Raptor: combining dual-shell representation, induced-fit simulation, and hydrophobicity scoring in receptor modeling: application toward the simulation of structurally diverse ligand sets.

We present a novel receptor-modeling approach (software Raptor) based on multidimensional quantitative structure-activity relationships (QSARs). To accurately predict relative free energies of ligand binding, it is of utmost importance to simulate induced fit. In Raptor, we explicitly and anisotropically allow for this phenomenon by a dual-shell representation of the receptor surrogate. In our concept, induced fit is not limited to steric aspects but includes the variation of the physicochemical fields along with it. The underlying scoring function for evaluating ligand-receptor interactions includes directional terms for hydrogen bonding and hydrophobicity and thereby treats solvation effects implicitly. This makes the approach independent from a partial-charge model and, as a consequence, allows one to smoothly model ligand molecules binding to the receptor with different net charges. We have applied the new concept toward the estimation of ligand-binding energies associated with the chemokine receptor-3 (50 ligands: r(2) = 0.965; p(2) = 0.932), the bradykinin B(2) receptor (52 ligands: r(2) = 0.949; p(2) = 0.859), and the estrogen receptor (116 ligands: r(2) = 0.908; p(2) = 0.907), respectively.