Jun 212011
ResearchBlogging.orgAs I have mentioned before on this blog, the use of tools like CS-ROSETTA holds the promise of determining protein structures using only the chemical shifts of its backbone atoms. In addition to potentially making NOEs and RDCs redundant, this technology allows biologists to determine the conformations of minor members of the structural ensemble, which are very difficult to obtain using conventional approaches in population-dominated techniques like NMR and X-ray crystallography. There are two limitations here, however. First, we only gain insight into the backbone, and as we know, the positions of side chains in minor states can be critical for function. In addition, backbone chemical shifts are not always available due to relaxation problems. Both weaknesses could, in principle, be addressed by extracting conformational information from the chemical shifts of methyl groups, which report on side-chain behavior and continue to give good signal even in very large proteins. This is the rationale behind a series of recent papers from the Kay lab [1-3] intended to determine changes in side-chain rotameric state from methyl relaxation-dispersion data.

The roots of this idea have been around for a while, dating back at least to a 1996 paper in J. Biomol. NMR [4]. I’ve reproduced one of MacKenzie et al.‘s figures at right, and as you can see, for this protein (a peptide of glycophorin A), the correlation between the Cδ chemical shift and JCδCα is quite striking. However, the quality of the correlation appeared to be protein-dependent, as the R2 for this relationship was significantly lower for staphylococcal nuclease side-chains, possibly because they were positioned in a less homogeneous chemical environment than a lipid bilayer.

A more systematic study was recently performed by Bob London and co-workers from the National Institute of Environmental Health Sciences [5]. They extensively compared side-chain rotameric angles extracted from the PDB to side-chain chemical shift data from the Biological Magnetic Resonance data Bank to see what correlations emerged. They expected to see that the chemical shifts of the carbons depended on the side-chain dihedral angles due to the “γ-substituent effect”, which is believed to alter chemical shifts due to bond polarization caused by steric interactions. Although there are some complications due to other effects, this prediction turned out to be true, broadly speaking.

The left Thr has χ1=-60° while the right one has
χ1=60°. The rotation around the Cα-Cβ bond from
N to Oγ defines the dihedral angle.

London et al. found clear correlations between chemical shift and rotameric state for threonine, for instance, which has a true chiral center at Cβ. For χ1 of ± 60° (these angles are also referred to as gauche±), the chemical shift of the methyl carbon was around 22 ppm, while for χ1 of 180° (also called trans) the chemical shifts cluster loosely around 19 ppm. More broadly, London et al. observed that sterically crowded rotamers tended to move aliphatic carbon chemical shifts upfield. Structurally, the difference between these dihedral angles is that in the ±60° positions, Cγ2 has steric interactions with only one heavy atom (i.e. the amide N or carbonyl C), while in the 180° position it interacts with two.

As one might expect given the results of Mackenzie et al., London et al. also found a straightforward relationship in the case of the leucine δ carbons, where the population of rotamers could be determined rather simply using the difference between the δ1 and δ2 chemical shifts. While this only specifically gives the population of the trans rotamer (where Cδ1 is on the opposite side of the Cβ—Cγ bond from Cα), it turns out that, due to unfavorable sterics, population of the gauche- conformation is vanishingly small in the PDB, so that one can assert with some confidence that everything not in trans is in gauche+. Also, London et al. noted that the χ1 and χ2 angles were highly correlated for leucines, so that in principle the entire side-chain conformation could be defined using just the difference in Cδ chemical shifts.

Hansen et al. [1] decided to use the chemical shift-rotamer relationship to analyze the minor conformations of leucines in mutants of the Fyn SH3 domain. The G48M mutant is in a rapid equilibrium between folded and unfolded forms, while the A39V/N53P/V55L triple mutant appears to primarily exchange to an intermediate state. Using a combination of CPMG-based relaxation-dispersion experiements and HSQC/HMQC, the Kay lab were able to determine the chemical shifts of the leucine methyls in the alternate state for each mutant, and thus derive populations for the trans rotamer. In the unfolded state, on expects to see ~60-70% population of the trans rotamer. The folded state of Fyn SH3 has several leucines that lie outside this range, but in the minor form of G48M nearly all of them lie within it, consistent with the existing finding that this state is unfolded. In the case of the triple mutant, some leucines move into the unfolded range in the minor state, while others remain outside of it. This is consistent with the assignment of the minor state as a partially-folded intermediate.

