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
Sep 062011
 

Over the last two decades, multiple kinds of NMR experiments have repeatedly shown that protein structures are quite variable, frequently shifting to minor conformations. The most striking evidence in this line has come from hydrogen-exchange experiments, which have demonstrated that virtually all proteins undergo excursions to partially-folded states at equilibrium. As R2 relaxation-dispersion experiments have become more widely used, excursions to alternative folded states have repeatedly been detected. The challenge now is to find ways to characterize these low-population states. Advanced crystallographic techniques have proven useful in determining some of these alternative structures. However, proteins are not always amenable to crystallography, and the minor state in the crystal may not correspond exactly to the minor state in solution. Therefore there is an ongoing effort to define these states by NMR. Lewis Kay’s group in Toronto is in the forefront of this effort, and recently reported the solution structure of a minor state of a T4 lysozyme mutant (1).

Lysozyme is an extremely common enzyme because it has the useful property of degrading the peptidoglycan that makes up bacterial cell walls. This makes it a natural antibiotic against gram-positive bacteria, and as a result it is found in many secretions and fluids, including saliva and egg whites. Because it is plentiful it has been widely studied, with many mutants made and characterized for their activity and stability. Lysozyme also crystallizes easily — doing this was actually part of my biochemistry lab class back in college. So, many structures of the enzyme and its mutants are available.

T4 lysozyme L99A with benzene boundOne lysozyme mutant that has interesting properties is the L99A mutant of the lysozyme from the T4 bacteriophage. This mutation creates a cavity in the upper part of the protein that is known to bind hydrophobic ligands such as benzene (right, benzene in purple, PDB code 3DMX). However, crystal structures show this binding pocket to be completely buried, even when empty. This poses the question of how the ligand gets in. Although the structure of L99A is very similar to WT, the Kay lab noticed that the NMR spectra of the mutant contained broadened peaks, indicating the presence of an exchange process between two conformations. Therefore, the Kay lab used R2 relaxation-dispersion to show that the protein sampled a minor state that accounted for 3% of the total protein, with a lifetime of about 1 ms (2). This conformation was presumed to be the binding-competent form of the protein. However, without a structure of this state, they could not confirm that the pocket was accessible. This led to their present attempts to characterize this low-population state using NMR.

As I have mentioned before, R2 relaxation-dispersion experiments can provide three important pieces of information: the populations of the two conformational states (pG, pE for ‘Ground’ and ‘Excited’), the rate of exchange between them (kEX = kGE + kEG), and the difference in chemical shift between the two states at each nucleus (|Δω|). Because the chemical shift is determined by the protein conformation, and because additional experiments can determine the sign of Δω, it should be possible to figure out the structure of the alternate state, given enough relaxation-dispersion data. Therefore, the Kay lab performed a large number of experiments to determine Δω for nearly all of the backbone 15N, 13C, and 1H atoms, as well as many side-chain methyl groups. They then fed this data to the CS-ROSETTA protocol, which can determine a protein structure using chemical shifts alone. While holding the majority of the protein in a single conformation, they allowed CS-ROSETTA to remodel the part of the mutant where they had detected conformational fluctuations.

Lysozyme minor state/major state overlay
Major state (green) and 5 lowest-energy conformers of the minor state (Excited) ensemble (blue)

Using this method, they were able to produce a structure of the transiently-populated minor state of the mutant protein, which I show to the left in comparison to the major conformation (PDB codes 2LCB and 3DMV, respectively, aligned using residues 10-100, 150-160). The most dramatic change is that two of the helices have been fused into one. As you can see, the new helix clashes with the usual position of phenylalanine 114 (pale green, because of the overlap it’s hard to see), which has in turn shifted so that it occupies part of the cavity where benzene binds (pale blue). This suggests, contra the Kay group’s earlier work, that the minor state is also incapable of binding to benzene.

This is a difficult prediction to test in the L99A system because the minor state (E) lives for such a short time that it’s difficult to tell whether anything binds to it or not. Therefore, Bouvignies et al. made a double-mutant protein with the L99A mutation and an additional G113A mutation that was predicted to stabilize the long helix observed in the minor form. This turned out to be the case: the E structure was enriched in the double mutant. In addition, the interconversion rate was slow enough that at low temperature distinct peaks could be observed for each conformation, as well as cross-peaks indicating exchange between them (I discussed this kind of experiment in my previous posts about cyclophilin). Under these conditions, the minor form is sufficiently populous and long-lived to determine whether ligands bind to it.

