Jun 042012
 

Allosteric regulation of proteins is often examined using two different models. The widely-known “induced-fit” (IF) model proposes that effectors form a loose complex with inactive proteins and cause them to shift into a new, active conformation. In the competing “conformational selection” model, effectors bind to and stabilize proteins that are already in an active conformation. An upcoming paper in the Journal of the American Chemical Society examines this question in the case of T. lanuginosis lipase (TLL) (1). The data show that the enzyme enters an activated state even when it is prevented from interacting with its activator. While this strongly suggests that the activation mechanism is CS, some data suggest that the mechanism is actually IF.

The paper in question relies on single-molecule kinetics techniques to characterize an enzyme. Previous studies in this field have shown that reaction time varies between enzyme molecules and over time for single molecules. These findings should not surprise us, knowing as we do that all machines have intrinsic variation in their rates of operation. Flexible proteins that can adopt many different folded structures (not to mention partially-folded and unfolded ones) should be expected to have even more operational differences. That said, there are a variety of ways to account for the observed distribution of reaction rates.

TLL is activated by lipid membranes. While tracking the activity of individual enzymes using fluorescence, Hatzakis et al. altered their ability to access a lipid membrane by changing the concentration of polyethylene glycol (PEG) in the solution; PEG blocks the (tethered) enzyme from accessing the liposome. They found that a model where the enzyme exists in an equilibrium of active (R) and inactive (T) states is most consistent with the distribution of reaction times they observe, even at PEG levels that completely occlude the membrane. Based on this finding, they conclude that TLL activation occurs by selection of an active conformation from a pre-existing equilibrium, rather than inducing a new conformation.

At this point things start to get a little confusing. The central problem is that CS and IF are used to identify both characteristics of the apo- ensemble and features of the activation pathway, and the former don’t necessarily coincide with the latter.

To understand what I mean, take a look at the figure below. Here, Ta and Ra are ligand-free T and R states, while Tb and Rb are ligand-bound T and R states. The typical ligand-free state is Ta, and the allosterically activated state is Rb. Ra (apo-R state) and TbL (“encounter complex”) are thermodynamic states that are viewed as characteristic of CS (red path) and IF (blue path), respectively. The rates kact and kin are the apparent rates of activation and inactivation, which are dependent on the microscopic rates noted for each pathway.

Comparison of CS and IF mechanisms

In a CS mechanism, the protein adopts both the R and T structures while free in solution, and ligand binds preferentially to the R state and stabilizes it, redistributing this pre-existing equilibrium without creating “new” states. Because binding follows conformational change, a pre-existing equilibrium in the apo- ensemble is a necessary condition of CS.

In the IF case, binding precedes conformational change: the ligand encounters the free T structure and allows it to adopt a “new” R structure. Traditionally, this has been interpreted to mean that the R structure never exists in solution at all. However, binding may proceed by an induced-fit mechanism even if an R state is populated in solution.

There are a couple of cases where we know this must happen. For instance, adenylate kinase, a protein that I have discussed before, undergoes conformational exchange between open (T) and closed (R) states in solution. However, in the closed state the ligand-binding site is completely occluded, and it is impossible for ligands to bind to this state. It therefore follows that binding-associated conformational change proceeds by an IF-like pathway, even though an equilibrium between the R and T structures exists in the apo- state. In this and similar cases, all four major states are populated, but kon,R≈0 and so the path through TbL dominates the reaction flux.

The thermodynamic implication of CS — that there is a detectable equilibrium between R and T states — is not synonymous with its mechanistic meaning — that conformational change precedes binding. This makes sense, because in the context of a constantly interconverting ensemble of conformations, even very unfavorable Ra states will be accessed occasionally. The strict thermodynamic definition of IF, that the R conformation be unattainable in the apo- state, may not apply to any real protein (2). However, the population of R conformers may be so low and short-lived as to be undetectable.

