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

Functional selectivity: Nature's Bach concerto

One of the great things about Bach’s organ music is how changes of a single note in a whole pattern can have rather dramatic effects on the sound. A unique and potentially very important similar phenomenon has been discovered recently in the area of GPCR research.

The understanding of the basic process by which GPCRs transmit signals from the cell exterior to the interior has seen remarkable advances in the last three decades, but much still remains to be deciphered. Our knowledge of signaling responses until now hinged on the action of agonists and antagonists. Central to this knowledge was the concept of ‘intrinsic efficacy’; according to this concept, there was no difference between two full agonists for instance, and both of them would produce the same response irrespective of the situation.

But this understanding failed to explain some observations. For instance, a full agonist would function as a partial agonist and even as an inverse agonist under different circumstances. Several such observations, especially in the context of GPCRs involved in neurotransmission, have forced a re-evaluation of the concept of intrinsic efficacy and led to an integrated formulation of a fascinating concept called ‘functional selectivity’.

So what is functional selectivity? It is the phenomenon by which the same kind of ligand (agonist, antagonist etc.) can modulate different signaling pathways activated by a single GPCR, leading to different physiological responses. Functional selectivity thus opens up a whole new method of modifying GPCR signaling in complex ways. It comprises a new layer of complexity and control that biological systems enforce at the molecular level to engage in complex signaling and homeostasis. Functional selectivity can allow the ‘tuning’ of ligands on a continuum scale of properties, from agonism to inverse agonism. In addition it can tightly regulate the strength of the particular property. It is what allows GPCRs to function as rheostats rather than as binary switches and allows them to exercise a fine layer of biological control and discrimination.

Functional selectivity is not just of academic interest. It can have clinical significance. Probably most tantalizingly, it may be one of the holy grails of pharmacology that allows us to separate the beneficial and harmful effects of a drug, leading to Paul Ehrlich’s ‘magic bullet’. Until now, side-effects have been predominantly thought to result from the lack of subtype-specificity of drugs. For instance, morphine’s side effects are thought to result from its activation of the μ-opioid receptor. But functional selectivity could provide a totally new avenue for explaining and possibly mitigating side-effects of drugs. For instance, consider the dopamine receptor agonist ropinirole, used in the treatment of Parkinson’s disease. There are several D-receptor agonists and just like them ropinirole interacts with several receptor subtypes. But unlike many of these, ropinirole does not demonstrate the dangerous side-effect named valvulopathy, a weakening of the heart valves that makes them stiff and inflamed. This can be a potentially life-threatening condition that seems to be facilitated by several dopamine agonists, but not ropinirole. The cause seems to be becoming clear only now; ropinirole is a functionally selective ligand that activates a certain pattern of second messenger pathways that is different from those activated by other agonists. Somehow this pattern of pathways is responsible for reduced valvulopathy.

Let’s go back to the organ/piano analogy to gauge the significance of such control. The sound produced by a piano depends on two variables- the exact identities of the keys pressed, and the intensity (how hard or softly you press them). The second variable can be as important as the first since a pressing a key particularly hard can drown out other notes and influence the very nature of the sound. The analogy to functional selectivity would be in looking at the keys themselves as different signaling pathways and the intensity of the notes as the strength of the pathways. Now, if one ligand binding to a single GPCR is able to activate a specific combination of these pathways, each with its own strengths, think of the permutations and combinations you could get from a set of even a dozen pathways- an astonishing number. Thus, functional selectivity could be the key that unlocks the puzzle of how one ligand can put into motion such a complex set of signaling events and physiological responses. One ligand- one receptor- several pathways with differing strengths. An added variable is the concentration of certain second messengers in a particular environment or cell type, which could add even more combinations. This picture could go a long way toward explaining how we can get such complex signaling in the brain from just a few ligands like dopamine, serotonin and histamine. And as described above, it also provides a fascinating direction - along with control of subtype selectivity (a much more well known and accepted cause) - for developing therapies that demonstrate all the good stuff without the bad stuff.

