Conquering the curse of morphine, one docked structure at a time

Morphine, one of the most celebrated molecules in history, also happens to be one of the deadliest. It belongs to the same family of opioid compounds that includes oxycodone and heroin. So does fentanyl, a weaponized form of which is rumored to have caused havoc and killed dozens in a terrorist standoff in Russia a few years ago. Potent painkillers which mitigate pain that is impossible to cure using traditional painkillers like ibuprofen, morphine and its cousins are also devils in disguise.

Constipation is the least concerning side effect of opioid use. Even in moderate doses they have a high risk of causing death from respiratory failure; in the jargon of drug science, their therapeutic index - the difference between a beneficial and a fatal dose - is very low. To get an idea of how potent these compounds are, it's sobering to know that in 2014, more than 28,000 deaths were caused by opioid overdoses, and half of these were because of prescription opioids; the addiction potential of these substances is frighteningly high. The continuing oxycodone epidemic in places like New Hampshire bears testimony to the hideous trail of death and misery that opioids can leave in their wake.

Quite unsurprisingly, finding a chemical agent that has the same unique painkiller profile as morphine without the insidious side effects has been a longstanding goal in drug discovery, almost a holy grail. It would be hard to overestimate the amount of futile resources that pharmaceutical companies and academic labs alike have spent in this endeavor. It was long thought that it might be impossible to decouple morphine's good effects from its bad ones, but studies in the last few decades have provided important insights into the molecular mechanism of morphine's action that may potentially help us accomplish this goal. Morphine binds to the so-called µ-opioid receptor (MOR), one of a family of several opioid receptors involved in pain, addiction and reward pathways. The MOR is a GPCR, a family of complex seven-helix protein bundle which are ubiquitously involved in pretty much every important physiological process, from vision to neuronal regulation.

Almost 30% of all drugs work by modulating the activity of GPCRs, but the problem until now has been the difficulty of obtaining atomic-level crystal structures of these proteins that would allow researchers to try out a rational drug design approach against them. All that changed with a series of breakthroughs that has allowed us to crystallize dozens of GPCRs (that particular tour de force bagged its inventors - Brian Kobilka and Robert Lefkowitz - the 2012 Nobel Prize, and I had the pleasure of interviewing Kobilka a few years ago). More recently they have crystallized the various opioid receptors, and this definitely represents a promising opportunity to find drugs against these proteins using rational drug design. When activated or inhibited by small molecules, GPCRs bind to several different proteins and engage several different biochemical pathways. Among other insights, detailed studies of GPCRs have allowed us to determine that the side effects of opioid drugs emerge mainly from engaging one particular protein called ß-arrestin.

A paper published this week in Nature documents a promising effort to this end. Using computational docking, Brian Shoichet (UCSF), Bryan Roth (UNC) and their colleagues have found a molecule called PZM21 that seems to activate the MOR without also activating the molecular mechanisms which cause dangerous side effects like respiratory depression. The team started with the crystal structure of the MOR and docked about 3 million commercial lead-like molecules against it. The top 2500 entries were visually inspected, and using known data on GPCR-ligand interactions (especially knowledge of the interaction between the ligands and an aspartate - Asp 147) and sound physicochemical principles (discarding strained ligands), they narrowed down the set to about 25 ligands which gave binding affinities ranging from 2 µM - 14 µM; interestingly, along with the Asp 147 interaction, the model located another unique hydrogen bond with Asp 147 that has never been seen before in GPCR-ligand binding (compound 7 in figure above). Using traditional structure-based design that allowed the group to access novel interactions, the affinity was then improved all the way to single digit nanomolar, which is an excellent starting point for future pharmacokinetic studies. The resulting compound hit all the right endpoints in the right assays; it had greatly reduced recruitment of ß-arrestin and subsequently reduced respiratory arrest and constipation, it mitigated pain efficiently in mice and showed much lesser addiction potential. It also showed selectivity against other opioid receptor subtypes, which may make it a more targeted drug.