Even though a pre-existing equilibrium is not probative, a quick examination of the figure above indicates how we can distinguish between these mechanisms. In the case of CS, the rate of interconversion between T and R states in solution sets an upper limit on the activation rate, because the ligand binds to the apo-R state. At high ligand concentrations, kact = kTR because the presence of ligand probably will not alter the energy landscape of a protein it is not bound to. In this mechanism, however, koff,R is expected to be much slower than kRT. This implies that kin should decrease significantly at high ligand concentrations.

In an IF mechanism, the energy landscape of the encounter complex need not be the same as that of the apo- protein. As such, in IF activation the T→R energy barrier can (and is expected to) become lower. Accordingly, if kact exceeds kTR at high ligand concentrations (3), then an IF mechanism must be at work. Because this energy barrier is variable in an IF mechanism, however, it’s somewhat difficult to predict what will happen with the R→T barrier; it might get larger, or it might not. The figure below summarizes the expectations.

Hatzakis et al. report that the rate of conversion from T to R (i.e. kact) increases as PEG concentration decreases (note: in the advance online version the schemes in Figures 2 and 4 are mislabeled, but the energy diagram in 4 is accurate). The kin rate, by contrast, remains constant. If we accept their (reasonable) assumption that the energy of Ta is not affected by the lipid membrane, this indicates that the T→R energy barrier decreases in the presence of the allosteric effector. That, in turn, implies that the membrane is associated with the protein prior to the transition state, and thus that the mechanism of activation is induced fit, even though an Ra state can be observed in solution.

“Conformational selection” is often used interchangeably with “pre-existing equilibrium”, but it is dangerous to employ this equivalence. The thermodynamic feature of a pre-existing equilibrium between apo -inactive and -active states does not necessarily imply that the pathway between apo-inactive and bound-active states proceeds through an apo-active intermediate. In some cases, the observed equilibrium indicates a kinetic dead-end where kon,R≈0 and the reaction flux is dominated by IF mechanisms.

Hatzakis et al. studied the single-molecule kinetics of several other allosterically-regulated monomeric enzymes and found that they also showed evidence of a pre-existing equilibrium between active and inactive states. This alone, however, is not sufficient to establish activation via CS. Only a detailed examination of the kinetics can indicate whether activation uses CS, IF, or some combination of these mechanisms.


1) Hatzakis, N., Wei, L., Jorgensen, S., Kunding, A., Bolinger, P., Ehrlich, N., Makarov, I., Skjot, M., Svendsen, A., Hedegård, P., & Stamou, D. (2012). Single Enzyme Studies Reveal the Existence of Discrete Functional States for Monomeric Enzymes and How They Are “Selected” upon Allosteric Regulation Journal of the American Chemical Society DOI: 10.1021/ja3011429

2) By the same token, apo-R states are almost certainly not exactly the same as bound-R states, so a strict version of CS is also quite improbable.

3) At substoichiometric concentrations of ligand, kact can exceed kTR because in this condition maximum activation can be reached by (rapid) binding of ligand to the existing pool of Ra, without any need to replenish Ra from Ta.

Feb 292012
 

Fluorescent sensors, be they proteins or small molecules, are extremely useful because they can be used to detect metabolic states and protein interactions in living cells. Fluorescent proteins are particularly useful because they can be produced inside the cell and, using tags, targeted to specific proteins, locations and organelles quite easily. Because of this, a large number of fluorescent proteins have been isolated and engineered, usually using the backbone of the green fluorescent protein. In this week’s Proceedings of the National Academy of Sciences, a group from Columbia University describe a fluorescent protein that can sense the viscosity of its surroundings (1).

Crystal model of dark-state Dronpa (PDB: 2POX), chromophore in green

Kao et al. were working with a protein called Dronpa. Isolated from coral, Dronpa is structurally homologous to GFP, but has unique photoswitching characteristics (2). Irradiation of Dronpa at 488 nm causes fluorescent emission at 518 nm, but the fluorescence is rapidly quenched, and emission ceases.