The basic foundation of functional selectivity is as tantalizing. Whatever the reasons for the phenomenon, the proximal cause for it has to concern the stabilization of different protein conformations by the same kind of ligands. Unravel these protein conformations and you would make significant inroads into unraveling functional selectivity. If you come to think of it, this principle is not too different from the current model of conformational selection used in explaining the action of agonists and antagonists in general, which involves the stabilization of certain conformations by specific molecules.

Nature never ceases to amaze. As we plumb its mysteries further, it reveals deeper, more subtle and finer layers of control and discrimination that allows it to generate profound complexity starting from some relatively simple events like the binding of a disarmingly simple molecule like adrenaline to a protein. And combined with the action of several proteins, the concerto turns into a symphony. We have been privileged to be in the audience.

Mailman, R., & Murthy, V. (2010). Ligand functional selectivity advances our understanding of drug mechanisms and drug discovery Neuropsychopharmacology, 35 (1), 345-346 DOI: 10.1038/npp.2009.117

Kelly, E., Bailey, C., & Henderson, G. (2009). Agonist-selective mechanisms of GPCR desensitization British Journal of Pharmacology, 153 (S1) DOI: 10.1038/sj.bjp.0707604

Fishin' in the membrane

Since we were talking about GPCRs the other day, here's a nice overview of some of the experimental challenges associated with membrane proteins and how researchers are trying to overcome them. These challenges are associated not just with the crystallization, but with the whole shebang. Although many clever tricks have emerged, we have a long way to go, and at least a few of the tricks sound like brute trial and error.

To begin with, it's not that easy to get your expression system to produce ample amounts of protein. As indicated, you often need liters of cell culture to get a few milligrams of protein. The workhorse for production is still good old E. coli. E. coli does not always fold membrane proteins well, but it still beats other expression systems because of its cost and efficiency. Researchers have discovered several tricks to coax E. coli to make better protein. For instance it turns out that cold, nutrient poor conditions and slower-growing bacteria produce better folded and functional protein (although the exact reasons are probably not known, I suspect it has to do with thermodynamics and the binding of chaperones). Adding lipids from higher organisms to the medium also seems to sometimes help.

What’s more interesting are efforts to do away with cellular production altogether and just add reagents to cell lysates to jiggle the protein-production machinery. For some reason, wheat-germ lysates seem to work particularly well. There are companies willing to use these lysates to produce hundreds of milligrams of protein. One of the advantages of such cell-free systems is that you can add solubilizing agents and detergents to stabilize the proteins. A striking fact emerging from the article is how many private companies are engaged in developing such technology for membrane proteins; the end "credits" list at least a dozen corporate entities. The list should be encouraging to visionaries who see more fruitful academic-industrial collaborations in the future.

Then of course, there’s the all-important problem of crystallization. Of the 50,000 or so structures in the PDB, hardly a dozen are of membrane proteins. Membrane proteins present the classic paradox; keep them stable in the membrane and methods like crystallography and NMR cannot study them, but take them out of the membrane and, divorced from the protective effects of the lipid bilayer, they fall apart. Scientists have worked for years and come up with dozens of tricks to circumvent this catch-22. Adding the right kind of detergents can help. In the landmark structure of the beta-2 adrenergic receptor that was solved in 2007, the researchers used two tricks: attaching a stabilizing antibody to essentially clamp two transmembrane helices together, and replacing a disordered section of the protein with a T4 lysozyme, both strategies geared toward stabilizing the protein.

In the end though, there is really no general strategy and that’s still the cardinal bottleneck; as the article's title says, a "trillion tiny tweaks" are necessary to make your system work. What works for one specific membrane protein fails for another. As one of the pioneers in the field, Raymond Stevens from Scripps says, “People are always asking what the one strategy that worked is. The answer is there wasn’t one strategy, there were about fifteen”.