This paper is noteworthy for several aspects, and not just for the promising result it achieved. Foremost among them is the fact that a relatively straightforward docking approach was used to find a novel ligand with novel interactions for one of the most coveted and intractable protein targets in pharmacology. But to me, equally noteworthy is the fact that the group examined 2500 docked ligands to pick the right ones and discard bad ones based on chemical intuition and existing knowledge; the final 25 ligands were not just selected from the top 50 or 100. That exercise shows that no modeling can compensate for the virtues of what chemist Garland Marshall called "a functioning pair of eyes connected to a good brain", and as I have found out myself, there's really no substitute for patiently slogging through hundreds of ligands and using your chemical intuition to pick the right ones.

There is little doubt that approaches such as this one will continue to find value in discovering new ligand chemistry and biology in valuable areas of pharmacology and medicine. As is becoming clear in the age of computing, the best results emerge not from computers or human beings alone, but when the two collaborate. This here is as good an example of that collaboration as I have recently seen.

The GPCR Network: A model for open scientific collaboration

This post was first published on the Scientific American Blog Network

The complexity of GPCRs is illustrated by this mechanical view of their workings (Image: Scripps Research Institute)

G Protein-Coupled Receptors (GPCRs) are the messengers of the human body, key proteins whose ubiquitous importance was validated by the 2012 Nobel Prize in chemistry. As I mentioned in a post written after the announcement of the prize, GPCRs are involved in virtually every physiological process you can think of, from sensing colors, flavors and smells to the action of neurotransmitters and hormones. In addition they are of enormous commercial importance, with something like 30% of marketed drugs binding to these proteins and regulating their function. These drugs include everything from antidepressants to blood-pressure lowering medications.

But GPCRs are also notoriously hard to study. They are hard to isolate from their protective lipid cell membrane, hard to crystallize and hard to coax into giving up their molecular secrets. One reason the Nobel Prize was awarded was because the two researchers – Robert Lefkowitz and Brian Kobilka – perfected techniques to isolate, stabilize, crystallize and study these complex proteins. But there’s still a long way to go. There are almost 800 GPCRs, out of which ‘only’ 16 have been crystallized during the past decade or so. In addition all the studied GPCRs are from the so-called Class A family. There’s still five classes left to decipher, and these contain many important receptors including the ones involved in smell. Clearly it’s going to be a long time before we can get a handle on the majority of these important proteins.

Fortunately there’s something important that GPCR researchers have realized; it’s the fact that many of these GPCRs have amino acid sequences that are similar. If you know what experimental conditions work for one protein, perhaps you can use the same conditions for another similar GPCR. Even for dissimilar proteins one can bootstrap based on existing knowledge. Based on the similarity you could also build computer models for related proteins. Finally, you can use a small organic molecule like a drug to essentially serve as a clamp that helps stabilize and crystallize the GPCR.

But all this knowledge represents a distributed body of work, spread over the labs of researchers worldwide and expected to be sequestered by them for their own benefits. These individual researchers working in isolation would not only face an uphill battle in figuring out the right conditions for studying their proteins but would also run the risk of reinventing the wheel and duplicating conditions from other laboratories. The central question asked by all these researchers is, how does the binding of a small molecule like a drug on the outside of a GPCR lead to the transmission of a signal to the inside?

Enter the GPCR Network, a model of collaborative science which promises to serve as a fine blueprint for other similar efforts. The network was created through a funding opportunity from the National Institute of General Medical Sciences in 2010 and has set itself the goal of structurally characterizing 15-25 GPCRs in the next five years. The effort is based at the Scripps Research Institute in La Holla and involves at least a dozen academic and industrial labs.

So how does this network work? The idea for the network came from the recognition that there are hundreds of GPCR researchers spread across the world. Each one is an expert on a particular GPCR but each one has largely worked separately. What the network does is to leverage the expertise from one researcher’s lab and apply it a similar GPCR in another lab (there are technical criteria for defining ‘similarity’ in this case). There are a variety of very useful protocols, ideas and equipment that can be shared between labs. This sharing cuts down on redundant protocols, saves money and accelerates the resolution of new GPCR puzzles much faster than what could be achieved individually.

