Alternative side-chain structures from methyl CPMG

As 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.

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The classic Alzheimer's aggregates are linked

The classic neuropathological hallmarks of Alzheimer's disease are the appearance of amyloid plaques composed primarily of amyloid beta (Aβ) peptides, and neurofibrillary tangles composed mainly of hyperphosphorylated tau protein. For many years, research into treatments for Alzheimer's disease proceeded on the hypothesis that the plaques were toxic to the surrounding neurons. More recently, however, evidence has shown that soluble Aβ oligomers may be the primary toxic species. A recent paper in Proceedings of the National Academy of Sciences supports this hypothesis by showing that Aβ oligomers isolated from the brains of Alzheimer's sufferers cause neuronal degradation and improper phosphorylation of tau (1). This paper is open access, so open it up and read along.

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

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

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Dynamic origins of PBX1 homeodomain allostery

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

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Costridium virulence: What's essential?

Clostridium difficile is an intestinal pathogen that causes diarrhea in hospitals and other healthcare settings (including nursing homes). Present as a commensal bacterium in a significant fraction of the population, C. difficile is usually rather harmless, its numbers suppressed by competition with the intestinal flora. When its competitors are decimated by antibiotics, however, C. difficile flourishes, releasing toxins that cause inflammation and diarrhea, which can be dangerous because the individuals suffering these effects are often already ill. There has been conflicting information, however, as to which of C. difficile's toxins are necessary to cause disease. A paper in the recent Nature (1) aims to resolve the question.

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Zombie cyclophilins catalyze HIV capsid isomerization

If 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.

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The M2 channel controversy rides again

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

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Can oil and water mix?

We all know that linear polymers of amino acids (proteins) adopt complex three-dimensional structures when they are dissolved in water. The process of forming these structures is called folding, and it is understood to occur because proteins are amphiphilic. Some parts of a protein chain like to interact with water (hydrophilic), while others are oily and want to get out of water (hydrophobic). Folding of the chain sticks all the oily parts together on the inside of the structure while the parts of the chain that have favorable interactions with water remain on the outside. An upcoming paper from the Journal of Physical Chemistry B suggests that sufficiently long alkanes might undergo a similar transition, even though they don't have any chemical groups that like to interact with water.

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Do metamorphic proteins mediate evolutionary structural transitions?

On several previous occasions on this blog I've discussed proteins that undergo significant changes in structure without drastic changes in their primary sequence or solution conditions. In some cases, a few mutations can take a protein to a novel fold, as with Philip Bryan's protein G work. In others, closely related sequences within a whole family populate different kinds of folds, as Matt Cordes illustrated for the case of Cro proteins. In addition, there are some cases such as lymphotactin, where interconversion between two very different structures takes place at equilibrium, as illustrated by Brian Volkman's research. Each time stories like this come up I have mentioned that this kind of behavior (termed "metamorphism" in a 2008 commentary by Alexey Murzin) suggests a means by which proteins could evolve from one structure to another without losing foldedness or function. Recently, a group from the Weizmann Institute published results in PNAS that speak to this possibility.

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Take cisplatin in the morning and call me

Although we are most familiar with the circadian rhythm from its effects on our physiological state, the roots of the phenomenon lie in the molecular biology of individual cells. The circadian rhythm is the result of a transcriptional control system that regulates the levels of many different proteins in the cell with the passing of time. Not all of the proteins subject to this control have yet been catalogued, and as a result some surprising effects are still being discovered. A recent article in Proceedings of the National Academy of Sciences from the Sancar lab at UNC suggests that circadian control of a DNA repair factor may be a way to enhance the effectiveness of a chemotherapeutic agent. The article is open access, so I encourage you to open it up and read along.

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How native-like is a cold-denatured structure?

A protein has several different levels of structure. The primary structure is the arrangements of atoms and bonds, and it is formed in the ribosome by the assembly of amino acids as directed by an RNA template. The secondary structure is the local topology, the helices and strands, and this forms mostly because of the release of energy through the formation of hydrogen bonds. The tertiary structure is the actual fold of the protein, the way helices, strands, and loops are arranged in space. The fold forms primarily because of the favorable entropy of burying the protein's hydrophobic groups where water cannot access them, analogous to the formation of an oil droplet in water. This suggests that, in addition to the well-known phenomenon of proteins denaturing, or losing their higher-order structure, under conditions of high heat, proteins might also denature when they get too cold.

