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

Aug 202009
This is the last of my series of posts about the dynamics-focused topical issue of JBNMR. There are plenty of other excellent papers in it, and I encourage you to at least glance over all of them, especially if you’re an NMR person.

ResearchBlogging.orgStandard NMR dynamics experiments on isotropically tumbling proteins cover a wide, but not comprehensive, swath of fluctuation timescales. Limited information about motions that take milliseconds or more can be obtained from hydrogen exchange data; the AMORE-HX experiment is meant to obtain this kind of information. Fluctuations with time constants in the range of μs-ms can be measured by relaxation-dispersion experiments, as were used in the Peng lab’s paper. Motions that are faster than the rotational correlation time of the protein can be characterized using dipolar relaxation data of the kind I collected for the field-cycling experiment. There are additional kinds of data that also cover these areas, but the glaring hole lies between the correlation time of the protein (several ns) and the low end of the chemical exchange regime (several μs). Several teams, including that of Christian Griesinger, propose to fill this gap using data derived from residual dipolar couplings (RDCs). In the topical dynamics issue of the Journal of Biomolecular NMR, his group demonstrates the use of this technique in measuring the dynamics of side chains in the small protein ubiquitin. The article is open access, so feel free to open it up and read along.

The strength of the dipolar coupling between two nuclei depends on their magnetic properties (specifically their gyromagnetic ratio), the distance between them, and the angle between the internuclear vector and the vector describing the external magnetic field. For solution NMR this last component is typically not important because most proteins tumble randomly with respect to the magnetic field, causing this interaction to be averaged away. However, if you were to somehow introduce a tiny amount of bias into the tumbling, very slightly aligning the protein parallel or perpendicular to the magnetic field, a residual portion of this coupling could be recovered. The effect is considerable: a net alignment of less than a fraction of a percent generates couplings on the order of 30 Hz or more.

There are many ways of inducing this alignment. Large charged particles such as phage or DNA nanorods have been used, as have assemblies such as charged or polar lipid bicelles. In addition, proteins can be labeled with paramagnetic metals to induce fractional orientation. Even mechanically manipulated media, such as acrylamide gels, can be used to achieve alignment if they are stretched or compressed along the field axis. When a new method of alignment is introduced, it is typically tried out on a small, abundant protein with good relaxation characteristics, most often the regulatory protein ubiquitin. The upshot of this is that there is a fantastic amount of RDC data on this protein, and Griesinger’s group uses this data to model the motions of its methyl-bearing side-chains.

This may sound strange, because RDCs are typically employed for structure determination. The angle defined by the measured coupling results from the overall tumbling bias of the protein (an alignment tensor) that is the same for each coupled pair, and their angle within that frame of reference, which can be used to uniquely define a structure. However, the dipolar coupling cannot be measured in an instantaneous fashion. It must evolve over time, just like chemical shift or a J coupling. As such, the dipolar coupling reflects an averaged orientation over the evolution period. In principle, the degree of averaging can be modeled as some kind of order parameter, similar to the S2 of the Lipari-Szabo system, reflecting all these motions. This should encompass not only the fast dynamics that determine dipolar relaxation rates, but also motions slower than the global correlation time, up to near the ms range.

In order to derive dynamics data from their set of experiments in 13 different alignment media, the authors first scaled the RDCs from C-H methyl bonds. The reason they did this is that the three hydrogens in a methyl group rotate constantly around an axis passing through the adjacent C-C bond (cyan in the isoleucine side chain depicted at right). Because this rotation is typically very fast, simple to model mathematically, and pretty uninteresting, it can be deconvoluted from the dynamics data to give us what we’re really interested in, the behavior of the C-C bond. Using an alignment tensor derived from a separate dataset of N-H RDCs in 36 different alignment media, the calculated C-C RDCs are combined into a matrix, from which simplified parameters describing motion can be derived. Fig. 1 depicts this schematically (note, the legend has the variables m and i reversed in meaning).