For instance, a favorite strategy for stabilizing a GPCR involves tagging it with an antibody that essentially holds the protein together. An antibody that worked for one GPCR can be lent to a researcher who is investigating another GPCR with a similar amino acid sequence. Or perhaps there is a chemist who has discovered a new molecule that binds very tightly to a particular receptor. The network would put him in touch with a crystallographer who could use that molecule to fish out that GPCR from a soup of other proteins and crystallize it. Once the crystallographer solves the structure of the protein using this molecule, he or she could then send the structure to a computer modeler who can use it to build a structure for another particularly stubborn GPCR which could not be crystallized. The computer model might explain some unexpected observations from a fellow network researcher who was using a novel instrumental technique. This novel technique would then be shared with everyone else for further studies.

Thus, what has happened here is that the individual pockets of knowledge from a biochemist, organic chemist, crystallographer and computer modeler – none of whom would have proceeded very far by themselves – are merged together to provide an integrated picture of a few important GPCRs. The entire pipeline of protocols including protein isolation, purification, structure determination and modeling also serves as a feedback loop, with insights from one step constantly informing and enriching others. This represents a fine example of how collaborative and open research can accelerate important research and save time and money. It's to the credit of these scientists that they haven't held their valuable reagents and techniques close to their chest but are sharing them for everyone's benefit.

In the three years since it has been up and running, the GPCR Network has leveraged the expertise of many experts in generating insights into the structure and function of important receptors. Its collaborative efforts have resulted in eight protein structures in just two years. These include the adenosine receptor which mediates the effect of caffeine, the opioid receptor which is the target for morphine and the dopamine receptor which binds to dopamine. Every one of these collaborations involved a dozen or so researchers across at least three or four labs, with each lab employing its particular area of expertise. Gratifyingly, there’s also a few industrial labs involved in the efforts and we can hope that this number will increase even as the pharmaceutical industry becomes more collaborative.

It’s also worth noting that the network was funded by the NIGMS, an institution which has been subject to the whims of budget and personnel cuts. This institution is now responsible for an effort that’s not only accelerating research in a fundamental biological area but is also contributing to a better understanding of existing and future drugs. Scientists, politicians and members of the public who are seeking a validation of basic, curiosity-driven scientific research and reasons to fund it shouldn’t have to look for.

GPCRs win 2012 Nobel Prize in Chemistry

What a nice surprise! Ever since Brian Kobilka's group solved the first GPCR-G protein structure I have been convinced that he and others will win the Nobel Prize. But I didn't think it would happen so soon.

In any case, amble over to my Scientific American blog for a writeup. This has to be one of the fastest discovery-to-prize transitions in recent years. It's interesting that the prize was awarded to Lefkowitz and Kobilka. I think this was done partly to recognize Lefkowitz's early pioneering work, but also because a purely structural prize would have had to recognize Raymond Stevens and Krzysztof Palczewski in my opinion. It was a shrewd move on the part of the committee to hand out a broader GPCR prize and include Lefkowitz. As for Kobilka, I think it's fair to say that among the three groups his has probably done the most detailed crystallography work.

Personally I feel very satisfied since GPCRs have been an interest of mine for a while. I have blogged about them several times and once wrote a major research proposal on them. However, as significant as the discovery is, there's still a long road ahead. There's almost a thousand GPCRs from class A-F. The present structures constitute only a handful of members of class A GPCRs (although I hear class B is coming up soon). We are far from any complete picture of GPCRs signaling and we also don't understand functional selectivity yet. This discovery has every indication of being a grand beginning than an end. 

