PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Virtual screening of GPCRs: an in silico chemogenomics approach
Laurent Jacob, Brice Hoffmann, Véronique Stoven and Jean-Philippe Vert
BMC Bioinformatics Volume 9, 363, 2008.

Abstract

Background The G-protein coupled receptor (GPCR) superfamily is currently the largest class of therapeutic targets. In silico prediction of interactions between GPCRs and small molecules in the transmembrane ligand-binding site is therefore a crucial step in the drug discovery process, which remains a daunting task due to the difficulty to characterize the 3D structure of most GPCRs, and to the limited amount of known ligands for some members of the superfamily. Chemogenomics, which attempts to characterize interactions between all members of a target class and all small molecules simultaneously, has recently been proposed as an interesting alternative to traditional docking or ligand-based virtual screening strategies. Results We show that interaction prediction in the chemogenomics framework outperforms state-of-the-art individual ligand-based methods in accuracy both for receptor with known ligands and without known ligands. This is done with no knowledge of the receptor 3D structure. In particular we are able to predict ligands of orphan GPCRs with an estimated accuracy of 78.1%. Conclusion We propose new methods for in silico chemogenomics and validate them on the virtual screening of GPCRs. The methods represent an extension of a recently proposed machine learning strategy, based on support vector machines (SVM), which provides a flexible framework to incorporate various information sources on the biological space of targets and on the chemical space of small molecules. We investigate the use of 2D and 3D descriptors for small molecules, and test a variety of descriptors for GPCRs. We show that incorporating information about the known hierarchical classification of the target family and about key residues in their inferred binding pockets significantly improves the prediction accuracy of our model.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:4430
Deposited By:Jean-Philippe Vert
Deposited On:13 March 2009