Why HisH doesn't fire until it sees the whites of PRFAR's eyes

The 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 Structure researchers from Yale report evidence that this change of pace results from a change in dynamics.

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Dynamic origins of PBX1 homeodomain allostery

In the Monod-Wyman-Changeux model for cooperative binding, proteins exist in an equilibrium of low-affinity and high-affinity states in solution, absent any ligand. In this view, although it may appear that the binding of a ligand causes a conformational transition, it actually stabilizes one conformation from a pre-existing equilibrium. In the past several years, advanced NMR techniques have yielded increasing evidence that these structural equilibria exist for a number of proteins, suggesting that this model for linkage between conformational change and binding may be quite general. An upcoming paper in the Journal of Molecular Biology (1) is typical of such findings.

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The M2 channel controversy rides again

Most 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?

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How do adamantane drugs block M2?

Vaccination plays such an important role in our seasonal influenza strategy in part because we don't have many medicines that can be brought to bear on the disease. The neuraminidase inhibitors (specifically Tamiflu) are widely stockpiled, and continue to work for now, but the specter of resistance is already lurking. If these drugs are too widely or too improperly used, there is a good chance that resistance mutations will eventually render these drugs ineffective. Universal drug resistance may already be the fate of the drugs amantadine and rimantadine, built on an adamantane backbone (1). The adamantane drugs inhibit the M2 proton channel from influenza A, a tiny tetrameric protein that equalizes pH between the virus and the endosome of the cell that has swallowed it. This process releases the virus contents so that they can do their damage to the cell, so these medicines can significantly retard the infection process. Or rather, they could, if so many influenza strains didn't harbor the S31N mutation that almost completely nullifies their effect. If we are to develop new drugs to attack the M2 channel, it would be helpful to know how this mutation causes drug resistance. Over the past few years a great deal of structural evidence has accumulated showing how adamantane drugs work on the older, non-resistant channels. The problem is that the evidence supports two different models of M2 inhibition, and so far it has proven difficult to determine which of them is probably correct.

How the question arose

The controversy is the result of two structures published in Nature early in 2008 (2,3). The first of these is a crystal structure of a tetramer of peptides encompassing the transmembrane (TM) region of the M2 channel reported by the DeGrado group at UPenn, which you can see at right (explore this structure at the PDB, noting that the numbering is off by 21). In the detergent used for crystallization, the peptides form a tetramer with a roughly conical pore, which amantadine (purple in these models) physically occludes, giving rise to the pore-blocking model (PBM). This model is consistent with previous results indicating that a single amantadine molecule is sufficient to inhibit the proton channel. In addition, in this model the drug binding site is adjacent to S31 (blue side chain), which is what we'd expect given that an S31N mutation is responsible for most amantadine resistance. The authors propose, given the position of the S31 side chain, that the mutant asparagines form a hydrogen-bonded network that is too constricted for amantadine to bind. Click on the picture for a larger view.

An alternative model was proposed by Schnell and Chou from Harvard University (3). They produced an NMR structure (left) of a 42 amino-acid peptide from M2 encompassing the TM region and an additional C-terminal helix (explore this structure at the PDB). In their structure, taken at pH 7.5 in detergent micelles, the tetramer forms a roughly cylindrical pore that is blocked by the side chains of the known gating residues W41 and H37 (light green in these models). Their structure shows rimantadine bound at four sites near the base of the helix but not in the pore. Using pH-dependent conformational exchange experiments, Schnell and Chou showed that a decrease in pH caused rapid structural changes in the channel, motions that rimantadine slowed. On the basis of this evidence, they proposed a mechanism in which protonation of the gating histidines destabilizes the packing of the TM helices and allows the conductance of protons. Rimantadine blocks the channel by stabilizing the helices, thus this is a dynamic quenching model (DQM). The position of S31 in this model is also somewhat different than the crystal structure, although these models were made at different pH conditions and so this may represent a difference between the closed and open states of the channel.

The distinction here is important. If Stouffer et al. are correct, then drug development should abandon the adamantane backbone altogether and start with a set of significantly different leads to address the resistance problem. The PBM implies that any molecule large enough to occlude the pore will be too large to fit in there following the S31N mutation that induces amantadine resistance. If the DQM is correct, however, then it is conceivable that further refinements to the adamantane base, or similar molecules, could improve affinity enough to overwhelm the mutational effect.

Unfortunately, neither result is unimpeachable. Although it agrees with a great deal of experimental evidence, the low resolution of the crystal structure means that the electron density called amantadine cannot be assigned unambiguously. It is also curious that a hydrophobic molecule like amantadine would bind tightly in the hydrophilic pore. In addition, the crystal form with amantadine bound contains a mutation, G34A (black side chain), which is near the drug binding site and could conceivably have altered the binding specificity of the protein.

The NMR structure has the advantage that it directly includes distance information in the form of NOEs. However, the authors used 40 mM rimantadine to obtain these results, meaning that there were as many rimantadine molecules in the solution as phosphate buffer molecules. Under these conditions, it is possible that the drug bound to a secondary, low-affinity site. Even if this is what happened, it is strange that the rimantadine never bound to the high-affinity site indicated by the crystal structure.

Both experiments use significantly truncated constructs and highly artificial systems to mimic a membrane environment. The structure of any membrane protein depends in often unexpected ways on the composition of the lipid bilayer in which it is embedded and on the structure of that bilayer. The intense curvature of the micelles may have distorted the structure in the NMR experiment, and possibly inappropriate lipids may have had effects on both structures. We know these considerations are relevant for this system, because Schnell and Chou report that the construct used for the crystal structure would not form stable tetramers in the micelle system. Also, as Chris Miller notes in his commentary on these papers (4), there were questions about both constructs with respect to their proton conductivity. Lacking significant stretches of the protein and placed in these environments, it is possible that both structures deviate from in vivo reality in significant ways.

Because the conditions diverge so much, it is difficult to weigh the mechanisms based on these structures alone. The binding site identified by Schnell and Chou is only at the very end of the construct used by Stouffer et al.. In addition, the inhibited crystal structure comes from a low-pH condition while the NMR structure exclusively represents a high-pH condition. Given these differences in conditions, it is not impossible that both models, in whole or in part, are correct. We must turn to additional experiments and alternative evidence to choose between them, specifically data on the stoichiometry of binding and the effects of mutations.

Binding stoichiometry

The crystal structure shows a single binding site for the drug, while the NMR structure implies four, and this is at odds with existing results that indicate that a single molecule of drug is sufficient to inhibit a single channel. Given the homotetrameric nature of the M2 channel, it is in principle not possible for the NMR experiment to distinguish between a single rimantadine binding event and four. That is, the NMR experiment cannot tell us whether the rimantadine-M2 inhibition occurs with a single binding event or requires four drug molecules to bind. Therefore, to argue that DQM is inconsistent with 1:1 stoichiometry overstates the case somewhat.

It may also be somewhat overstating the case to say that there is only one amantadine binding site on M2. Washing amantadine out of your buffer does not reverse inhibition, in part because of slow kinetics of leaving the binding site and in part because these drugs, being very greasy, preferentially partition into the lipid membranes and are therefore not readily removed from a system when its aqueous phase is replaced. It is difficult to measure a binding constant for the drugs because the equilibria under consideration will be quite complex. The studies often cited on the 1:1 stoichiometry (5,6) use structural and kinetic evidence to get at this question.

Czabotar et al. (5) measured tryptophan fluorescence in M2 as a function of pH and rimantadine concentration. They found that fluorescence from W41 was quenched by decreased pH, but recovered when 1 equivalent rimantadine per tetramer was added. This result implies that structural or dynamic changes caused by histidine protonation are reversed by rimantadine inhibition, but this is so general that it cannot be taken to support either the PBM or DQM.