The order parameter (S2rdc) reflects the rigidity of the bond and ranges from 0 (highly flexible) to 1 (perfectly rigid). The values measured for the methyl groups of ubiquitin cover almost this entire range (Fig. 2a), which is typical of side chains which tend to have less constrained motions than the backbone. It’s also evident from this figure that S2rdc is roughly anticorrelated with the number of dihedral angles between a given methyl group and the peptide backbone. The anisotropy of motion (ηrdc) is generally low, and appears to be roughly correlated with the number of intervening dihedrals. Both of these observations agree with previous data from other dynamics experiments, as well as reasonable expectations about the movements of these groups.

Farès et al. compare their S2rdc to order parameters determined using other approaches. As one would expect, for most methyls the S2rdc, which encompasses motions from a wide array of timescales, is lower than the S2 determined using a Lipari-Szabo model-free approach that is only sensitive to ps-ns motions. The most notable exceptions are three residues for which the RDC method fit order parameters greater than 1. Probably these represent some unanticipated failure of the model, although these violations occur at groups that are expected to be rigid (two are alanines) and which have high S2 from the Lipari-Szabo model. One potential culprit is the previously discussed scaling, which may need to be adjusted if the axial rotation is unusually slow. The best agreement with existing data comes from an approach that combined J-coupling data with less-specific RDC information, but only when those data are corrected for very fast motions using backbone order parameters.

Because the RDC fundamentally contains data about the orientation of a given bond, it should be possible, with a minimal degree of modeling, to extract specific information about the kinds of motions being made, data that cannot be obtained from methods such as the Lipari-Szabo model-free interpretation. The authors modeled side-chain motions around the dihedral angles χ1 and χ2 using the MFA data. The agreement between these results and the existing ensembles is not particularly good, but it’s not completely obvious where the fault for this lies. Similarly there was limited agreement between these order parameters and those derived from existing ensembles. Agreement with the EROS ensemble structure determined last year was good, but it is difficult to judge what this means, as that ensemble was calculated using the same or similar data to what was used in this paper.

It’s fair to ask whether this sort of data analysis will be possible in systems with less comprehensive data. Even this extremely rich dataset proved problematic, for instance in the cases of the alanines and the disagreement concerning rotameric states. It remains to be seen whether this approach will be practical in cases where significantly fewer alignment media can be used. However, it is also true that methyl groups have the best relaxation properties of any group in a protein, and that the experiments for the determination of RDCs are simple and sensitive. This means that if this approach can be made to work, it can provide important dynamic data even in the largest proteins studied by NMR. Farès et al. have performed an impressively comprehensive analysis of dynamics in a time regime that NMR has had difficulty accessing. Hopefully they will next bend their considerable talents towards reproducing as much of this analysis as possible in a more difficult system with sparser data.

Farès, C., Lakomek, N., Walter, K., Frank, B., Meiler, J., Becker, S., & Griesinger, C. (2009). Accessing ns–μs side chain dynamics in ubiquitin with methyl RDCs Journal of Biomolecular NMR, 45 (1-2), 23-44 DOI: 10.1007/s10858-009-9354-7 OPEN ACCESS

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Aug 182009
This post continues my series about selected articles from the dynamics-focused topical issue of JBNMR.

ResearchBlogging.orgIt is helpful, in examining some NMR articles, to understand that NMR spectroscopists have a long and resilient tradition of giving their pulse sequences silly names. You can think of it as the biophysical equivalent of fly geneticist behavior. From the basic COSY and NOESY experiments (pronounced “cozy” and “nosy”) to the INEPT spin-echo train, to more complicated pulse trains such as AMNESIA and DIPSI (which, I am not making this up, is used in an experiment sometimes called the HOHAHA), the field is just littered with ludicrous acronyms (look upon our words, ye mighty, and despair). A team from Josh Wand’s lab now joins this club by developing a multiple optimization for radially enhanced NMR-based hydrogen exchange (AMORE-HX) approach. The name is ridiculous, but the experiment fills an important role and illustrates a very active area of technical development in NMR.