Dronpa can be restored from this ‘dark state’ by hitting it with light at 405 nm, completely recovering its fluorescence. A pulse of light at 405 nm while also illuminating at 488 nm causes a rapid spike in fluorescence, followed by a slower decay over several milliseconds as the bright state quenches. Kao et al. decided to look further into Dronpa’s photoswitching behavior by examining the kinetics of this process.

When they examined the decay rates, they noticed something interesting: the rate of quenching seemed to depend on the viscosity of the solvent, which they controlled by varying the percentage of glycerol in their measurement buffer. Unfortunately, as you can see below, the distributions overlapped significantly, meaning that this protein would not be a particularly good viscosity sensor.

Modeled distributions for WT Dronpa at various glycerol concentrations

This prompted Kao et al. to ask if they could do better. The magnitude of the changes in rates that they saw suggested that viscosity-related changes in the photoswitching rate were responsible for the effect. This makes sense, because the structural models suggest that certain translational motions are needed to switch the chromophore from its light state to its dark state. These motions would be subject to drag from the surrounding liquid, which increases with viscosity.

An alternative possibility not discussed by Kao et al. is that the internal fluctuations of the protein are directly coupled to solvent dynamics. This coupling, called ‘solvent slaving’ (3), has long been suggested by Hans Frauenfelder based on his work in myoglobin.

In either case, if protein flexibility is the key to the photoswitching rate, then a more flexible protein might be more sensitive to viscosity. As it turns out, some flexible mutants of Dronpa already exist. Specifically, Dronpa-3 has been engineered to have both a steric clash and a void, and has a reduced quantum yield that is consistent with a more flexible interior.

When Kao et al. repeated their viscosity experiments using Dronpa-3, they found that it had substantially better measurement characteristics. The decay rates still had fairly broad distributions, but were much better separated (see below). Also, consistent with their hypothesis that protein flexibility contributed significantly to the switching rate, the decays were much faster overall (compare the scales). The response is not linear over the entire viscosity range, but it still seems that Dronpa-3 could produce relatively sensitive measurements.

In order to test that idea, the authors expressed Dronpa-3 alone, and as a fusion with a histone protein, in HEK 293T cells. In these experiments, the researchers were able to measure local viscosity in live cells, both during stable phases and mitosis. The results suggested, not surprisingly, that the nucleus is more viscous than the cytoplasm, though the environment seemed to be more heterogeneous once chromatin had condensed for mitosis. All this is more or less as expected, and in some cases cross-validated by other experiments. In the future, Dronpa-3 may be useful for examining solution dynamics during other processes that reshape cells and tissue or depend on molecular diffusion, as well as in calibrating in vitro experiments to better reflect the relevant biological environments.

1. Kao, Y., Zhu, X., & Min, W. (2012). Protein-flexibility mediated coupling between photoswitching kinetics and surrounding viscosity of a photochromic fluorescent protein Proceedings of the National Academy of Sciences, 109 (9), 3220-3225 DOI: 10.1073/pnas.1115311109

2. Ando, R., Mizuno, H., & Miyawaki, A. (2004). Regulated Fast Nucleocytoplasmic Shuttling Observed by Reversible Protein Highlighting Science, 306 (5700), 1370-1373 DOI: 10.1126/science.1102506

3. Fenimore, P.W., Frauenfelder, H., McMahon, B.H., & Parak, F.G. (2002). Slaving: Solvent fluctuations dominate protein dynamics and functions Proceedings of the National Academy of Sciences, 99 (25), 16047-16051 DOI: 10.1073/pnas.212637899

 Posted by at 10:46 PM
Jan 252012
 

Imagine that you could get an injection of a protein that would chop up arterial plaques. Imagine that you could drop a plastic bottle into a pool of bacteria that would transform it back into high-grade oil. Imagine that you could take any organic material at all and, with a minimum of planning, transform it into any kind of desired organic chemical with a bare minimum of energy input and no need to purify intermediates. This is the vision behind the applied structural biology of protein design, the holy grail of which is to come up with a way to make enzymes that will perform novel chemistry. A study recently published online in Nature Biotechnology by David Baker’s group (1) suggests that the design process could be improved by crowdsourcing certain parts of the problem to gamers (the paper is paywalled at Nature but freely available via the Foldit site).