Wang et al. (6) measured the reduction of surface currents in X. laevis oocytes after addition of various concentrations of amantadine. From these results they are able to construct a Hill plot with a coefficient of 1, showing that binding of amantadine is not cooperative. In further results, Wang et al. find that amantadine inhibits M2 channels slightly better at high pH (when the pore is closed) than at low pH, and that amantadine inhibits proton conductance in either direction (rather than favoring one). Both these outcomes are unexpected for PBM, but can be easily explained by DQM. However, the differences in the binding constants are relatively minor and the linearity of the current-voltage relationship may result from some other idiosyncratic feature of the M2 channel, so these results are not unequivocal.

Neither experiment refutes DQM because they do not measure the number of binding sites, but rather the number of efficacious binding sites. If there are four binding sites, but 95% or more of the inhibitory or structural effect is caused by the first drug molecule bound, then these experiments would be unable to distinguish DQM from PBM. Overall, the evidence on the question of binding stoichiometry does not eliminate the possibility of four binding sites existing, but it does place a requirement on DQM that the inhibitory effect of amantadine on the tetramer result from a single binding event. Because the proposed DQM binding site for rimantidine lies between monomers and is linked to the gating tryptophan, this is not unbelievable. Other evidence from these experiments is equivocal, but can be seen as somewhat more problematic for PBM than DQM.

Functional effects of mutations

A serious problem for DQM is that the mutations known to give M2 resistance to adamantane drugs are all located near the PBM binding site. In particular, S31 is adjacent to the drug in the crystal structure and quite distant in the NMR structure. As Miller notes in his commentary, mutational studies are substantially more difficult to interpret than is typically suggested, so this isn't absolutely probative. In general, however, one predicts mutations to have short-range rather than long-range effects, so at least some resistance mutations ought to evolve at the binding site. However, many of the residues surrounding the DQM site are almost absolutely conserved, presumably because they are essential to the function of the channel. As a result, it would be very difficult to interpret any studies on point mutants in this area. What would be ideal, however, would be to find a set of mutations that produced a functional protein and abrogated amantadine inhibition.

This is the basis for an interesting experiment conducted by the lab of Robert Lamb and reported last year in PNAS (7). In this case, the authors took advantage of the fact that the M2 protein from influenza B virus is not sensitive to adamantane drugs. They constructed a chimeric protein containing about a dozen residues from influenza A M2 — specifically, the dozen or so residues surrounding the PBM site. If PBM is correct, then we would expect that these residues, which define that site, would impart amantadine susceptibility to the influenza B channel. This is what happens, sort of. For your benefit, I have shamelessly stolen their figure (right), but you can check out this paper yourself because it is open access. In this assay, again involving X. laevis oocytes, the hybrid channel is sensitive to amantadine (bottom trace), but only half as sensitive as the wild-type influenza A channel (second from top). This result suggests that there is important context conferring susceptibility outside the PBM site. However, this could be something as simple as helix orientation, so the result does not necessarily imply that there is an external binding site.

Additionally, the authors made point mutations at residues (L38, D44, and R45) that were presumed to be important in the DQM mechanism or have long-range effects on amantadine binding. None of these mutations appeared to affect amantadine resistance. In contrast, experiments in liposomes reported by the Chou group this May showed that a D44A mutation prevented rimantidine from having an effect (8). This conflict in results is difficult to reconcile, but may result from the different constructs used (the Chou group used a truncated form of M2 while the Lamb group used the full-length protein) or from changes in ion specificity caused by the D44A mutation. It might be of value to repeat these experiments with the alternative construct: truncated in oocytes, full-length in liposomes. Because the D44A mutant does not appear to conduct protons as efficiently as WT, the proposition that the function of this mutant is too deranged to provide trustworthy information should also be considered.

Additional experiments in the Chou paper are meant to address the relationship between the DQM site and the mutations at the PBM site. They show that the S31N mutation prevents rimantidine binding to the remote site, and also that this mutation makes the protein generally more dynamic. From this evidence they propose that this mutation, at least, disrupts amantadine binding by destabilizing the helical packing of the channel and thus interfering with the organization of the lipid-facing pocket.

They also examine an S31A mutation and find that it is not rimantadine-resistant or destabilizing to the packing. This supports their dynamic model in a limited way, because it demonstrates that only certain mutations at the S31 site will generate resistance. It does not cast any doubt on PBM, however, because in that model resistance in the S31N mutant is explained by the idea that its side chain will partially obstruct the pore so that rimantadine will not fit. I do not think it was ever proposed that specific contacts between S31 and the drug stabilize the binding; in fact, the general absence of such contacts strikes me as a concern about PBM.

Chou et al. also examine the effect of rimantadine on the shorter construct used for the crystal studies. They find that the inhibition of this construct is substantially weaker. However, it also conducts protons at a much slower rate in this assay, suggesting that there may be additional serious problems with the function of this construct. It may be that it simply is not appropriate to use this construct for studies in solution or living membranes. That doesn't necessarily imply that this peptide will give incorrect information in the stabilizing environment of a crystal.

What do we know, and what do we need?

Very little of this evidence unequivocally prefers one model to the other. We know that a single adamantane molecule is sufficient to inhibit M2, and while this is most obviously compatible with PBM it need not be inconsistent with DQM. It is also apparent that various constructs of the M2 channel retain adamantane susceptibility after ablation of the DQM site, either by truncation, mutation, or the construction of a chimeric protein. In all assays, however, the adamantane drugs lose a considerable amount of inhibitory power, so these results are not entirely consistent with PBM either. And, at least in the Chou lab's assays, interference with the DQM site also reduces adamantane susceptibility and deranges function. Moreover, the NMR data from the Chou lab shows that mutations at PBM site have a long range effect on the DQM site, which mitigates the probative power of the S31N mutation.

How do we address this question? One important step would be to start comparing like to like. We are considering evidence from a plethora of constructs and conditions, and the evidence in conflict is often collected in very divergent experiments. Ideally we would like to have structures of the wild-type channel at low and high pH in a lipid environment that closely mimics the composition and curvature of a mature influenza virion. As this is unlikely in the near term, we must hope for NMR and crystal structures that at least use the same construct, minimally mutated, under similar conditions. NMR studies at low pH would be of value in assessing whether these studies in fact contradict one another. Additionally, it would be useful to make adamantane derivatives labeled with a free radical or other paramagnetic label; this would presumably allow the identification of a binding site at lower drug concentrations in an NMR experiment. Labeling the drug with a metal might also allow its identification in a crystal structure without any need to push the resolution significantly higher. Finally, actual structures of the S31N mutant, positively identifying the disposition of this side chain, would be of great value in judging the question.

Structural experiments can take a great deal of time and careful tuning, a requirement exacerbated by the often-fickle behavior of membrane proteins. As such, additional mutational studies could prove useful. Inverting the chimera experiment of Jing et al. to create a chimeric protein with the upper channel from influenza B and the lower channel from influenza A may be a helpful supplement to the existing experiments. If the C-terminal portion of the channel makes a contribution to adamantane inhibition this chimera will also be rimantidine-sensitive. In addition, new mutations at S31 could help distinguish the possibilities. The PBM supposes that the N31 side chains stick into the pore and form hydrogen bonds, while the DQM supposes that they stick into the interface of the TM helices and destabilize them. An S31L mutant should disrupt the helical packing but not form hydrogen bonds or extend the L31 side chains into the pore. If functional, such a mutant ought to be rimantidine resistant if the DQM is correct, but not if PBM is correct. Assuming the geometry of the longer side chain is wrong for formation of a hydrogen bonding network, an S31Q mutation might be useful as well. Similarly, mutations that increase the size of the L40, I42, or L43 side chains could prevent adamantane binding to the DQM site without degrading the channel's transport capabilities; the drug sensitivity of such a mutant would be a powerful argument either way. Any experiments of this kind would likely be easier to perform than to interpret, but could provide valuable insight. Obviously, it would also be important to establish that each mutant was competent at transporting protons.