In a subsequent paper, Hansen et al. derived a relatively simple method for estimating the population of the gauche- rotamer state for the isoleucine δ carbon and applied it to the same system [2]. The situation for the Ile Cδ is somewhat more complicated than that of leucine. Because it is an isolated methyl group, and the rest of the side chain has a complicated topology, as many as four unique rotamer positions are distinctly populated in the PDB. However, in solution only the trans and gauche- configurations are expected to be significantly populated.

The Fyn SH3 domain has two Ile residues, which by this technique appear to be populated primarily in the gauche- rotamer (I28) and the trans rotamer (I50) respectively. In the intermediate state (results from the unfolded state are not reported) both isoleucines populate the gauche- rotamer to about 20%. The authors interpret this as a non-native interaction in the case of I28 and a slight increase in dynamics in the case of I50. However, it seems that these values could also support a case that both side chains are totally (or almost totally) solvent-exposed in the intermediate state, and thus adopting random-coil configurations.

One might also take issue with the idea that an increase from 0 to 20% of an alternate rotamer population represents a “slight” increase in dynamics. It’s difficult to make any firm statement in this regard because we don’t actually know the rotamer distribution in the folded state: Cδ1 may be entirely in trans, or averaged somehow between all of the non-gauche states. The authors take the folded state to be essentially pure trans, from which one would plausibly expect to observe an order parameter of 0.8 or higher for the methyl group (according to the rough calculations in [6], see reproduced figure on left). Based on the population, the order parameter would decrease to around 0.5 in the intermediate, a fairly large change.

However, this does not undermine the conclusion that the core is relatively well-formed in the intermediate. One perplexing feature of methyl side-chain order parameters is that they correlate poorly with nearly every structural feature one might expect to explain them [7]. Solvent-accessible surface area, packing density, and depth of burial are all rather poor predictors of side-chain dynamics. By the same token, more rudimentary measures, such as methyl distance from the backbone, are relatively robust predictors of dynamics, even though they ignore the higher-order structure of the protein. The upshot of this is that any data obtained about the dynamics of side-chains in minor states will need to be interpreted conservatively.

In the most recent offshoot of this research, Hansen and Kay published a paper correlating the chemical shift of valine Cγ methyls with the rotameric state [3]. Unfortunately, this is not a case where there’s a simple calculation that can accurately spit out the χ1 angle, and because of the β-branched structure of the amino acid, it’s not possible to rule out one of the possible angles a priori. As their Figure 2 shows, the relationship between the chemical shifts and the rotamer is complicated and may also vary with the local secondary structure. Instead of a simple formula, they were able to derive a “surface” reflecting probabilities of particular rotamer arrangements based on the shifts, which can then be analyzed using a program they wrote. They subsequently validated this approach on a very large protein complex, the half-proteasome, by comparing the chemical shift-derived rotameric states to those observed in crystallographic data.

I tested their chemical-shift based predictions against some of my own data (a web-based version of the program is available at Flemming Hansen’s website) and wasn’t exactly blown away by the results. Of 10 methyls in my protein, the primary rotamer was completely wrong (as determined by experimental 3J measurements) in two cases, and the population of the primary rotamer was dramatically overestimated in another two. However, the two side-chains with incorrect rotamer determinations were both adjacent to tryptophan side-chains, and in those cases the ring currents may have altered the chemical shift enough to inferfere with the calculation. Because aromatic rings are likely to be present in the core and may enhance the possibility of observing chemical exchange, this may bear further investigation. Nonetheless, the primary rotamer was usually correct chosen, and so these calculations can serve as at least a starting point for structural analysis.

These introductory studies are fairly encouraging, and suggest that it should be possible to use CPMG experiments to assess structural features of minor states beyond just the backbone conformation, even in very large systems. This may be especially helpful in analyzing the dynamics of proteins with hydrophobic active or regulatory sites. As hydrophobic surfaces are often involved in protein-protein interactions, an improved understanding of these critical binding events may result.

Disclosure: I have co-authored a paper with Bob London’s group, as well as several (obviously) with Andrew Lee’s.