And I have to say that the whole "But is this chemistry?!" meme is getting quite boring. Binding of a small molecule to a GPCR is as much of a molecular interaction as anything in chemistry. Plus, think about the downstream chemistry that GPCRs do, including phosphorylation of the G proteins and salt-bridge breakage in the crucial helices that modulate the signal transduction. I thought chemists were supposed to rub their hands with glee at the reduction of biology to chemistry while biologists fret and fume. But I see the opposite, biologists being quite sanguine about proteins being awarded medicine Nobels while chemists continue to complain about proteins (chemicals!) being awarded chemistry Nobels. Something's not quite right here. In addition, this year's Nobel continues the proud tradition of honoring crystallographers, a tradition that goes back to 1962 when Kendrew and Perutz won it for hemoglobin and myoglobin. The point is that chemistry has traditionally been defined as structure and function. Chemists have studied the molecular constitution of matter since the birth of the science, and biological matter is no different in principle. Why would chemists complain when structure - of any kind - is recognized by a Nobel Prize?

However there is a bright side to the arguments. As I have said before, this very bickering shows the astonishing reach and diversity of the field. If you can't even agree on a definition for your field, well, that means your field is truly everywhere.

Congratulations to Kobilka and Lefkowitz, and a toast to more GPCR research!

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

Slices from the literature

1. A decade ago, MIT biologist Robert Weinberg who has been a contender for the Nobel Prize for his discovery of oncogenes wrote a seminal article in Cell called "The Hallmarks of Cancer". This article which became the highest cited article in Cell ever laid out the myriad ways in which a cell circumvents normal regulatory mechanisms to metamorphose into a monster. Weinberg and co-author Hanahan now follow up with a second "Hallmarks of Cancer" review in Cell to take stock of the ensuing decade's major discoveries and their implications for our understanding of cancer. It's a must read.

2. GPCR drugs: Magic bullets or magic "shotguns"? Over the last few years, the traditional paradigm of drug discovery laid out a century ago which posits that the ideal drug should be a "magic bullet" hitting a single protein target has been revised. While selectivity is still a valuable property, the importance of "selectively non-selective" drugs that hit a chosen subset of proteins is now much appreciated. No other family of drugs exemplifies this new paradigm more than CNS drugs which usually work by acting on a judicious set of GPCRs like those for serotonin, dopamine and norepinephrine. These compounds have been called "magic shotguns" and the general mechanism has been termed "polypharmacology".

However we are still light years away from actually designing such drugs which hit a pre-decided family of proteins on demand; a lot of the selective non-selectivity in these molecules has been designed in serendipitously and discovered in retrospect. The first step towards this goal would be to develop biochemical tools that would allow us to asses the exact polypharmacological activity of such compounds. UNC pharmacologist Bryan Roth details magic shotguns and efforts to unravel their complexities in a comprehensive review.

3. Directed evolution has been a valuable approach to speed up sluggish natural evolutionary processes to produce diverse libraries of biomolecules for functional screening. Here's a nice new review on using directed evolution to dissect protein-protein interactions which are of intense current interest.

4. And finally, a look at the human kinome and its interactions using a combination of sequence-based and ligand-based similarity methods. When each approach is limited, simply combine the two.

New kid on the GPCR block

The CXCR4 GPCR structure has been solved by Raymond Stevens's group at Scripps. It joins the exclusive august list of previously crystallized GPCRs- the beta-adrenergic, rhodopsin and the adenosine A2A receptor.

This could be quite important for HIV drug discovery since the CXCR4 is a chemokine receptor expressed on the surface of lymphocytes that HIV uses as a co-receptor to gain entry into cells. People struggling with structure-based drug design with CXCR4 should be elated.

Welcome to the club, although many more members have to be still inducted.

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

Steering library bias toward A2A adenosine receptor ligand discovery

The A2A adenosine receptor is an important GPCR, well-known for binding caffeine. Adenosine receptors are emerging as relevant drug targets for a variety of disorders including Parkinson's disease, and there is interest in discovering new ligands that bind to them. Among adenosine receptor subtypes, the A2A receptor is one of the few GPCRs whose crystal structure is available. Thus the A2A is amenable to structure-based design efforts, and virtual screening is an especially attractive endeavor in this regard.

