A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology

Durrant, Jacob D. and Amaro, Rommie E. and Xie, Lei and Urbaniak, Michael D. and Ferguson, Michael A. J. and Haapalainen, Antti and Chen, Zhijun and Di Guilmi, Anne Marie and Wunder, Frank and Bourne, Philip E. and McCammon, J. Andrew (2010) A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology. PLoS Computational Biology, 6 (1). ISSN 1553-7358

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Abstract

Conventional drug design embraces the "one gene, one drug, one disease" philosophy. Polypharmacology, which focuses on multi-target drugs, has emerged as a new paradigm in drug discovery. The rational design of drugs that act via polypharmacological mechanisms can produce compounds that exhibit increased therapeutic potency and against which resistance is less likely to develop. Additionally, identifying multiple protein targets is also critical for side-effect prediction. One third of potential therapeutic compounds fail in clinical trials or are later removed from the market due to unacceptable side effects often caused by off-target binding. In the current work, we introduce a multidimensional strategy for the identification of secondary targets of known small-molecule inhibitors in the absence of global structural and sequence homology with the primary target protein. To demonstrate the utility of the strategy, we identify several targets of 4,5-dihydroxy-3-(1-naphthyldiazenyl)-2,7-naphthalenedisulfonic acid, a known micromolar inhibitor of Trypanosoma brucei RNA editing ligase 1. As it is capable of identifying potential secondary targets, the strategy described here may play a useful role in future efforts to reduce drug side effects and/or to increase polypharmacology.

Item Type:
Journal Article
Journal or Publication Title:
PLoS Computational Biology
Additional Information:
Copyright: © 2010 Durrant et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1300/1312
Subjects:
ID Code:
66391
Deposited By:
Deposited On:
17 Sep 2013 08:08
Refereed?:
Yes
Published?:
Published
Last Modified:
24 May 2020 04:15