1. Hansen, D., Neudecker, P., Vallurupalli, P., Mulder, F.A.A., & Kay, L. (2010). “Determination of Leu Side-Chain Conformations in Excited Protein States by NMR Relaxation Dispersion.” Journal of the American Chemical Society, 132 (1), 42-43 DOI: 10.1021/ja909294n

2. Hansen, D.F., Neudecker, P., & Kay, L.E. (2010). “Determination of Isoleucine Side-Chain Conformations in Ground and Excited States of Proteins from Chemical Shifts.” Journal of the American Chemical Society, 132 (22), 7589-7591 DOI: 10.1021/ja102090z

3. Hansen, D.F., & Kay, L.E. (2011). “Determining Valine Side-Chain Rotamer Conformations in Proteins from Methyl 13C Chemical Shifts: Application to the 360 kDa Half-Proteasome.” Journal of the American Chemical Society, 133 (21), 8272-8281 DOI: 10.1021/ja2014532

4. MacKenzie KR, Prestegard JH, & Engelman DM (1996). “Leucine side-chain rotamers in a glycophorin A transmembrane peptide as revealed by three-bond carbon-carbon couplings and 13C chemical shifts.” Journal of Biomolecular NMR, 7 (3), 256-60 PMID: 8785502

5. London, R., Wingad, B., & Mueller, G. (2008). “Dependence of Amino Acid Side Chain 13C Shifts on Dihedral Angle: Application to Conformational Analysis.” Journal of the American Chemical Society, 130 (33), 11097-11105 DOI: 10.1021/ja802729t

6. Hu, H., Hermans, J., & Lee, A. (2005). “Relating side-chain mobility in proteins to rotameric transitions: Insights from molecular dynamics simulations and NMR” Journal of Biomolecular NMR, 32 (2), 151-162 DOI: 10.1007/s10858-005-5366-0

7. Igumenova, T., Frederick, K., & Wand, A. (2006). “Characterization of the Fast Dynamics of Protein Amino Acid Side Chains Using NMR Relaxation in Solution.” Chemical Reviews, 106 (5), 1672-1699 DOI: 10.1021/cr040422h

Dec 022010

ResearchBlogging.orgIn 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.

Farber and Mittermaier studied the behavior of a homeodomain, a small, all-helical domain that typically binds to DNA, often in concert with other homeodomains. In particular, they were interested in the homeodomain of PBX1 (PBX-HD) which binds DNA cooperatively with a homeodomain from HOXB1 (HOX-HD). The domains interact with the DNA target and with each other. Peptides representing the binding site from the HOX-HD bind detectably to PBX-HD only in the presence of the target DNA, suggesting that the two binding sites communicate. The third helix of the PBX-HD is likely to mediate the allostery since it’s involved in both binding interactions, but it’s not clear from the available structures how this would happen. Additionally, there is a C-terminal sequence, with no defined fold in the free structure, that forms a helix in the ternary complex. It does not interact directly with the DNA, but removal of this extension decreases the affinity of PBX-HD for DNA and weakens the cooperativity between PBX-HD and HOX-HD.

Helix folding has a low energy barrier, so it is reasonable to suspect that this helix could form even in the absence of DNA. Farber and Mittermaier examined this possibility using a technique I have often discussed on this blog: CPMG relaxation dispersion. As you may recall, this technique is sensitive to fluctuations between states (chemical exchange) that persist for microseconds or milliseconds. One can in principle determine the rate of exchange (kex), the population of each state (pA, pB), and the chemical shift difference (|Δω|) between them, although if the motion is too fast or too slow only composites of some of these can be reliably determined. When they performed the experiment, the authors found that residues throughout PBX-HD had significant broadening, indicating chemical exchange and suggesting that the protein does not spend all its time in one folded state. The relaxation-dispersion profiles they obtained at 10 °C and 15 °C were in the intermediate regime, where all three of the aforementioned parameters can be determined.

For the C-terminal extension, the |Δω| determined by fitting the relaxation-dispersion data were linearly correlated with the chemical shift change that was observed in an HSQC upon binding (|Δδ|). The correspondence wasn’t exactly 1:1, but this is still reasonably good evidence that the helix is folding independent of binding. The authors used the |Δω| from the 10 °C fits to pull populations and rates from the experiments performed at higher temperatures, where only a composite parameter can be reliably determined (due to the speed of the fluctuation). Arrhenius plots derived from these data indicate thermodynamic parameters that are consistent with the folding of a single helix, again supporting the proposition that the C-terminal helix can fold on its own.

