Rights statement: 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.
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology
AU - Durrant, Jacob D.
AU - Amaro, Rommie E.
AU - Xie, Lei
AU - Urbaniak, Michael D.
AU - Ferguson, Michael A. J.
AU - Haapalainen, Antti
AU - Chen, Zhijun
AU - Di Guilmi, Anne Marie
AU - Wunder, Frank
AU - Bourne, Philip E.
AU - McCammon, J. Andrew
N1 - 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.
PY - 2010/1/22
Y1 - 2010/1/22
N2 - 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.
AB - 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.
KW - Algorithms
KW - Catalytic Domain
KW - Cluster Analysis
KW - Computational Biology
KW - Computer Simulation
KW - Databases, Protein
KW - Drug Discovery
KW - Humans
KW - Models, Biological
KW - Proteins
KW - Sequence Homology, Amino Acid
KW - Structural Homology, Protein
U2 - 10.1371/journal.pcbi.1000648
DO - 10.1371/journal.pcbi.1000648
M3 - Journal article
C2 - 20098496
VL - 6
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-7358
IS - 1
M1 - e1000648
ER -