PrGen: Pseudoreceptor Modeling Using Receptor-mediated Ligand Alignment and Pharmacophore Equilibration

Abstract

Based on the structures of known ligand molecules, a pseudoreceptor modeling concept developed at our laboratory allows the construction of a peptidic binding-site model for a structurally uncharacterized bioregulator. Such a three-dimensional receptor surrogate — validated using an external set of test compounds — should be able to semi-quantitatively predict the binding affinities of related molecules. To reduce problems resulting from the mutual obscuring of functional groups within a pharmacophore hypothesis, we have devised a procedure referred to as receptor-mediated ligand alignment. It permits to identify an alternate position, orientation and conformation for each ligand molecule by means of conformational search within a primordial receptor model, constructed about the most potent ligands of a series. To derive an energetically relaxed model with a high correlation between calculated and experimental free energies of ligand binding, we have developed a ligand equilibration protocol. During this iterative procedure, the receptor surrogate and the pharmacophore are allowed to relax individually, with and without correlation-coupled energy minimization, respectively, until a high correlation is achieved in a relaxed state. In our approach (software PrGen), free energies of ligand binding are estimated based on ligand-pseudoreceptor interactions using a directional force field, ligand desolvation energy and the change of both ligand-internal energy and ligand entropy upon receptor binding. The concept was tested by generating and evaluating a pseudoreceptor for the cannabinoid receptor. The binding-site surrogate for this system was constructed about a pharmacophore comprising 18 cannabinoid antagonists. It consists of 26 amino-acid residues and is capable of predicting free energies of ligand binding, ΔG°, for an external set of 10 test molecules to within 0.8 kcal/mol (RMS) of their experimental value, corresponding to an uncertainty factor of 4 in the binding affinity. Maximal individual errors of predicted binding affinities do not exceed a factor of 12.