The experiment they developed is intended to measure the rate of hydrogen/deuterium exchange at amide groups on the backbone of the protein. This sort of exchange reaction proceeds pretty quickly for most residue types, and can be either acid- or base- catalyzed. For it to happen, however, two things must be true. One of them is that the amide proton must not be in a hydrogen bond already. Also, the site of the reaction must be accessible to water. These requirements should indicate to you that HX measures the rate of local unfolding and can therefore be interpreted as a measure of fold stability at each NH group on the backbone. This data is of obvious interest to researchers studying protein folding. In addition, because some structural transitions are proposed to involve an unfolded state this may have explanatory power for protein interactions and regulation.

A typical HX experiment involves taking your protein, switching it rapidly into >75% D2O buffer, then placing it in the magnet and taking a series of HSQC or HMQC spectra that separate signals from backbone NH groups by the proton and nitrogen chemical shift. These spectra can be taken with very high time resolution (<2 min each), and the rate of exchange can then be measured by the decay of peak intensity as hydrogen is replaced by deuterium. Assuming that the chemical step occurs significantly faster than the rate of local unfolding and refolding, this decay can be directly interpreted as a local unfolding rate. This works quite well, but as proteins get larger there is a significant likelihood of signal overlap. It would be nice, with these large proteins, to separate the hydrogen signals using an additional chemical shift — say, that of the adjacent carbonyl. Unfortunately, taking these decay curves using 3-dimensional spectra like the HNCO turns out to be impossible because of the way these experiments are collected. Multidimensional NMR spectra rely on a series of internal delays during which a coherence acquires the frequency characteristics of a particular nucleus. In a typical experiment, the delays are multiples of a set dwell time, the length of which is determined by the frequency range one wishes to examine. Typically the collection proceeds linearly through the array, so for m y dwell times and n z dwell times you would collect 1D spectra with the delays:

0,0 0,y 0,2y 0,3y … 0,my


z,0 z,y z,2y z,3y … z,my

and so on until

nz,0 nz,y nz,2y nz,3y … nz,my

This is called Cartesian sampling, and it has some advantages. The numerous data points typically do a good job of specifying resonance frequencies, and processing this data is a fairly straightforward proposition. The glaringly obvious disadvantage is time, of which a great deal is required. Completely sampling either one of these dimensions separately can take less than 30 minutes, but sampling both can push a triple-resonance experiment into the 60 hour range. Most annoyingly, because triple-resonance spectra can be really rather sparse, this extremely long experiment often over-specifies the resonance frequencies. That is, much of this time is spent collecting data you don’t need.

Because spectrometer availability and sample stability are not infinite, there is considerable interest in making this process more efficient. One of the methods for doing so is called radial sampling. In this approach, the spectrum is built up from a series of “diagonal” spectra that lie along a certain defined angle with respect to the two time domains (imagine the above array as a rectangle with sides of my and nz to get a rough idea of what this means). If these angles are judiciously chosen, the spectrum can then be rebuilt from just a few of them with only modest losses in resolution. Gledhill et al. apply this approach as a means of addressing their time-resolution problem. Guided by a selection algorithm, they use just four angles (at 500 MHz) to resolve more than 90% of the peaks possible in myelin basic protein. As a result, they were able to collect HNCO-based HX data with 15-minute resolution. This isn’t enough to catch the fastest-exchanging peaks, but it’s more than sufficient to catch core residues.

Gledhill et al. used some additional tricks to gain extra speed in the experiment, however. Using band-selective excitation, they cut down the experiment’s relaxation delay to 0.6 s, which is important because this delay is a considerable portion of the duration of each transient. Having done this, they started to get really clever. Because this experiment is being used to measure the intensities of known frequencies, it is possible to significantly reduce the amount of processing required by employing the 2D-FT only for those regions that contained actual peak intensity. Moreover, they could extract peak intensities from each individual angle plane. Because they did not interleave the collection, this enabled them to substantially increase the time-resolution when necessary.

