An integrated structure- and system-based framework to identify new targets of metabolites and known drugs.
BACKGROUND
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 11 drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs-two that are of high pharmacological interest, the peroxisome proliferator-activated receptor gamma and the oncogene B-cell lymphoma 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.


METHODS
The program, datasets and results are freely available to academic users at http://sfb.kaust.edu.sa/Pages/Software.aspx.
Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center.
Hammad
H
Naveed
Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Umar S
US
Hameed
Inserm U1054, Centre de Biochimie Structurale and CNRS UMR5048, Universités Montpellier 1 & 2, Montpellier, France.
Deborah
D
Harrus
Inserm U1054, Centre de Biochimie Structurale and CNRS UMR5048, Universités Montpellier 1 & 2, Montpellier, France.
William
W
Bourguet
Computational Bioscience Research Center, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Stefan T
ST
Arold
Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center.
Xin
X
Gao
D001665:Binding Sites; D019295:Computational Biology / Q000379:methods; D055808:Drug Discovery; D006801:Humans; D008660:Metabolism; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D011336:Probability; D011506:Proteins / Q000737:chemistry / Q000378:metabolism
D004364:Pharmaceutical Preparations; D011506:Proteins