GPCR modeling: The devil hasn't left the details

The last decade has been a bonanza decade for the elucidation of structures of G Protein-Coupled Receptors (GPCRs), culminating with the landmark structure of the first GPCR-G protein complex published a few weeks ago. With 30% of all drugs targeting these proteins and their involvement in virtually every key aspect of health and disease, GPCRs remain glowingly important targets for pure and applied science.

Yet there are miles to go before we sleep. Although we now have more than a dozen structures of half a dozen GPCRs in various states (inactive, active, G-protein coupled), there are still hundreds of GPCRs whose structures are not known. The existing GPCRs all fall into the 'Class A' GPCRs. We still have to mine the vast body of Class B and C GPCRs which comprise a huge number of functionally relevant proteins. The crystal structures which we do have comprise an invaluable resource but from the point of view of drug discovery, we still don't have enough.

In the absence of crystal structures, homology modeling wherein a protein of high sequence homology is used to build a computational model for an unknown structure has been the favorite tool of modelers and structural biologists. Homology modelers were recently provided an opportunity to pit their skills against nature when a contest asked them to predict the structures of the D3 and CXCR4 receptors just before the real x-ray structures came out. Both proteins are important targets involved in multiple processes like neurotransmission, depression, psychoses, cancer and HIV infection. The D3 structure prediction involved predicting the ligand-bound structure of the protein complexed with eticlopride, a D3 antagonist.

The results of the contest have been published before, but in a recent Nature Chemical Biology paper, a team led by Brian Shoichet (UCSF) and Bryan Roth (UNC-Chapel Hill) perform another test of homology modeling, this time connected to the ability to virtually screen potential D3 receptor ligands and discover novel active molecules with interesting chemotypes.

Two experiments provided the comparison. One protocol used the D3 homology model to screen about 3 million compounds by docking, out of which about 20 were picked and tested in assays based on docking scores and inspection. The homology model was built on the basis of the published structure of the ß2 adrenergic receptor which has been structurally heavily studied. Then, after the x-ray structure of the D3 was released, they repeated the virtual screening protocol with the crystal structure; again, 3 million compounds out of which roughly 20 were picked and tested.

First the somewhat surprising and heartening result; both homology model and crystal structure demonstrated similar hit rates- about 20%. In both the cases the actual affinity of the ligands ranged from about 200 nM - 3 µM. In addition, the screen revealed some novel chemotypes that did not resemble known D3 antagonists (although not surprisingly, some hits were similar to eticlopride). As an added bonus, the top ranked ligands using the homology model did not measurably inhibit the template ß2 adrenergic receptor, which means that the homology model probably did not retain the "memory" of the original template.

Now for the bee in the bonnet. The very fact that the homology model and the crystal structure produced different hits means that the two models were not identical (only one hit overlapped between the two). Of course, it's too much to expect a model of a protein with thousands of moving parts to be identical to the experimental structure, but it goes to show how careful homology modeling has to be performed and how it can still be imperfect. What is more disturbing is that the differences between the model and the crystal structure responsible for the different hits were small; in one case the difference between two carbons was only 1 Å between the two models. Other amino acids differed by less than that.

And all this even after generating a stupendous number of models of unbound and ligand-bound protein. As the paper says, the team generated about 98 million initial ligand-bound homology models. Screening the top models among these involved generating multiple conformations and binding modes of the 3 million compounds; the total number of discrete protein-ligand complexes resulting from this exercise numbered about 2 trillion. That such kind of evaluation is possible is a tribute to the enormous computing power we have at our fingertips. But it's also a commentary on how relatively primitive our models are so that we are still at a loss to predict minute structural differences with significant consequences in finding new active molecules.

So where does this lead us? I think it's really useful to be able to perform such comparisons between homology models and crystal structures and we can only hope more such comparisons will be possible by virtue of an increasing pipeline of GPCR structures. Yet these exercises demonstrate how challenging it is to generate a truly accurate homology model. A few years ago a similar study demonstrated that a difference in a single torsional angle of a phenylalanine residue (and that too resulting in a counter-intuitive gauche conformation) affected the binding of a ligand to a homology model of the ß2 adrenergic receptor. Our ability to pinpoint such tiny differences in homology models is still in its infancy. And this is just for Class A GPCRs for which relatively accurate templates are available. Get into Class B and Class C territory and you start looking for the proverbial black cat in the dark.

Now throw in the fascinating phenomenon of functional selectivity and you have a real wrench in the works. Functional selectivity, whereby different conformations of a GPCR binding to the same ligand modulate different signal transduction pathways and cause the ligand to change its mode of action (agonist, inverse agonist etc.) takes modeling of GPCRs to unknown levels of difficulty. Most modeling currently being done does not even attempt to consider protein flexibility which is at the heart of functional selectivity. Routinely including protein flexibility in GPCR modeling has some way to go.

That is why I think that, as much as we will continue to learn from GPCR homology modeling, it's not going to contribute massively to GPCR drug discovery anytime soon. Constructing accurate homology models of even a fraction of the GPCR universe will take a long time. Using such models would be like throwing darts at a board for which the center is unknown. Until we can locate the center and are plagued with the complexities of functional selectivity, we may be better off pursuing experimental approaches that that can map the effect of ligands on a particular GPCR using multifunctional assays. Fortunately, such approaches are definitely seeing the light of day.

Carlsson, J., Coleman, R., Setola, V., Irwin, J., Fan, H., Schlessinger, A., Sali, A., Roth, B., & Shoichet, B. (2011). Ligand discovery from a dopamine D3 receptor homology model and crystal structure Nature Chemical Biology DOI: 10.1038/nchembio.662