Chemogenomics Approaches for Receptor Deorphanization and Extensions of the Chemogenomics Concept to Phenotypic Space

Abstract

Chemogenomic approaches, which link ligand chemistry to bioactivity against targets (and, by extension, to phenotypes) are becoming more and more important due to the increasing number of bioactivity data available both in proprietary databases as well as in the public domain. In this article we review chemogenomics approaches applied in four different domains: Firstly, due to the relationship between protein targets from which an approximate relation between their respective bioactive ligands can be inferred, we investigate the extent to which chemogenomics approaches can be applied to receptor deorphanization. In this case it was found that by using knowledge about active compounds of related proteins, in 93% of all cases enrichment better than random could be obtained. Secondly, we analyze different cheminformatics analysis methods with respect to their behavior in chemogenomics studies, such as subgraph mining and Bayesian models. Thirdly, we illustrate how chemogenomics, in its particular flavor of 'proteochemometrics', can be applied to extrapolate bioactivity predictions from given data points to related targets. Finally, we extend the concept of 'chemogenomics' approaches, relating ligand chemistry to bioactivity against related targets, into phenotypic space which then falls into the area of 'chemical genomics' and 'chemical genetics'; given that this is very often the desired endpoint of approaches in not only the pharmaceutical industry, but also in academic probe discovery, this is often the endpoint the experimental scientist is most interested in.

Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets

“…It resembles the real-world situation of deorphanizing receptors where information from related proteins of the ‘orphan receptor’ is taken into account to identify bioactive chemical matter for a receptor for which no ligands have been identified yet [55,56]. Moreover, this concept is applicable to predict which drug to use against a particular receptor mutant in case of e.g .…”

Proteochemometric modeling in a Bayesian framework

“…These techniques are based on the similarity principle [4,5], which follows the premise that similar compounds (and targets) [6] are more likely to exhibit akin bioactivity profiles in comparison to structurally distant structures. Among others, chemogenomic approaches have enabled: (i) the prediction of protein targets for new compounds based on the bioactivity profiles of similar compounds, [7-9] (ii) the study of protein similarity on the basis of the similarity of their ligands, [10,11] and (iii) receptor deorphanization [12]. …”

Mapping small molecule binding data to structural domains

“…Chemogenomic approaches can be used to systematically examine and explore the binding of small molecules to large target families such as kinases [6,7] or G-protein coupled receptors (GPCRs) [8,9] or for the design of compounds targeting multiple proteins [10]. One of the current limitations of these approaches is the biased distribution of data that is available for individual targets.…”