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Dynamics conservation in the Ras superfamily

The proposition that general fold architecture is preserved within a family of evolutionarily-related proteins is not controversial. The amino acid sequence of a protein determines its structure, and countless studies have substantiated the idea that proteins with similar sequences will adopt similar folded conformations. Because structure and dynamics are intrinsically linked, one could reasonably assume that many features of a protein's dynamics get conserved along with the fold. A growing number of experiments show that this is indeed the case, including a recent paper in Structure (1).

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Prion diseases: protein is enough

Spongiform encephalopathies are transmissible diseases that can have a major economic impact on agricultural exports, and pose a significant challenge for surveillance of the food supply. Scientists generally believe that these diseases are transmitted via a self-propagating, aberrant conformation of the prion protein (PrP). This prion hypothesis suggests that PrP alone should be sufficient to cause symptoms or death. If this hypothesis is true, then it should be possible to reproduce the disease using recombinant proteins expressed in yeast or bacteria. In tomorrow's Science, researchers from Columbus and Shanghai report that they have managed to do this, establishing that PrP alone can account for prion disease transmission.

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A single residue dictates a fold

Anfinsen's dogma — that the amino acid sequence of a protein uniquely determines its structure — naturally leads one to the idea that identity between amino acid sequences means identity between structures. This has proven to be a successful paradigm: sequence similarity reliably predicts structural and functional similarity. Evidence accruing in recent years, however, suggests that for small proteins, at least, this assumption may not be entirely safe. Adding to this view, in an article in PNAS this week (it's open access, so open it up), a research team led by Philip Bryan reports that they are able to generate significantly different folds with divergent functions from sequences that differ by a single amino acid.

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Non-native hydrogen bonds mediate structural transitions

A few weeks ago, I wrote that the goal of a structural biology research program ought to be to "characterize the conformation and energy of key, functionally-relevant members of the protein's structural ensemble and identify the pathways between them." The Nature paper last week, among other examples I mentioned in the preceding post, described functionally significant minor members of the native-state ensemble, and this is certainly an area where structural studies are making a lot of progress. But what about the other part of that statement, the transition pathways? How are we to study them, and what can we learn about them? Experiments alone are unlikely to tell us everything we want to know about the intermediates between different native structures. We can, however, use simulations validated by experiments to investigate the mechanisms of structural change. Today in Cell, research primarily performed by my coworkers Alexandra Gardino and Janice Velos demonstrates that the bacterial signaling protein NtrC  rapidly samples its active conformation even when it is not phosphorylated. Moreover, they confirm predictions that the intermediates between these two states are stabilized by hydrogen bonds not present in either one.

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Alternate structures and catalysis in cyclophilin

Of all the sources of structural variability in proteins, the hardest to pin down is side-chain conformational heterogeneity. Side chains aren't always easy to model into their primary conformation in the first place — you need excellent crystal diffraction or NMR data to do it. Even if you pulled that off, it's not always clear how (or if) side-chain fluctuations relate to a protein's activity. If we carefully examine our data for the faintest signals, however, we can sometimes find evidence of secondary conformations that play an important role in function. In an article in this week's Nature, James Fraser, myself, and others show that in the case of the proline isomerase cyclophilin A (CypA), alternative conformations of side chains play a key role in catalysis.

Previous experiments on CypA had established that the backbone amide groups of many residues were sensitive to a conformational fluctuation on the millisecond timescale. Under conditions where this enzyme is saturated with a peptide substrate, the fluctuation rate for some of these residues is very similar to the catalytic rate, suggesting that the dynamics and catalysis are linked in some way (2). Later experiments also showed that this fluctuation was an intrinsic property of the enzyme, continuing even in the absence of substrate (3). What we didn't know, however, was how the dynamics of cyclophilin were related to catalysis. We couldn't know, because we had no idea what the motion we were detecting was.