The unspoken assumption of the debate so far is that these mechanisms are mutually exclusive, but there is no particular reason to believe this must be so. The structural experiments definitively show that binding to both sites is at least possible — even if one clings tenaciously to the idea that the density observed in the crystal is not in fact amantadine, that structure at least shows that the PBM site is capable of accommodating the drug. It might therefore be plausible that adamantane drugs inhibit M2 using both mechanisms simultaneously, or that DQM predominates at high pH and PBM at low pH. Redundancy in inhibitory mechanisms may explain the curious features of amantadine inhibition noted by Wang et al., and the inability of experiments specific to a single site to completely account for adamantane inhibition. In addition, the fact that S31N interferes with both mechanisms may explain why it is the primary resistance mutation.

An experiment with the alternate chimera mentioned above could test this possibility. In addition, if the mechanisms switch off in a pH-dependent fashion, then this should be testable with the hybrids: specifically, the A/B M2 used by Jing et al. should have lower susceptibility to adamantane drugs at high pH than at low pH. Similarly, the B/A M2 chimeric protein, if inhibited by amantadine, would be more resistant at low pH.

Doubtless these suggestions are nothing new to the members of the labs working on this perhaps unexpectedly hairy question. Membrane protein structure and function is one of the most difficult experimental subjects in biochemistry, and constitutes a critically important frontier in scientific efforts to improve human health. It is infinitely easier to propose most of these experiments than it is to perform them, and I would be remiss if I did not temper the persistently critical tone of this post with some praise for the efforts of all the scientists involved in this research, and for their commitment to getting the right answer. These papers represent years of work by incredibly talented people using some of mankind's most advanced scientific techniques. The lack of clarity on the question of adamantane drugs binding to M2, even in the face of this amazing effort, is a testament to the enormous difficulty of researching these critical systems.

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1. Deyde, V., Xu, X., Bright, R., Shaw, M., Smith, C., Zhang, Y., Shu, Y., Gubareva, L., Cox, N., & Klimov, A. (2007). Surveillance of Resistance to Adamantanes among Influenza A(H3N2) and A(H1N1) Viruses Isolated Worldwide The Journal of Infectious Diseases, 196 (2), 249-257 DOI: 10.1086/518936 OPEN ACCESS

2. Stouffer, A., Acharya, R., Salom, D., Levine, A., Di Costanzo, L., Soto, C., Tereshko, V., Nanda, V., Stayrook, S., & DeGrado, W. (2008). Structural basis for the function and inhibition of an influenza virus proton channel Nature, 451 (7178), 596-599 DOI: 10.1038/nature06528

3. Schnell, J., & Chou, J. (2008). Structure and mechanism of the M2 proton channel of influenza A virus Nature, 451 (7178), 591-595 DOI: 10.1038/nature06531

4. Miller, C. (2008). Ion channels: Coughing up flu's proton channels Nature, 451 (7178), 532-533 DOI: 10.1038/451532a

5. 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

6. Wang, C., Takeuchi, K., Pinto, L.H., & Lamb, R. (1993) Ion Channel Activity of the Influenza A Virus M2 Protein: Characterization of the Amantadine Block J. Virol. 67 (9) 5585-5594 Available free from PubMed Central

7. 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 of the United States of America, 105 (31), 10967-10972 DOI: 10.1073/pnas.0804958105 OPEN ACCESS

8. 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 of the United States of America, 106 (18), 7379-7384 DOI: 10.1073/pnas.0902548106

The Scientific Activist and Discovering Biology in a Digital World also have interesting posts on this subject.

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cAMP gives CAP a twist

The catabolite activator protein (CAP), which plays a significant role in telling bacterial metabolism to digest sugars other than glucose, is a classic example of allosteric activation. Binding of the small signaling molecule cyclic AMP (cAMP) switches CAP into an active state that recruits RNA polymerase to certain metabolic genes. The biochemistry of cAMP activation is well understood, but the structural basis is not as clear, because a structure of the inactive protein was not available. This week in PNAS, researchers from Rutgers University and the University of Wisconsin-Madison report a structure of the free state of CAP that illuminates this allosteric effect.

Many excellent structural studies have examined CAP in its activated and DNA-bound form. CAP is a dimer, and each monomer has two domains: a DNA-binding domain (DBD) that recognizes its specific sequence, and a cAMP-binding domain (CBD). The monomers bind to each other through a coiled-coil interaction between two long helices. When CAP is activated it binds to DNA, with two helices (the recognition helices) sliding into the major groove and specifically identifying the sequences to which it should recruit the transcription apparatus. Without cAMP bound, CAP can still interact with DNA, but this interaction is of low affinity and not specific for any particular sequence. There are a number of ways this could conceivably happen, but it's difficult to be certain about any model in the absence of a structure of the free (apo-) protein.

In order to determine the structure, Popovych et al. used NMR. The 50 kDa size of the dimer means that it requires some extra effort for NMR work, but it is still well within the capabilities of the technique. The fact that the protein is a symmetric homodimer makes assigning the spectra somewhat easier, as the researcher only needs to deal with 209 residues rather than 418. The authors determined the structure using short-range distance restraints from nOe experiments, long-range restraints from paramagnetic relaxation enhancement, and angular restraints from residual dipolar couplings (RDCs). These angular restraints allowed the authors to unambiguously determine the relative orientation of the DBD and CBD in each monomer.

Getting that orientation right is key to the story here, as you can see from the image to the left. Here I'm showing you the DBD and coiled-coil helix (lower left) of a single monomer in the two different states. The activated CAP is in green (PDB code: 1G6N, and the apo- structure is in red (PDB code: 2WC2). You're looking down the coiled-coil, and the recognition helix is in a brighter color right at the front. If you superpose these structures on the bottom end of the coiled-coil, you can see that the recognition helix is rotated by 60° when cAMP binds. This twist of the DNA binding domain gives the recognition helices the right orientation and spacing to slide into the major groove of DNA and identify genes to activate. In the apo- state, these helices cannot both fit into the major groove simultaneously, explaining the low affinity and lack of specificity in that state.

Although the DBDs undergo a radical change in position following cAMP binding, they don't actually have any direct interactions with the signaling molecule, which binds down in the CBD near the coiled-coil helix. This helix, which links the CBD to the DBD, turns out to be key to communicating the allosteric signal. In the apo- state, the top part of this helix (near the DBD) is actually somewhat disordered and loop-like, not helical. Binding of cAMP to the CBD forms several contacts and causes several structural shifts that result in the formation of regular helical structure at the top of the coiled-coil. This in turn swings the DBDs around so that the recognition helices are in position to interact with target sequences (the authors provide a short movie of this in the Supplementary Information). A similar molecule, cGMP, that does not activate CAP, fails to make the key contacts with T127 and S128 that mediate this structural change.

The fact that the upper part of the coiled-coil is unstructured suggests that CAP may sample a range of conformations in the apo- state. This possibility is supported by one of the mutational studies in the paper. As you can see from Figure 5, a G141S mutation and binding of various effectors to the mutant causes the NMR resonances of DBD residues to shift on a line between the WT apo- and WT cAMP-bound states. This, in conjunction with the broadening of those intermediate peaks, suggests that the DBDs are exchanging between the two states on a timescale of microseconds. It seems quite likely that one or both DBDs in the inactive dimer occasionally samples the active conformation. In this model, the function of cAMP would be to stabilize, rather than enable the active conformation. The negative cooperativity of cAMP binding may help keep CAP switched "off" in the face of this conformational heterogeneity.

