Jan 132009
ResearchBlogging.orgAlthough we still do not know the full breadth of our flavor-sensing capabilities, human beings are known to possess receptors for at least five basic tastes. Probably you have known about the sweet, sour, salty, and bitter flavors since you were in grade school, but the fifth, umami, was less widely accepted in the West until recently. Umami is a savory flavor element that is found in many foods, including tomatoes, parmesan cheese, truffles, and many kinds of meat and seafood. The umami taste primarily detects the amino acid glutamate (hence the popularity of the food additive monosodium glutamate, or MSG), but the effect is also intensified by the presence of the nucleotide inosine monophosphate (IMP). In a recent (open access) paper in PNAS, researchers from two corporations examined the umami taste receptor to understand how this happens.

The umami flavor is detected by a pair of G-protein coupled receptors (GPCRs) that have an external venus flytrap (VFT) domain in addition to their classic 7-helix trans-membrane domain (TMD). This complex is closely related to the sensor for the sweet flavor: in fact one of the receptors (called T1R3) is the same in both sensors. It is the second receptor (T1R1 for umami, T1R2 for sweet) that determines what taste is recognized. What we don’t know for sure is whether it is the TMD or the VFT of these receptors that identifies the flavor component.

In order to answer this question, the researchers performed an experiment known as a “domain swap”. Using recombinant DNA technology they assembled two chimeric proteins, one with the VFT of umami and the TMD of sweet, and one with the VFT of sweet and the TMD of umami. They then inserted these proteins into cultured cells that would fluoresce when the receptors were activated. The authors suspected that the VFT is primarily responsible for binding the ligand. As you can see from figures 1 & 2 (this is an open access paper, so go ahead and take a look), the experiment bears this out. The chimera with the VFT of sweet caused a fluorescent response in the presence of compounds such as sucrose and aspartame, while the umami-VFT chimera reacted to glutamate and aspartate. You can also see in figure 2C that the presence of IMP dramatically enhanced the activity of glutamate in this chimera. This indicates that the VFT is also responsible for IMP synergy in the umami receptor.

The hurdle in going further than this is that no structure of the umami VFT is available, which makes it difficult to figure out exactly how everything fits together. However, T1R1 has a close evolutionary relationship to the metabotropic glutamate receptors (mGluR), and a crystal structure of that VFT is available. Using conserved and homologous residues as a guide, the authors made a model of the T1R1 fold from the mGluR data. Based on this model they predicted certain amino acids that would be essential for glutamate binding in T1R1 and then mutated them in order to measure the effect. Residues that were predicted by the model to interact with the zwitterionic amino acid backbone proved to be essential for ligand recognition. Interestingly, the amino acids that contact the side-chain carboxylic acid of glutamate in mGluR are not conserved in T1R1, and mutations at the matching sites do not alter glutamate binding. However, these mutations eliminate the effect of IMP.

In order to understand this behavior, the authors modeled the binding cleft in the closed state, with IMP and glutamate in place. Glutamate binds at the bottom of the cleft, with its side chain pointed outwards. This conformation puts several positively-charged residues from the two lobes of the VFT close together higher up in the cleft. The authors propose, in keeping with previous models of VFT behavior, that the binding of the glutamate lowers the energy barrier between the open and closed states of the domain, but that glutamate alone is not sufficient to hold the domain closed. Their model places IMP higher up in the cleft, where its negatively-charged phosphate interacts with the positive residues. Thus, IMP stabilizes the closed conformation of the VFT domain.

Some more work here would be welcome, particularly in the form of experimental crystal structures of the T1R1 VFT that can confirm the homology model. The VFT is rather large, but using a perdeuterated sample in a high-field magnet it might be possible to confirm the population-shift mechanism using NMR experiments. Lower-resolution techniques such as FRET may also be able to catch this stabilization behavior. If the model proves to be accurate, it would serve as an interesting example of positive allostery from a population shift.

Although these experiments only concerned the umami taste receptor, this allosteric mechanism may be a more general feature of certain GPCRs. The authors indicate that they have unpublished data showing similar behavior in the sweet receptor, and it may be possible to design an allosteric stabilizer for any GPCR with a VFT domain. Because the related mGluR receptors are involved in many neurological and psychological diseases, successful design of such activators may have some therapeutic value.