In the present report, a team of researchers from NIH and UCSF led by Brian Shoichet, John Irwin and Kenneth Jacobson use virtual screening to discover new ligands for the A2A. There are several points to note here. The authors use the ZINC library of drug like molecules to dock about a million and a half compounds into the binding pocket of the A2A crystal structure. They pick the best-scored 500 (0.035% of the total) ligands and investigate their fit in the binding site. Using criteria like electrostatic and VdW complementarity and novelty of chemotype, they finally select 20 of these 500 hits and test them in assays. Out of these 20, 7 inhibited binding by more than 40% at 20 μM concentration, thus constituting a hit rate of 35%. While the compounds formed the same kinds of interactions as some other A2A ligands, they were also relatively diverse in structure. The ligands were also tested in aggregation-based screens to determine that their activity was not a spurious artifact of aggregation-based inhibition.

This is a pretty good hit rate. Generally virtual screening campaigns are lucky to have a hit rate of a few percent. Curiously, the authors also found a similarly high hit rate during a past VS campaign against the well-known β2 adrenergic receptor. What could be responsible for this high hit rate against GPCRs? The reasons are interesting. One reason could be that GPCRs are very well adapted to bind small molecules in compact pockets, enclosing them and forming many kinds of productive interactions. But more intriguingly, as the authors have noted earlier, there is "biogenic bias" in favor of certain target-specific chemotypes in commercial libraries that are screened, both during VS as well as HTS. This in turn reflects the biases of medicinal chemists in picking and synthesizing certain kinds of chemotypes based on the importance of drug targets and past successes in hitting these targets. GPCRs clearly are enormously important, and GPCR-friendly ligand chemotypes thus constitute a large part of screening libraries. These chemotypes are much more prevalent than those for kinases or ion channels for instance.

This observation has both positive and negative implications. The positive implication is that one is likely to keep finding high hit rates for GPCRs using VS. However, the negative implication is that one is also going to be constrained by biogenic bias, and this might preclude finding more diverse and entirely novel subtypes. Thus, while VS campaigns for GPCRs might find a good number of hits, the novelty of these hits might not always be satisfying. One other quite intriguing point emerging in this study is that the kind of hits found (agonist, inverse agonist, antagonist etc.) reflects the ligand which the target structure used for VS is co-crystallized with. Thus the A2A houses an antagonist in the binding site, leading to a preponderance of antagonists in the top docking hits. Indeed, agonists ranked abysmally low in the list.

GPCR ligand discovery is one of the most important goals in drug discovery. This and other similar studies demonstrate that, with all its caveats, VS can be productively used to mine for new GPCR drugs.

Carlsson, J., Yoo, L., Gao, Z., Irwin, J., Shoichet, B., & Jacobson, K. (2010). Structure-Based Discovery of A2A Adenosine Receptor Ligands Journal of Medicinal Chemistry DOI: 10.1021/jm100240h

Scaling further GPCR summits

There'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

The importance of being patient

The determination of the ß-adrenergic receptor GPCR structure in 2007 was a breakthrough in structural biology. Combined with the earlier structure of rhodopsin, this provided a template for structure-based design for GPCRs. However, there was a lurking mystery in the structure, a mystery which was not always discussed but which has started to come to light recently.

Th mystery is exemplified by a recent paper in which authors from D E Shaw Research in New York use extremely long molecular dynamics simulations to uncover a peculiar conformational characteristic of the ß2 AR. The original structure was crystallized bound to an inverse agonist named carazolol. The receptor as crystallized was thought to be in an inactive state. In this state, two helices of the receptor were at some distance from each other. However, this observation did not square with biochemical experiments that indicated proximity of the two helices mediated by a crucial ionic lock, a salt bridge between a glutamate and arginine. This lock however was absent in the crystal structure, raising questions about the exact role of the lock in activating the receptor and the nature of the inactive state.

In the present study, the authors used extremely long, microsecond MD simulations on the crystal structure. They used the DESMOND program recently introduced by Schrodinger and D E Shaw to perform simulations of the GPCR in a lipid bilayer.

All they really had to do was wait.

The first 150 ns were not very interesting from the perspective of the salt bridge. However, the salt bridge spontaneously formed after 150 ns and then stayed put like a fly on fly paper. Notice the N-O distance (blue) and how it stabilizes after 150 ns.




