Aug 032010
ResearchBlogging.orgMost people never learn about an actual scientific controversy. Almost every “controversy” that bubbles into the public eye is manufactured, often reflecting social or ethical differences rather than genuine disagreements between experts about how different models fit to reality. Actual scientific controversies tend to be highly technical, and often concern points that lay people find to be esoteric. That doesn’t mean that the issues involved aren’t important, or that they’re even difficult to understand. One controversy that has unfolded over the past few years and now may be over relates to a seemingly simple question. Where do adamantane drugs bind to the influenza A M2 channel?

Previously, on As the Channel Twists

Bill DeGrado and James Chou, whose
competing structures began the controversy

The M2 proton channel plays an essential role in the life cycle of the influenza virus. The activity of the channel could be blocked, at least in influenza A, by drugs called adamantanes, including amantadine and rimantadine. Unfortunately, these antiviral drugs have been fading in efficacy due to the spread of an S31N mutation that interferes with their binding. On January 31, 2008, two articles appeared in the scientific journal Nature showing adamantanes bound to the M2 channel. Unfortunately, the structures had different answers about where the drug was bound. The X-ray crystal structure from Bill DeGrado’s group at the University of Pennsylvania placed amantadine in the center of the channel’s pore, suggesting a simple pore-blocking model (PBM) for inhibition. The NMR structure from James Chou’s group at Harvard University located rimantadine on the outside of the channel, ultimately giving rise to an allosteric, dynamic quenching model (DQM) of adamantane activity.

As outlined in my previous post on the M2 channel, there was conflicting functional evidence as to which site was actually relevant in vivo, and reasons to doubt the conclusions from both structures. Since that time, several papers have been published that substantially clarify the issue. At this point, the evidence strongly supports the PBM as an explanation of adamantane activity in vivo.

Sure adamantanes bind there, but does it matter?

The direct observation of NOEs, even weak ones, between the adamantane and the protein proved that the drugs were binding at the DQM site, but there were some significant areas of concern with this finding. The greatest worry was due to the extremely high concentration of ligand used in the NMR experiment. This opened up the possibility that the DQM site was a low-affinity site that would not see binding under normal circumstances. Because both models had explanations for the efficacy of the S31N mutation, the only way to address the question would be to make mutations that would abolish binding at the DQM site and see if adamantanes were still effective. Because aspartate 44 was proposed to form a hydrogen bond to rimantadine, it was thought that a D44A mutation would eliminate binding, and if DQM was true, adamantane activity. This prediction was borne out by an experiment performed in liposomes by the Chou lab (6), but Robert Lamb’s group from Northwestern University was not able to replicate this result in X. laevis oocytes (4).

Robert Lamb has studied the
M2 channel since the 80s.

What Lamb’s group did do was test different parts of the influenza A channel for adamantane sensitivity by fusing them to the adamantane-insensitive influenza B channel. These A/B M2 chimeras should in principle have adamantane susceptibility if the legitimate binding site got imported from A to B. Their first results in this experiment were somewhat inconclusive. Adding the N-terminal portion of the A channel to the C-terminal portion of the B channel (essentially sticking the PBM site into B M2) created a chimera that was somewhat sensitive to amantadine treatment, but the effect was nowhere near what occurred for WT A channel (4). Subsequently, Lamb’s group expanded these experiments to add a little bit more of the N-terminal sequence to the chimera, which then almost perfectly matched the WT A channel’s susceptibility. Notably, when they made the opposite chimera that incorporated the DQM site from A into the B channel, only a very slight inhibitory activity was observed upon addition of amantadine (5). While the conclusions that can be drawn from the chimeras are limited by their particularly odd provenance, the fact that transplanting the PBM site from one channel to another confers adamantane susceptibility suggests that this is the functional binding site.

An upcoming paper in PNAS clarifies the picture somewhat using surface plasmon resonance (SPR) (7). This technique detects the binding of a ligand as a change in physical force exerted by a protein tethered to the surface of a gold chip. In this case, the tethering was mediated by a DMPC liposome. This was a tricky experiment because adamantanes like the greasy portions of lipid bilayers, so they can bind to the liposome itself. Rosenberg and Casarotto, however, were able to control for this effect. Their SPR experiments detect two distinct adamantane binding sites on M2 with vastly different affinities. Rimantadine binding at the high-affinity site could be abrogated by S31N and V27A mutations, but not a D44A mutation. At the low-affinity site, rimantadine binding could be knocked out by a D44A mutation or an S31N mutation, but not a V27A mutation. This result indicates that both binding sites are functional (and, incidentally, that the S31N mutation does indeed exert an allosteric effect on the DQM site). However, the authors note that the adamantane concentrations used in actual treatment are too low to significantly populate the low-affinity site, given the dissociation constants they calculated. This argues that the DQM site is irrelevant in vivo.