This study only dealt with a single protein, but the results are likely to be applicable to a number of systems. The allosteric mechanism described here seems to fit observations in at least some other members of the protein family to which CAP belongs. As such, this structural work and the dynamics investigations that will probably ensue are likely to provide important insights into a number of regulatory pathways in bacteria.

Popovych, N., Tzeng, S., Tonelli, M., Ebright, R., & Kalodimos, C. (2009). Structural basis for cAMP-mediated allosteric control of the catabolite activator protein Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.0900595106

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Activated caspases stick together

In a post last week I mentioned a technique for obtaining the high-resolution structure of a protein inside a living cell, but I also pointed out that this technique was difficult and expensive, and might not be applicable to large proteins. Techniques improve and become more powerful, of course, but you might not want to wait for NMR to catch up to your question. Fortunately, high-resolution in vivo structures may not be necessary if you already have relevant dilute-solution structures of your protein and merely want to distinguish between different known conformational states. In a recent paper in PNAS, researchers from San Francisco used conformation-specific antibodies to locate activated caspase-1 in cultured cells.

Caspase-1 is a cysteine protease that plays a role in the immune response, as well as being released during apoptosis. From crystal structures we know that this protein can adopt two different structures, of which only one represents a catalytically competent state of the enzyme (the on-form). Caspase-1 also possesses an allosteric site where an inhibitor can bind, locking the enzyme in an inactive conformation (the off-form). When it's not bound to anything (the apo-form) caspase-1 is presumed to have a conformation similar to the off-form. Like many proteases, caspase-1 has a large, inactivating tail when it is made (the pro-form) that must be cleaved off before activation is possible. The structure of the caspase-1 proenzyme is not known. Current models of inflammatory response propose that after processing, the on-form binds to scaffolding proteins in an "inflammasome". In order to confirm this proposition, the authors decided to generate antibodies that would bind specifically to the on-form or the off-form of caspase-1.

The key to this experiment was combining irreversible inhibitors that could essentially lock the caspase into one conformation with the phage-display technique for optimizing antibody recognition. The authors had the advantage that both the active site and the allosteric site have cysteines in them. In an oxidizing environment, small molecules can covalently bind to the protein via disulfide bonds, thus locking the enzyme into the on-form or off-form. The authors immobilized these "locked" forms of caspase-1 and used them to screen antibody fragments (Fabs) using phage display. In addition to the typical selection approach, the authors performed anti-selection at one point using the "wrong" conformation to increase the specificity. After several rounds of selection, and some controls to ensure that the antibodies were binding to caspase and not the inhibitors, Gao et al. had several candidates for further optimization and screening. After they completed that process, they had two antibodies, Fab_on and Fab_off, specific for the two conformations. Each antibody bound to its intended target with a K_D of less than 5 nM. The authors also made full antibodies (IgG_on and IgG_off) from these Fabs for expression in mammalian cells.

The authors took these new antibodies for a spin with the apo-form of caspase-1. One might naively expect that only Fab_off would bind to this protein, but in fact Fab_on bound as well, albeit with substantially reduced affinity relative to the on-form. One possible interpretation of this finding is that the apo-form is equivalent to the off-form, but that Fab_on can convert it to the on-form via an induced-fit mechanism. If this is the case, then we would expect Fab_off to have the same affinity for the apo-form as it has for the off-form. However, the authors find that Fab_off has reduced affinity for the apo-form relative to the off-form. This indicates that the apo-form is an ensemble of conformational states, most of which more closely resemble the off-form than the on-form. Consistent with this view, the authors found that they can activate or inhibit apo-form activity by adding Fab_on or Fab_off, respectively.

By contrast, IgG_on did not bind detectably to a model of the pro-form, suggesting that this form's conformational ensemble contains no members that are close in structure to the active form. The weak affinity of IgG_off for the pro-form suggests that there are substantial differences between this conformation and the off-form as well.

At this point we know that IgG_on will bind tightly to the on-form of caspase-1, weakly to the apo-form, but not to the pro-caspase. This means it will likely be an effective probe of active caspase-1 in cells. The authors performed this experiment in THP-1 cells that they differentiated into macrophages. While IgG_off produced diffuse fluorescence in these cells, IgG_on stained small, concentrated bodies in a fraction of the cells. This suggests that active caspase-1 is localized to supramolecular structures in these cells, which the authors argue are identical to a structure previously identified as the "pyroptosome".

Although this particular experiment took advantage of binding-site cysteines that are particular to caspase-1, it should be possible to extend this approach to other proteins. Even non-covalent inhibitors or activators should be useful in this approach as long as the concentration is held high enough to saturate the target site during the selection step. Of course, the conformational change must alter the structure enough that the antibodies have something to grasp — it may not be possible to get specific antibodies if the shift is too subtle. If this requirement is met, however, it should be possible to determine the distribution of specific conformational states in cells, or even (as the authors suggest) to use antibodies as activators or inhibitors in vivo.

J. Gao, S. S. Sidhu, J. A. Wells (2009). Two-state selection of conformation-specific antibodies Proceedings of the National Academy of Sciences, 106 (9), 3071-3076 DOI: 10.1073/pnas.0812952106

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Allostery in the CBP KIX domain

Classically, allosteric and cooperative effects have been identified with large complexes of multiple protein subunits, in which the binding of a ligand to one subunit enhances ligand binding in a different subunit. While some features of the models developed to deal with these systems do not translate well to cases of allostery within a single protein or domain, many of their core ideas continue to illuminate these single-subunit systems. In an upcoming paper in the Journal of the American Chemical Society, a team of European researchers examine an allosteric effect based on population shifts in a transcriptional activator, comparing it to a famous model for explaining allostery in hemoglobin (1).

The CREB binding protein (CBP) is a large molecular scaffold that brings pieces of the transcriptional machinery together in order to turn on a gene. Like many scaffold proteins it contains several protein-protein interaction domains linked together by large unfolded regions. One of these domains is KIX, a small bundle of helices that binds other proteins at two distinct sites. In one case, a protein called MLL binds to one site while a protein called c-Myb binds at the other. What is so interesting about this is that KIX is much more likely to bind c-Myb when it is already bound to MLL. Brüschweiler et al. used NMR techniques to try and understand how this happens.

In order to pull this off they performed relaxation-dispersion experiments on the amide nitrogen, α-carbon, and some methyl carbon atoms of the KIX domain bound to a peptide derived from MLL. Many of the amino acids in the protein showed a significant contribution to R₂ from exchange, suggesting a global conformational switch between two states. In order to cover their bases, the authors performed experiments to prove that this behavior was not related to the unfolding of the protein. Satisfied that the protein was stable, they used standard methods to calculate the rate of the conformational change, the population of the two states, and the chemical shift difference between them. They found that the minor state of the KIX-MLL complex is 7% of the total population of protein molecules. They also noticed that the chemical shift difference between the two states correlates very well with the chemical shift difference between the KIX-MLL complex and the KIX-MLL-c-Myb complex. Assuming that the conformation of KIX is the primary determinant of chemical shift in the bound state, this suggests that the dynamics are sensing a switch between a state that doesn't bind c-Myb and a state that does.

In order to determine whether MLL binding gave rise to this conformational switching behavior, the authors measured relaxation dispersion in KIX at several different concentrations of MLL. Excluding residues highly sensitive (by chemical shift) to MLL binding, they found that the exchange contribution to relaxation increases as MLL is added. Although Brüschweiler et al. were unable to fit this small number of residues quantitatively, these results strongly suggest that the addition of MLL increases the population of the c-Myb binding state. Moreover, under conditions where KIX forms a saturated complex with MLL and a peptide from another protein (pKID), the chemical exchange contribution to relaxation vanishes, suggesting that the protein has been pushed completely to the binding-competent state.