In the case of enzymes like adenylate kinase, there is a dramatic rearrangement of structural elements, and the population of conformations corresponding to the "end points" of that motion can be significantly enriched by altering the amount of substrate present in solution. In the case of CypA, neither of these things seems to be true. Supplementary Fig. 1a (freely accessible from the article page) neatly encapsulates the problem. For this figure, 48 structures of CypA, some with ligand and some without, were aligned, and the variation between them was determined. While there is some variability in the chain conformation, it is primarily limited to a group of residues known to undergo fluctuations that are not related with catalysis (blue chain). The residues involved in the catalysis-related dynamics don't seem to have much variability, even across this fairly large group. So we can't trap the unknown, minor state of CypA by adding substrate, and there's no evidence of an alternate state that explains the NMR data.

Knowing this, we suspected that some kind of side-chain motion accounted for the observed dynamics, probably involving an aromatic group of some kind. Our efforts to gather evidence for this, however, ran into some typical NMR problems — resonance overlap and poor sensitivity exacerbated by chemical exchange. Fortunately, the crystallographers came to our rescue, in the form of Tom Alber and his super-talented grad student Jaime Fraser. Jaime had determined a crystal structure of CypA at cryogenic temperature and analyzed the data using their algorithm RINGER, which examines electron density below the threshold typically considered "noise" in order to identify possible alternative rotameric states of side chains. He found evidence of multiple conformations for a few residues, but nothing that would explain the NMR results. Jaime had the bright idea to redo the experiment at room temperature, which Tom was convinced would result in nothing more than a radiation-damaged crystal and bad diffraction data.

What actually happened was that when Jaime examined the electron density from that experiment he could identify a group of side chains that had more than one conformation in the crystal, which you can see in Fig. 1. These residues included serine 99, methionine 61, and the catalytic arginine 55. Right in the middle of this group was phenylalanine 113, a residue with an aromatic side chain capable of causing changes in chemical shift at relatively long range. For context, the image to the left shows a structure of cyclophilin (PDB code: 1RMH) in complex with the model substrate we used in our own experiments (succinyl-Ala-Ala-Pro-Phe-p-nitroaniline), with the side chains of S99, F113, M61, and R55 in red. As you can see, F113 and M61 form part of the floor of the binding pocket, with S99 rather remote.

So here we have an alternative structure of CypA, hidden below the threshold typically considered when determining a crystal structure. It was certainly plausible that fluctuations in this ensemble of side chains could give rise to the NMR observations, but plausibility isn't proof. One way to address this would be to try and force CypA to adopt the less-populated conformation. If you look at Fig. 1d you can see that the two conformations of S99 lie at the standard rotameric positions, and that the less-populated rotamer of S99 would run into the more-populated rotamer of F113. So, if you replaced one of the side-chain hydrogens of S99 with a methyl group (i.e. mutated the serine to threonine), that might push the other residues of this group into their minor conformational state. So, that's what we did.

To the right you can see an overlay of structures for wild-type (WT) CypA (red) and S99T (green), aligned using structural elements on the opposite side of the protein from the active site. As you can see, the backbone traces match very closely, except for the helix and loop on the right. These elements are involved in crystal contacts in the S99T structure, but not the WT; a lower-resolution structure of S99T shows no differences here. Another key difference between these structures, of course, is the position of the side chains (thick neon); as shown here (and more clearly in Fig. 2c) they seem to have adopted the minor conformation from the WT structures. Although this mutation inspires widespread chemical shift changes (Fig. 3a) consistent with the hypothesis that this concerted side-chain rotation gives rise to the NMR observations, the structures seem very similar. Yet, S99T CypA differs from WT in two important ways.

The first difference is that the conformational fluctuations are dramatically slower, but only for residues that showed catalysis-related dynamics in WT (Fig 3d). In fact, this rate is now so slow that due to a quirk of NMR we can only determine the slowest rate of the process. At 10 °C, this fluctuation in S99T is about 60 times slower than the slowest process in WT.

The second key difference between the mutant and the WT is that catalysis is dramatically slowed. Because CypA does not consume its substrate (it acts on both cis- and trans- proline bonds) its activity can be assayed by NMR, as you can see in Fig. 4. As with any enzymatic assay, the net activity is proportional to the amount of enzyme added, so just glancing at these spectra (and knowing the enzyme concentration) you can estimate that S99T has at least 40-fold lower activity than WT enzyme. If you actually perform the fits, it turns out that the reaction velocity for S99T is about 240 times lower than that for normal CypA, but this includes a contribution due to the fact that S99T does not bind its substrate as tightly either. If you correct for this, it turns out that S99T has about 70-fold less activity than the normal enzyme. Not only is this similar to the change in dynamics, it's also quite comparable to another mutation, R55K, that removes a group that performs some of the chemistry.