To do this, the Baker group used their program Foldit, which they have used previously for predicting three-dimensional protein structures from their amino acid sequences. Rather than predicting a structure from a known sequence, however, the Baker group asked the Foldit players to figure out an amino acid sequence that would generate a desired structure. The goal was to enhance an enzyme that would perform the chemically useful Diels-Alder reaction.

An enzyme is a protein that increases the rate of (catalyzes) a chemical reaction, often by incredible amounts. The best enzymes can increase reaction rates by factors of up to 1017 relative to the same reaction occurring in pure water. Protein design aims to produce artificial enzymes with rate enhancements comparable to their natural counterparts. To do this, biochemists try to design an active site that stabilizes the transition state of a chemical reaction. The transition state is the point of a reaction where the molecules are in their least stable state, and equally likely to revert to substrates or continue on and become products.

Unfortunately, it’s not just as simple as stabilizing a transition state. Enzymes have to bind and release their substrates and products, producing energy landscapes that are at least as complex as the one I have drawn below. Using a protein design protocol they had described in previous publications, Baker’s group managed to produce a weak enzyme. They then asked the Foldit players to help out, by posing some specific challenges to try and stabilize the bound substrates. The Foldit players eventually produced an 18-fold improvement in the enzyme’s kcat/KM value. To understand what that means and what the players accomplished, let’s examine this reaction coordinate:

That’s a busy little figure, but it’s not as bad as it looks. The position up or down in the figure indicates how much energy a state has. The more energy, the less likely the system is to occupy that state. Left to right positions show us how close we are to the desired state of the system, which is to have the product (P) we want separate from the enzyme (E) that catalyzed its production from substrate (S). To move from one stable state to another stable state, you have to push the system over hills (energy barriers) in the landscape, just like pushing a car up a hill. The higher the barrier, the slower that step becomes. For simplicity, this diagram shows only one substrate, but the artificial enzyme had two. We can pretend that the Foldit effort started with an enzyme that resembled the blue curve.

We start with E and S separate from each other in solution (E+S). E and S bind to each other to form ES, releasing binding energy. Here I’ve shown a small barrier between E+S and ES, but in many cases there is no barrier here, or it is negligible. Next S is converted to P, and as you can see there is usually a large energy barrier, at the top of which is the transition state (TS). The height of the barrier is determined by the activation energy, which is affected by the structure of the enzyme-substrate complex. Once P has been formed, the complex dissociates so we have free enzyme and product (E+P). Here I have shown E+P to be a lower-energy state than EP, but this won’t necessarily be true.

In the language of Michaelis-Menten kinetics, this landscape is described by two main parameters. KM, also called the Michaelis constant, describes the balance between E+S and ES, and therefore primarily reflects the binding energy. The larger the binding energy, the more ES will be favored, and the lower KM will be. The turnover number, or kcat (maybe we should call this the Menten constant?) describes the creation of product over time, and in this diagram it depends on the activation energy. Again, the larger the activation energy, the lower kcat will be. However, kcat really just depends on the slowest step of the catalytic cycle. If the largest energy barrier was between EP and E+P, kcat would depend on that barrier. Because kcat/KM is something like a normal rate constant, and combines the values in an easy-to-understand way (a higher kcat/KM means a better enzyme), it’s often used to describe an enzyme’s activity.

So how did the Foldit players improve the activity by a factor of 18? The original enzyme design left part of the active site open to water. Through a series of iterations, the Foldit players filled in this void with a self-stabilizing helix-loop-helix motif (Figure 1b). The upshot of this was that the affinity of the enzyme for both substrates increased. Thus, KM decreased, as shown in Table 1, for both substrates. At the end of the process, the diene bound six times as tightly and the affinity for the dienophile improved by about a factor of three. This accounts for all the observed change in kcat/KM, because kcat was not improved.

