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

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

Mar 062009
 
ResearchBlogging.orgVirtually everything we know about protein conformation comes from experiments performed in environments that do not resemble the biological context of proteins in action. Our data generally come from solutions that lack the significant array of salts, sugars, and metabolites that fill the cytosol of living cells, and often these data are acquired at a pH far removed from cytosolic. In addition, the dilute solution conditions used in almost all structural biology experiments do not capture the crowding and excluded volume effects that are likely to play a role in determining protein conformation in densely-packed cellular environments. As techniques in biology and NMR have advanced, however, the determination of protein structures in living organisms has become possible, despite the significant challenges. This week in Nature, a research team from several institutions in Japan and Germany reports that they have solved a high-resolution NMR structure of a protein in the cytosol of living E. coli bacteria (1).

The authors chose a relatively small and simple protein to work out their technique, in this case a 66-residue metal-binding protein from a thermophilic organism. They expressed the protein in E. coli using standard methods, exchanging the bacteria into isotopically-enriched media once they reached the appropriate density for induction. At the end of the induction period, the bacteria were gently centrifuged, resuspended into a thick slurry, and put in an NMR tube. Samples produced in this way were stable for about 6 hours, which is generally not enough time to perform the kinds of 3-dimensional experiments necessary for NMR structure determination.

To get around this problem, the authors used non-linear sampling and maximum entropy processing. This approach allowed them to reduce the number of data points they took, without losing much of the frequency discrimination that is vital to successful NMR. In this way they were able to compress the essential assignment and structural experiments to about 3 hours, although they found it necessary to repeat experiments and add them together in order to get enough signal to proceed. In order to ensure that the data were not contaminated by sample degradation, they ran short two-dimensional experiments to check sample quality in between the 3-D spectra. With this approach they managed to take 9 assignment spectra, several relaxation spectra, and several NOESY spectra for structural data. Apparently, each spectrum required its own, new sample due to the short lifetime of the bacteria under these conditions.

The authors performed control experiments in order to address some of the problems that affected previous research on proteins in living E. coli. They found that removing the cells from the NMR tube eliminated most of the protein signal, and that lysates of the bacteria contained protein signal. These experiments showed that the data collected in their experiments genuinely came from protein inside the bacteria rather than protein that had leaked out.

After all this work, the authors were eventually able to solve a structure of TTHA1718 in live E. coli, which you can see to the right (explore this structure at the PDB). This result would not have been possible, however, had the authors not employed specific methyl labeling in order to get additional long-range restraints, a technique typically used for very large proteins. As you can see in the supplementary information, attempts to solve the structure without the methyl NOEs gave rise to a fairly disordered ensemble. Even this ensemble is nowhere near as tight as the in vitro structure that the authors also solved. Because the in vivo structure used many fewer NOEs than the one from dilute solution it is difficult to tell whether differences between these ensembles reflect real conformational changes or simple uncertainty. The loop near the metal-binding cysteines (shown as fat sticks in this image) is a case in point — it looks quite different from the solution structure, especially in the positioning of the critical side chains, but there are almost no NOE restraints for this loop in the in vivo structure (Figure 4e). Chemical shifts support the idea of a conformational change, and inside the cells many of the signals in that region are too broad to detect. This, in conjunction with some metal-enrichment experiments the authors performed, suggests that the protein is regularly binding to metals in vivo, but the structure of this bound state is essentially a mystery. There are also a few clear structural differences in well-defined regions of the protein, but their significance is also unclear at this time.

This experiment serves as proof of principle, but NMR spectroscopists weary of years of promising experiments that turn out to only work on ubiquitin might rightly question whether this approach has any further applicability. In order to address this, the authors expressed the protein to a lower level in order to demonstrate that the procedure could still work for less-concentrated proteins. In addition, they show spectra from calmodulin in the supplementary data, suggesting that this approach will at least be applicable to proteins up to the 20 kilodaltons. However, the relaxation data the authors show in the supplementary data indicate that tumbling in the bacteria is significantly slower than in dilute solution, and the T2 of the protein is 5-6 times shorter in vivo. If this result is general, then structural work on larger proteins may not be possible.

Why did this experiment work when other experiments on globular proteins in E. coli have led to leaking protein or an absence of signal (2)? Part of this may be that not all E. coli are created equal: the authors of this study used the JM109(DE3) rather than the popular BL21(DE3) strain. Genetic differences between the cells used may be responsible for the altered outcome. This will be a difficult thing to nail down, however, as overexpression typically involves the introduction of foreign DNA, an antibiotic, and an exotic activator of some kind, not to mention that isotopic labeling requires nutrient-poor minimal media. The difference between CI-2 that leaks out of cells and TTHA1718 that stays in may be as simple as the amount of magnesium sulfate in the M9. Until the factors causing the excretion of overexpressed proteins are more fully understood, careful controls will be an absolute necessity of in vivo experiments.

Because of the difficulty and expense this is clearly not an approach to be taken up lightly. For the time being, at least, you want to save this sort of experiment for systems where there is a real inconsistency between structural data from dilute solutions and results in vivo. As we improve the NMR approaches and increase our ability to manipulate E. coli behavior, however, this technique will grow more powerful and broadly applicable. Moreover, the Japanese part of the team reports in the same issue of Nature that they have managed to acquire spectra from proteins transferred into cultured human cells (3). This suggests the possibility of purifying labeled proteins at high yield, transferring them into living human cells, and then monitoring their structural and dynamic properties in their biological context.

1) Daisuke Sakakibara, Atsuko Sasaki, Teppei Ikeya, Junpei Hamatsu, Tomomi Hanashima, Masaki Mishima, Masatoshi Yoshimasu, Nobuhiro Hayashi, Tsutomu Mikawa, Markus Wälchli, Brian O. Smith, Masahiro Shirakawa, Peter Güntert, Yutaka Ito (2009). Protein structure determination in living cells by in-cell NMR spectroscopy Nature, 458 (7234), 102-105 DOI: 10.1038/nature07814

2) Li, C., Charlton, L.M., Lakkavaram, A., Seagle, C., Wang, G., Young, G.B., Macdonald, J.M., Pielak, G.J. (2008). Differential Dynamical Effects of Macromolecular Crowding on an Intrinsically Disordered Protein and a Globular Protein: Implications for In-Cell NMR Spectroscopy. Journal of the American Chemical Society DOI: 10.1021/ja801020z

3) Kohsuke Inomata, Ayako Ohno, Hidehito Tochio, Shin Isogai, Takeshi Tenno, Ikuhiko Nakase, Toshihide Takeuchi, Shiroh Futaki, Yutaka Ito, Hidekazu Hiroaki, Masahiro Shirakawa (2009). High-resolution multi-dimensional NMR spectroscopy of proteins in human cells Nature, 458 (7234), 106-109 DOI: 10.1038/nature07839