These results indicate that a conformational change in a group of side chains including F113 is primarily responsible for the chemical exchange behavior observed in WT. The S99T mutation stabilizing the minor conformation dramatically and similarly reduces both the conformational fluctuation rate and the catalytic rate. This suggests that dynamics and catalysis are linked not by happenstance but by some direct relationship. Unfortunately, these experiments do not provide any direct insight into the mechanism by which dynamics contribute to catalysis. They do establish, however, that in CypA coherent fluctuations of side chains, barely detectable in protein crystals, nonetheless make a critical contribution to function.

1) Fraser, J.S., Clarkson, M.W., Degnan, S.C., Erion, R., Kern, D., & Alber, T. (2009). Hidden alternative structures of proline isomerase essential for catalysis Nature, 462 (7273), 669-673 DOI: 10.1038/nature08615

2) Eisenmesser, E.Z., Bosco, D.A., Akke, M., & Kern, D. (2002). Enzyme Dynamics During Catalysis Science, 295 (5559), 1520-1523 DOI: 10.1126/science.1066176

3) Eisenmesser, E., Millet, O., Labeikovsky, W., Korzhnev, D., Wolf-Watz, M., Bosco, D., Skalicky, J., Kay, L., & Kern, D. (2005). Intrinsic dynamics of an enzyme underlies catalysis Nature, 438 (7064), 117-121 DOI: 10.1038/nature04105

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The role of dynamics in catalysis

For some enzymes, dynamics on the millisecond timescale play a critical role in catalysis. I don't think this is a particularly controversial or unclear statement, but then, I know what I mean by it. In the process of communication, however, the intended meaning sometimes gets lost or transformed. A statement that addresses an entire catalytic cycle, for instance, might be interpreted as addressing only the chemical step. This seems to have happened in a pair of papers that concern the transfer of energy from conformational rearrangements to a chemical reaction.

Consider a reaction scheme in which an enzyme loosely associates with substrates (E.S), then "closes" to form a tight, catalytically-competent complex that then undergoes a reaction with the rate k_chem:

Pisliakov et al. (1) ask whether the closing process can accelerate k_chem. They ask this question primarily because a group from Harvard University proposed that this was possible in a paper printed last year in J. Phys. Chem. B (2). In that paper, Min et al. performed some simulations suggesting that such an acceleration was at least possible, and consistent with some enzymatic data. Pisliakov et al. approach the question with simulations of the reaction of the phosphotransfer enzyme Adk with 2 ADP molecules to form ATP and AMP. As part of the catalytic cycle, the enzyme goes from an open state (PDB: 4AKE) where the ATP and AMP binding sites are exposed to solvent, to a closed state (PDB: 1ANK) where the substrates are shielded from the surrounding solution by ATP and AMP "lids" that close down over the active site.


One can, perhaps, imagine that when the enzyme closes around the substrates, some motion will occur that promotes the transfer of a phosphate group from one molecule to another. Pisliakov et al. use a three-tiered system of simulations to address the question, as a way of trying to get around the difficulty of dealing with the long timescales required. Their simulations allow them to adjust the energy barrier to match the experimental rates or accelerate the reaction so that the whole pathway can be simulated. In general, they find that conformational fluctuations do not enhance the chemical reaction rate in this system.

I have two main concerns about the science that was performed here. The first is that the energy barriers in the long-timescale experiment appear to be improperly paramaterized. In estimating these barriers for the phosphotransfer reaction in Adk, Pisliakov et al. used 260 /s as k_chem. However, although the actual reaction carried out by Adk follows an extremely complex scheme, the analysis performed by Wolf-Watz et al. utilized a simplified scheme that combined all post-association steps into a single k_cat. This is why the concordance between k_cat and k_open justifies the conclusion that lid-opening is rate-limiting. In principle, the experiments used for that paper are incapable of separating the opening and closing steps from the chemical step. Therefore we have no experimental knowledge of the phosphotransfer rate, except that it is greater than 260 /s. This perplexing error appears to have originated with Min et al., but I am surprised Warshel's group did not catch it.