Numerous residues in the folded portion of the domain also experienced chemical exchange, which could mean that the helix is not the only thing undergoing a structural transition. The authors fit these residues individually, then tried again while fixing kex to the value determined from the helix behavior. The latter fits were not much worse in terms of their residuals than the floating fits were, so the fluctuations here could reasonably be seen as consistent with the helix-folding fluctuation.

If this is so, then removing this unstable helix should quench the dynamics in the folded part of the protein. This turned out to be the case — when the helix was removed, the dispersion curves for residues in the folded part of the protein became flat. This reinforces the case that the dynamics detected in the folded domain are related to the folding of the helix, and therefore represent an excursion to the “bound” structure for ligand-free protein.

Farber and Mittermaier note that for residues in the folded portion of the domain, the |Δω| determined through the CPMG analysis does not appear to agree with the |Δδ| observed upon binding DNA. From this they conclude that the conformational change in solution is actually going to some unknown third state that is different from both the free and bound structures. I disagree somewhat with this interpretation. Because the ligand (in this case a piece of double-stranded DNA) is large relative to the protein and possesses substantial negative charge, there’s a significant possibility of long-range electrostatic effects on the chemical shift of the PBX-HD. That is, the protein’s bound state might have different chemical shifts free in solution and bound to the ligand even without any major conformational changes. If this is the case, the |Δω| will correlate best with |Δδ| for residues that are far from the interface. Probably the structure sampled by the free protein is not exactly the same as the bound structure, but I think further data would be needed to determine whether the alternative structure in the free state differs significantly from the bound structure with DNA.

The uncertainty about the alternative structural state of the free protein makes it more difficult to make a firm argument about whether the binding mechanism more closely resembles conformational selection or induced fit, or whether it’s some kind of middle ground between the two. Although it’s suggestive, the observation of a structural equilibrium in the free state does not actually indicate how binding occurs. Moreover, because this is a complicated ternary complex, it is possible that, say, the protein-binding mechanism is conformational selection, while the DNA mechanism is induced-fit. This latter possibility might seem more sensible in light of existing studies indicating that long-range (e.g. electrostatic) interactions may predispose a system to induced-fit binding.

Complications aside, these data seem to support a model in which the PBX-HD transiently adopts the bound conformation in the absence of ligand. Binding of the PBX-HD domain to DNA shifts its population towards the state that is the minority in solution. This new structure has high affinity for the HOX-HD, promoting the formation of the ternary complex. In principle, binding of the HOX-HD to PBX-HD could precede DNA binding by both modules, but the interaction between these proteins appears to be weak in the absence of DNA. However, proving that the excursion to the bound (or near-bound) PBX-HD structure represents an actual intermediate in the binding process rather than  just an interesting fluctuation on the side will require some determination of the binding kinetics in various conditions.

(1) Farber, P., & Mittermaier, A. (2010). Concerted Dynamics Link Allosteric Sites in the PBX Homeodomain Journal of Molecular Biology DOI: 10.1016/j.jmb.2010.11.016

Aug 262010
ResearchBlogging.orgIf you’re going to study the role an enzyme plays in a biological pathway, it’s often useful to “kill” it with a mutation. For example, the proline cis-trans isomerase cyclophilin A (CypA) needs a particular arginine residue for its chemistry, so mutations that remove or alter that functional group, like R55K and R55A, should destroy the protein’s function and have effects on the related pathways that help illustrate its role. The hydrophobic pocket it uses to bind substrates is made by residues like H126, F113, and W121. Growing or shrinking those residues should alter the shape of the pocket and change binding or activity, leaving the enzyme “dead”.

Using model reactions and various binding assays, researchers have previously examined a number of these mutants (4,7) and found that they diminish isomerase activity and alter inhibition. However, a detailed study of the effects of the mutations on CypA’s catalytic cycle has not been performed. Former Kern lab members Daryl Bosco (now a professor at UMass Medical) and Elan Eisenmesser (now at UCHSC) examined these mutants in greater detail to see how they really behaved. I also contributed some data at the last minute, when the third reviewer requested we study an additional mutant, prompting a scene that I promise was not too much like that Downfall parody. In every case we found that these enzymes, although significantly impaired, weren’t as dead as they had seemed.

You had me at “dunno”

CAN bound to CypA, from PDB structure 1AK4 (5).
CypA residues are labeled in black, CAN residues in red.