Amantadine caught in the pore

Mei Hong has studied M2
extensively by NMR

One of the problems with the PBM was that the crystal structure that supported it was unsatisfactory in a variety of ways. The structure was made using a construct that consisted of only the transmembrane segment of the protein. This construct could not be reconstituted in micelles, and functional experiments showed that it was not very similar to the WT in terms of its activity. In addition, the extra electron density in the pore could not be unambiguously assigned as amantadine. In a paper from February of this year, the DeGrado group collaborated with Mei Hong’s group at Iowa State University to produce a structure of amantadine bound to M2 using solid-state NMR (2). An important advantage of this approach is that one can take spectra of proteins embedded in a membrane without penalty, because there is no requirement for the protein to tumble freely. While there are some trade-offs in terms of resolution and the kinds of data that can be obtained, biomolecular solid-state NMR can help us answer some very tricky questions.

Amantadine (yellow) in the
pore binding site.

The approach proved to be especially fruitful here. Cady et al. used a technique that allowed them to determine whether the amantadine was near a particular residue, by labeling residues with 13C and the amantadine with 2H. If a 13C nucleus is coupled to a 2H nucleus by a dipolar interaction, a pulse that dephases the 2H nucleus will affect the 13C nucleus in a distance-dependent manner. When Cady et al. made samples at a ratio of one amantadine per channel, they found that the signal from S31 was significantly broadened, but that from D44 was not, proving that the amantadine is close to S31 under these conditions. The D44 signal was affected at higher amantadine concentrations, but never to the same degree as the S31 signal. Other residues at the PBM site were also affected by the presence of amantadine. Using an alternative version of the REDOR experiment, Cady et al. were able to generate distance constraints and, in combination with other data, generate a structure for the channel with amantadine bound (see their figure at right) (2). Note the residues that are close to the amantadine in this structure: V27, S31, G34, and H37. This will be important in a minute.

The Cady et al. paper has several advantages over the DeGrado group’s original crystal structure. The first, and most important, is that the protein was reconstituted in DMPC vesicles rather than OGP bilayers. The lipids themselves, and the vesicle structure, more closely mimic the likely environment in vivo than the crystal conditions, and they are also more biologically-relevant than the DHPC micelles employed in Chou’s original determination. In addition, the pH of 7.5 matches the experimental conditions used by Chou and allows for a direct comparison of results under conditions where the channel’s conformation should in principle be the same. The REDOR results provide unambiguous evidence that amantadine binds preferentially in the pocket for this construct. Their observation that amantadine has approximately 100-fold higher affinity for the PBM site, but can also bind the DQM site, agrees nicely with the functional data, especially the SPR results.

Support for an allosteric mechanism?

Bob Griffin,
SSNMR master

However, Cady et al. still used the truncated construct that possesses significantly altered activity relative to WT, which brings us to an upcoming paper in JACS by Andreas et al. (1). Chou’s group collaborated with Robert Griffin’s group at MIT to map the chemical shifts of a somewhat larger construct of M2 that is known to have relatively normal activity. The longer construct is also known to form a fairly stable tetramer, and this may have improved its spectral properties. The construct was inserted into membrane bilayers that were lyophilized for the NMR experiment, with and without rimantidine present. The chemical shifts of 15N and 13C nuclei of residues in the transmembrane helices were compared between these two states as a way of localizing the adamantane binding site. As Andreas et al. show in their figure 2 (recapitulated below in a slightly altered fashion), the chemical shift changes in response to the binding event are quite widespread. The authors argue that this observation favors an allosteric inhibition mechanism. That may well be true, in a sense, but unfortunately that does not mean that the observation favors DQM.

