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

 Posted by at 1:00 AM
Aug 022008
 
ResearchBlogging.orgLoads of interesting stuff is going on in Alzheimer’s research right now. While the hot news is about a trial showing significant benefits from going after tau tangles, a recent paper in PLoS ONE continues to investigate the pathology of the amyloid-β peptide. As I’ve mentioned in previous posts, cleavage of the amyloid precursor protein to a 42-residue peptide (called Aβ1-42 in this paper) initiates the formation of peptide oligomers and eventually plaques. Recent research has indicated that these oligomers are sufficient to cause the development of Alzheimer’s disease, but the mechanism by which they do so remains uncertain. Sara Sanz-Blasco and colleagues show that Aβ oligomers disrupt calcium homeostasis in neurons, damaging the mitochondria and promoting apoptosis, and that certain NSAIDs can suppress these adverse mitochondrial effects (1). PLoS ONE is open access, so go ahead, open the article up in another window, and follow along.

Although the appearance of plaques and neuronal death are classic hallmarks of Alzheimer’s pathology, the relationship between these features is not well understood. For instance, it is possible the plaques themselves kill neurons or impair neural function. However, it seems equally likely that the appearance of plaques and the death of neurons are two distinct effects with a single cause. This view is supported by the oligomer toxicity study, but that study fails to resolve the question of exactly how Aβ oligomers kill neurons. Previous work has associated Aβ with derangement of cellular calcium (Ca2+) management — a 2005 paper by Demuro et al. (2) showed that soluble Aβ induced an increase in intracellular Ca2+ in a neuroblastoma cell line. Sanz-Blasco et al. therefore decided to directly test whether Aβ oligomers were increasing Ca2+ levels in neurons, and specifically in mitochondria. In order to do this last bit they used a low-affinity aequorin targeted specifically to mitochondria.

Allow me digress… to many of my readers that probably sounds like a terrible idea. If you’re trying to detect a particular chemical in the cell, it seems like the best thing to do would be to get a high-affinity binding partner. And if figuring out whether there is any calcium in the mitochondria is what you want to do, then a high-affinity detector makes sense. However, when you’re using a small amount of a sensor to detect changes in the concentration of a large amount of ligand, a low-affinity sensor is what you want.

To see why, take a look at the graph on the right. This is just a rough calculation based on a situation where the detector is at a concentration of 100 µM and the concentration of its ligand (that you’re trying to detect) changes from 10 mM to 100 mM. Note that the concentration of the detector is at most 1% that of the ligand. If the dissociation constant KD of this complex is 1 mM (blue) (a lower KD means higher affinity), then the detector is almost saturated when you start, and the percentage occupied doesn’t change very much over the course of the experiment. This means that it will be very difficult to tell the difference between, say, 50 mM ligand and 100 mM ligand, because that amounts to a signal difference of 1% of the maximum response. The situation gets a little better if the KD is 10 mM (green). The lowest affinity detector here (KD = 50 mM, red) actually does the best job of distinguishing between 50 mM and 100 mM ligand, because the difference in response amounts to 17% of the total dynamic range. Ideally, you want to tune the KD of your detector in such a way that its response to changes in ligand concentration is large and linear over the range you are likely to be observing. For the last detector, this range lies between 10 and 40 mM of ligand, so that would likely be the ideal range to investigate with it.

The precise numbers are different in the present paper, but the principle is the same. The affinity you want in your detector will depend on what you are trying to detect and the circumstances under which you are trying to detect it. In this case, the researchers are trying to measure changes in calcium ions over a fairly wide range, which have a pretty high concentration in mitochondria, and they’re doing it using a luminescent protein, which isn’t very concentrated. As a result, a relatively low-affinity detection system is best.

So, what did they find? The results in Figure 1 show that Aβ oligomers and fragments cause an influx of calcium into the cytoplasm of cultured neurons, but preparations of Aβ fibrils did not cause this effect. Moreover, exposure of the cells to Aβ oligomers caused a clear influx of calcium into the mitochondria (Figure 3). This is a problem for a cell because Ca2+ overload in mitochondria can cause programmed cell death, or apoptosis. Using the classic TUNEL assay, the authors of this study showed that the Aβ oligomers caused apoptosis. In addition, they showed that treatment with the oligomers caused the release of mitochondrial cytochrome c (a step in the apoptotic pathway) and that the addition of cyclosporin A, which inhibits the release of proteins from the mitochondrion, blocked cell death (Figure 4). Together, these pieces of evidence support the idea that Aβ-induced Ca2+ influx into the mitochondria activates the apoptotic cascade, leading to neuronal death. These results are consistent with a very cool study published this week in Neuron (3) showing that amyloid plaques correlated with high neuronal Ca2+ levels in vivo (in live mice).