The Kay group did this by adding an equimolar amount of benzene to the reaction and observing whether there were exchange peaks. If you examine their figure 3c, it’s clear that exchange occurs between all three possible states: (G)round, (E)xcited, and (B)ound. This might seem to contradict their hypothesis. However, the E→B exchange peaks have very low intensity and take significantly longer to reach a maximum than the other exchange peaks. Therefore, this exchange peak may represent a low-frequency E→G→B event rather than direct exchange between the E and B states. Fits of the exchange curves seem to substantiate this interpretation, as the fit tended towards a value of zero for kEB and the χ2 jumped up significantly when kEB was fixed to a very low number.

My only concern with this result is that the kEG rate changes from ~31 to ~36 s-1 when benzene is added (kGE remains the same). It’s possible that the presence of benzene really does accelerate this process, or that the errors are underestimated. The model might also be janky in some hidden way, but my back-of-the-envelope check of the parameters suggests that the results are consistent with what is known about benzene binding to the L99A mutant, e.g. various ways of calculating the KD from these data produce a value of approximately 1 mM, matching earlier results.

If the E state does not represent a binding-competent state, that means the protein must be exchanging to yet another, still-undetected state. According to Bouviginies et al., the E structure they determined can account for all of the observed chemical exchange. If the alternative state that is capable of admitting benzene to the hydrophobic pocket cannot be detected by relaxation-dispersion experiments, it must constitute a very small fraction of the overall protein population (< 1%) and undergo very fast exchange. In principle, the existence of such a process can be detected using experiments designed to measure the intrinsic R2 of a residue, and also should be detectable using 1H experiments directed towards the methyl groups (the side chains likely represent the best bet for explaining the phenomenon). It does not appear that those experiments have been done yet, but I’m certain they’re underway.

Bouvignies et al. made a third construct incorporating the R119P mutation to stabilize the E state even further. This succeeded, producing a protein that spent most of its time in the E state and occasionally sampled the G state. The paper contains no data as to whether benzene detectably binds this mutant, although that strikes me as an obvious experiment to try. Presumably the obligate route through a high-energy intermediate would slow the kinetics of binding relative to the single mutant. If the penalty for adopting the G fold in this mutant is high enough, it might also significantly reduce the affinity.

The findings in this paper are not of any immediate practical use. The L99A mutant is a biophysical curiosity, not a disease target, and most of these techniques have been presented before, at least individually. However, this does serve as a very nice example of the advanced NMR methods that allow the determination of minor states, and of the surprising findings that can be derived from them. This paper should serve as a model approach to this sort of question, which may find broad applicability in the study of signaling, ligand binding, and protein evolution.


Disclaimer: I am currently collaborating with David Baker’s lab on a research project using ROSETTA.

1) Bouvignies G, Vallurupalli P, Hansen D, Correia B, Lange O, Bah A, Vernon R, Dahlquist FW, Baker D, & Kay LE (2011). Solution structure of a minor and transiently formed state of a T4 lysozyme mutant Nature, 477 (7362), 111-114 DOI: 10.1038/nature10349

2) Mulder FA, Mittermaier A, Hon B, Dahlquist FW, & Kay LE (2001). Studying excited states of proteins by NMR spectroscopy. Nature structural biology, 8 (11), 932-5 PMID: 11685237

Jan 272011
 

ResearchBlogging.orgThe enzyme imidazole glycerophosphate synthase (IGPS) can be a bit of a lump. If you bind just one substrate it doesn’t do anything, even though its two active sites are separated by more than 30 Å. Only if the second substrate also binds does catalysis actually go at anything like a respectable rate. In a recent paper in Structureresearchers from Yale report evidence that this change of pace results from a change in dynamics.

Apo- IGPS from Thermatoga maritima
PDB code: 1GPW

IGPS consists of two different protein subunits, HisH and HisF (above). HisH performs a relatively standard hydrolysis of glutamine, producing ammonia and glutamic acid. The ammonia molecule is then used by HisF as part of a cyclization reaction involving a weird nucleotide called PRFAR (with an IUPAC name that’s just too long to bother with). The products of this reaction feed into the biosynthesis of histidine (as you might guess from the name) and the purines. In an example of poor planning, however, the active sites for these two reactions are separated by a great distance. Glutamine hydrolysis takes place near the interface between the proteins (which bind to each other with nM affinity), while PRFAR cyclization takes place at the far end of HisF (near the bottom of the image). This is too far for the ammonia to be efficiently transferred by any direct action of the enzyme itself. Therefore, the reaction proceeds when the NH3 travels down the β-barrel of HisF to its distant active site (see image below left). The upside of this system is that ammonia gets where it needs to go. The downside of it is that unless the hydrolysis reaction only occurs when PRFAR is in position, this enzyme will be a little ammonia factory, costing the cell a fortune in nitrogen. Therefore, the cleavage reaction must be tightly regulated.

