1 research outputs found
Protein–Ligand-Based Pharmacophores: Generation and Utility Assessment in Computational Ligand Profiling
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