On its own this is pretty interesting, but Sanz-Blasco et al. push it a bit further. Because they had shown previously that some NSAIDs prevent mitochondrial Ca2+ uptake in a cancer cell line, they decided to find out if they would work in this instance, too. As you can see from Figure 6, the three NSAIDs tested kept the mitochondria calcium-free, even if the cells were treated with Aβ oligomers. NSAIDs also prevented cytochrome c release and cell death (Figure 8).

Some readers may recall that Kukar et al. showed that certain NSAIDs prevent oligomerization of Aβ1-42, hinting at a possible explanation of these results. However, the controls performed by Sanz-Blasco et al. show that under the conditions of these experiments the NSAIDs they used have no effect on cytosolic Ca2+ concentrations (Figure 7). If it is amyloid oligomers that let Ca2+ through plasma membranes, then this would appear to rule out structural disruption as a mechanism. Instead, Sanz-Blasco et al. propose that these NSAIDs specifically alter the polarity of the mitochondrial membrane in such a way as to prevent Ca2+ uptake.

If this is true, then NSAIDs may be able to perform a double-whammy on Alzheimer’s disease. On the one hand, they appear to be capable of altering Aβ cleavage patterns to reduce the formation of toxic oligomeric precursors. In addition, they appear to have an ability to block mitochondrial breakdown and subsequent apoptosis directly. While this is encouraging, and speaks to the value of pursuing refinements of existing NSAIDs as possible Alzheimer’s treatments, this experiment doesn’t necessarily prove any therapeutic value. Even if the neurons are saved from death, the calcium flood may impair their function to such a degree that their continued survival doesn’t matter. Only clinical trials and further research can firmly establish whether current or optimized NSAIDs can provide significant protection against Alzheimer’s disease.

1. Sara Sanz-Blasco, Ruth A. Valero, Ignacio Rodríguez-Crespo, Carlos Villalobos, Lucía Núñez (2008). Mitochondrial Ca2+ Overload Underlies Aβ Oligomers Neurotoxicity Providing an Unexpected Mechanism of Neuroprotection by NSAIDs PLoS ONE, 3 (7), 0-0 DOI: 10.1371/journal.pone.0002718 OPEN ACCESS

2. A. Demuro, E. Mina, R. Kayed, S.C. Milton, I. Parker, C.G. Glabe (2005). Calcium Dysregulation and Membrane Disruption as a Ubiquitous Neurotoxic Mechanism of Soluble Amyloid Oligomers Journal of Biological Chemistry, 280 (17), 17294-17300 DOI: 10.1074/jbc.M500997200 OPEN ACCESS

3. K Kuchibotla, S Goldman, C Lattarulo, H Wu, B Hyman, B Backsai (2008). Aβ Plaques Lead to Aberrant Regulation of Calcium Homeostasis In Vivo Resulting in Structural and Functional Disruption of Neuronal Networks Neuron, 59 (2), 214-225 DOI: 10.1016/j.neuron.2008.06.008

Jun 272008
 
ResearchBlogging.orgAs I mentioned in my post on the recent paper by Kukar et al., the disruption of amyloid plaques has been an ongoing focus in Alzheimer’s disease research. However, plaques and inclusions are of concern in many diseases, and as a result there is a great deal of interest in finding molecules that can either dissociate, or prevent the formation of, amyloids of many different kinds of proteins. In the most recent issue of Nature Structural and Molecular Biology there is a paper suggesting that (-)-epigallocatechin gallate (EGCG) may be able to interfere with multiple proteins that form β-rich aggregates.

EGCG is a chemical found in green tea (although it is doubtful you could realistically drink enough green tea to absorb the concentrations used in this study). Previous studies had suggested that it altered the aggregation behavior of α-synuclein (αS) and huntingtin. So, Ehrnhoefer et al. use highly purified EGCG in a number of experiments to determine what effect it had on αS and the amyloid-β peptide (Aβ) (1). What these proteins have in common, besides the fact that their aggregation is associated with disease, is that the single proteins take on a β-strand structure that assembles into fibrils.

Ehrnhoefer et al. find that EGCG interferes with some aspect of this process, reducing the formation of the fibrils while inducing the formation of some alternate oligomeric structure. In the case of αS, the result is a spherical oligomer (Figure 1), although the gel filtration results indicate that a large spectrum of oligomeric states is formed at lower concentrations (the trace suggests that these oligomeric forms are interconverting during elution). NMR and other data show that EGCG associates directly, but non-specifically, with the protein backbone, and that the compound has strongest affinity for the C-terminus of αS, which may play a role in preventing aggregation. The EGCG-treated oligomers had reduced β-strand content (as assayed by CD). Treatment with EGCG appeared to reduce αS toxicity in cultured cells, although this was measured strictly in terms of cell death.

