Field of Science

Scaling further GPCR summits

ResearchBlogging.orgThere's a nice review on GPCRs and their continuing challenges in the British Journal of Pharmacology this month. The authors focus on both structural and functional challenges in the characterization of this most important class of signaling proteins. As is well-known, drugs targeting GPCRs generate the highest revenue among all drugs. And given their basic roles in signal transduction, GPCRs are also clearly very important from an academic standpoint. Yet there is a wall of obstacles confronting us.

For starters there are the well-known problems with crystallization plaguing all membrane proteins like GPCRs. Until now only four GPCRs- rhodopsin, beta1 and beta2 adrenergic receptors and A2a adenosine receptor- have been crystallized, and the publication of each structure was considered a breakthrough. As the review mentions, the proteins are unstable outside the membrane and conditions for stabilization and crystallization are frequently incompatible; for instance stabilization is often effected by long-chain detergents while the opposite is true for crystallization. To circumvent these problems clever strategies have been adopted and immense trial and error and hard work were required. The rhodopsin and adrenergic receptors were crystallized by point mutations and special techniques; in one case an antibody was tethered to the protein and in another case a fusion protein was attached to stabilize the domain.

It's when we enter the dense jungle of GPCR biology that crystallization problems almost start sounding trivial. GPCRs couple to a variety of ligands including well-known biogenic amines (like adrenaline and serotonin), peptides, proteins and nucleotides. Where is starts to become complex is in the kind of response these ligands elicit, which could be full agonism, partial agonism, inverse agonism and full antagonism.

What structural features distinguish these different responses from each other? This is a key question in GPCR biology. But not only can ligands be agonists or antagonists but they can act in different ways on the same GPCR, activating different pathways. The case of partial agonists is especially interesting and more protein-partial agonist structures would be quite valuable.

The traditional model of protein binding assumes two dominant states, inactive and active. Agonists stabilize the active state, antagonists stabilize both states, and inverse agonists stabilize the inactive state. But, as the authors say, the traditional model is slowly undergoing a revision:

The concept of a receptor existing in a simple pair of active and inactive states (R and R*) is no longer sufficient to explain the observations of pharmacology. Agonists vary considerably in their efficacy and how this relates to the bound conformational states is unclear. A partial agonist with 50% efficacy could fully activate 50% of the receptors or could activate 100% of the receptor by 50%. Alternatively, a partial agonist might stabilize a different form of the receptor to a full agonist state and this different conformation might activate the G protein with a lower efficiency. The study of rhodopsin suggests that activation of the receptor involves the release of key structural constraints within the E/DRY and NPxxY regions. Energy provided by agonist binding must be sufficient to break these constraints and stabilize the new active conformation. In the case of rhodopsin, whether this transition is complete or partial depends on the chemical nature of the ligand (Fritze et al., 2003). The retinal analogue 9-demethyl-retinal is a partial agonist of rhodopsin which only poorly activates G protein in response to light. Spin-labeling studies (Knierim et al., 2008) suggest that in the presence of this ligand, only a small proportion of receptors are in the active conformation equivalent to all-trans-retinal. However, this can also result in a new state that is not formed with the full agonist. Therefore, rhodopsin studies suggest that that partial agonism may result in either a reduced number of fully active receptors or conformations which are not capable of fully engaging the signal transduction process. Structures of other GPCRs in complex with partial agonists are required to determine their effects on conformation.
An example makes the hideous complexity clear. The mu-opioid receptor is activated by several ligands including morphine, etorphine and fentanyl. However, morphine acts only as a partial agonist in effecting a phosphorylation endpoint whereas the other two act as full agonists. But it gets more interesting. While morphine effects phosphorylation of the kinase ERK through activation of PKC (protein kinase C), etorphine also activates ERK but by activation of beta-arrestin. Thus the same endpoint can be effected through different pathways. And it doesn't even stop there. Morphine causes the phosphorylated ERK to stay in the cytoplasm while etorphine causes the ERK to translocate to the nucleus. Not done yet; in addition, morphine can reverse its role and act as a full agonist on the adenylyl cyclase pathway.

Thus, the same ligand adopts different roles when activating different pathways. To begin with it's not even clear which pathway is activated under what circumstances. And the problem is only accentuated by the participation of different G proteins in inducing different responses.

Another dense layer of complexity is added by the fact that GPCRs have been found to dimerize and oligomerize. Crystallography can often be misleading in studying these dimers since there are several documented reports of dimers being formed as misleading artifacts of the crystallization conditions.