Enzymes can deal with this kind of demand in two ways. The first is to make the binding of one substrate depend on another. This is called K-type allostery because what is changed is the affinity (KD) of the enzyme for its substrates. Alternatively, the rate of catalysis can be altered, which is called V-type allostery because the velocity (Vmax) of the reaction is changed. IGPS uses the latter approach. When glutamine binds, NH3 gets eliminated at a stately pace of about 10-3 /s. If PRFAR also binds, however, HisH starts firing NH3 down the barrel at about 5 /s, which may not win many races but is a substantial enhancement. The question, then, is how the HisF active site lets the HisH active site know that PRFAR has arrived, when they are separated by more than 30 Å. Examining the enzyme complex in the presence of various ligands, James Lipchock and Pat Loria find evidence that changes to the dynamics of HisF are responsible for this communication.

A rotated view, looking through the barrel
towards the HisH active site.

The authors start by examining the energetics of PRFAR binding to IGPS. This event is endothermic, with an unfavorable enthalpy of binding. However, the entropic contribution is sufficiently large to overwhelm this effect. This could indicate a major increase in conformational entropy upon binding, or it could just be related to the behavior of water. Lipchock and Loria found that PRFAR binding to form the ternary complex had similar energetics. Of course, you can’t form a ternary complex with actual substrates for very long, because catalysis would occur and change the affinities. They dealt with this using acivicin, a glutamine analogue that binds covalently to the active C84 of HisH.

Unfortunately, these thermodynamic data aren’t particularly illuminating, so the authors proceeded with a high-resolution examination of the system. Because IGPS is a bit over 50 kDa in size, they chose to use methyl groups as their primary probes. Most of the remaining work in the paper uses ILV (Isoleucine, Leucine, Valine) labeling, which takes advantage of the favorable relaxation properties of the methyl groups of those side chains.

Lipchock and Loria started by examining the enzyme in its apo- state using relaxation-dispersion experiments. As I’ve mentioned before, these experiments detect exchange between different conformations on the microsecond to millisecond timescale. If this represents motion between two well-defined states, then the apparent relaxation rate at a given refocusing field strength will be a function of total process rate (kex = kab + kba), the populations of the two states (pa and pb), and the chemical shift difference between them (Δω). If the exchange rate is fast on the NMR timescale (meaning that kex >> Δω), the last three parameters can be combined into a factor called φex.

This is how the authors fit their data, a choice they justified by stating that fitting the data to the full Carver-Richards formula (SI equations 8-18) gives similar answers for kexbut yields large errors in the populations and chemical shift differences. However, most of the dispersion curves look like data from slower exchange regimes. Unfortunately, I’m having trouble reconstructing their fitted curves from the parameters in any convincing way, in part because the equations in SI contain a few errors, so it’s difficult to discuss where the vulnerabilities in this fitting procedure lie.

Using their approach, Lipchock and Loria find that only a few residues are experiencing conformational exchange, and they believe that the motions are primarily local. I’m not so certain on that point: a quick examination of SI Table 1 indicates that all but two methyls have kex within error of 150 /s or so, which may indicate that most residues belong to a single process. However, most of the residues with similar fluctuation rates don’t physically group in any obvious way (although V100 and V79 are adjacent).

Regardless of the particulars, it’s clear that in the apo- state, few of the methyl groups in HisF are experiencing any kind of µs – ms fluctuation. Binding of acivicin to HisH doesn’t change this too much. Within the bounds of the fitted error, the extracted dynamics parameters are the same for many residues. The exceptions are the adjacent residues V79 and V100, and L153δ1, which has an odd halving of both rate and the combined parameter.

Also, as you can see in SI Table 2, the R2° values in this state are significantly lower than apo- IGPS. This is difficult to interpret without knowing exactly how the experiment was performed; they could represent additional ns fluctuations, the removal of some very fast global process, or simply different deuteration efficiency. However, some methyls do not appear to have large changes in their R2° values (e.g. V56γ2, I73δ1, L94δ1). Most of the spurious factors that would give rise to the observed changes in R2° should affect all residues more or less equally; the lack of uniformity suggests this may be worth following up on.

