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

Jan 272011

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

Apo- IGPS from Thermatoga maritima
PDB code: 1GPW

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

HisH active site, looking up the barrel from HisF

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

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

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

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