The binding of a ligand generally alters the chemical shifts of a protein by changing its structure, reshaping the arrangement of bond angles that dictates the electron distribution around the relevant nuclei. It is of course possible that a small change in the protein’s conformation at the binding site propagates into a large change elsewhere, but this is not typically observed. The most reasonable interpretation of chemical shift changes (Δδ) upon ligand binding is that the largest shifts observed are nearest to the ligand, while the smaller shifts are farther away. The original version of figure 2 presents the data in a fairly simple way, and does not distinguish very large changes from relatively small ones. So, I made a new version of the figure for you, where I’ve taken the additional step of scaling the color according to the magnitude of Δδ.

M2 channel showing Δδ
due to rimantidine binding

This adaptation of the Andreas et al. figure, shows the NMR micelle structure (PDB code: 2RLF) with purple rimantadine at the DQM binding site and the helices colored in blue according to the Δδ. This was done very roughly, by eyeballing the graphs to the left in figure 2, scaling the Δδ based on the nucleus measured, and adding it all up across all three nuclei. The more intense the blue, the larger the Δδ. It should be immediately apparent that the largest chemical shift changes are located a substantial distance from the DQM site (although there are some smaller changes near that site). What’s perhaps less immediately obvious is that the largest chemical shift changes belong to residues located in the pore. To make this point more clear, check out the figure below and to the left. This is the same structure, that I have now tilted so we are looking down the barrel of the channel. The side chains of the four residues with the largest chemical shift changes (V27, S31, G34, H37) are shown in light green for contrast (obviously there’s nothing for G34). Three of them are sticking into the pore in this model (G34′s Cα faces the pore); the fourth is S31. You might recall that the structure from Cady et al. (above) puts all four of these large Δδ residues right next to the ligand.

The largest chemical shift changes in response to rimantidine binding occur near the proposed PBM site and far from the DQM site, and the residues most affected are those facing the pore. Andreas et al. argue that this information is insufficient to positively localize the drug, due to the large chemical shift change at H37 Cα, but this doesn’t seem particularly convincing. The concentration of large chemical shift changes at the N-terminal end of the channel strongly argues for the ligand binding in this region. As for the significant Δδ at H37, the observations could quite possibly be due to ring-current effects from the repositioning of the adjacent tryptophan side chains (light red in the figure to the left). We know that binding of adamantanes has at least some effect on W41 thanks to Czabotar et al. (3), who measured adamantane binding by observing changes in intrinsic tryptophan fluorescence. Of course, changes in fluorescence are a very general indicator of structural or dynamic change, but the previous finding supports the possibility of interpreting the H37 observations as side-chain effects rather than ligand proximity.

In contrast, there is no such explanation for the significant chemical shift changes at the N-terminal end of the channel, which has no aromatic residues. The significant Δδ in this region must be due to rearrangements of these residues themselves, rather than long-range effects or ring currents. Thus, the most plausible model explaining these data remains one in which adamantanes bind near V27 and S31, propagating some kind of structural change to W41, rather than the other way around. In this regard, I agree that the data from figure 2 establish that adamantane binding has an allosteric effect. These data, however, do not support the DQM Chou proposed previously.

Conclusions, Lessons

The DQM hit its high-water mark with the Pielak et al. PNAS paper back in 2009 (6), in which the model’s prediction that the D44A mutation would significantly alter adamantane sensitivity was borne out by experiments in liposomes. Since that time, however, the evidence has started to weigh mostly against it. The D44A results could not be replicated in X. laevis oocytes, and lovely chimera experiments in this system demonstrated that the N-terminal region of M2 was critical for adamantane sensitivity (4)(5). Live viruses remained sensitive to adamantanes even if they were reverse-engineered to have the D44A mutation. Rosenberg and Casarotto showed that the D44A mutation only affected binding at absurdly high rimantadine concentrations (7). Finally, the Cady et al. study provided unambiguous evidence for adamantane in the pore of the channel (2). In light of these findings, only a crystal-clear result in favor of the DQM could really save it.

Although their findings convincingly illustrate an allosteric effect from rimantidine binding, Andreas et al. do not provide that result. Even their own chemical shift data seem to support the PBM model. Of course direct dipolar couplings would provide a totally unambiguous answer as to the location of the rimantidine, but in light of our existing knowledge about the system, that experiment doesn’t seem necessary. At this point there is no serious reason to doubt that the physiological inhibition of M2 channel results from adamantane drugs binding to the pore.