This is not a major problem because the bulk of the conclusions of the experiment were drawn from a different simulation in which the energy barriers were lower, but this leads to my second concern. If the structural transition involves a very smooth and coherent rearrangement of the protein, then simply manipulating energy barriers should not result in a serious error of analysis. In reality, however, ensemble motions of protein elements are not going to be so directed or uniform. Structural rearrangements are not highly singular steps, but involve a large number of intermediates and transition states. Motions in the late stages of the structural transition that promote catalysis may well be missed by simplified models, or accelerated beyond productivity by lowering the energy barrier.

That said, I'm not particularly surprised that Pisliakov et al. find that energy from the conformational coordinate does not transfer to the chemical coordinate, nor do I disagree with the finding. Despite what Pisliakov et al. appear to believe, the papers that have come out of Dorothee's group don't argue that the millisecond motions contribute directly to the chemistry. Doro doesn't believe that for a second. Neither do I. The importance of dynamics has little to do with shoving the reaction along the chemistry coordinate, but everything to do with getting substrates bound and into a state where chemistry is possible.

Dynamics allow an enzyme to reconcile incompatible functional requirements. To efficiently function as a phosphotransfer enzyme (as opposed to a hydrolytic phosphatase), Adk must expel water from the active site during catalysis. If the active site is inaccessible to solution, however, there is no way for the substrates to diffuse into it. It is difficult to create a single, rigid fold that can accommodate both these demands, but by fluctuating between two states the problem is resolved quite easily. So yes, the dynamics are essential to catalysis, but that does not imply that the conformational and chemical energy coordinates are coupled.

More perplexing is the discussion of the hierarchy of motion, which Pisliakov et al. take to mean that nanosecond motions somehow contribute to the chemical coordinate. As I discussed when that paper was initially published, the question being addressed was whether and how motions on the fast timescale (ps-ns) in Adk were related to the slower (ms) motions of the lids. In a hierarchy of motion, fast timescale fluctuations enable or promote slow timescale dynamics. In the case of Adk, this means that nanosecond flexibility at structural hinges allow the millisecond motions of the ATP and AMP lids. It was not implied, then or since, that the nanosecond motions in question make a direct contribution to movement along the chemical coordinate. This is not to say that there are no researchers who believe that ns motions contribute to catalysis — I've previously mentioned some work on hydrogen tunneling that makes precisely this argument. In the specific case of Adk, however, the contribution of ns motions to catalysis consists entirely in their enabling of the slower ensemble motions of the nucleotide binding domains, and nobody but the Warshel group has suggested otherwise.

There is an ongoing disconnect in the literature concerning the role of dynamics in catalysis. While it is true that in many cases rates of structural transitions correlate with rates of catalysis, this does not imply that the conformational transition coordinate is linked to the chemical reaction coordinate by direct transfer of energy. It is more likely that the dynamics of the enzyme contribute to catalysis by generating reaction-competent states from reaction-incompetent states. This is not to say that dynamics cannot possibly make a contribution to phenomena such as hydrogen tunneling, but it strikes me as unlikely that motions on the millisecond timescale will contribute to a chemical coordinate. Experiments, rather than simulations, will be the ultimate test of the idea. However, in principle, this hypothesis can only be tested experimentally on enzymes where the conformational changes do not limit the chemical reaction rate. Because the rate of the chemical step is unknown in Adk, it may not be an appropriate model system for addressing this question.

1. Pisliakov, A., Cao, J., Kamerlin, S., & Warshel, A. (2009). Enzyme millisecond conformational dynamics do not catalyze the chemical step Proceedings of the National Academy of Sciences, 106 (41), 17359-17364 DOI: 10.1073/pnas.0909150106

2. Min, W., Xie, X., & Bagchi, B. (2008). Two-Dimensional Reaction Free Energy Surfaces of Catalytic Reaction: Effects of Protein Conformational Dynamics on Enzyme Catalysis The Journal of Physical Chemistry B, 112 (2), 454-466 DOI: 10.1021/jp076533c

