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

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A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology. / Durrant, Jacob D.; Amaro, Rommie E.; Xie, Lei; Urbaniak, Michael D.; Ferguson, Michael A. J.; Haapalainen, Antti; Chen, Zhijun; Di Guilmi, Anne Marie; Wunder, Frank; Bourne, Philip E.; McCammon, J. Andrew.

In: PLoS Computational Biology, Vol. 6, No. 1, e1000648, 22.01.2010.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Durrant, JD, Amaro, RE, Xie, L, Urbaniak, MD, Ferguson, MAJ, Haapalainen, A, Chen, Z, Di Guilmi, AM, Wunder, F, Bourne, PE & McCammon, JA 2010, 'A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology', PLoS Computational Biology, vol. 6, no. 1, e1000648. https://doi.org/10.1371/journal.pcbi.1000648

APA

Durrant, J. D., Amaro, R. E., Xie, L., Urbaniak, M. D., Ferguson, M. A. J., Haapalainen, A., Chen, Z., Di Guilmi, A. M., Wunder, F., Bourne, P. E., & McCammon, J. A. (2010). A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology. PLoS Computational Biology, 6(1), [e1000648]. https://doi.org/10.1371/journal.pcbi.1000648

Vancouver

Durrant JD, Amaro RE, Xie L, Urbaniak MD, Ferguson MAJ, Haapalainen A et al. A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology. PLoS Computational Biology. 2010 Jan 22;6(1). e1000648. https://doi.org/10.1371/journal.pcbi.1000648

Author

Durrant, Jacob D. ; Amaro, Rommie E. ; Xie, Lei ; Urbaniak, Michael D. ; Ferguson, Michael A. J. ; Haapalainen, Antti ; Chen, Zhijun ; Di Guilmi, Anne Marie ; Wunder, Frank ; Bourne, Philip E. ; McCammon, J. Andrew. / A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology. In: PLoS Computational Biology. 2010 ; Vol. 6, No. 1.

Bibtex

@article{222b8e3063ee491eb2013c284028a973,
title = "A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology",
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.",
keywords = "Algorithms, Catalytic Domain, Cluster Analysis, Computational Biology, Computer Simulation, Databases, Protein, Drug Discovery, Humans, Models, Biological, Proteins, Sequence Homology, Amino Acid, Structural Homology, Protein",
author = "Durrant, {Jacob D.} and Amaro, {Rommie E.} and Lei Xie and Urbaniak, {Michael D.} and Ferguson, {Michael A. J.} and Antti Haapalainen and Zhijun Chen and {Di Guilmi}, {Anne Marie} and Frank Wunder and Bourne, {Philip E.} and McCammon, {J. Andrew}",
note = "Copyright: {\textcopyright} 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.",
year = "2010",
month = jan,
day = "22",
doi = "10.1371/journal.pcbi.1000648",
language = "English",
volume = "6",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "1",

}

RIS

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 - 1553-734X

IS - 1

M1 - e1000648

ER -