Protein-ligand interaction prediction: an improved chemogenomics approach

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.

Fig. 1.

Distribution of the number of training points for a target for the enzymes, GPCR and ion channel datasets. Each bar indicates the proportion of targets in the family for which a given (x-axis) number of data points is available.

Fig. 2.

Target kernel Gram matrices (K_tar) for ion channels with multitask, hierarchy and local alignment kernels.

Fig. 3.

Relative improvement of the hierarchy kernel against the Dirac kernel as a function of the number of known ligands for enzymes, GPCR and ion channel datasets. Each point indicates the mean performance ratio between individual and hierarchy approaches across the targets of the family for which a given (x-axis) number of training points was available.