Apart from the stated problems, there are even more differences in further downstream signaling and receptor internalization induced by oligomerization. It's clearly a jungle out there. No wonder the design of drugs targeting GPCRs needs a measure of faith. For instance consider the various drugs targeting CNS proteins. CNS drug discovery has long been considered a black box for a good reason. Once a drug enters the brain, one can imagine it not only targeting a diverse subset of GPCRs (and even other classes of proteins) but, given the above complexities, also acting separately as agonist and antagonist at the various receptors. We clearly have a long way to go before we can prospectively design a CNS drug that will do all this on cue.

It would be a tall order trying to explain all these differences simply through structural modifications induced by the ligands. Yet whatever signal is eventually transmitted to the G proteins must begin with a crucial structural movement. It seems that elucidating the differences in helix and loop movements induced by partial and full agonists, inverse agonists and antagonists is a tantalizing part of the GPCR puzzle.

Since crystal structure data on GPCR is lacking, modeling approaches especially based on homology modeling have proved especially fruitful. Earlier attempts were all based on the single rhodopsin template. Since then the higher resolution adrenergic and adenosine receptor structures have provided significant insight. But here again numerous caveats abound. Modeling the helices is relatively easy since all GPCRs share the same general 7TM helix topology which is highly conserved, but modeling the fine differences between helices that lead to structural changes upon ligand binding is harder. And most difficult and important of all is modeling the extracellular loops which actually bind the ligands. Subtle changes in loop movement, salt-bridge breakage, hydrophobic effects and interaction of loops with helices is difficult to model. Often a change in conformation of a single residue can be enough to throw the modeling off balance. Nonetheless, the paucity of structural data means that modeling when done right will continue to be valuable. In the absence of structural data, computational ligand-based approaches which search for ligands similar to known compounds could be useful.

We have made a lot of progress in understanding the structure and function of these key proteins. But investigations seem to have unearthed more questions than answers. Which is always good for science since then it can have more choice fodder for contemplation.

Congreve, M., & Marshall, F. (2009). The impact of GPCR structures on pharmacology and structure-based drug design British Journal of Pharmacology DOI: 10.1111/j.1476-5381.2009.00476.x

Zheng, H., Loh, H., & Law, P. (2010). Agonist-selective signaling of G protein-coupled receptor: Mechanisms and implications IUBMB Life DOI: 10.1002/iub.293


  1. Nice post. If people had known this degree of complexity existed when they started, I wonder if they would have continued. Another issue is the idea of 'the' crystal structure. Presumably the differential effects of the various drugs detailed above depend on the slightly different conformations of the receptor they induce (or based on another set of ideas, how many pre-existing conformations they find and stabilize). The early success of protein crystallography of proteins with the mindset that a protein had 'a' shape -- because the pictures produced were so compelling.

    All of the post-receptor pathways mentioned above involve feedback, which is, in effect, an n-body problem in physics and one which is inherently insoluble mathematically in closed form.

    I'm not sure that our brains are powerful enough to understand the myriad events occurring after a drug binds to a 7TM receptor.

    But keep on truckin' -- our future longevity depends on it


  2. Indeed! But I believe we can can definitely make headway in understanding the basic transmitting mechanism in GPCRs at least on a case by case basis. As LBJ said, "We can do our best. That is all we can do".

  3. People are now taking cognizance of the inadequacy of one crystal structure. That is where MD simulations can help in gaining access to alternative conformations which can then be used for, say, docking. "Ensemble docking" is slowly becoming common practice.

  4. Your article covers many good points concerning the difficulties in modeling GPCRs. Our work has tried to address many of these issues - see: Bio Balance Graphics for a description of a two-state model that describes partial agonism and receptor desensitization. Also, this article gives a detailed method to show how the model works in practice: Optimal Agonist/Antagonist Combinations Maintain Receptor Response by Preventing Rapid Beta-1 adrenergic Receptor Desensitization Intl. J. Pharmacol., 1(2): 122-131, 2005. pdf In addition, we've developed a biophysical model that accounts for these two receptor states as well as the redox control of receptor response - see: Activation of G Protein-Coupled Receptors Entails Cysteine Modulation of Agonist Binding, J. Molecular Structure (Theochem), 430/1-3: 57-71 (1998). pdf and Molecular dynamics of a biophysical model for beta-2-adrenergic and G protein-coupled receptor activation Journal of Molecular Graphics and Modelling 25: 396-409 (2006) Only time will tell if we are right, but these attempts do try to address many of the issues that you raise in your excellent article.

  5. Thanks a lot for the references. I will take a look.


Markup Key:
- <b>bold</b> = bold
- <i>italic</i> = italic
- <a href="">FoS</a> = FoS