The bridge also involved local movement of the helices and some important residues. The authors also did the simulation in the presence and absence of the ligand and found that this lock forms irrespective of the presence of the ligand. They follow up with some mutagenesis experiments that reconcile the conformational changes with experimental observations. Interestingly, they mutate an aspartate that is also proximal to the arginine in the salt bridge. Mutation of this aspartate would be expected to "free up" the arginine and further encourage its interaction with the glutamate. However, the opposite seemed to happen, indicating an interesting role for the aspartate as a something of lock itself in holding the aspartate fixed.

The overall conclusion is that there are probably two inactive states, one in which the salt bridge is formed (the dominant one) and one in which it's broken (not highly populated) and the receptor recycles between the two. The less populated conformation is nonetheless the one that is crystallized which is interesting. This kind of observation is clearly important for further structure-based design since it implies that one could encourage GPCR activation if the "right" conformation of the receptor could be preferentially stabilized.

The thing to note here is the time. Nothing interesting would have been observed had the simulation been run for less than 150 ns. Researchers who ran the simulation for less than 150 ns may not have had something worth reporting. 150 ns is a reasonably long amount of time for any MD program or simulation. The fact that such simulations can be run for microseconds attests to the rapid development of hardware and software exemplified by D E Shaw's program DESMOND and their processor named ANTON.

Sometimes simply waiting long enough can lead to productive results. Echoing an unpleasant man's ominous pronouncement, "Quantity has a quality of its own".

Dror, R., Arlow, D., Borhani, D., Jensen, M., Piana, S., & Shaw, D. (2009). Identification of two distinct inactive conformations of the ß2-adrenergic receptor reconciles structural and biochemical observations Proceedings of the National Academy of Sciences, 106 (12), 4689-4694 DOI: 10.1073/pnas.0811065106

New ligands for everyone's favorite protein

A landmark event in structural biology and pharmacology occurred in 2007 when the structure of the ß2-adrenergic receptor was solved using xray crystallography by Brian Kobilka's and Raymond Stevens's groups at Stanford and Scripps respectively. The structure was co-crystallized with the inverse agonist carazolol. Until then the only GPCR structure available was that of rhodopsin and all homology models of GPCR were based on this structure. The availability of this new high resolution structure opened new avenues for structure-based GPCR ligand discovery.

The ß2 binding pocket is especially suited for drug design since it is tight, narrow and lined with mostly hydrophobic residues with polar residues well-separated. Two crucial residues, an Asp and a Ser bind to the ubiquitous charged amino nitrogen present in most catecholamines and the aromatic section of the molecule docks deep into the hydrophobic pocket. These particular features also make computational docking more facile; a mix of polar and non-polar features with bridging waters can make docking and scoring more challenging.

Since the ß2 structure has been published, attempts are being made to use it as a template to build homology models of other GPCRs. A couple of months back I described an interesting proof-of-principle paper by Stefano Costanzi that sought to investigate how well a homology model based on the ß2 would perform. In that study carazolol itself was used as a ligand for docking into the homology model. Comparison with the original crystal structure revealed that while the ligand docked more or less satisfactorily, an important deviation in its orientation could be explained by a counterintuitive orientation of a Phe residue in the binding site. The study indicated that the devil is in the details when one is considering homology models.

However, finding ligands for the ß2 itself is also an important and interesting endeavor. Virtual screening could help in such studies. To this end Brian Shoichet, Brian Kobilka and their group have used the DOCK program to virtually screen one million lead-like ligands from their ZINC database against the ß2. Out of the 1 million ranked poses, they chose and clustered the top 500 compounds (0.05% of the database) into 25 unique chemotypes, a choice also guided by visual inspection of the protein-ligand interactions and commercial availability. They then tested these 25 compounds against the ß2 and found 6 compounds with IC50s better than 4 µM. One of these compounds with an IC50 of 9 nM is perhaps the most potent inverse agonist of the ß2 known. The binding poses revealed substantial overlap of similar functional groups with the carazolol structure. Two compounds turned out to have novel chemotypes and bore very little similarity with known ß2 ligands. A negative test was also run where a known predicted binder was chemical modified so that it would not bind.