F. Zhang, B. Klebansky, R. M. Fine, H. Xu, A. Pronin, H. Liu, C. Tachdjian, X. Li (2008). Molecular mechanism for the umami taste synergism Proceedings of the National Academy of Sciences, 105 (52), 20930-20934 DOI: 10.1073/pnas.0810174106 OPEN ACCESS

 Posted by at 11:30 PM
Jan 072009
ResearchBlogging.orgAntibiotics such as chloramphenicol suppress infections by inhibiting bacteria from making proteins. They achieve this by binding to and blocking the peptidyl transferase center (PTC) of the ribosome, a large complex of RNA and protein that performs nearly polypeptide synthesis in living cells. Although PTC-binding antibiotics comprise several different families of compounds, mutations in the ribosome that confer resistance to one family often produce cross-resistance to other families. This is difficult to understand because the PTC itself is highly conserved and not very tolerant of mutations. In an upcoming paper (open access, read along) in the Proceedings of the National Academy of Sciences, a team of researchers from the Weizman Institute of Science analyze several crystal structures of the ribosome to understand how this cross-resistance arises.

Davidovich et al. mapped nucleotide mutations known to confer resistance to PTC antibiotics onto x-ray crystal structures of the large ribosomal subunit from D. radiodurans in complex with antibiotics. One interesting facet of the resistance mutations became immediately apparent: they were almost all clustered on one side of the antibiotic binding site.

You can see this pretty clearly in Figure 2 panels B&D. Although the antibiotics (large pink surface) are surrounded by nucleotides, most of those that are on the left side (thin tan sticks) do not confer resistance if mutated. Resistance-conferring mutations instead cluster around the “rear wall” of the PTC (to the right). The authors explain that in this region ribosomal functions primarily rely on the sugar-phosphate backbone of the rRNA. Because the backbone elements are the same for all ribonucleotide bases, mutations in this region are more likely to be tolerated without significant loss of function.

Another striking feature of resistance mutations is visible in Figure 2 and quantified in Figure 3A, namely that many of these mutated bases do not contact the antibiotics directly. In particular, mutation of G2032 appears to play a role in conferring resistance to several different antibiotics. Overall, however, it appears that numerous long-range interactions can interfere with antibiotic binding.

The lynchpin of these interactions seems to be U2504, a base that directly contacts the bound antibiotic in most cases. Mutations to U2504 itself do not appear to be well-tolerated, but many of the long-range mutations occur in the layer of bases surrounding it. The authors describe in detail several mechanisms by which the observed mutations might increase the flexibility of U2504, allowing it to adopt positions that could allow continued protein synthesis while reducing the binding of antibiotics. The commonality of interactions with U2504, and the importance of the structural context of the surrounding nucleotides, explains why many mutations can give rise to cross-resistance.

The practical upshot of these findings is that they may serve as a guide for the design of future antibiotics. Since the majority of the drug-resistance mutations lie on the rear wall of the PTC, the effectiveness of these antibiotics may be enhanced by improving their binding to other parts of the site. With further modeling it may also be possible to design antibiotics that can compensate for flexibility at U2504. These findings also remind us that dynamics and long-range interactions can be important to the function of any biomolecule with a folded three-dimensional structure, not just proteins.

C. Davidovich, A. Bashan, A. Yonath (2008). Structural basis for cross-resistance to ribosomal PTC antibiotics Proceedings of the National Academy of Sciences, 105 (52), 20665-20670 DOI: 10.1073/pnas.0810826105 OPEN ACCESS

 Posted by at 4:00 AM
Oct 232008
ResearchBlogging.orgThe practical aim of the investigation of allostery is the manipulation of this property as a means to aid human health and industry. We already have in hand the sequences of numerous enzymes that carry out unique and useful chemical reactions, and recent advances suggest that we will in the near future be able to design man-made enzymes that efficiently carry out completely novel reactions. Making the fullest use of these abilities demands that we be able to regulate the enzymatic activity of interest. Fortunately, just as nature possesses a rich array of enzymatic activities, it also holds a number of binding proteins, so we don’t have to start from scratch. The trick is that there must be some way to communicate the binding event from one domain into the active site of the other domain. In last week’s edition of Science researchers from the University of Pennsylvania and the University of Texas Southwestern Medical Center claim to have achieved just that.