Similar results were seen with Aβ—addition of EGCG reduced the formation of fibrils and the toxicity of amyloids towards cultured cells. Again, the oligomers formed in the presence of EGCG could be quite large, and took a spherical shape.

Based on these data, the authors propose that EGCG binds preferentially to unfolded proteins and interferes with the formation of regular β-strand structure. The EGCG-bound proteins are unable to form fibrils, and therefore EGCG oligomers compete with fibrils for monomers, slowing the formation of the latter. The net effect is to divert these unfolded proteins out of amyloidogenic pathways and into alternate oligomeric structures, which appear to be nontoxic, or at least less toxic.

Can EGCG or a derivative be turned into a drug to treat Alzheimer’s disease, or a general treatment for amyloidoses? This is an uncertain proposition. As the authors of a commentary (2) in the same issue of NSMB note, EGCG’s nonspecific assault on amyloids may damage some normal structures built on this architecture. Moreover, because EGCG seems to bind unfolded regions nonspecifically, it has the potential to interfere with any of the numerous signaling proteins that possess such regions. The potential for side effects is very high, and the continued viability of cultured cells in the presence of EGCG, while reassuring, is not a particular reason to believe the compound is safe at high concentrations in the human nervous system.

The promiscuity of EGCG’s interactions with unfolded regions poses another problem, in that all these proteins will act to interfere with EGCG’s action on its intended target. Fairly high ratios of EGCG were necessary in these assays, and they mostly involved purified proteins. In vivo, all unfolded proteins will act to titrate EGCG out of plasma, meaning that significant quantities of this (or any other non-specific molecule like it) would need to be used in order to achieve the desired effect. This again raises the likelihood of side effects.

Of course, the most severe complication arises from the nature of amyloidoses themselves. Although the obvious presence of plaques and inclusions naturally leads us to suspect that these aggregates are the agents causing the disease, increasing evidence suggests that amyloids are merely the endpoints of some other process that is the actual culprit. In the case of Alzheimer’s disease, for instance, a recent article in Nature Medicine has provided very strong evidence that soluble Aβ dimers are the dominant contributors to Alzheimer’s pathophysiology (go check out Ashutosh’s excellent discussion of this article over at The Curious Wavefunction for more information). Now, given that these dimers do eventually form amyloid, it seems likely that they have β structure in their pathogenic form, which EGCG will probably disrupt, but this is not guaranteed. Small molecules like EGCG that prevent deposition into amyloid may actually exacerbate the problems they are meant to solve. Further research is needed to establish that the EGCG oligomers of αS and Aβ are not toxic in vivo.

Diseases associated with protein aggregation continue to pose a challenge precisely because we have such a poor handle on their pathogenesis. Ehrnhoefer et al. clearly demonstrate that EGCG possesses the ability to alter the behavior of amyloidogenic unfolded proteins. While that may imply that it has promise as a broad-spectrum drug to attack these diseases, the promiscuity of its action is a cause for concern from the perspective of dosage and side effects. And, because soluble oligomers may well be the pathogenic species in many (if not all) of these diseases, our optimism about this approach must be tempered with an awareness that the actual effect of EGCG may be to enhance, rather than diminish, the toxicity of the relevant protein targets.

1. Ehrnhoefer, D.E., Bieschke, J., Boeddrich, A., Herbst, M., Masino, L., Lurz, R., Engemann, S., Pastore, A., Wanker, E.E. (2008). EGCG redirects amyloidogenic polypeptides into unstructured, off-pathway oligomers. Nature structural & molecular biology, 15(6), 558-566. DOI: 10.1038/nsmb.1437

2. Roberts, B.E., Shorter, J. (2008). Escaping amyloid fate. Nature Structural & Molecular Biology, 15(6), 544-546. DOI: 10.1038/nsmb0608-544

Jun 142008
 
ResearchBlogging.orgOne of the best-known features of Alzheimer’s disease pathology is the formation of proteinaceous amyloid plaques in the brain. In Alzheimer’s disease these plaques are primarily formed by the amyloid-β peptide (Aβ) derived from the amyloid precursor protein (APP) by the action of β- and γ-secretase. The length of the Aβ peptide varies, but the 42-residue form (Aβ42) is more likely to form plaques and fibrils. Although it remains uncertain whether plaques are a cause of Alzheimer’s disease symptoms, or merely an effect of some underlying derangement, finding some way to prevent or reduce plaque formation is a major goal in the field. This week in Nature, a team of researchers from institutions all over the US and Europe show that non-steroidal anti-inflammatory drugs (NSAIDs) may be able to accomplish these goals by binding to APP and Aβ directly.