In order to identify the pathway by which the MLL binding site communicates to the c-Myb binding site, the authors examined the residues in KIX that had the largest chemical shift change associated with the chemical exchange behavior. As it happens, residues satisfying these criteria cluster in a region stretching from the MLL site to the c-Myb site, as you can see to the right (explore this structure at the PDB). Here, KIX is blue, the MLL peptide is red, and the c-Myb peptide is green. The side chains of the residues Brüschweiler et al. identify are shown as sticks inside the pink atomic surface. As you can see, these residues constitute a contiguous body stretching from one site to the other. Presumably, this set of residues provides a pathway for communication between the two sites. A trio of isoleucines at the core of this region (I 611, 660, and 657) are present in KIX domains from many different species (supporting information), suggesting that this communication pathway is evolutionarily conserved. Mutational studies centered on this trio of residues may teach us more about the mechanism of information flow in this domain.

Although this allosteric pathway is known to work in reverse (binding of c-Myb enhances the binding of MLL), the authors were unable to detect any exchange contribution to R₂ when only c-Myb or pKID was bound. While this may suggest that communication in the opposite direction uses a completely different mechanism, such that KIX has two unidirectional allosteric pathways, that's not a necessary conclusion from this result. Alteration of R₂ due to conformational exchange is dependent on the populations of the two states, the difference in chemical shift between them, and the rate of the switch. Actually detecting a dispersion curve requires that all these parameters lie within a 'sweet spot' that allows observation. This doesn't always happen, even when a dynamic process is occurring with a μs-ms rate. Because the chemical shift changes that result from MLL binding appear to be quite large (2) the exchange process may be slow on the NMR timescale.

One minor concern I have with the paper is that the experiments were carried out at a pH of 5.8, which is lower than the pH of cytosol (7.2). The only groups likely to change their charge over that range are histidines, but one of the key residues for this paper is H651 in the KIX domain. The experiments that established the allosteric effect of MLL on c-Myb binding (2) were performed at pH 7.0 so it is formally possible that the dynamics and allostery are a coincidence (although the chemical shift data argue against this). It would probably be worthwhile to perform HMQC experiments to clarify the protonation state of the histidine, or to repeat the binding experiments at a lower pH. The latter might be preferable; I assume that mildly acidic conditions are used for the NMR experiments because KIX has undesirable spectral characteristics nearer neutral pH. Additionally, it might be interesting to perform experiments that explore the effects MLL has on the kinetics of binding, seeing as this appears to be a dynamic process.

Brüschweiler et al. identify their results with the Monod-Wyman-Changeux model of allostery. Although this model was formally developed for systems with multiple subunits, what the authors really wish to emphasize is the idea from the MWC model that proteins in solution exist in an equilibrium of high-affinity and low-affinity forms. The evidence from the relaxation-dispersion experiments indicates that a very small proportion of free KIX exists in a (unfavorable) conformation that's ready to bind c-Myb. The binding of MLL enhances KIX affinity for c-Myb by stabilizing this structure — the allosteric effect arises because MLL binding defrays the energetic cost of adopting this fold. This manifests as a shift in the population of KIX proteins towards the binding-competent state. This kind of binding cooperativity may play a significant role in CBP's transcriptional activation function.

(1) Sven Brüschweiler, Paul Schanda, Karin Kloiber, Bernhard Brutscher, Georg Kontaxis, Robert Konrat, Martin Tollinger (2009). Direct Observation of the Dynamic Process Underlying Allosteric Signal Transmission Journal of the American Chemical Society DOI: 10.1021/ja809947w

(2) N. K. Goto, T. Zor, M. Martinez-Yamout, H. J. Dyson, P. E. Wright (2002). Cooperativity in Transcription Factor Binding to the Coactivator CREB-binding Protein (CBP). Journal of Biological Chemistry, 277 (45), 43168-43174 DOI: 10.1074/jbc.M207660200

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How we taste umami

Although we still do not know the full breadth of our flavor-sensing capabilities, human beings are known to possess receptors for at least five basic tastes. Probably you have known about the sweet, sour, salty, and bitter flavors since you were in grade school, but the fifth, umami, was less widely accepted in the West until recently. Umami is a savory flavor element that is found in many foods, including tomatoes, parmesan cheese, truffles, and many kinds of meat and seafood. The umami taste primarily detects the amino acid glutamate (hence the popularity of the food additive monosodium glutamate, or MSG), but the effect is also intensified by the presence of the nucleotide inosine monophosphate (IMP). In a recent (open access) paper in PNAS, researchers from two corporations examined the umami taste receptor to understand how this happens.

The umami flavor is detected by a pair of G-protein coupled receptors (GPCRs) that have an external venus flytrap (VFT) domain in addition to their classic 7-helix trans-membrane domain (TMD). This complex is closely related to the sensor for the sweet flavor: in fact one of the receptors (called T1R3) is the same in both sensors. It is the second receptor (T1R1 for umami, T1R2 for sweet) that determines what taste is recognized. What we don't know for sure is whether it is the TMD or the VFT of these receptors that identifies the flavor component.

In order to answer this question, the researchers performed an experiment known as a "domain swap". Using recombinant DNA technology they assembled two chimeric proteins, one with the VFT of umami and the TMD of sweet, and one with the VFT of sweet and the TMD of umami. They then inserted these proteins into cultured cells that would fluoresce when the receptors were activated. The authors suspected that the VFT is primarily responsible for binding the ligand. As you can see from figures 1 & 2 (this is an open access paper, so go ahead and take a look), the experiment bears this out. The chimera with the VFT of sweet caused a fluorescent response in the presence of compounds such as sucrose and aspartame, while the umami-VFT chimera reacted to glutamate and aspartate. You can also see in figure 2C that the presence of IMP dramatically enhanced the activity of glutamate in this chimera. This indicates that the VFT is also responsible for IMP synergy in the umami receptor.

The hurdle in going further than this is that no structure of the umami VFT is available, which makes it difficult to figure out exactly how everything fits together. However, T1R1 has a close evolutionary relationship to the metabotropic glutamate receptors (mGluR), and a crystal structure of that VFT is available. Using conserved and homologous residues as a guide, the authors made a model of the T1R1 fold from the mGluR data. Based on this model they predicted certain amino acids that would be essential for glutamate binding in T1R1 and then mutated them in order to measure the effect. Residues that were predicted by the model to interact with the zwitterionic amino acid backbone proved to be essential for ligand recognition. Interestingly, the amino acids that contact the side-chain carboxylic acid of glutamate in mGluR are not conserved in T1R1, and mutations at the matching sites do not alter glutamate binding. However, these mutations eliminate the effect of IMP.

In order to understand this behavior, the authors modeled the binding cleft in the closed state, with IMP and glutamate in place. Glutamate binds at the bottom of the cleft, with its side chain pointed outwards. This conformation puts several positively-charged residues from the two lobes of the VFT close together higher up in the cleft. The authors propose, in keeping with previous models of VFT behavior, that the binding of the glutamate lowers the energy barrier between the open and closed states of the domain, but that glutamate alone is not sufficient to hold the domain closed. Their model places IMP higher up in the cleft, where its negatively-charged phosphate interacts with the positive residues. Thus, IMP stabilizes the closed conformation of the VFT domain.

Some more work here would be welcome, particularly in the form of experimental crystal structures of the T1R1 VFT that can confirm the homology model. The VFT is rather large, but using a perdeuterated sample in a high-field magnet it might be possible to confirm the population-shift mechanism using NMR experiments. Lower-resolution techniques such as FRET may also be able to catch this stabilization behavior. If the model proves to be accurate, it would serve as an interesting example of positive allostery from a population shift.

