Structure-based chemogenomics: analysis of protein family landscapes

Predictive in silico off-target profiling in drug discovery

Drug action can be rationalized as interaction of a molecule with proteins in a regulatory network of targets from a specific biological system. Both drug and side effects are often governed by interaction of the drug molecule with many, often unrelated, targets. Accordingly, arrays of protein–ligand interaction data from numerous in vitro profiling assays today provide growing evidence of polypharmacological drug interactions, even for marketed drugs. In vitro off-target profiling has therefore become an important tool in early drug discovery to learn about potential off-target liabilities, which are sometimes beneficial, but more often safety relevant. The rapidly developing field of in silico profiling approaches is complementing in vitro profiling. These approaches capitalize from large amounts of biochemical data from multiple sources to be exploited for optimizing undesirable side effects in pharmaceutical research. Therefore, current in silico profiling models are nowadays perceived as valuable tools in drug discovery, and promise a platform to support optimally informed decisions.

Chemogenomics of allosteric binding sites in GPCRs

Chemogenomic techniques connect the chemical and biological domains to establish ligand and target relationships not evident from the individual disciplines. Chemogenomics has been applied in lead generation, target classification, focused library design as well as selectivity and polypharmacology profiling. This review describes recent developments structured into ligand-, target- and combined chemogenomic techniques and applications to allosteric GPCR ligands. It also outlines relative strengths and limitations of these techniques and the impact of the increasing crystallographic data.

Recipes for the selection of experimental protein conformations for virtual screening

The use of multiple X-ray protein structures has been reported to be an efficient alternative for the representation of the binding pocket flexibility needed for accurate small molecules docking. However, the docking performance of the individual single conformations varies widely, and adding certain conformations to an ensemble is even counterproductive. Here we used a very large and diverse benchmark of 1068 X-ray protein conformations of 99 therapeutically relevant proteins, first, to compare the performance of the ensemble and single-conformation docking and, second, to find the properties of the best-performing conformers that can be used to select a smaller set of conformers for ensemble docking. The conformer selection has been validated through retrospective virtual screening experiments aimed at separating known ligand binders from decoys. We found that the conformers cocrystallized with the largest ligands displayed high selectivity for binders, and when combined in ensembles they consistently provided better results than randomly chosen protein conformations. The use of ensembles encompassing between 3 and 5 experimental conformations consistently improved the docking accuracy and binders vs decoys separation.