Interestingly all the compounds found were inverse agonists. The ZINC library is somewhat biased against aminergic ligands as is most of chemical space. The catecholamine scaffold is one of the favourite scaffolds in medicinal chemistry. However, subtle difference in protein structure can sometimes turn an inverse agonist into an agonist. In this case, small changes in the orientation of the crucial Ser residue near the mouth of the binding pocket. In a past study for instance, slightly changing the rotameric features of the Ser residue thus resulting in a different orientation of the hydroxyl was sufficient to retrieve agonists.

The study thus shows the value of virtual screening in the discovery of new ß2 ligands and indicates the effect of library bias and protein structure on such ligand discovery. Many factors can contribute to the success or failure of such a search; nature is a multi-armed demon.

Reference:
Kolb, P., Rosenbaum, D., Irwin, J., Fung, J., Kobilka, B., & Shoichet, B. (2009). Structure-based discovery of ß2-adrenergic receptor ligands Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.0812657106

Computational modeling of GPCRs: not too bad

GPCRs constitute one of the most important family of proteins in our body, both for their innate importance in signal transduction and neurotransmission, and as important targets for drugs. Many of the important drugs on the market today target GPCRs. And yet there is an unusual gap between knowledge and application when it comes to this important family. That's because only two crystal structures of GPCRs are known. And one of them was derived last year, so there's been a real dearth of structural information about GPCRs for a long time.

We do know something about many GPCRs, however. We know that they are 7-TM receptor-spanning proteins. And the two structures we do know about shed valuable insight on GPCR function. One is rhodopsin which has been around for a while. Then there was big news last year about the second important GPCR whose structure was determined- the ß-2 adrenergic receptor.

Given the paucity of structural information and the availability of two structures, a logical question is whether computational modeling can teach us something new about GPCRs whose structure is unknown. To this end, Stefano Costanzi at the NIH did a nice set of experiments which he published in J. Med. Chem. He attempted to build a homology model of the adrenergic receptor based on the sequence and structure of rhodopsin. Since we now have a crystal structure of the adrenergic GPCR, we have something concrete to compare modeled structures and ligand orientations to.

Costanzi was particularly interested in knowing how a small molecule-carazolol- binds to the modeled GPCR. This is important both from a structural and functional drug-discovery point of view. His results indicate that we can do pretty well. In essence, he built two models of the receptor, one of them de novo. While the models were similar to rhodopsin in the conserved regions, the important differences were with respect to a loop that flaps on top of the protein. In one model the loop was buried inside the binding pocket, and in the other one it was open. Docking of carazolol into the buriled-loop model using the Glide program from Schrodinger gave a binding pose in which the ligand was, not surprisingly, buried deeper into the cavity compared to the crystal structure. This was naturally the effect of the loop blocking part of the pocket. The other model in which the loop was not buried gave much better results. Curiously, the ligand was buried a little deep in the pocket even in this model, even though it was much less buried compared to the previous one. It still misaligned considerably with the experimental pose. Inspection revealed that there was a Phe in the pocket which was anti in the model but +gauche in the crystal structure. Since the corresponding residue in rhodopsin was Ala, there was no way this unusual conformation could have been predicted ab initio. Fixing the conformation of this residue to +gauche suddenly gave excellent alignment with the ligand orientation in the crystal structure.

An instructive piece of work that shows that homology modeling and docking of ligands into GPCRs of unknown structure can be fruitful. However, it also indicates caveats like the Phe conformation which are hard to account for de novo. However, since structures of members in this important family of proteins are unavailable anyway, even some predictive ability might be welcome in this area.

Costanzi, S. (2008). On the Applicability of GPCR Homology Models to Computer-Aided Drug Discovery: A Comparison between In Silico and Crystal Structures of the ß2-Adrenergic Receptor. Journal of Medicinal Chemistry DOI: 10.1021/jm800044k