Why modeling GPCRs is (still) hard

Well, it's hard for several reasons which I have discussed in previous posts, but here's one reason demonstrated by a recent paper. In this paper they crystallized the ß2 adrenergic receptor with an antagonist. Previously, in the landmark publication of the ß2 structure in 2007, the protein had been crystallized with an inverse agonist. Recall that an inverse agonist inhibits the basal activity of the GPCR whereas an antagonist stabilizes both active and inactive states but does not affect the basal activity.

In this case they crystallized the ß2 with an antagonist and compared the resulting structure to that of the agonist-GPCR complex. And they saw...nothing in particular. The protein backbone and side-chain locations are very similar for the antagonist (compound 3) and inverse agonist (compound 2) shown in the figure below.



As we can see in the figure, about the only side-chain that shows any movement is the tyrosine on the left (Y316). No wonder that cross-docking the two ligands (that is, docking one ligand into the other ligand's protein conformation) gave very accurate ligand orientations; this was essentially a softball problem for a docking program since the antagonist was being docked into a protein conformation that was virtually identical to its own.

But of course, we know that antagonists and agonists affect GPCR function quite differently. As this study shows, clearly the action is not taking place in the ligand-binding pocket where things aren't really moving. So where is the real action? It's naturally taking place on the intracellular side, where the GPCR interacts with a medley of other proteins. And as the paper accurately notes, the difference between antagonist and inverse agonist binding is probably also reflected in the protein dynamics corresponding to the two ligands.

Good luck modeling that. That's the whole deal with modeling GPCRs. Simply modeling the ligand-binding pocket is not going to help us understand the differences between the binding of various ligands; one has to model multiprotein interactions and subtle effects on dynamics that are relayed through the helices. The program Desmond which I described in a earlier post is a powerful MD program, but even MD is going to really turn heads when it can take account of multiprotein interactions, and such interactions happen on a time-scale much longer than what even Desmond can access. We have a long way to go before we can do all this. But please, don't stop.

Wacker, D., Fenalti, G., Brown, M., Katritch, V., Abagyan, R., Cherezov, V., & Stevens, R. (2010). Conserved Binding Mode of Human β-2 Adrenergic Receptor Inverse Agonists and Antagonist Revealed by X-ray Crystallography Journal of the American Chemical Society, 132 (33), 11443-11445 DOI: 10.1021/ja105108q