Although it may not seem like it, we can also learn a great deal from the fact that kcat did not change. This observation shows that the changes made by the Foldit players did stabilize the TS. Otherwise, the energy barrier would have increased when they stabilized the ES complex. However, the best-case scenario would have been for them to uniquely stabilize TS without improving the energy of ES, because this would effectively lower the energy barrier and increase the reaction rate. Because this didn’t happen, the situation follows the orange curve in the figure above: the ES and TS states have shifted down in energy by the same amount, with no change to the activation energy.

The lack of change in kcat also indicates that the Diels-Alder reaction itself, rather than product dissociation, is rate-limiting for the enzyme. My reasoning here is that the increase in affinity is general. We know that both the ES and TS complexes were stabilized by the changes, so EP probably was too, as shown in the orange curve. If the EP → E+P transition were rate-limiting, these stabilizing mutations would have made the enzyme slower.

The Foldit players made this a better enzyme, but that doesn’t exactly mean that it’s an impressive one. The observed kcat is significantly slower than almost any natural enzyme, and the overall rate enhancement is on the order of 103-104, which is not much better than catalytic antibodies. The success of the Foldit players at improving the affinity of the enzyme for all the bound states suggests that it might be possible to use crowdsourced systems like Foldit to accomplish the more difficult feat of stabilizing a TS, or at least to generate folds that support a pre-defined TS. The ultimate goal is to produce something like the green curve, where substrate binding is stronger and activation energy is lower. I hope that such efforts will be taking place among the Foldit players soon, if they haven’t started already.

Disclaimer: I am part of an ongoing collaboration with David Baker’s group unrelated to the Foldit program.

1) Eiben, C., Siegel, J., Bale, J., Cooper, S., Khatib, F., Shen, B., Players, F., Stoddard, B., Popovic, Z., & Baker, D. (2012). Increased Diels-Alderase activity through backbone remodeling guided by Foldit players Nature Biotechnology DOI: 10.1038/nbt.2109 Also available for free from the Foldit site.

Jan 042012
 

One of the most serious challenges facing medical science today is the development of drug resistance by bacteria and viruses. Almost as quickly as we can develop drugs that attack the machinery of infectious disease, evolution, aided in some cases by careless use, defeats our efforts. In some cases this is because the specific target of a drug changes in response to the challenge, as has happened in the evolution of resistance to rimantidine in influenza. Bacteria have an additional mechanism to attack our medicines, however, in the form of multidrug resistance genes. These proteins can recognize an array of toxic molecules, often using general properties, and expel them from the cell. As such, every single one of these genes can take out multiple medicines.

One of these multidrug resistance exporters is EmrE, a member of the small multidrug resistance (SMR) family of genes. EmrE is a proton-drug antiporter that pushes positively-charged polyaromatic molecules out of the cell while letting two protons in. The import of the protons provides the energy to expel the toxins against a concentration gradient. Today in Nature, a research group led by my friend Katie Henzler-Wildman published new details of EmrE’s mechanism and topology (1). Using NMR and fluorescence techniques, we show that EmrE does, or at least can, operate as an antiparallel, asymmetric dimer that exposes a single active site to alternating sides of the membrane by simultaneously switching the conformation of the monomers.

This was a long and difficult project, in which I played a small role. Unfortunately, multidrug resistance exporters like EmrE tend to be integrated into the bacterial membrane, which makes them challenging subjects for biophysical studies. In order to investigate proteins like this, we must reconstitute them in lipid environments that suitably mimic their natural setting, while maintaining sufficient purity and concentration to perform our experiments. The controversy over the effect of rimantidine on influenza’s M2 channel provides just one example of the difficulty of reliably recreating a membrane environment.

