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Liu, X. ; Baarsma, H.A. ; Thiam, C.H.* ; Montrone, C. ; Brauner, B. ; Fobo, G. ; Heier, J.S.* ; Duscha, S. ; Königshoff, M. ; Angeli, V.* ; Ruepp, A. ; Campillos, M.

Systematic identification of pharmacological targets from small-molecule phenotypic screens.

Cell Chem. Bio. 23, 1302-1313 (2016)
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Phenotypic drug discovery offers some advantages over target-based methods, mainly because it allows drug leads to be tested in systems that more closely model distinct disease states. However, a potential disadvantage is the difficulty of linking the observed phenotype to a specific cellular target. To address this problem, we developed DePick, a computational target de-convolution tool to determine targets specifically linked to small-molecule phenotypic screens. We applied DePick to eight publicly available screens and predicted 59 drug target-phenotype associations. In addition to literature-based evidence for our predictions, we provide experimental support for seven predicted associations. Interestingly, our analysis led to the discovery of a previously unrecognized connection between the Wnt signaling pathway and an aromatase, CYP19A1. These results demonstrate that the DePick approach can not only accelerate target de-convolution but also aid in discovery of new functionally relevant biological relationships.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Drug Target-phenotype Relations ; High-throughput Chemical Screens ; Target De-convolution ; Target Prediction; Anion Transporting Polypeptide; Amyloid Precursor Protein; Bioactive Small Molecules; Wnt/beta-catenin; Beta-catenin; Web Server; Drug Discovery; E-cadherin; Sr-bi; Cholesterol
ISSN (print) / ISBN 2451-9448
e-ISSN 2451-9456
Journal Cell Chemical Biology
Quellenangaben Volume: 23, Issue: 10, Pages: 1302-1313 Article Number: , Supplement: ,
Publisher Cell Press
Publishing Place Cambridge, Massachusetts
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Bioinformatics and Systems Biology (IBIS)
Research Unit Lung Repair and Regeneration (LRR)