When Lipchock and Loria added PRFAR to the system, all hell broke loose. Many of the amide groups in the protein had their signals broadened beyond the detection limit, indicating conformational exchange on the intermediate timescale. In addition, a large number of methyl groups showed evidence of conformational exchange.

Here the fluctuation is obviously a genuinely incoherent one. Not only do the fitted kex values vary wildly across the protein, they also have poor fitting characteristics (including fitted errors greater than 100%), and enormous differences between adjacent methyls on a side-chain (e.g.L153δ1,2). This suggests that the two-state model might be inappropriate, which is what you would expect for widespread and incoherent fluctuations among contiguous residues. For atoms that are in close proximity, a two-state exchange model presupposes some kind of coherent fluctuation, because in a chaotically fluctuating system, well-defined, relatively long-lived states don’t exist.

Of course, in the absence of well-defined, relatively long-lived states, it’s difficult to understand what all this motion does. It’s therefore very interesting that when acivicin and PRFAR are bound to the enzymes, forming the ternary complex, all the methyls can be fit to a single conformational exchange process with a rate of about 225 /s. That is, the formation of the ternary complex causes the dynamics to become a global (or nearly so), coherent process.

So, what does all that wiggling accomplish? Lipchock and Loria point out that in the apo- structure of HisH, the backbone amide group of V51 is improperly positioned. Its role in the reaction is to stabilize the negatively charged oxygen in the tetrahedral intermediate of the reaction. However, as you can see in their Figure 9, this amide points away from the reactive cysteine in the apo- state. In order to fulfill its function, this loop must rotate about 180° from the apo- position.

The authors hypothesize that the coherent fluctuations of HisF in the ternary complex are transmitted to the active site of HisH and make it possible for this rotation to occur. Consistent with this model, the binding of PRFAR to HisF causes the amide resonance of G50 to broaden out due to chemical exchange. The titration (in Figure 9) looks a little strange; it’s not clear why the peak shifts between 4% and 20% saturation, or why no points are shown from 33% to 100%. While neither glutamine nor acivicin was bound in this experiment, it at least confirms that information about PRFAR binding to HisF can reach the binding site of HisH as changes in dynamics.

HisH active site, looking up the barrel from HisF

This might seem like an odd mechanism, because this particular loop in HisH has no points of close contact with HisF in the crystal structure. By contrast, there appear to be several points of contact between HisF and the region around catalytic triad members H178 and E180, so one could argue that they are more likely responsible for the observed effect. In the apo- state, however, the backbone amide of V51 is hydrogen-bonded to the carbonyl oxygen of P10 (see figure on the right). Fluctuations in that loop, perhaps transmitted from HisF through contacts to HisH residues N12, N15, R18, and R22, could destabilize that bond and encourage rotation. The HisF residues I93 and I73, which are part of the dynamic network in the ternary complex, lie in this region. However, the bulk of the contacts are to the backbone, and alanine dynamics (reflective of main-chain motions) do not appear to have been studied in the ternary complex. A good look at HisF A70, A89, and A97 when both ligands are bound may give some insight into whether this is the transmission point, and some data on the ILV residues of HisH in this region would also help examine this hypothesis. It might also be valuable to mutate P10 to something more flexible to see if the regulation is altered.

The authors point out that the fluctuation rate is many times larger than kcat. The relevant rate, however, is the rate at which the complex enters the catalytically-competent state, which is probably much lower than the total kex. Here, a fit to the full Carver-Richards equation yielding populations would have been enormously valuable. It’s therefore possible (but not yet proven) that the HisF fluctuations are rate-limiting for HisH catalysis, which would after all be an easy way to achieve V-type regulation.

This is another case in which dynamics allow a protein to reconcile incompatible functional requirements. IGPS must be nearly inactive in the absence of PRFAR, yet still achieve a significant rate enhancement in its presence. Although much work remains to confirm the hypothesis that the dynamics are solely responsible, it appears that fluctuations in HisF may enable HisH to adopt an alternate conformation that is catalytically competent while generally favoring the inactive structure.

Lipchock, J., & Loria, J. (2010). “Nanometer Propagation of Millisecond Motions in V-Type Allostery” Structure, 18 (12), 1596-1607 DOI: 10.1016/j.str.2010.09.020

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

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