Bioinformatics Advance Access published online on August 1, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn409
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Protein-ligand interaction prediction: an improved chemogenomics approach
1Centre for Computational Biology, École des Mines de Paris, 35 rue Saint Honoré, 77305 Fontainebleau Cedex, France
*To whom correspondence should be addressed. Laurent Jacob, E-mail: Laurent.Jacob{at}ensmp.fr
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Abstract |
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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
Associate Editor: Prof. Alfonso Valencia
Received on April 4, 2008; revised on June 17, 2008; accepted on July 20, 2008
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