The papers cited in this article represent decades of man-hours and significant amounts of money spent in resolving what might seem like an esoteric point. Given the enormous effort that went into resolving the  seemingly simple question, you might be tempted to ask what went wrong. The answer is, “nothing”. This is how the scientific process is supposed to work. Two groups came at the same problem in different ways and got different answers, which is hardly a surprise because no experiment is perfect. More experiments were carried out to determine which model best represented the physical reality. Eventually, the weight of the evidence strongly supported one model over the other. The best data we have right now really point to a single conclusion. The process succeeded, and nobody needed a superior court judge or a congressional hearing.

That doesn’t mean we can’t take some lessons from the experience. Most prominent among these is that we must have serious reservations about NMR structures derived from proteins bound to or inserted in micelles. What we know about the M2 channel tells us that adamantanes prefer to bind in the pore. That they did not do so (or at least, could not be detected doing so) in the micelle-based structure suggests that something about the micelle itself made that impossible. We know that the forces exerted on proteins by membrane curvature can be substantial, and the structure of a micelle is very unlike the structure of a cell membrane. Solution NMR in bicelles may yet prove to be a superior approach for some systems, but in this case it was solid-state NMR that provided the vital evidence. Solid-state has its own set of limitations, but it’s clear proper membrane context is absolutely vital to getting good answers about membrane protein structure and function.

Knowing the actual binding site of adamantanes may prove to be very important in aiding the design of alternative drugs that achieve the same inhibition of the channel. The papers from the DeGrado and Hong groups have already made several interesting recommendations in this regard. Even what has been learned about the remote site may not be fruitless. Though it is not the source of the physiological activity of adamantanes, several experiments have made it clear that there is some kind of allosteric interaction between S31 and the DQM site. It may be possible to attack M2 through this site with a specifically-designed high-affinity drug, even if adamantanes themselves don’t work this way.  If that proves to be the case, then this will be the best kind of scientific controversy: one where we learn something important from both sides.


(1) Andreas, L., Eddy, M., Pielak, R., Chou, J., & Griffin, R. (2010). “Magic Angle Spinning NMR Investigation of Influenza A M2: Support for an Allosteric Mechanism of Inhibition.” Journal of the American Chemical Society DOI: 10.1021/ja101537p

(2) Cady, S., Schmidt-Rohr, K., Wang, J., Soto, C., DeGrado, W., & Hong, M. (2010). “Structure of the amantadine binding site of influenza M2 proton channels in lipid bilayers.” Nature, 463 (7281), 689-692 DOI: 10.1038/nature08722

(3) Czabotar, P., Martin, S.R., & Hay, A.J. (2004). “Studies of structural changes in the M2 proton channel of influenza A virus by tryptophan fluorescence.” Virus Research, 99 (1), 57-61 DOI: 10.1016/j.virusres.2003.10.004

(4) Jing, X., Ma, C., Ohigashi, Y., Oliveira, F., Jardetzky, T., Pinto, L., & Lamb, R. (2008). “Functional studies indicate amantadine binds to the pore of the influenza A virus M2 proton-selective ion channel.” Proceedings of the National Academy of Sciences, 105 (31), 10967-10972 DOI: 10.1073/pnas.0804958105 OPEN ACCESS

(5) Ohigashi, Y., Ma, C., Jing, X., Balannick, V., Pinto, L., & Lamb, R. (2009). “An amantadine-sensitive chimeric BM2 ion channel of influenza B virus has implications for the mechanism of drug inhibition.” Proceedings of the National Academy of Sciences, 106 (44), 18775-18779 DOI: 10.1073/pnas.0910584106

(6) Pielak, R., Schnell, J., & Chou, J. (2009). “Mechanism of drug inhibition and drug resistance of influenza A M2 channel.” Proceedings of the National Academy of Sciences, 106 (18), 7379-7384 DOI: 10.1073/pnas.0902548106

(7) Rosenberg, M., & Casarotto, M. (2010). “Coexistence of two adamantane binding sites in the influenza A M2 ion channel.” Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.1002051107

Apr 282010
ResearchBlogging.orgOn several previous occasions on this blog I’ve discussed proteins that undergo significant changes in structure without drastic changes in their primary sequence or solution conditions. In some cases, a few mutations can take a protein to a novel fold, as with Philip Bryan’s protein G work. In others, closely related sequences within a whole family populate different kinds of folds, as Matt Cordes illustrated for the case of Cro proteins. In addition, there are some cases such as lymphotactin, where interconversion between two very different structures takes place at equilibrium, as illustrated by Brian Volkman’s research. Each time stories like this come up I have mentioned that this kind of behavior (termed “metamorphism” in a 2008 commentary by Alexey Murzin) suggests a means by which proteins could evolve from one structure to another without losing foldedness or function. Recently, a group from the Weizmann Institute published results in PNAS that speak to this possibility.