For peaks that exchanged quickly Gledhill et al. took relaxation data from the individual angle spectra, to maximize the time-sensitivity of the data. For slowly-exchanging peaks, they averaged the data from the angle spectra to maximize the signal-to-noise ratio. The resulting intensity curve seems a bit noisy, but this is an acceptable price to access new peaks. More importantly, the precision of the overall rate (as opposed to the instantaneous intensity) appears to be on par with simpler methods of measuring HX.

Successful use of the AMORE-HX experiment will depend on a wise selection of acquisition angles, a process that may benefit from further optimization. Because the HNCO has relatively good dispersion, the pulse sequence should enable HX measurements for just about any protein that is suitable for NMR. This would allow for a direct assessment of large enzymes and complexes, as well as a measurement of local stabilities in domain-domain interfaces.

Gledhill, J., Walters, B., & Wand, A. (2009). AMORE-HX: a multidimensional optimization of radial enhanced NMR-sampled hydrogen exchange Journal of Biomolecular NMR DOI: 10.1007/s10858-009-9357-4

Aug 112009
My field-cycling article (previous post) is part of a dynamics-focused topical issue of JBNMR. In my next few science posts I’ll describe some of the other contributions.

ResearchBlogging.orgResearch into the interplay between protein structural dynamics and function is a window into important fundamental knowledge about biochemistry, but the general justification for public funding of these studies by medical agencies is that they will have the ultimate effect of improving our ability to design and optimize drugs. However, even though our ability to characterize macromolecular dynamics has increased dramatically in the past few decades, there are few, if any, cases in which this knowledge has been applied successfully to the design of therapeutic agents. In part this is because incorporating data on fluctuations into the design algorithms poses a significant challenge. It’s also true, though, that we understand only part of each system, i.e. the dynamics of the protein target, not the small molecules it binds. If dynamics studies are to make the maximum possible contribution to pharmaceutical sciences, the motions of the ligand must be characterized. In their article in Journal of Biomolecular NMR, Jeffrey Peng and students from Notre Dame attempt to address this shortcoming in the case of a substrate for the phosphorylation-directed prolyl isomerase Pin1.

Pin1 is implicated in a number of regulatory and signaling pathways, which seems strange because it doesn’t possess any intrinsic transcriptional regulation ability, nor does it covalently add or remove phosphate groups. Instead, Pin1 has an enzymatic activity that accelerates, generally without altering the relative populations, the conversion of prolines from their cis- to trans- state and vice versa. This activity is specifically targeted to prolines that are adjacent to phosphorylated serines or threonines. In addition to the catalytic domain that does this work, Pin1 possesses a WW domain that has identical specificity. Because Pin1 does not alter the balance between cis- and trans- Pro, only the rate at which one changes to the other, its role in signaling has been difficult to ascertain, although there is intense interest in this area.

You don’t need to understand an entire pathway to design an effective inhibitor. What you do need to understand is the relationship between specific chemical groups and binding affinity. Getting that knowledge can be very difficult if the proposed drug is flexible. In that case, refinement methods that focus only on the particular chemical groups rather than their dynamic properties could go badly astray. Unfortunately, the dynamics of protein-bound drug molecules are difficult to measure. Their proton signals are likely to be swamped by the protein, and small molecules are often difficult to label with isotopes convenient for NMR. Peng et al. propose to address this by studying 13C relaxation at natural abundance.

