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.

A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus

“…Pharmacophores were constructed using the receptor-ligand pharmacophore generation protocol in Discovery Studio version 4.1 (Biovia, San Diego, CA) with a maximum number of pharmacophores (10), minimum features (4), and maximum number of features (6) as are described elsewhere 20 .…”

Molecular dynamic simulations and structure-based pharmacophore development for farnesyltransferase inhibitors discovery

“…Generally, these pharmacophore points are generated directly from the protein-ligand complex, considering both ligand and the protein structures [19][20][21][22][23][24][25][26] . In this analysis, Functional groups in ligands Figure 6.…”

“…The ligand-based drug design approaches are used when 3D structure of the protein is not available; however, it considers compounds (especially active compounds) those have activity on the particular target. The QSAR, pharmacophore analyses are best-suited techniques applied for ligand-based analysis [19][20][21][22][23][24][25][26] . In the present investigation, we have applied both structure-and ligand-based drug design approaches to generate structure-based pharmacophore models to identify the FTase inhibitors from data set of natural compounds.…”

“…The structure or receptor-based pharmacophore models provide an efficient alternative to docking-based virtual screening of small molecules, still representing specific ligand-protein interactions [19][20][21][22][23][24][25][26] . The generation of pharmacophore models directly from the complex crystal structures is more reliable because it imposes the necessary constraints required for interaction and selectivity 20 .…”

“…The generation of pharmacophore models directly from the complex crystal structures is more reliable because it imposes the necessary constraints required for interaction and selectivity 20 . The protein-ligand-derived pharmacophores was applied by Langer et al in order to identify potential targets of bioactive ligands in a series of retrospective screening experiments focusing on small protein-ligand matrices 21,22 . The advanced experimental techniques, such as X-ray crystallography and NMR, are available for the determination of a protein structure, and hence, the reliable and robust techniques to construct pharmacophores from a receptor structure is important 23,24 .…”