An integrated structure- and system-based framework to identify new targets of metabolites and known drugs

Handle URI:
http://hdl.handle.net/10754/575525
Title:
An integrated structure- and system-based framework to identify new targets of metabolites and known drugs
Authors:
Naveed, Hammad ( 0000-0002-1867-974X ) ; Hameed, Umar Farook Shahul ( 0000-0002-0552-7149 ) ; Harrus, Deborah; Bourguet, William; Arold, Stefan T. ( 0000-0001-5278-0668 ) ; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
Motivation: The inherent promiscuity of small molecules towards protein targets impedes our understanding of healthy versus diseased metabolism. This promiscuity also poses a challenge for the pharmaceutical industry as identifying all protein targets is important to assess (side) effects and repositioning opportunities for a drug. Results: Here, we present a novel integrated structure- and system-based approach of drug-target prediction (iDTP) to enable the large-scale discovery of new targets for small molecules, such as pharmaceutical drugs, co-factors and metabolites (collectively called ‘drugs’). For a given drug, our method uses sequence order–independent structure alignment, hierarchical clustering, and probabilistic sequence similarity to construct a probabilistic pocket ensemble (PPE) that captures promiscuous structural features of different binding sites on known targets. A drug’s PPE is combined with an approximation of its delivery profile to reduce false positives. In our cross-validation study, we use iDTP to predict the known targets of eleven drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the nuclear receptor PPARγ and the oncogene Bcl-2, were successfully validated through in vitro binding experiments. Our method is broadly applicable for the prediction of protein-small molecule interactions with several novel applications to biological research and drug development.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Biological and Environmental Sciences and Engineering (BESE) Division
Citation:
An integrated structure- and system-based framework to identify new targets of metabolites and known drugs 2015:btv477 Bioinformatics
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
Issue Date:
18-Aug-2015
DOI:
10.1093/bioinformatics/btv477
Type:
Article
ISSN:
1367-4803; 1460-2059
Additional Links:
http://bioinformatics.oxfordjournals.org/lookup/doi/10.1093/bioinformatics/btv477
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorNaveed, Hammaden
dc.contributor.authorHameed, Umar Farook Shahulen
dc.contributor.authorHarrus, Deborahen
dc.contributor.authorBourguet, Williamen
dc.contributor.authorArold, Stefan T.en
dc.contributor.authorGao, Xinen
dc.date.accessioned2015-08-23T10:31:06Zen
dc.date.available2015-08-23T10:31:06Zen
dc.date.issued2015-08-18en
dc.identifier.citationAn integrated structure- and system-based framework to identify new targets of metabolites and known drugs 2015:btv477 Bioinformaticsen
dc.identifier.issn1367-4803en
dc.identifier.issn1460-2059en
dc.identifier.doi10.1093/bioinformatics/btv477en
dc.identifier.urihttp://hdl.handle.net/10754/575525en
dc.description.abstractMotivation: The inherent promiscuity of small molecules towards protein targets impedes our understanding of healthy versus diseased metabolism. This promiscuity also poses a challenge for the pharmaceutical industry as identifying all protein targets is important to assess (side) effects and repositioning opportunities for a drug. Results: Here, we present a novel integrated structure- and system-based approach of drug-target prediction (iDTP) to enable the large-scale discovery of new targets for small molecules, such as pharmaceutical drugs, co-factors and metabolites (collectively called ‘drugs’). For a given drug, our method uses sequence order–independent structure alignment, hierarchical clustering, and probabilistic sequence similarity to construct a probabilistic pocket ensemble (PPE) that captures promiscuous structural features of different binding sites on known targets. A drug’s PPE is combined with an approximation of its delivery profile to reduce false positives. In our cross-validation study, we use iDTP to predict the known targets of eleven drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the nuclear receptor PPARγ and the oncogene Bcl-2, were successfully validated through in vitro binding experiments. Our method is broadly applicable for the prediction of protein-small molecule interactions with several novel applications to biological research and drug development.en
dc.language.isoenen
dc.publisherOxford University Press (OUP)en
dc.relation.urlhttp://bioinformatics.oxfordjournals.org/lookup/doi/10.1093/bioinformatics/btv477en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.comen
dc.titleAn integrated structure- and system-based framework to identify new targets of metabolites and known drugsen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.identifier.journalBioinformaticsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionInserm U1054, Centre de Biochimie Structurale, Montpellier, France; CNRS UMR5048, Universités Montpellier 1 & 2, Montpellier, Franceen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorNaveed, Hammaden
kaust.authorHameed, Umar Farook Shahulen
kaust.authorArold, Stefan T.en
kaust.authorGao, Xinen
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