many limitations remain.…”

Section: Discussionmentioning

confidence: 99%

Target identification using drug affinity responsive target stability (DARTS)

Lomenick¹,

Hao²,

Jonai³

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

765

707

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Identifying the molecular targets for the beneficial or detrimental effects of small-molecule drugs is an important and currently unmet challenge. We have developed a method, drug affinity responsive target stability (DARTS), which takes advantage of a reduction in the protease susceptibility of the target protein upon drug binding. DARTS is universally applicable because it requires no modification of the drug and is independent of the mechanism of drug action. We demonstrate use of DARTS to identify known small-molecule-protein interactions and to reveal the eukaryotic translation initiation machinery as a molecular target for the longevity-enhancing plant natural product resveratrol. We envisage that DARTS will also be useful in global mapping of protein-metabolite interaction networks and in label-free screening of unlimited varieties of compounds for development as molecular imaging agents.aging ͉ label-free ͉ proteomics ͉ small molecules

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Section: Discussionmentioning

confidence: 99%

Target identification using drug affinity responsive target stability (DARTS)

Lomenick¹,

Hao²,

Jonai³

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

765

707

View full text Add to dashboard Cite

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“…The former has become increasingly important in view of the recent tendency in the pharmaceutical industry to switch from target-based drug discovery to phenotypic screening where prior knowledge of the target is not a prerequisite. Although various approaches have been developed, target identification remains challenging (40,41). In analogy to the threehybrid assays that have been successfully applied to identify drug targets in yeast (42), we developed a three-hybrid variant of KISS that enables drug-target analysis in living mammalian cells.…”

Section: Discussionmentioning

confidence: 99%

Kinase Substrate Sensor (KISS), a Mammalian In Situ Protein Interaction Sensor

Lievens

Gerlo

Lemmens

et al. 2014

Molecular & Cellular Proteomics

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Probably every cellular process is governed by proteinprotein interaction (PPIs), which are often highly dynamic in nature being modulated by in-or external stimuli. Here we present KISS, for KInase Substrate Sensor, a mammalian two-hybrid approach designed to map intracellular PPIs and some of the dynamic features they exhibit. Benchmarking experiments indicate that in terms of sensitivity and specificity KISS is on par with other binary protein interaction technologies while being complementary with regard to the subset of PPIs it is able to detect. We used KISS to evaluate interactions between different types of proteins, including transmembrane proteins, expressed at their native subcellular location. In situ analysis of endoplasmic reticulum stress-induced clustering of the endoplasmic reticulum stress sensor ERN1 and ligand-dependent ␤-arrestin recruitment to GPCRs illustrated the method's potential to study functional PPI modulation in complex cellular processes. Exploring its use as a tool for in cell evaluation of pharmacological interference with PPIs, we showed that reported effects of known GPCR antagonists and PPI inhibitors are properly recapitulated. In a three-hybrid setup, KISS was able to map interactions between small molecules and proteins. Taken A protein's function is largely mediated through its interactions with other proteins, hence the critical importance of protein-protein interaction (PPI) 1 maps for understanding cellular mechanisms of action in health and disease. Whereas many proteins are organized in stable multi-protein complexes, the majority of cellular processes are governed by transient protein encounters, the dynamics of which are directed by a diversity of both intra-and extracellular signals. Our view of protein networks is still, however, mainly a static one (1). Current interactomes consist mainly of data generated by yeast 2-hybrid (Y2H) (2) and (tandem) affinity purification combined with mass spectrometry (3) and should be interpreted as scaffolds of potential PPIs that might occur at a certain time and place in the cell or as snapshots of PPIs taking place under a specific cellular condition. Although very robust and highly efficient, these approaches do not allow

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“…I dentifying molecular pathways targeted by a compound (drug effects), and the specific compound-substrate interactions (drug mode of action-MoA), is of paramount importance for the development of new drugs, and also for new clinical applications of already existing drugs (1)(2)(3). Systems biology approaches are naturally suited to capture the complexity of drug activity in cells (4)(5)(6).…”

mentioning

confidence: 99%

Discovery of drug mode of action and drug repositioning from transcriptional responses

Iorio

Bosotti²,

Scacheri³

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

755

728

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A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmacology. We developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA. We constructed a "drug network" of 1,302 nodes (drugs) and 41,047 edges (indicating similarities between pair of drugs). We applied network theory, partitioning drugs into groups of densely interconnected nodes (i.e., communities). These communities are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compoundtargeted biological pathways. New compounds can be integrated into the network to predict their therapeutic and off-target effects. Using this network, we correctly predicted the MoA for nine anticancer compounds, and we were able to discover an unreported effect for a well-known drug. We verified an unexpected similarity between cyclin-dependent kinase 2 inhibitors and Topoisomerase inhibitors. We discovered that Fasudil (a Rho-kinase inhibitor) might be "repositioned" as an enhancer of cellular autophagy, potentially applicable to several neurodegenerative disorders. Our approach was implemented in a tool (Mode of Action by NeTwoRk Analysis, MANTRA, http://mantra.tigem.it).computational drug discovery | drug repurposing | systems biology | chemotherapy

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Target deconvolution strategies in drug discovery

Cited by 387 publications

References 72 publications

Target identification using drug affinity responsive target stability (DARTS)

Target identification using drug affinity responsive target stability (DARTS)

Kinase Substrate Sensor (KISS), a Mammalian In Situ Protein Interaction Sensor

Discovery of drug mode of action and drug repositioning from transcriptional responses

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