Yadid et al. are looking at a class of proteins called β-propellers. Characteristics of the sequences of these proteins, especially high homology between different blades within a protein, suggest that these proteins are “built up” by gene duplication and fusion from precursors that were either multimeric in nature or made from a smaller number of blades (or both). In particular, they worked with a protein called tachylectin-2, that binds sugars. You can see its structure at right (or examine it at the PDB). The color-coding recognizes that the N and C termini are adjacent to one another, meaning that each “blade” of the propeller actually incorporates one strand from a neighbor. The whole protein is a bit under 250 residues in size. Previously, the authors of this paper had randomly chewed up the tachylectin-2 DNA from either end, a process that one might expect would produce a bunch of useless garbage. Some of the products of this experiment, however, were functional pentamers. But, they were about 100 amino acids long, suggesting that each monomer incorporated two blades. This meant that the structure of the pentamers had to differ from that of the original protein in some key way, but the proteins could not be crystallized due to low yields and instability.

To solve this problem, Yadid et al. performed directed-evolution refinement of the sequences of two promising candidates. From the pool of proteins thus produced they were able to crystallize two, which had interesting properties. As one might expect given the known data, both these proteins formed ten-bladed propellers (the structures have PDB codes 3KIF and 3KIH) in the form of two five-bladed propellers that were linked to each other. In the case of the mutant called Lib2-D2-15, 3 of the 100-residue subunits contributed two blades to the propeller apiece, while the remaining two monomers each contributed to three blades. That doesn’t add up to 10 because each of the two oddballs contributed four strands to one blade, three strands to another, and one strand to the third. That means one blade was uniquely of that monomer and the other two were shared. One of these blades was shoved into the second propeller, generating an asymmetric pentamer. Note also that the two oddballs weren’t equivalent: one was arranged 4-3-1 and the other 3-1-4. The other mutant, Lib1-B7-18, was even weirder in some ways. In that mutant, four of the monomers contributed to three blades in a 1-3-4 manner. The last monomer, however, contributed to four blades, two from each propeller, with a pattern of 1-3-1-3. Because these structures cannot form unless the monomers adopt multiple structures (3 in the case of Lib2-D2-15 and 2 in the case of Lib1-B7-18), it follows that the monomers must be metamorphic.

The evolved fragments didn’t have higher stability to guanidinium hydrochloride than the source fragments, suggesting that the improved expression and solubility was not due to improved stability. The authors argue that the improved expression was mostly due to a change in the isoelectric point of the mutants, which decreased towards neutral in both cases. However, the evolved fragments also were able to refold from the denatured state, which the source fragments could not do. To the authors, this suggests that the directed evolution process actually selected for metamorphism; that is, the proteins were stabilized by an increased ability to sample states that formed productive pentamers.

The observations in this study, although very interesting, do not tell us anything directly about the evolution of tachylectin-2. While it is possible that the current protein evolved from a metamorphic precursor like these fragments, there is no direct evidence that this is the case, nothing to indicate that evolution performed in reverse what Yadid et al. did here. Whether metamorphism contributed to the diversity of β-propeller folds in general, or to the evolution of this protein in particular, remains very much an open question. The metamorphism observed here may be more a consequence of a particularly robust fold tending towards its original state than evidence of hidden metamorphic potential in singular structures. As such, the article’s title strikes me as overbold given the data. That said, it is certainly not implausible that odd assemblies like these pentamers played a role somewhere along the evolutionary path that created the rich library of β-propellers we have today, and this study establishes that even very strange steps along the way can occur without destroying the protein’s function or certain gross features of its structure.

Yadid, I., Kirshenbaum, N., Sharon, M., Dym, O., & Tawfik, D. (2010). “Metamorphic proteins mediate evolutionary transitions of structure.” Proceedings of the National Academy of Sciences, 107 (16), 7287-7292 DOI: 10.1073/pnas.0912616107

 Posted by at 2:39 AM
Mar 222010
ResearchBlogging.orgThe proposition that general fold architecture is preserved within a family of evolutionarily-related proteins is not controversial. The amino acid sequence of a protein determines its structure, and countless studies have substantiated the idea that proteins with similar sequences will adopt similar folded conformations. Because structure and dynamics are intrinsically linked, one could reasonably assume that many features of a protein’s dynamics get conserved along with the fold. A growing number of experiments show that this is indeed the case, including a recent paper in Structure (1).