Although these experiments only concerned the umami taste receptor, this allosteric mechanism may be a more general feature of certain GPCRs. The authors indicate that they have unpublished data showing similar behavior in the sweet receptor, and it may be possible to design an allosteric stabilizer for any GPCR with a VFT domain. Because the related mGluR receptors are involved in many neurological and psychological diseases, successful design of such activators may have some therapeutic value.

F. Zhang, B. Klebansky, R. M. Fine, H. Xu, A. Pronin, H. Liu, C. Tachdjian, X. Li (2008). Molecular mechanism for the umami taste synergism Proceedings of the National Academy of Sciences, 105 (52), 20930-20934 DOI: 10.1073/pnas.0810174106 OPEN ACCESS

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Long-range effects in the ribosome

Antibiotics such as chloramphenicol suppress infections by inhibiting bacteria from making proteins. They achieve this by binding to and blocking the peptidyl transferase center (PTC) of the ribosome, a large complex of RNA and protein that performs nearly polypeptide synthesis in living cells. Although PTC-binding antibiotics comprise several different families of compounds, mutations in the ribosome that confer resistance to one family often produce cross-resistance to other families. This is difficult to understand because the PTC itself is highly conserved and not very tolerant of mutations. In an upcoming paper (open access, read along) in the Proceedings of the National Academy of Sciences, a team of researchers from the Weizman Institute of Science analyze several crystal structures of the ribosome to understand how this cross-resistance arises.

Davidovich et al. mapped nucleotide mutations known to confer resistance to PTC antibiotics onto x-ray crystal structures of the large ribosomal subunit from D. radiodurans in complex with antibiotics. One interesting facet of the resistance mutations became immediately apparent: they were almost all clustered on one side of the antibiotic binding site.

You can see this pretty clearly in Figure 2 panels B&D. Although the antibiotics (large pink surface) are surrounded by nucleotides, most of those that are on the left side (thin tan sticks) do not confer resistance if mutated. Resistance-conferring mutations instead cluster around the "rear wall" of the PTC (to the right). The authors explain that in this region ribosomal functions primarily rely on the sugar-phosphate backbone of the rRNA. Because the backbone elements are the same for all ribonucleotide bases, mutations in this region are more likely to be tolerated without significant loss of function.

Another striking feature of resistance mutations is visible in Figure 2 and quantified in Figure 3A, namely that many of these mutated bases do not contact the antibiotics directly. In particular, mutation of G2032 appears to play a role in conferring resistance to several different antibiotics. Overall, however, it appears that numerous long-range interactions can interfere with antibiotic binding.

The lynchpin of these interactions seems to be U2504, a base that directly contacts the bound antibiotic in most cases. Mutations to U2504 itself do not appear to be well-tolerated, but many of the long-range mutations occur in the layer of bases surrounding it. The authors describe in detail several mechanisms by which the observed mutations might increase the flexibility of U2504, allowing it to adopt positions that could allow continued protein synthesis while reducing the binding of antibiotics. The commonality of interactions with U2504, and the importance of the structural context of the surrounding nucleotides, explains why many mutations can give rise to cross-resistance.

The practical upshot of these findings is that they may serve as a guide for the design of future antibiotics. Since the majority of the drug-resistance mutations lie on the rear wall of the PTC, the effectiveness of these antibiotics may be enhanced by improving their binding to other parts of the site. With further modeling it may also be possible to design antibiotics that can compensate for flexibility at U2504. These findings also remind us that dynamics and long-range interactions can be important to the function of any biomolecule with a folded three-dimensional structure, not just proteins.

C. Davidovich, A. Bashan, A. Yonath (2008). Structural basis for cross-resistance to ribosomal PTC antibiotics Proceedings of the National Academy of Sciences, 105 (52), 20665-20670 DOI: 10.1073/pnas.0810826105 OPEN ACCESS

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Making a molecular switch

The practical aim of the investigation of allostery is the manipulation of this property as a means to aid human health and industry. We already have in hand the sequences of numerous enzymes that carry out unique and useful chemical reactions, and recent advances suggest that we will in the near future be able to design man-made enzymes that efficiently carry out completely novel reactions. Making the fullest use of these abilities demands that we be able to regulate the enzymatic activity of interest. Fortunately, just as nature possesses a rich array of enzymatic activities, it also holds a number of binding proteins, so we don't have to start from scratch. The trick is that there must be some way to communicate the binding event from one domain into the active site of the other domain. In last week's edition of Science researchers from the University of Pennsylvania and the University of Texas Southwestern Medical Center claim to have achieved just that.

The core idea of their approach is disarmingly simple. Identify a protein that has a long-range conformational response to a binding event, and locate the distal region on its surface where this response gets read out. Then, on an enzyme of interest, find a region of the surface that has an energetic connection to the active site. Join the proteins at these surfaces and voila! Now you have a regulatory switch for your enzyme!

The reality, of course, is likely to be trickier. In order for efficient communication between sites to occur via these pathways, the structural dynamics of allostery at the points of attachment must be compatible. For nearly all proteins, the precise nature of the conformational reactions that drive communication are essentially unknown. Thermodynamic mutant cycle analysis cannot provide detailed mechanistic information, and structural dynamics experiments from NMR and other techniques can provide only general information about what occurs on these pathways. Only molecular dynamics simulations are likely to give us the information we need to tune the allosteric control precisely. Absent that, all you can do is just stick things together and hope for the best, which is essentially what Lee et al. did.

Of course, they didn't go in totally blind. Lee et al. used the statistical coupling analysis (SCA) technique pioneered by Dr. Ranganathan to identify distal surface sites linked to the light sensitivity of a PAS domain and the enzymatic activity of a bacterial dihydrofolate reductase (DHFR). I've mentioned this technique before in connection with Ranganathan's research on the PDZ domain. The SCA results indicated that a surface loop of DHFR was energetically linked to its active site. The analysis also indicated that a region encompassing the N- and C- termini of the LOV2 PAS domain was likely to be a readout for its detection of light. These results accorded with existing knowledge about these proteins. The result with the PAS domain was particularly convenient. Because the N- and C- termini are adjacent, it meant that the PAS domain could simply be inserted at a loop site. Also conveniently, the surface identified for DHFR was a loop.

Thus, Lee et al. inserted the PAS domain at two sites in DHFR. One was the loop identified by SCA, and the other was a control site equally distant from the active site but not predicted to be linked. Figure 3 of the paper shows the key result: insertion of the PAS domain at the SCA-identified site (A site), but not the control location (B site), resulted in a modest light-dependence of the hydride transfer rate for DHFR. All of the A site chimeras had substantially reduced DHFR activity, similar to the effect of a G121V mutation. Interestingly, shifting the insertion site by even a single residue completely abolished the light-dependence of the activity.

Granted, the light dependence is less than twofold at room temperature; this approach did not generate a genuine light-dependent on/off switch for DHFR. However, for the reasons I mentioned before, a perfect switch is hardly something that could have been expected. What this experiment does do is prove that this approach is workable. Conceivably, with further tuning the hybrid PAS-DHFR can be made to carry out its catalytic function exclusively in the presence (or absence) of light. Since PAS domains bind a wide array of ligands, the approach can probably be adapted for various chemical triggers.

On a more fundamental level, the authors claim that this result supports the view that specific surface locations in many domains may be evolutionarily-conserved loci for allosteric control. This does not mean that every PAS domain (or PDZ domain, or DHFR) actually possess allosteric properties, but it does imply that all of them have the potential to exert or receive allosteric influences. If this is true, then it may be possible to adapt a wide array of binding modules as allosteric regulators for natural and designed enzymes. As our understanding of intradomain signaling improves, our ability to make use of these approaches will only increase.