EmrE has also been embroiled in a controversy between structural biologists and biochemists. Although the minimal functional unit is agreed to be a dimer, biochemical studies have indicated that that the dimer is symmetric, and that the proteins have a parallel orientation in the membrane. That is, each EmrE protein has the same shape and is pointing the same way. Relatively low-resolution data from crystallography and electron microscopy, however, has suggested that the protein units are asymmetric and antiparallel. However, these studies were performed in lipid environments where the protein may not have been active, and at frozen temperatures far from physiological relevance. One would like to get a look at EmrE in a state where it is active and at a somewhat more reasonable temperature.

Caught in the Act

Solution NMR provides one way to achieve this goal. A protein can be embedded in a small bit of membrane, and allowed to tumble freely in an aqueous environment, allowing sufficient signal for us to make some kinds of measurements. Historically, micelles have been used for this, but multiple lines of evidence now suggest that they may produce artifacts due to the unnatural local curvature. Consequently, Katie and her student Emma worked out a system for observing EmrE in bicelles, which are small, flat-ish discs of lipids that still tumble freely enough to allow solution-state NMR measurements. They also established that EmrE in this bicelle preparation could still bind to tetraphenylphosphonium (TPP+), one of its ligands.

The NMR spectrum, however, was perplexing. As you can see in the HSQC to the right, the peaks in the spectrum are fairly spread out. That’s unusual for a protein composed entirely of α-helices, but because electron currents from the aromatic rings of TPP+ induce significant changes in chemical shift it’s still reasonable. What is more troubling, and perhaps less obvious, is that there are twice as many peaks in this spectrum as you would expect.

An HSQC of a 15N-labeled protein, in principle, shows one peak for every N-H in the protein, such that you get a two-dimensional spectrum showing the chemical shift of the nitrogen on one axis and its bonded proton on the other. This means there should be one peak per amino acid, except prolines. In addition, peaks usually appear for tryptophan indoles (they are at bottom left in the spectrum) and, depending on your setup, glutamine and asparagine side-chains. Other side-chains usually exchange with water too quickly to be seen. EmrE has about 100 residues, and the spectrum has about 200 peaks. This indicates that there are two different structures of EmrE in the sample.

We decided to ask whether EmrE switched between these two structures and how fast. Observing two peaks per residue, of roughly equal intensity, told us that if EmrE did change its structure, it was doing so slowly, at a rate of 10 times a second or less. So we used an experiment called ZZ-exchange, which is similar to the HSQC but includes a relatively long pause between determining the 15N chemical shift and the 1H chemical shift. If a significant proportion of the sample changes conformation during the pause, you will see a spectrum that includes all the HSQC peaks, as well as cross-peaks that have the 15N chemical shift of one structure and the 1H chemical shift of the other, producing a little rectangle of peaks. This ended up being tricky because the bicelle distorts the signals, but Katie, Greg DeKoster, and I managed to come up with a setup that got around this problem on the 800 at Brandeis, starting from a pulse sequence written by Art Palmer.

As you can see above, we observed cross peaks, shown in blue to differentiate them from the HSQC peaks. By varying the pause between chemical shift determinations, we were able to fit a rate of about 5 /s, which roughly correlates to a fluorescence fluctuation observed in previous experiments. In addition, experiments using paramagnetic relaxation enhancement agents show that the two states have different accessibility to water, suggesting that they represent a change in which side of the active site is open. This supports the conclusion that what we are seeing here is the fundamental conformational change inherent to EmrE’s function: the opening of the binding site to one side of the membrane, then the other.

The ABBA Model?

So now we have evidence for two distinct structures that interconvert during the export process. Two models can be consistent with these data, shown in the figure below. The most obvious possibility, consistent with almost all of the biochemical data, is that the structures represent two states of a symmetric, parallel dimer converting from an AA state to a BB state. Alternately, consistent with the crystallographic data, one could have an asymmetric, antiparallel dimer that exchanges from an AB state to a BA state.

Top: Symmetric, parallel AA-BB model. Bottom: Asymmetric antiparallel AB-BA model.

