Modeling Membrane Protein-Ligand Binding Interactions: The Human Purinergic Platelet Receptor

Abstract

Membrane proteins, due to their roles as cell receptors and signaling mediators, make prime candidates for drug targets. The computational analysis of protein-ligand binding affinities has been widely employed as a tool in rational drug design efforts. Although efficient implicit solvent-based methods for modeling globular protein-ligand binding have been around for many years, the extension of such methods to membrane protein-ligand binding is still in its infancy. In this study, we extended the widely used Amber/MMPBSA method to model membrane protein-ligand systems, and we used it to analyze protein-ligand binding for the human purinergic platelet receptor (P2Y₁₂R), a prominent drug target in the inhibition of platelet aggregation for the prevention of myocardial infarction and stroke. The binding affinities, computed by the Amber/MMPBSA method using standard parameters, correlate well with experiment. A detailed investigation of these parameters was conducted to assess their impact on the accuracy of the method. These analyses show the importance of properly treating the nonpolar solvation interactions and the electrostatic polarization in the binding of nucleotide agonists and non-nucleotide antagonists to P2Y₁₂R. On the basis of the crystal structures and the experimental conditions in the binding assay, we further hypothesized that the nucleotide agonists lose their bound magnesium ion upon binding to P2Y₁₂R, and our computational study supports this hypothesis. Ultimately, this work illustrates the value of computational analysis in the interpretation of experimental binding reactions.

Figure 1

Dielectric constant regions in the P2Y₁₂R system. The protein/ligand is shown in yellow (dielectric: epsin), the implicit membrane is shown in red (dielectric: epsmem), and the surrounding implicit water solvent is shown in blue (dielectric: epsout).

Figure 2

Thermodynamic cycle of the MMPBSA method for a membrane protein-ligand system. The membrane protein is depicted in yellow, the ligand is depicted in orange, the implicit membrane is shown in red, the water solvent is shown with blue, and the vacuum is shown with black. ΔG⁰ values are labelled for the various transitions from one state to another.

Figure 3

Parameter optimization for the non-polar solvation model, inp (epsin=20, epsmem=4). The R value indicates the correlation for the data set at the given inp value.

Figure 4

Parameter optimization for the protein dielectric constant, epsin (inp=2, epsmem=4). The R value indicates the correlation for the data set at the given epsin value.

Figure 5

Parameter optimization for the membrane dielectric constant, epsmem (inp=2, epsin=20). The R value indicates the correlation for the data set at the given epsmem value.

Figure 6

Absolute binding free energy (ΔG) correlation plots (inp=2, epsin=20, and epsmem=4). The plot that was corrected for the removal of the magnesium ion appears on the left while the plot that did not take into account the magnesium correction appears on the right.

Figure 7

Relative binding free energy (ΔΔG) correlation plots (inp=2, epsin=20, and epsmem=4). The plot that was corrected for the removal of the magnesium ion appears on the left while the plot that did not take into account the magnesium correction appears on the right.

Figure 8

Cross-sectional comparison of the ATP binding site for A) P2Y₁₂R, B) GRK1, and C) FlaH. Blue and red coloring reflects a net positive or negative charge, carbon bonds are shown in cyan, phosphorus bonds are depicted in orange, and the magnesium ions are shown in green.