One key aspect of this work is that it involves a physiological substrate of CypA, namely the N-terminal domain of the HIV-1 capsid protein (CAN). Mature HIV-1 virions contain CypA that is bound to proline 90 of CAN. The absence of CypA dramatically reduces their ability to infect their target cells, which we know from experiments with mutant CA proteins as well as ones involving the CypA inhibitor cyclosporin (3). What we don’t know about the system is exactly what CypA does for HIV-1. The crystal structure (right) of CAN in complex with CypA appears to only capture the trans isomer configuration (5), but for reasons I have discussed previously on this blog, that’s not particularly informative. We know, largely from Daryl and Elan’s previous research on the system (2), that when CAN is floating free in solution CypA will catalyze isomerization, but in the context of a fully assembled capsid that situation could conceivably change.

This leaves us with three possibilities for CypA’s function in the capsid. Catalysis of cis-trans isomerization of the proline bond could be important. Or, maybe all capsid needs is for CypA to bind at P90, and catalysis is irrelevant. And perhaps neither of these functions matters and CypA just needs to be hanging around for some other reason. To address these possibilities, Saphire et al. performed an elegant series of experiments where they sneaked an engineered CypA protein into another part of the capsid by fusing it to a protein called Vpr. When they replaced the normal CypA sequence with a mutant (H126A) that was supposed to abrogate both binding and catalysis, HIV-1 could still infect CD4+ cells (6). But, how sure can we be that H126A, or any other mutant, is actually “dead”?

You can’t measure what you can’t see

The problem with proline isomerization, from a biochemist’s standpoint, is that it’s a difficult reaction to detect. While switching between isomerization states may have structurally significant effects, there’s no direct spectroscopic signal to tell you whether a proline bond is in the cis or trans conformation. Even if there was, most proteins have many prolines and so the signal of the bond you care about might be difficult to separate from the bonds you don’t.

You can get around this difficulty using a coupled reaction with a model substrate. CypA catalyzes the isomerization of tetrapeptides of the form AXPF pretty efficiently. As it turns out, sequences like this are also good substrates for the protease chymotrypsin, but there’s a catch. Chymotrypsin only cleaves substrates where the proline bond is in the trans configuration. So, what you can do is take a substrate like succinyl-Ala-Ala-Pro-Phe-p-nitroanilide, add a tiny amount of cyclophilin, and then dump in a huge amount of chymotrypsin. With enough chymotrypsin, the peptide that’s already trans will be cleaved before the solution stabilizes, causing a color change (due to the pNA) that can be measured with a conventional spectrophotometer. Then you can monitor the conversion of the remaining substrate from cis to trans, because there’s so much chymotrypsin that cleavage after isomerization is essentially instantaneous.

This works reasonably well, but it has some limitations. You’re stuck with a model peptide that may not behave very much like your particular protein substrate. You’re only following the cis-to-trans reaction, and even that comes with limited detail. Also, performing the experiment takes some careful work, because if you add too much of your CypA the reaction will end before the solution turbulence settles, and if you add too little, the intrinsic cis-trans isomerization will interfere with your catalytic measurement.

Although proline isomerization is a difficult reaction to follow by spectrophotometry, it’s actually quite convenient to assay by NMR. Because CypA catalyzes the reaction in both directions, it’s impossible to exhaust the substrate. The kinetics can therefore be measured at equilibrium using NOESY and ZZ-exchange experiments (2). Of course the experiment is limited by our ability to express isotopically-labeled substrate proteins, but provided we can do that and visualize the active site in our spectra, then we can observe catalysis of the native substrate. When you perform this experiment on these various “dead” forms of CypA using CAN as a substrate, it becomes evident they’re still active after all.

Night of the living “dead” enzymes

Panels B-G of Figure 3 in this paper directly show that every single one of the CypA mutants catalyzes CAN isomerization in solution (1). These spectra show peaks representing the chemical shifts of the nitrogen and hydrogen atoms of CAN‘s backbone amide groups in the presence of a small amount of CypA, so we are not looking at P90 directly. Fortunately, the chemical shift of the G89 amide is dependent on the isomerization state of P90. If the G89-P90 bond is in trans, G89 shows up as the large peak at lower right in these panels, but if the bond is in cis you get the small peak at upper left.