The core idea of their approach is disarmingly simple. Identify a protein that has a long-range conformational response to a binding event, and locate the distal region on its surface where this response gets read out. Then, on an enzyme of interest, find a region of the surface that has an energetic connection to the active site. Join the proteins at these surfaces and voila! Now you have a regulatory switch for your enzyme!

The reality, of course, is likely to be trickier. In order for efficient communication between sites to occur via these pathways, the structural dynamics of allostery at the points of attachment must be compatible. For nearly all proteins, the precise nature of the conformational reactions that drive communication are essentially unknown. Thermodynamic mutant cycle analysis cannot provide detailed mechanistic information, and structural dynamics experiments from NMR and other techniques can provide only general information about what occurs on these pathways. Only molecular dynamics simulations are likely to give us the information we need to tune the allosteric control precisely. Absent that, all you can do is just stick things together and hope for the best, which is essentially what Lee et al. did.

Of course, they didn’t go in totally blind. Lee et al. used the statistical coupling analysis (SCA) technique pioneered by Dr. Ranganathan to identify distal surface sites linked to the light sensitivity of a PAS domain and the enzymatic activity of a bacterial dihydrofolate reductase (DHFR). I’ve mentioned this technique before in connection with Ranganathan’s research on the PDZ domain. The SCA results indicated that a surface loop of DHFR was energetically linked to its active site. The analysis also indicated that a region encompassing the N- and C- termini of the LOV2 PAS domain was likely to be a readout for its detection of light. These results accorded with existing knowledge about these proteins. The result with the PAS domain was particularly convenient. Because the N- and C- termini are adjacent, it meant that the PAS domain could simply be inserted at a loop site. Also conveniently, the surface identified for DHFR was a loop.

Thus, Lee et al. inserted the PAS domain at two sites in DHFR. One was the loop identified by SCA, and the other was a control site equally distant from the active site but not predicted to be linked. Figure 3 of the paper shows the key result: insertion of the PAS domain at the SCA-identified site (A site), but not the control location (B site), resulted in a modest light-dependence of the hydride transfer rate for DHFR. All of the A site chimeras had substantially reduced DHFR activity, similar to the effect of a G121V mutation. Interestingly, shifting the insertion site by even a single residue completely abolished the light-dependence of the activity.

Granted, the light dependence is less than twofold at room temperature; this approach did not generate a genuine light-dependent on/off switch for DHFR. However, for the reasons I mentioned before, a perfect switch is hardly something that could have been expected. What this experiment does do is prove that this approach is workable. Conceivably, with further tuning the hybrid PAS-DHFR can be made to carry out its catalytic function exclusively in the presence (or absence) of light. Since PAS domains bind a wide array of ligands, the approach can probably be adapted for various chemical triggers.

On a more fundamental level, the authors claim that this result supports the view that specific surface locations in many domains may be evolutionarily-conserved loci for allosteric control. This does not mean that every PAS domain (or PDZ domain, or DHFR) actually possess allosteric properties, but it does imply that all of them have the potential to exert or receive allosteric influences. If this is true, then it may be possible to adapt a wide array of binding modules as allosteric regulators for natural and designed enzymes. As our understanding of intradomain signaling improves, our ability to make use of these approaches will only increase.

J. Lee, M. Natarajan, V. C. Nashine, M. Socolich, T. Vo, W. P. Russ, S. J. Benkovic, R. Ranganathan (2008). Surface Sites for Engineering Allosteric Control in Proteins Science, 322 (5900), 438-442 DOI: 10.1126/science.1159052

Aug 082008
ResearchBlogging.orgThe binding of a ligand to a protein rarely occurs with the simplicity of a block sliding into an appropriately-shaped hole. Protein and ligand often engage in complementary conformational changes to adapt their shapes to each other. As a result, the structure of a protein bound to its target may differ substantially from the structure of the free protein. Unfortunately, it is virtually impossible to view the binding process in fine structural detail; as a result, most of our knowledge comes from the relatively stable bound and free states. Improving biophysical techniques, however, have brought a change in the way we view some binding events.

