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. 2014 Jul;42(Web Server issue):W32-8.
doi: 10.1093/nar/gku293. Epub 2014 May 3.

SwissTargetPrediction: a web server for target prediction of bioactive small molecules

David Gfeller  1 , Aurélien Grosdidier  1 , Matthias Wirth  1 , Antoine Daina  1 , Olivier Michielin  2 , Vincent Zoete  3
Affiliations

SwissTargetPrediction: a web server for target prediction of bioactive small molecules

David Gfeller et al. Nucleic Acids Res. 2014 Jul.

Abstract

Bioactive small molecules, such as drugs or metabolites, bind to proteins or other macro-molecular targets to modulate their activity, which in turn results in the observed phenotypic effects. For this reason, mapping the targets of bioactive small molecules is a key step toward unraveling the molecular mechanisms underlying their bioactivity and predicting potential side effects or cross-reactivity. Recently, large datasets of protein-small molecule interactions have become available, providing a unique source of information for the development of knowledge-based approaches to computationally identify new targets for uncharacterized molecules or secondary targets for known molecules. Here, we introduce SwissTargetPrediction, a web server to accurately predict the targets of bioactive molecules based on a combination of 2D and 3D similarity measures with known ligands. Predictions can be carried out in five different organisms, and mapping predictions by homology within and between different species is enabled for close paralogs and orthologs. SwissTargetPrediction is accessible free of charge and without login requirement at http://www.swisstargetprediction.ch.

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Figures

Figure 1.
Figure 1.
Prediction result page. This page shows the list of predicted targets for the query molecule (here chlorotrianisene). Targets are ranked according to their scores. Links to GeneCards (under ‘Common name’ column), UniProt and ChEMBL (when available) are provided. Green bars indicate the estimated probability of a protein to be a true target given its score. The sixth column (# sim cmpds 3D/2D) shows the number of ligands of the predicted target or its homologs that display similarity with the query molecule based on either 2D or 3D similarity measures. These numbers are linked to pages containing information about these ligands. For instance, the number circled in red provides a link to the list of ligands of ESR1 or its homologous proteins that display similarity with the query molecule (see Figure 2A). The pie chart shows the distribution of target classes. Predictions based on homology are indicated with ‘(by homology)’ (see the green box).
Figure 2.
Figure 2.
(A) List of ligands of ESR1 or its homologous proteins displaying 3D similarity with a query molecule (here chlorotrianisene). This page is obtained by following the link in the red circle in Figure 1. Molecules are ordered based on their 3D similarity with the query molecule. (B) List of ligands of ESR2 or its homologous proteins displaying similarity with a query molecule (here chlorotrianisene) obtained by following the link in the green circle in Figure 1. If a molecule is a ligand of a homologous protein of the predicted target, the actual target as well as its organism is indicated (see the green box). When the most similar molecule is a ligand of a homologous protein, the prediction is labeled as ‘by homology’ in the result page (Figure 1). A link to the ChEMBL entry is provided for each compound.
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