3. Wolf-Watz, M., Thai, V., Henzler-Wildman, K., Hadjipavlou, G., Eisenmesser, E., & Kern, D. (2004). Linkage between dynamics and catalysis in a thermophilic-mesophilic enzyme pair Nature Structural & Molecular Biology, 11 (10), 945-949 DOI: 10.1038/nsmb821

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Proteins stick together when it's crowded

One of the many ways that a living cell does not resemble a test tube is in the degree to which its internal environment is crowded. Cells are crammed full of massive protein complexes, vesicles, organelles, carbohydrates, peptidoglycan, and other assemblies that occupy a great deal of space. The tubes and cuvettes used for biochemical experiments, in contrast, typically contain nothing more than a few proteins and small molecules of interest along with a relatively dilute set of salts and buffering agents. Because many complexes appear to be stabilized by crowding, and excluded volume effects are known to favor compact, folded protein chains, there is considerable interest in estimating how protein behavior changes in the spatially-restricted environment of the cell. In some cases this is approached using NMR to study changes in dynamics and structure inside cells. In a recent issue of Biophysical Journal, groups from Florida State University and Rehovot University, in separate papers, address how crowding affects protein-protein interactions.

Both groups perform biochemical experiments in vitro to examine this question. In order to crowd the solution, they add reagents such as ficoll, polyethylene glycol (PEG), and dextran, and compare the changes in binding and kinetics to solutions that have merely been made more viscous (through the addition of glucose, for instance). Batra et al. study the association of two components of the E. coli DNA polymerase III and find that the presence of crowding agents slightly stabilizes the complex. However, as the size of the crowders is increased, this stabilization is diminished. Batra et al. develop a relatively simple mathematical model that suggests this observation results from the fact that larger particles pack less efficiently, leaving larger "holes" in which the protein complex sees something more like dilute solution.

Phillip et al. study several protein complexes. Similar to Batra's group, they find that crowding with dextran modestly increases the binding affinity of two of their protein pairs, but that this is not replicated for crowding with PEG. Particularly for PEG-1000, there was a clear decrease in affinity, although a non-crowding viscogen (ethylene glycol) had an even greater effect. Phillip et al. also measured the kinetics of binding, and found that the association rates were significantly lower in crowded solutions, as compared to buffer. However, when the rates were corrected for the effect of viscosity, it appeared that the crowding agents slightly increased the association rate. The authors attribute this to excluded volume effects in the binding transition state. The dissociation rate was also slightly reduced in crowded solutions, which the authors explain by the longer lifetime of the encounter complex (allowing a larger fraction of complexes to fall back to the lower-energy bound state).

Given that crowding appears to have a profound effect on the function of certain complexes, the relatively small effects observed in these studies might seem confusing. Batra et al. argue that although each individual binding interaction is only modestly stabilized, the effect should be cumulative. As a result, multi-subunit complexes will experience a greater effect than small heterodimers. Additionally, the most famous examples of crowding enhancement involve very large complexes — the ribosome, decameric assemblies, hemoglobin polymers, etc. In comparison, the complexes formed in these model studies are quite small. It may be that the stabilizing effect of crowding depends to some degree on the size of the complex to be formed. While similar size is difficult to achieve in strictly heterodimeric systems, it should be possible to monitor the assembly of large complexes like GroES/GroEL under crowded conditions. A study of the relationship between the size or number of components in a complex and crowding stabilization may prove instructive.

Batra, J., Xu, K., Qin, S., & Zhou, H. (2009). Effect of Macromolecular Crowding on Protein Binding Stability: Modest Stabilization and Significant Biological Consequences Biophysical Journal, 97 (3), 906-911 DOI: 10.1016/j.bpj.2009.05.032

Phillip, Y., Sherman, E., Haran, G., & Schreiber, G. (2009). Common Crowding Agents Have Only a Small Effect on Protein-Protein Interactions Biophysical Journal, 97 (3), 875-885 DOI: 10.1016/j.bpj.2009.05.026

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Filling the donut hole in dynamics

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.

Standard 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 (S²_rdc) 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 S²_rdc is roughly anticorrelated with the number of dihedral angles between a given methyl group and the peptide backbone. The anisotropy of motion (η_rdc) 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 S²_rdc to order parameters determined using other approaches. As one would expect, for most methyls the S²_rdc, 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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Apparently, the moon hit their eyes like a big pizza pie

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

It 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

then

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

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