If you don’t wait very long between determining the 15N chemical shift (y-axis) and the 1H chemical shift (x-axis), you get something that looks like panel A. If, however, you pause between determining the 15N shift and the 1H shift, you get cross-peaks representing the portion of CAN proteins that started the experiment in trans and ended it in cis, or vice-versa. The presence of these cross-peaks in the CAN/CypA samples, and their absence in the CAN-only sample (panel A), proves that catalysis is occuring. I’ve blown up the figure for H126A on the right to make things a little clearer. In this case the cross-peaks were pretty weak, but still in evidence.

There was more variability when it came to affinity, the strength with which CypA binds the CAN substrate. I’ve shown a complete titration for H126A on the left. As you can see, progressive addition of H126A causes the free CAN peaks to disappear while the new bound CAN peak grows in. This behavior is characteristic of slow chemical exchange on the NMR timescale, and indicates a high-affinity binding interaction. WT CypA binds CAN with a KD of 13 µM, and H126A probably has similar affinity. Note also that the bound state has a single peak for each residue, while the free state of CAN has separate cis and trans peaks. This indicates that the cis and trans isomers are interconverting rapidly on the enzyme, and constitutes additional evidence that H126A CypA is catalytically active.

This pattern was not repeated for all the mutants, however. H126A and W121Y had affinity similar to WT, while R55A, R55K, and F113W had significantly higher KD (lower affinity). You can see this clearly from the titrations in Figure 5. For each of these mutants, adding CypA to CAN caused the CAN peaks to move around in the spectrum, rather than disappearing and reappearing (R55K had a mixture of behaviors because the NMR timescale also depends on chemical shift). This peak shifting is characteristic of fast chemical exchange on the NMR timescale and indicates relatively low affinity. This wasn’t the only change for those mutants.

Shifts in the rate-limiting step

An enzyme that doesn’t have very high affinity for its substrate isn’t necessarily in trouble. The NMR titrations of the R55A and R55K mutants indicate that their KDs are near 1 mM (Table 1). This is comparable to the affinity the WT protein has for the AAPF peptide, which gets catalyzed pretty efficiently. What does seem strange about this result is that the ZZ-exchange spectra are very similar.

The presence of single peaks for residues of CAN bound to WT suggests that the isomerization step is fast. Using relaxation-dispersion techniques, Daryl established that the net process rate (kct+ktc) for CAN on WT CypA was about 2200 /s. From the ZZ-exchange spectra we know that the total catalytic cycle goes a great deal slower (closer to 75 /s), from which we can deduce that isomerization is not the rate-limiting step. An analysis of the lineshapes suggested that the unbinding rate (koff) was about 45 /s, which is close enough to the catalytic rate to indicate that this step is rate-limiting.

But if koff is rate-limiting for this reaction, and the koff for R55A and R55K is dramatically increased (as it must be, with lower affinity), we ought to be seeing a higher rate in the ZZ-exchange experiments, or maybe even not seeing independent cross-peaks at all. How can this reaction be going slowly enough to be seen by this technique? As it turns out, the titrations hold the key. When CAN is saturated with R55A CypA, you can clearly see independent cis and trans peaks in the bound state (Figure 6, partially reproduced at right). This means that cis-trans interconversion on the enzyme has gotten much slower.

In fact, the presence of those two peaks means that we can use the ZZ-exchange experiment again, this time to determine the on-enzyme interconversion rate directly. The answer we get is about 20 /s, which is, within error, equivalent to the rate of the full cycle for this mutant. That means the rate-limiting step is no longer the unbinding of substrate, but rather the isomerization step itself. There’s only a minor change in overall catalytic efficiency, but this is the result of large changes in the rates of the individual steps that happen to cancel each other out.

Implications for the study of CypA-associated pathways

The best evidence available at the time supported the decisions Saphire et al. made in setting up their experiment. Previous work had clearly shown that an H126Q mutation of CypA significantly reduces the protein’s incorporation into virions (4,7). Saphire et al. made an H126A mutation on this basis and seemingly assumed that the activity would be similar (6). Unfortunately, the evidence from the NMR spectra is that H126A binds to the capsid protein perfectly well and also catalyzes its isomerization. This does not prove that the catalytic function of CypA is important for HIV-1 infectivity. However, on the basis of the existing experiments that possibility cannot yet be ruled out.