A little less than 99% of the world’s carbon is in the form of NMR-inactive 12C, which is a problem for NMR because carbon is very abundant in proteins and drugs. Of the rest, most (about 1% of all carbon) is dipolar, NMR-detectable 13C, which is usually not enough to accomplish anything in terms of protein NMR. As a result, NMR researchers typically adopt the strategy of expressing their proteins using bacteria grown in media containing 13C6 D-glucose. Such enrichment of drug molecules probably could not be carried out for pharmaceutical research due to the cost and the limited availability of properly labeled reagents. Fortunately, advances such as magnets stronger than 17 T and cryoprobes make sensitive detection of natural-abundance 13C a plausible approach. Because natural-abundance measurements also simplify the experiments and analysis considerably, Peng et al. adopt this approach in their study.

Peng et al. measure μs-ms fluctuations in a 10-residue peptide in the presence and absence of Pin1. Keeping in mind that such motions can only be detected when they are associated with a change of chemical shift, it is reasonable that no such motions are detected when the peptide is all by itself. In the presence of Pin1, however, methyl groups on phospho-Thr 5 and Val 7 experience some kind of chemical exchange process on the order of several 100 /s (at 278 K), as does a methylene group in Pro 6.

Peng et al. rationalize their observations with reference to a previously-determined structure of the Pin1 WW domain in complex with this peptide (explore it at the PDB). As you can see from the lowest-energy member of this NMR ensemble (left), the residues where they detect these fluctuations in the methyls and methylenes are those that are most involved in the binding interaction. The WW domain is represented as blue ribbons, while the peptide is shown as sticks down at the bottom. That the Pro and pThr form part of the interface is unsurprising, as they constitute the specific binding sequence, while the Val side chain appears to be in position to make some hydrophobic contacts. Everything makes sense, but that doesn’t mean it’s telling us what we want to know.

The structure above shows us the interaction of the peptide with the WW domain, while what we’re really interested in getting at is the catalytic domain. Using an exchange spectroscopy experiment, Peng et al. determined that the ms dynamics they were observing probably reflected the binding of the peptide to the WW domain. To avoid this interaction, they created an artificial Pin1 that contained only the catalytic domain, and found that this also caused chemical exchange in the methyls and methylenes. Cross-checking against the exchange spectroscopy rates suggested that the ms dynamics in this case reflect the result of Pin1 catalytic activity, namely the interconversion from cis to trans and vice versa.

Unfortunately, this experiment did not report the most desired data, i.e. the dynamics of the ligand on the enzyme. On-enzyme fluctuations certainly contribute to the exchange experienced by the ligand, but because the on-enzyme population is so small (at most 2.5% of ligand) this would only be detectable in the case of an extremely large change in chemical shift. In principle one could deconvolute the dynamics from a partial-occupancy system where 50% or more of the ligand is bound to enzyme, but reliably fitting all the parameters for a two-state chemical exchange system from CPMG data is an already-dicey proposition. Fitting a four-state process from data like these is unlikely to be practical. So, in order to observe on-enzyme dynamics the drug of interest will need be saturated with its target protein, which would require millimolar protein concentrations for most ligands. Under those conditions, the spectra will also contain significant signal from the protein. The 70% deuteration used in this experiment, combined with 13C depletion, will probably be enough to suppress this, although these isotopes will increase the cost of the technique (and diminish protein yields).

Nevertheless, this paper establishes that the natural-abundance approach to measuring ligand dynamics on the µs-ms timescale is feasible. Because methylenes and methyls are common moieties in drugs and small molecules this technique may have broad applicability. Investigating the motions of small molecules bound to large proteins poses a unique problem because these systems don’t have the advantages of either small molecules (low R2) or proteins (exotic labeling schemes). The ongoing work of Peng et al. suggests that this problem is tractable, which may have positive consequences for our ability to design and optimize drugs.

Peng, J., Wilson, B., & Namanja, A. (2009). Mapping the dynamics of ligand reorganization via 13CH3 and 13CH2 relaxation dispersion at natural abundance Journal of Biomolecular NMR DOI: 10.1007/s10858-009-9349-4

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