The NMR data support the second model in two ways. The first is that peaks for the two states have almost equal intensity, which can only be the case if both states are almost equal in free energy. This happens automatically in the case of the asymmetric dimer, because each dimer contains one of each conformation, making the exchange to the alternate state energy-neutral. In the case of a symmetric dimer, it requires that each individual conformation have the same energy, which is unlikely, but not impossible. Also, in the NMR data, regions of the protein that show the largest difference in chemical shift between the two states also show the most significant conformational differences in the crystal structure of the asymmetric dimer. Unfortunately, these lines of evidence are not enough to be sure about what we’re seeing.

Flash in the Pan

To get a better idea of EmrE’s topology, Katie and her team performed a number of FRET and crosslinking experiments to establish the relative orientation of dimers in the membrane. In bulk FRET experiments, they fluorescently labeled EmrE that was in liposomes, as shown in Figure 3. In the first experiment, EmrE was exposed to one label while in liposomes, then broken out into bicelles and exposed to another. For antiparallel proteins, excitation of the green dye should result in fluorescent output from the red dye, and this is exactly what was observed. Also, EmrE in liposomes was exposed to both dyes simultaneously, which should result in an observation of FRET for parallel but not antiparallel dimers. Some FRET was observed, but it wasn’t clear whether this was due to dye leaking into the liposomes.

Katie answered this question using single-molecule FRET. EmrE dimers with a single cysteine mutated into them were labeled with fluorescent dye and then examined on a slide to determine the efficiency of energy transfer. Because there is only one labeling site, a high-efficiency transfer would imply that both fluorophores were on the same side of the membrane, and thus a parallel topology. However, the observed efficiency suggested a distance of 50 Å between the fluorophores, more consistent with an antiparallel topology where the labeling sites are separated by the membrane.

In one final experiment, Katie used a molecule that covalently links a lysine side chain to a cysteine side chain. There is only one lysine in EmrE, and Katie created a mutant that has a single cysteine on the opposite side of the membrane. This distance is too great for the linker molecule to bridge, so in a parallel dimer no cross-linking should be observed. Instead, the experiment resulted in nearly complete cross-linking, supporting an antiparallel topology.

How an Antiporter Works

Cumulatively, these results strongly support the model shown below, where EmrE swaps two protons for a drug molecule using a conformational exchange between energetically-equivalent asymmetric, antiparallel dimer states that are open to different sides of the membrane. In this model, there is a single binding site, consistent with the biochemical data, in the context of an antiparallel, asymmetric dimer, consistent with previous structural data. Because EmrE binds TPP+ with high affinity under our conditions, and because the cysteine mutations made for the FRET experiments did not significantly change the NMR spectra, we can be confident that these experiments plausibly reproduce normal protein behaviors. However, some mutational studies indicate that EmrE functions as a parallel dimer in vivo, and further experiments are necessary to either reconcile these observations or determine where the errors originate.

Exchange between identical antiparallel, asymmetric structures allows EmrE to exchange two protons for one molecule of toxin.

In terms of the implications for fighting drug resistance in bacteria, this is an early step on a long road. EmrE is not the only drug exporter in bacteria, nor is it the most critical. It is also too soon to say whether the particular mechanism outlined here is general to the SMR family or a peculiarity of this single protein. However, these results give us confidence that the crystal structures are reliable (Katie’s group is currently is working on improving them), and that we can cleanly measure exchange rates to determine what effect drug candidates are having. The goal would be to develop accessory drugs that attack the exporters while a primary drug attacks the bacterium’s basic functions. A great deal more work is necessary before we reach that point, but this is one strategy that may allow us to defeat drug resistance, or at least prolong the usefulness of our current antibiotic arsenal.

(1) Morrison, E., DeKoster, G., Dutta, S., Vafabakhsh, R., Clarkson, M.W., Bahl, A., Kern, D., Ha, T., & Henzler-Wildman, K. (2011). Antiparallel EmrE exports drugs by exchanging between asymmetric structures Nature, 481 (7379), 45-50 DOI: 10.1038/nature10703

 Posted by at 10:10 PM