We already have some evidence of fold-dependent dynamics. An NMR study from my mentor Andrew Lee’s lab comparing fast fluctuations of side chains among three related proteins from the PDZ family suggested that motions on this timescale could be evolutionarily conserved (2). That study compared the model-free order parameters of methyl groups from one protein to those of their counterparts in other PDZ domains. Predicting an order parameter using dynamics data from a structurally equivalent residue in another protein was shown to be slightly more accurate than calculations from structural considerations such as packing or methyl type. In a similar vein, I have previously discussed studies on adenylate kinase enzymes from E. coli and a thermophilic organism that show they have similar backbone dynamics under conditions where their enzymatic activity is about equal, although they differ substantially from each other at room temperature.

Of course, these studies were limited and involved just a few proteins, because getting experimental data about dynamics is costly and time-consuming. For comparisons across large numbers of different proteins, computational approaches may therefore be of great value. Previously, other groups have made use of short molecular dynamics simulations or normal mode analysis. Raimondi et al. continue in this vein, combining normal-mode analysis of single structures with principal component analysis of a large set of structures from the Ras superfamily of proteins.

The Ras superfamily encompasses several groups of related folds with nucleotide-dependent activity. When GTP is bound to them, they are active and propagate a particular signal. Over time, the GTP gets hydrolyzed to GDP and the signal turns off. This catalytic process is pretty inefficient, but it can be enhanced by the action of a GTPase Activating Protein (GAP). The exchange of GDP for GTP can be enhanced by the action of a Guanine nucleotide Exchange Factor (GEF). The GTP/GDP state manifests primarily in the positioning of two loops, termed the switch regions (SwI and SwII). This mechanism allows for several different modes of control, so the Ras architecture has been repurposed many times throughout evolution for a variety of different roles.

Because the different members of the superfamily play key roles in their respective pathways, there are many structures available, often in several different states (GTP-bound, GDP-bound, GEF-bound, etc.). Raimondi et al. aligned these structures using the common features of the Ras fold and used PCA to identify flexibility across this evolutionary ensemble. The goal of PCA is to take a dataset with many potentially correlated data points (in this case, the relative positions of the backbone Cα atoms) and identify a small set of variables that explain as much of the variance as possible. Here, the principal components (PC) are expected to describe the structural variability of the fold.

The first PC, which is expected to explain the largest amount of the variability, can separate the structures by their families. That is, the displacement along PC1 can distinguish a Rho family domain from an Arf family domain. The authors call this variability function-independent, because this principal component doesn’t seem to make any meaningful distinction between the GTP/active and GDP/inactive states. That appears to be a property of the second PC, which for some families does a very good job of separating the GTP from the GDP-bound forms (for others there appears to be more mixing). According to this analysis, function-dependent variability appears to be confined to one half of the protein, while function-independent variability seems to be distributed across the whole fold.

The authors also performed normal mode analysis on individual proteins from the Ras superfamily using an elastic network model. In this kind of simulation the protein is modeled as a group of Cα “nodes” connected by spring-like harmonic potentials representing covalent and non-covalent interactions. Although any one of these “bonds” can be stretched, compressed, and moved, such deformations exert a force on other bonds connected to the nodes involved, which tends to damp most motions. Certain collective deformations will be favored as a result, and these can be calculated as “normal modes” that probably reflect slow fluctuations of the fold.

The deformations detected by ENM for all individual proteins overlapped significantly with the second PC identified in the evolutionary analysis. That is, the conformational variability of a conserved domain over evolutionary time is correlated with the conformational fluctuations of a single domain on a biological time scale. This makes sense, especially in this case, because the switch regions are areas of significant conformational variability, and are connected with the conserved catalytic function of these proteins. The fact that PC1 doesn’t line up with the low-frequency normal modes probably means that the conformational transitions between different family members cannot be mimicked by ordinary thermal motion, i.e. the fold cannot change this way without the aid of mutations.