This is why chemistry (or economics) is not like physics. Although there are general rules, every specific case still invokes its own principles. In fields like membrane protein chemistry, it is unlikely that a single holy-grail strategy could be discovered that could work for all of them. The medley of techniques applied to membrane proteins makes the science seem sometimes like black magic and trial-and-error. All this makes chemistry hard, but also very interesting; if only a dozen membrane proteins have their structures solved, think of how many more are waiting in the shadows, awaiting the fruits of our sweat and toil.

Baker, M. (2010). Making membrane proteins for structures: a trillion tiny tweaks Nature Methods, 7 (6), 429-434 DOI: 10.1038/nmeth0610-429

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

Computational modeling of GPCRs: What are the challenges?

GPCRs are extremely important proteins both for pure and applied science research, but they are also very difficult to crystallize and hence structural information on them has been sparse. Naturally in such a case, computational modeling can be expected to be of great value of providing insight into GPCR structure and function. However, even though progress has been impressive, such modeling still has to overcome many challenges. A recent review lists some of them.

Firstly, in the absence of crystal structure, homology modeling wherein a sequence for an unknown structure is 'threaded' through that of a known one is well-established as a valuable technique. However the technique is tricky. First and foremost one has to get the right sequence alignment between the target and the template. As the article notes, recent studies have suggested that using multiple structures for alignment instead of a single one provides better results. Particularly noteworthy is this detailed study. Once a homology model has been obtained, it must be meticulously examined, both for internal consistency (bad contacts, incorrect hydrogen bonding interactions etc.) and for its agreement with experiment. Data from cross-linking studies and mutagenesis can be used to achieve this. A recent promising development has been termed 'ligand-supported homology modeling'. In this process, topographical protein-ligand interaction data from mutagenesis and other studies is used to limit the number of homology models. Such data-driven homology modeling is becoming increasingly popular.

Once a good homology model has been obtained, many things can be done with it. Molecular dynamics (MD) simulations provide a very valuable avenue for exploring protein motion and be used to detect structural features not obvious in static models. A recent MD simulation of the beta-adrenergic receptor helped to resolve discrepancies between biochemical and structural observations. MD simulations can be used to investigate protein dynamics and to refine the models. Several challenges present themselves during this procedure. Firstly, while helices in GPCRs can be well-modeled, loops (of which there are six- three intracellular and three extracellular) are much harder to model because of their higher flexibility and because they are often ill-resolved in crystal structures. Unfortunately, it's these loops which are important ligand-interacting elements, so getting them right is key. Recently developed algorithms for loop-refinement based on either first-principles energy minimization or by statistical modeling based on a database of known loop conformations have been used in getting loops right. Also, state-of-the-art long MD simulations spanning several microseconds can be used to model large-scale structural changes in GPCRs.

There are still immense challenges still to be overcome in understanding GPCRs. One of the biggest concerns the cycling between several inactive and active states (and not just one active and one inactive state) that present often conflicting features that can be subject to varying interpretation. For instance, for class A GPCRs (which is the largest class), it has been well-established that activated states involve the breakage of the "ionic lock", a salt bridge between arginines and glutamates on transmembrane helices 6 and 3. Breaking this lock allows TM6 to shift away from TM3 and towards TM5, a hallmark of GPCR activation. Yet the MD study on the beta2 cited above indicated that even an inactive state may feature breakage of this lock.

In the GPCR jungle, strange shape-shifting creatures appear and clutch gems of insight in their palms. It is only fitting that we throw the kitchen sink at them to unravel their secrets, and computational techniques can only be a valuable arrow in this quiver.

Yarnitzky T, Levit A, & Niv MY (2010). Homology modeling of G-protein-coupled receptors with X-ray structures on the rise. Current opinion in drug discovery & development, 13 (3), 317-25 PMID: 20443165