Previous research from the Koo lab indicated that some NSAIDs specifically reduced the production of the amyloidogenic Aβ42 fragment (1) both in cultured cells and in a mouse model of the disease. APP was still processed into peptides, but these were shorter and less likely to form amyloid plaques than Aβ42. Significantly, the cleavage of other γ-secretase targets was not affected, meaning that side-effects of NSAID treatment might be minimal. Although NSAIDs were expected to ameliorate Alzheimer’s symptoms by reducing inflammation, Weggen et al. found that the beneficial effects were not the result of cyclooxygenase inhibition. In a follow-up paper (2), Weggen et al. used experiments on cultured cells to show that the drugs were directly modulating γ-secretase activity. These experiments also showed that mutations to presenilin-1, a core component of the γ-secretase complex, could either increase or decrease the effect of NSAIDs, suggesting that it was the protein directly affected by these drugs.

Kukar et al. set out to test this hypothesis using photaffinity labeling. They took a few compounds known to alter Aβ42 levels and added a functional group that would react with a protein in the presence of UV light. These covalently-labeled proteins could then be detected, and this would serve as a relatively easy way to determine which component of the γ-secretase complex was actually binding NSAIDs. Like many cleverly-designed experiments, this failed in an interesting way: no known components of the γ-secretase complex were labeled. Fortunately, the researchers realized that there was another component to the complex they hadn’t tested yet: the substrate.

It turned out that the NSAIDs could label a 99-residue fragment of APP. Moreover, this labeling was reduced by other γ-secretase modulators (GSMs) and unaffected by non-GSM NSAIDs. Using a series of progressively shorter constructs, Kukar et al. localized the binding activity of GSMs to residues 28-36 of amyloid-β.

This on its own is a very useful finding because it provides a target for refinement of these compounds. Knowing where and to what protein a possible drug binds makes it easier to develop assays to test new potential drugs, as well as enabling structure-based design. However, the authors took the next step and asked whether these drugs, because they bind to a region of APP known to be involved in the formation of amyloid plaques, might inhibit plaque formation directly. In cultured cells, they found that treatment with certain substrate-targeting GSMs decreased the formation of Aβ dimers and trimers even under conditions where the overall concentration of Aβ42 was not altered.

This suggests that these GSMs may be able to fight the buildup of amyloid plaques in two ways. By altering where γ-secretase cleaves APP, they reduce the concentration of Aβ42. Moreover, by interfering with Aβ oligomerization they fight the formation of plaques directly. With luck, further work in medicinal chemistry will arrive at compounds that enhance both these activities. The development of compounds that significantly reduce or prevent the formation of amyloid plaques will be a great step forward for Alzheimer’s research. Even if such drugs do not prove to be a cure, a clear indication that plaques don’t cause Alzheimer’s would be a critical insight.

I want to emphasize that although these results are quite promising, they do not prove the efficacy of NSAIDs in ameliorating actual Alzheimer’s symptoms. Transforming these findings into a cure or even an effective treatment will require a great deal of additional research, if it is even possible. You should not attempt to treat Alzheimer’s with NSAIDs, or begin a regimen of NSAIDs or any other kind of drug or supplement, unless you have first discussed the possible risks and benefits with your doctor. And no, Minnesota, I do not mean a naturopath.

1. Weggen, S., Eriksen, J.L., Das, P., Sagi, S.A., Wang, R., Pietrzik, C.U., Findlay, K.A., Smith, T.E., Murphy, M.P., Bulter, T., Kang, D.E., Marquez-Sterling, N., Golde, T.E., Koo, E.H. (2001). A subset of NSAIDs lower amyloidogenic Aβ42 independently of cyclooxygenase activity. Nature, 414(6860), 212-216. DOI: 10.1038/35102591

2. Weggen, S. (2003). Evidence That Nonsteroidal Anti-inflammatory Drugs Decrease Amyloid β42 Production by Direct Modulation of γ-Secretase Activity. Journal of Biological Chemistry, 278(34), 31831-31837. DOI: 10.1074/jbc.M303592200 OPEN ACCESS

3. Kukar, T.L., Ladd, T.B., Bann, M.A., Fraering, P.C., Narlawar, R., Maharvi, G.M., Healy, B., Chapman, R., Welzel, A.T., Price, R.W., Moore, B., Rangachari, V., Cusack, B., Eriksen, J., Jansen-West, K., Verbeeck, C., Yager, D., Eckman, C., Ye, W., Sagi, S., Cottrell, B.A., Torpey, J., Rosenberry, T.L., Fauq, A., Wolfe, M.S., Schmidt, B., Walsh, D.M., Koo, E.H., Golde, T.E. (2008). Substrate-targeting γ-secretase modulators. Nature, 453(7197), 925-929. DOI: 10.1038/nature07055