More broadly, these results demonstrate that claims about CypA’s role in biology cannot be based on mutant studies alone. The mutants discussed here alter, rather than abolish, CypA’s catalytic activity towards a biological substrate. Even those that appear not to bind the substrate in certain assays still display catalysis, because the strength of binding that is required for successful catalysis is considerably less than what is required for, say, a successful co-precipitation. The standard for assessing how a mutation has changed an enzyme’s behavior needs to be one that pays attention to the various steps of the reaction and how changes in particular rates can compensate one another. Experiments that rely on overexpression of a CypA mutant are particularly vulnerable to erroneous interpretation, because adding more enzyme is always an efficient way to compensate for a loss of activity and binding.

CypA and its related domains are very highly conserved across all vertebrates, yet its function was preserved even when apparently critical residues were dramatically altered by mutation. Our existing knowledge of protein sequences is limited to just a few examples from a relatively tiny number of species, and our structural and biochemical data encompass just a fraction of that. Assessments based on these databases are likely to underestimate the functionally viable sequence space. Descriptions of function based on model systems are also suspect. That goes for this system too — the findings about CypA activity made with respect to CAN are not necessarily any more generalizable than the chymotrypsin assay results. CypA can bind an enormous number of potential targets, and what is true for one may not be true for another. Whenever possible, catalytic activity and binding affinity ought to be verified directly on the substrate of interest. Otherwise, you might find that an enzyme you thought was dead is still stumbling along.


1) Bosco, D.A., Eisenmesser, E.Z., Clarkson, M.W., Wolf-Watz, M., Labeikovsky, W., Millet, O., & Kern, D. (2010). “Dissecting the Microscopic Steps of the Cyclophilin A Enzymatic Cycle on the biological substrate HIV-capsid by NMR” Journal of Molecular Biology DOI: 10.1016/j.jmb.2010.08.001

2) Bosco, D.A., Eisenmesser, E.Z., Pochapsky, S., Sunquist, W.I., and Kern, D. (2002) “Catalysis of cis/trans isomerization in native HIV-1 capsid by human cyclophilin A.” Proceedings of the National Academy of Sciences, 99(8), 5247-5252. DOI: 10.1073/pnas.082100499

3) Braaten, D., & Luban, J. (2001). “Cyclophilin A regulates HIV-1 infectivity, as demonstrated by gene targeting in human T cells” The EMBO Journal, 20 (6), 1300-1309 DOI: 10.1093/emboj/20.6.1300 OPEN ACCESS

4) Dorfman, T., Weimann, A., Borsetti, A., Walsh, C.T., & Göttlinger, H.G. (1997). “Active-site residues of cyclophilin A are crucial for its incorporation into human immunodeficiency virus type 1 virions.” Journal of Virology, 71 (9), 7110-3 PMCID: PMC192007 OPEN ACCESS

5) Gamble, T.R., Vajdos, F.F., Yoo, S., Worthylake, D.K., Houseweart, M., Sundquist, W.I., & Hill, C.P. (1996). “Crystal Structure of Human Cyclophilin A Bound to the Amino-Terminal Domain of HIV-1 Capsid” Cell, 87 (7), 1285-1294 DOI: 10.1016/S0092-8674(00)81823-1 OPEN ACCESS

6) Saphire, A.C.S., Bobardt, M.D., & Gallay, P.A. (2002). “trans-Complementation Rescue of Cyclophilin A-Deficient Viruses Reveals that the Requirement for Cyclophilin A in Human Immunodeficiency Virus Type 1 Replication Is Independent of Its Isomerase Activity” Journal of Virology, 76 (5), 2255-2262 DOI: 10.1128/jvi.76.5.2255-2262.2002 OPEN ACCESS

7) Zydowsky, L., Etzkorn, F., Chang, H., Ferguson, S., Stolz, L., Ho, S., & Walsh, C. (1992). “Active site mutants of human cyclophilin A separate peptidyl-prolyl isomerase activity from cyclosporin A binding and calcineurin inhibition” Protein Science, 1 (9), 1092-1099 PMCID: PMC2142182 OPEN ACCESS

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

Previously, on As the Channel Twists

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

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

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

Sure adamantanes bind there, but does it matter?

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

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

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

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

Amantadine caught in the pore

Mei Hong has studied M2
extensively by NMR

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

Amantadine (yellow) in the
pore binding site.

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

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

Support for an allosteric mechanism?

Bob Griffin,
SSNMR master

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

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

M2 channel showing Δδ
due to rimantidine binding

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

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

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

Conclusions, Lessons

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

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

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

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

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


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

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

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

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

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

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

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