J. Lee, M. Natarajan, V. C. Nashine, M. Socolich, T. Vo, W. P. Russ, S. J. Benkovic, R. Ranganathan (2008). Surface Sites for Engineering Allosteric Control in Proteins Science, 322 (5900), 438-442 DOI: 10.1126/science.1159052

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Do conformational changes precede or follow binding?

The binding of a ligand to a protein rarely occurs with the simplicity of a block sliding into an appropriately-shaped hole. Protein and ligand often engage in complementary conformational changes to adapt their shapes to each other. As a result, the structure of a protein bound to its target may differ substantially from the structure of the free protein. Unfortunately, it is virtually impossible to view the binding process in fine structural detail; as a result, most of our knowledge comes from the relatively stable bound and free states. Improving biophysical techniques, however, have brought a change in the way we view some binding events.

Most alterations of conformation during a binding event have historically been interpreted using the induced fit model. In this view, the protein stably maintains the free or "open" structure until it comes into contact with a ligand molecule. This encounter stimulates a conformational change so that the protein adopts the "closed" conformation that tightly holds onto the ligand. Thus, the ligand induces the conformational change necessary to form the bound, closed (BC) structure from the unbound, open (UO) structure, and the intermediate on this path is some kind of bound, open (BO) structure. This model is physically reasonable and has been very successful in interpreting many systems.

However, for the past few decades an increasing amount of evidence has suggested that this is not the whole story. NMR investigations indicated that instead of remaining in a single, well-defined backbone conformation most of the time, many proteins experienced significant changes in their structure while floating free in solution. These results suggested an alternative mechanism of population shift. In this view, the protein actually samples the "closed" conformation (or something very similar) while unbound, and it is this conformation that binds to the ligand. We still go from UO to BC, but now the intermediate is an unbound, closed (UC) structure.

This sounds very arcane, but it is not without functional relevance. Consider, for instance, a protein that is activated by a particular ligand. If we wish to make a drug that binds exclusively to the BC form, then we may experience unforeseen side-effects if our target protein occasionally samples a UC state. It would be useful to have a general idea of what kinds of circumstances are likely to favor a population shift model vs. an induced fit model. That is precisely what Kei-Ichi Okazaki and Shoji Takada aim to provide in an upcoming paper in Proceedings of the National Academy of Sciences (1).

Okazaki and Takada performed a coarse-grained molecular dynamics simulation of glutamine binding protein. In the bound and unbound states they employed a double-well Gō model, a simplified representation of molecular forces, to represent "opening" and "closing". To switch between these states (i.e. to represent binding) they used a Monte Carlo algorithm. This approach has the advantage of being quick and relatively inexpensive from a computational standpoint, but the results must be interpreted cautiously because the physics of the model are greatly simplified. They observe UO ↔ UC and UC ↔ BC events in this system, but they also observe UO ↔ BO and BO ↔ BC events. This suggests that the simulation will be able to make predictions about both population-shift and induced-fit mechanisms.

In order to try to make some predictions about the circumstances in which a particular mechanism is favored, Okazaki and Takada varied the strength and range of the binding interaction. By monitoring whether the simulated system entered the BC state from BO or UC, they could tell whether the system obeyed the induced-fit or population-shift mechanisms, respectively. They find that as either the strength or the range increase, the induced-fit mechanism is increasingly favored (Figure 4). These results make sense. If the protein regularly samples the closed state while unbound, then the amount of energy needed to reach that state is probably small, so it makes sense to see a population-shift mechanism associated with low-energy binding. Similarly, if a ligand is to associate productively with a non-optimal protein conformation, it makes sense that key interactions will be effective at long range.

From these results Okazaki and Takada suggest that the binding of small hydrophobic ligands is generally likely to proceed via population shift, while the binding of large, charged ligands (such as DNA) will likely proceed via induced fit. They acknowledge, however, that the simulation is limited, particularly in its view of conformational change. Unitary transitions in which the whole protein changes its structure simultaneously are probably not the norm, particularly in the case of very large conformational changes. These changes may instead be stepwise or hierarchical. For instance, a protein or complex recognizing multiple features of a DNA strand may proceed by an apparently induced-fit mechanism, even though each individual binding event more closely resembles population-shift behavior.

An additional limitation of this study is that it considers only one protein, but mechanisms of binding and conformational change may be idiosyncratic properties of particular folds. One could consider the behavior of lymphotactin, which displays clear hallmarks of the population-shift mechanism despite binding to macromolecules (heparin and a GPCR) much larger than itself, as a counterpoint to the predictions developed here. Similarly, the population shift of NtrC involves a charged phosphate group likely to have long-range interactions, although this is a post-translational modification and not a strict ligand-binding event. While the authors point to some examples that match their expectations, overall the data are not unanimously in support of their predictions. Still, the general rules laid out here provide a starting point for experimental work.

Despite the limitations of the simulation, it provides a relatively efficient tool for assessing these processes in other proteins. While no simulation can yet replace experimental data, coarse-grained models like this can serve as a means to formulate testable hypotheses about the energetics of protein-ligand systems.

1. Okazaki, K., Takada, S. (2008). Dynamic energy landscape view of coupled binding and protein conformational change: Induced-fit versus population-shift mechanisms. Proceedings of the National Academy of Sciences 105(32) 11182-11187. DOI: 10.1073/pnas.0802524105

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Structural dynamics of PDZ allostery

If Michele Vendruscolo were trying to get me to blog about one of his papers, he could hardly have assembled a more perfect lure than his upcoming paper in JACS. It brings together all sorts of things I've been talking about on this webpage: NMR dynamics, MD simulations, and dynamics-driven allostery (in the PDZ domain, no less). Previous investigations of this PDZ domain indicated the existence of a network of residues that had a dynamic response to ligand binding. Dhuselia et al. extend this work using molecular dynamics simulations constrained by the existing dynamics results. This leads them to discover not one, but two networks in the PDZ domain, with different properties.

NMR experiments have enormous power to sensitively detect changes in dynamics resulting from a perturbation, but they are also quite limited. Because of the models we use, the parameters we can fit out of relaxation data only give us information about the magnitude and timescale of fluctuations. Chemical shift overlap and interference caused by nearby dipoles limit the number of probes. Moreover, because NMR can only measure an ensemble, it is practically impossible to extract anything other than the most general information about correlated motions. MD has answers to all of these problems, but as a general rule has done poorly at reproducing NMR data about side-chain motions, calling the validity of the conclusions into question. Vendruscolo has taken some interesting strides in this regard by employing the limited experimental dynamics data as a component of the energy function. By constraining the simulation to mimic the known dynamics, we can hopefully learn more about the sites to which we are blind, as well as what kinds of motions the experiment is sensing and how they are linked.

In this instance, the authors make use of the PDZ domain previously studied by Ernesto Fuentes in Drew Lee's lab (there was also some hack working there at the time). Ernie's research followed on previous evolutionary studies indicating a network of communication in PDZ domains (local summary here), and Ernie found, by comparing the dynamics of the free and ligand-bound states, that changes in motions propagated away from the binding site to two distal surfaces. The pathways of communication compared pretty well with the evolutionary results. Dhulesia et al. aim to extend these results by determining which motions are correlated and identifying the mechanisms by which energy is transmitted. They accomplished this by running multiple parallel simulations of the free and ligand-bound states of the PDZ domain constrained by Ernie's dynamics results, as well as NOE and ³J data.