Although the results in these studies might seem rather pedestrian and expected, I find them quite encouraging. We’re not particularly good at predicting structure from sequence yet, and our understanding of protein dynamics is even more primitive. What these studies indicate is that it should be possible to predict the conformational fluctuations of a given protein or domain using our knowledge of a related, homologous protein. This could have positive consequences for fields such as rational drug design and protein design, which have met with limited success in part, perhaps, because they do not sufficiently account for a protein’s structural fluctuations.

(1) Raimondi, F., Orozco, M., & Fanelli, F. (2010). Deciphering the Deformation Modes Associated with Function Retention and Specialization in Members of the Ras Superfamily. Structure, 18 (3), 402-414 DOI: 10.1016/j.str.2009.12.015

(2) Law, A., Fuentes, E., & Lee, A. (2009). Conservation of Side-Chain Dynamics Within a Protein Family. Journal of the American Chemical Society, 131 (18), 6322-6323 DOI: 10.1021/ja809915a

 Posted by at 10:00 PM
Dec 172009
ResearchBlogging.orgAnfinsen’s dogma — that the amino acid sequence of a protein uniquely determines its structure — naturally leads one to the idea that identity between amino acid sequences means identity between structures. This has proven to be a successful paradigm: sequence similarity reliably predicts structural and functional similarity. Evidence accruing in recent years, however, suggests that for small proteins, at least, this assumption may not be entirely safe. Adding to this view, in an article in PNAS this week (it’s open access, so open it up), a research team led by Philip Bryan reports that they are able to generate significantly different folds with divergent functions from sequences that differ by a single amino acid.

The researchers in this study took two independently folded domains from streptococcal protein G that have different structures and sequences, called GA and GB, which bind human serum albumin (HSA) and part of an antibody (IgG), respectively. As you can see from several figures in this paper, GA has an all α-helical structure, while GB has a helix and a four-strand β-sheet. In previous papers they have undertaken a process of slowly mutating these two domains towards each other, most recently generating a pair of proteins they called GA88 and GB88 (which I have mentioned before) that retained their original structures and functions despite differing in their amino acid sequence at only seven places (i.e. they were 88% identical). Beyond this point, mutating the sequences towards each other causes the protein stability to degrade. However, they manage to complete the project by generating sequences that are 91, 95, and 98% identical while retaining much of their original function.

GA98 and GB98 differ only in the identity of the amino acid at position 45, which is leucine in GA98 and tyrosine in GB98. The tyrosine seems to be critical in stabilizing a hairpin turn in the GB structure, possibly by interactions with an adjacent aromatic residue (F52 – see Fig. 6). In the structure of the GA95 protein (Fig. 5), L45 is partially solvent exposed, and therefore probably does not contribute substantially to stability. Of course, as a leucine is also hydrophobic, one might expect that it would also interact favorably with F52. This seems to be the case, as the GA98 protein binds to both HSA and IgG, suggesting that it transiently samples the GB structure.

As with studies I have talked about previously on lymphotactin, which has two structures in its native state, and the cro proteins, which can have different structures despite similar sequences, this research indicates that the relationship between sequence space and fold space may not be as straightforward as previously believed. We have known for some time that distance in sequence space does not imply distance in fold space — the convergent evolution of numerous proteins has shown us that, at least. These experiments inform us that distance in fold space need not imply distance in sequence space, that it is possible for a protein to undergo a structural saltation in which a single mutation gives rise to a dramatic difference in structure.

It remains to be seen whether this is true in larger proteins as it seems to be in small proteins and domains. If it is generally the case that proteins can drift towards new folds without losing their native function, then “jump” over to a completely different structure, without transiting the molten-globule state, then our understanding of evolution, particularly early evolution, may change significantly. For instance, the objection that proteins could not evolve new functions without wandering into the unfolded state loses its weight. Moreover, given that in two of these cases the proteins actually sample both structures (and hence, both functions), it is not even necessarily true that a protein must lose its original function before evolving a new one. Equilibrium switching between structures would allow a protein to fulfill both the old and new functions, albeit likely at reduced efficiency. As I’ve mentioned before, one answer to this lack of efficiency would be altering protein concentration by adjusting gene dosage, i.e. gene duplication. From that point, the generation of fold and functional diversity by evolution might be a straightforward process.

Alexander, P., He, Y., Chen, Y., Orban, J., & Bryan, P. (2009). From the Cover: A minimal sequence code for switching protein structure and function Proceedings of the National Academy of Sciences, 106 (50), 21149-21154 DOI: 10.1073/pnas.0906408106 OPEN ACCESS

 Posted by at 2:51 AM