Most alterations of conformation during a binding event have historically been interpreted using the induced fit model. In this view, the protein stably maintains the free or “open” structure until it comes into contact with a ligand molecule. This encounter stimulates a conformational change so that the protein adopts the “closed” conformation that tightly holds onto the ligand. Thus, the ligand induces the conformational change necessary to form the bound, closed (BC) structure from the unbound, open (UO) structure, and the intermediate on this path is some kind of bound, open (BO) structure. This model is physically reasonable and has been very successful in interpreting many systems.

However, for the past few decades an increasing amount of evidence has suggested that this is not the whole story. NMR investigations indicated that instead of remaining in a single, well-defined backbone conformation most of the time, many proteins experienced significant changes in their structure while floating free in solution. These results suggested an alternative mechanism of population shift. In this view, the protein actually samples the “closed” conformation (or something very similar) while unbound, and it is this conformation that binds to the ligand. We still go from UO to BC, but now the intermediate is an unbound, closed (UC) structure.

This sounds very arcane, but it is not without functional relevance. Consider, for instance, a protein that is activated by a particular ligand. If we wish to make a drug that binds exclusively to the BC form, then we may experience unforeseen side-effects if our target protein occasionally samples a UC state. It would be useful to have a general idea of what kinds of circumstances are likely to favor a population shift model vs. an induced fit model. That is precisely what Kei-Ichi Okazaki and Shoji Takada aim to provide in an upcoming paper in Proceedings of the National Academy of Sciences (1).

Okazaki and Takada performed a coarse-grained molecular dynamics simulation of glutamine binding protein. In the bound and unbound states they employed a double-well Gō model, a simplified representation of molecular forces, to represent “opening” and “closing”. To switch between these states (i.e. to represent binding) they used a Monte Carlo algorithm. This approach has the advantage of being quick and relatively inexpensive from a computational standpoint, but the results must be interpreted cautiously because the physics of the model are greatly simplified. They observe UO ↔ UC and UC ↔ BC events in this system, but they also observe UO ↔ BO and BO ↔ BC events. This suggests that the simulation will be able to make predictions about both population-shift and induced-fit mechanisms.

In order to try to make some predictions about the circumstances in which a particular mechanism is favored, Okazaki and Takada varied the strength and range of the binding interaction. By monitoring whether the simulated system entered the BC state from BO or UC, they could tell whether the system obeyed the induced-fit or population-shift mechanisms, respectively. They find that as either the strength or the range increase, the induced-fit mechanism is increasingly favored (Figure 4). These results make sense. If the protein regularly samples the closed state while unbound, then the amount of energy needed to reach that state is probably small, so it makes sense to see a population-shift mechanism associated with low-energy binding. Similarly, if a ligand is to associate productively with a non-optimal protein conformation, it makes sense that key interactions will be effective at long range.

From these results Okazaki and Takada suggest that the binding of small hydrophobic ligands is generally likely to proceed via population shift, while the binding of large, charged ligands (such as DNA) will likely proceed via induced fit. They acknowledge, however, that the simulation is limited, particularly in its view of conformational change. Unitary transitions in which the whole protein changes its structure simultaneously are probably not the norm, particularly in the case of very large conformational changes. These changes may instead be stepwise or hierarchical. For instance, a protein or complex recognizing multiple features of a DNA strand may proceed by an apparently induced-fit mechanism, even though each individual binding event more closely resembles population-shift behavior.

An additional limitation of this study is that it considers only one protein, but mechanisms of binding and conformational change may be idiosyncratic properties of particular folds. One could consider the behavior of lymphotactin, which displays clear hallmarks of the population-shift mechanism despite binding to macromolecules (heparin and a GPCR) much larger than itself, as a counterpoint to the predictions developed here. Similarly, the population shift of NtrC involves a charged phosphate group likely to have long-range interactions, although this is a post-translational modification and not a strict ligand-binding event. While the authors point to some examples that match their expectations, overall the data are not unanimously in support of their predictions. Still, the general rules laid out here provide a starting point for experimental work.

Despite the limitations of the simulation, it provides a relatively efficient tool for assessing these processes in other proteins. While no simulation can yet replace experimental data, coarse-grained models like this can serve as a means to formulate testable hypotheses about the energetics of protein-ligand systems.

1. Okazaki, K., Takada, S. (2008). Dynamic energy landscape view of coupled binding and protein conformational change: Induced-fit versus population-shift mechanisms. Proceedings of the National Academy of Sciences 105(32) 11182-11187. DOI: 10.1073/pnas.0802524105