They find that two regions of the protein have correlated motions internally and move in an anticorrelated fashion relative to each other (Figure 3A). One of these regions consists of part of the binding site and all of distal surface 2 (DS2), while the other includes the other half of the binding site and all of distal surface 1 (DS1). When the ligand binds, something interesting happens. The motions of DS2 become more tightly correlated to the motion of an area around V30. The tight correlation between the motions of DS1 and α2 (an element of the binding cleft) switch to a slight anticorrelation.

When a ligand binds to a protein we expect a broad increase in rigidity of the complex so that the proper orientations of bonding pairs are maintained. For the most part, the simulations affirm this expectation, but not for all regions. For the binding site and DS2, the backbone mobility decreases, as expected, but the backbone mobility of DS1 increases (I am going off the text and Table 3 here, rather than Figure 3). The side chains have a similar response. This agrees with other studies indicating that the change in conformational entropy upon binding a ligand need not be homogeneous. What is more interesting is that these results imply that opposite coherent responses can be induced in a small domain by a single stimulus.

Although (as far as I know) this PDZ domain has no allosteric behavior in vivo, one can imagine that the binding of a ligand at the cleft could alter the binding of other modules to this domain. The entropic penalty for binding to DS2 would be lower in this case, while the penalty for binding to DS1 would be higher. The opposed nature of the dynamic responses may be related to the broad regional anticorrelation of free-state motions; disruption of this mode (by linking the motion of β2 and α2) may shunt that energy into DS1.

The authors also find, using a series of structural parameters, that a set of residues have clear structural changes. Some of them appear to be associated with coupled changes in rotameric states; the authors map out one pathway in Figure 5. Because it is a rotameric pathway, it should be possible to test whether it is essential to communication experimentally—mutation of the intermediary residues should abolish the linkage. The authors also carry out a network analysis to identify the most connected residues, a prediction that may also be testable by mutagenesis. These "structural network" residues overlap only slightly with the dynamic network, and indeed do not generally intersect with the evolutionary network either. In the absence of identified allosteric behaviors or clear energetic connectivities it's difficult to say what this disjunction means. However, the residues undergoing structural changes surround most of the residues undergoing dynamic changes. It is possible that these changes in structure provide the context that allows the changes in dynamics (or vice-versa); the two properties are inextricably linked.

Although communication between the binding site and distal surfaces is proven in this PDZ domain, and appears to be a general feature of the fold, the absence of a known function for the propagation in this instance makes it tough to assess the quality of these results. However, the findings of Dhulesia et al. make it clear that this approach can produce testable predictions and explanations. Hopefully this approach will be employed in the near future to study PDZ domains known to possess allosteric properties.

1. Dhulesia, A., Gsponer, J., Vendruscolo, M. (2008). Mapping of Two Networks of Residues That Exhibit Structural and Dynamical Changes upon Binding in a PDZ Domain Protein. Journal of the American Chemical Society DOI: 10.1021/ja0752080

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DegP 24-mers are spacious chaperones

A few weeks back I mentioned a paper in PNAS on allosteric regulation of DegP protease function by its PDZ domains. This week in Nature, the same group (same first author, even) provides some intriguing new insights into the workings of this combined chaperone and protease. Using different arrangements of a base trimer structure, DegP assembles into multimers of 6, 12, and 24 protein units. Krojer et al. determine the structures of the larger complexes using cryo-electron microscopy and X-ray crystallography, and how these structures might achieve the refolding and degradative functions of this HtrA protein family member.

As I mentioned last time, a single DegP protein consists of a protease domain and two PDZ domains, a domain I talk about a lot. This makes for a decently-sized protein, but in fact DegP is rarely encountered in vivo as a monomer. It is known to form trimers and hexamers, and now dodecamers and whatever fancy Latin or Greek word you would use for a 24-mer. In all of these cases what we're really dealing with are higher assemblies of trimers. For instance, the dodecamer is a tetramer of trimers. In addition to its ability to degrade proteins, DegP is known to have a chaperone function, and also to be able to shepherd OMPs (outer membrane proteins) through the periplasm of E. Coli.

In the case of the 24-mer, the DegP molecules assemble into a giant, hollow octahedral shape with an interior cavity 110 Å wide, which is larger than the cavity of the well-known chaperone GroEL. The PDZ domains mediate contact between adjacent trimers, and the protease domains form the "faces" of the octahedron. From the first figure in the paper it almost looks like you could cram 2 folded OMP proteins into the cavity formed by this structure. This oligomeric complex is so large it could conceivably span the whole periplasmic space between the inner and outer membranes of an E. Coli. Because the 24-mer has fairly large pores, it seems possible that it could form a tunnel that protects OMPs from aggregation and degradation as they cross the periplasm. In addition, positively-charged residues concentrated on the surfaces of the PDZ domains appear to give the multimer some affinity for membranes; these positive charges are concentrated around the edges of the pores.

To check this idea in vivo, Krojer et al. made a DegP-null strain of bacteria and measured the concentration and location of OMPs. They found that for several OMPs, deleting DegP did not change the concentration of the OMP in a whole cell lysate, but reduced levels of these proteins in the outer membrane. Further experiments indicated that DegP oligomers can protect OMPs from proteases. DegP itself can degrade unfolded OMPs, but stabilizes the folded proteins.

The researchers also managed to catch a glimpse of an OMP inside a DegP oligomer. In the case of the 24-mer this was difficult, probably because the sheer size of the enclosed space allows so many orientations of the OMP that getting a regular structure is impossible. However, they found that the structure of DegP dodecamers bound to OMP was fairly homogeneous, allowing an investigation by cryo-EM. You can see an image of the structure (shamelessly stolen from Figure 5) at left; the DegP molecules are in warm tones, and a molecule of OmpC is visible in blue. Again, the protease domains form the faces of this tetrahedral cage, while the PDZ1 domains (not PDZ2 in this case) mediate trimer-trimer contacts.

But what about DegP's protease function? Chromatography experiments (Figure 2) suggest that at room temperature, the presence of unfolded substrate molecules induces the formation of higher-order oligomers. These experiments cannot tell us whether these 12- and 24-mers have the same conformations as determined in the experiments above, but the fact that the PDZ1 domains mediate trimer-trimer contacts in both complexes suggests a possible mechanism for the allosteric activation noted previously. However, at higher temperatures where protease activity is markedly increased, the dominant species in solution was the trimer itself, even in the presence of substrates.

The authors propose that the hexameric form of DegP is a resting state, and that DegP₁₂ and DegP₂₄ form in response to specific stimuli. This model certainly fits the observations in solution, but it seems possible to me that the presence of membranes could induce formation of DegP₂₄. In vivo studies using fluorescence or TEM may be able to address what form actually predominates in the periplasm. Additionally, these results do not directly address the role of oligomerization in the switch between proteolytic and chaperone function. Particularly crucial in this regard is the question of whether the larger complexes that form during proteolysis are the same as those that form around OMPs. While it's reasonable to think that they are, the demonstrated structural versatility of DegP trimers suggests that these large assemblies may represent alternative conformations. Alternatively, it could be that features of the periplasm make DegP₂₄ and DegP₁₂ poor proteases in bacteria, and that the more rapidly diffusing naked trimer is the only efficient protease in vivo among DegP oligomers.

If, however, the chaperone and protease oligomers are structurally equivalent, a whole new class of questions opens up. Is formation of 12- and 24-mers sufficient to activate proteolysis, or is some additional step required? What protects folded OMPs from degradation by the protease subunits? These are challenging questions, but the new structures will be of great assistance in guiding the design of the genetic and biochemical experiments that answer them.

1. Krojer, T., Sawa, J., Schäfer, E., Saibil, H.R., Ehrmann, M., Clausen, T. (2008). Structural basis for the regulated protease and chaperone function of DegP. Nature, 453(7197), 885-890. DOI: 10.1038/nature07004

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