title: Protein-ligand-based pharmacophores: generation and utility assessment in computational ligand profiling.
abstract: Ligand profiling is an emerging computational method for predicting the most likely targets of a bioactive compound and therefore anticipating adverse reactions, side effects and drug repurposing. A few encouraging successes have already been reported using ligand 2-D similarity searches and protein-ligand docking. The current study describes the use of receptor-ligand-derived pharmacophore searches as a tool to link ligands to putative targets. A database of 68,056 pharmacophores was first derived from 8,166 high-resolution protein-ligand complexes. In order to limit the number of queries, a maximum of 10 pharmacophores was generated for each complex according to their predicted selectivity. Pharmacophore search was compared to ligand-centric (2-D and 3-D similarity searches) and docking methods in profiling a set of 157 diverse ligands against a panel of 2,556 unique targets of known X-ray structure. As expected, ligand-based methods outperformed, in most of the cases, structure-based approaches in ranking the true targets among the top 1% scoring entries. However, we could identify ligands for which only a single method was successful. Receptor-ligand-based pharmacophore search is notably a fast and reliable alternative to docking when few ligand information is available for some targets. Overall, the present study suggests that a workflow using the best profiling method according to the protein-ligand context is the best strategy to follow. We notably present concrete guidelines for selecting the optimal computational method according to simple ligand and binding site properties.
authors.affiliation: Laboratoire d'Innovation Thérapeutique, UMR7200 Université de Strasbourg/CNRS, 74 route du Rhin, 67400 Illkirch, France.
authors.forename: Jamel
authors.initials: J
authors.lastname: Meslamani
authors.forename: Jiabo
authors.initials: J
authors.lastname: Li
authors.forename: Jon
authors.initials: J
authors.lastname: Sutter
authors.forename: Adrian
authors.initials: A
authors.lastname: Stevens
authors.forename: Hugues-Olivier
authors.initials: HO
authors.lastname: Bertrand
authors.forename: Didier
authors.initials: D
authors.lastname: Rognan
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D062313:Databases, Pharmaceutical; D055808:Drug Discovery; D058492:Drug Repositioning; D057166:High-Throughput Screening Assays; D006801:Humans; D006860:Hydrogen Bonding; D057927:Hydrophobic and Hydrophilic Interactions; D008024:Ligands; D016013:Likelihood Functions; D062105:Molecular Docking Simulation; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry*; D054852:Small Molecule Libraries / Q000737:chemistry*; D055672:Static Electricity; D013329:Structure-Activity Relationship
chemical_list: D008024:Ligands; D011506:Proteins; D054852:Small Molecule Libraries