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Bioinformatics Advance Access originally published online on August 1, 2008
Bioinformatics 2008 24(19):2149-2156; doi:10.1093/bioinformatics/btn409
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Protein-ligand interaction prediction: an improved chemogenomics approach

Laurent Jacob 1,2,3,* and Jean-Philippe Vert 1,2,3

1Mines ParisTech, Centre for Computational Biology, 35 rue Saint Honoré, F-77305 Fontainebleau, 2Institut Curie and 3INSERM, U900, F-75248, Paris, France

*To whom correspondence should be addressed.


   Abstract

Motivation: Predicting interactions between small molecules and proteins is a crucial step to decipher many biological processes, and plays a critical role in drug discovery. When no detailed 3D structure of the protein target is available, ligand-based virtual screening allows the construction of predictive models by learning to discriminate known ligands from non-ligands. However, the accuracy of ligand-based models quickly degrades when the number of known ligands decreases, and in particular the approach is not applicable for orphan receptors with no known ligand.

Results: We propose a systematic method to predict ligand–protein interactions, even for targets with no known 3D structure and few or no known ligands. Following the recent chemogenomics trend, we adopt a cross-target view and attempt to screen the chemical space against whole families of proteins simultaneously. The lack of known ligand for a given target can then be compensated by the availability of known ligands for similar targets. We test this strategy on three important classes of drug targets, namely enzymes, G-protein-coupled receptors (GPCR) and ion channels, and report dramatic improvements in prediction accuracy over classical ligand-based virtual screening, in particular for targets with few or no known ligands.

Availability: All data and algorithms are available as Supplementary Material.

Contact: laurent.jacob{at}ensmp.fr

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Alfonso Valencia


Received on April 4, 2008; revised on June 17, 2008; accepted on July 30, 2008

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