Aug 302008
ResearchBlogging.orgIt is rare, but not unheard of, for a human baby to be born with a tail. Atavism of this kind is generally understood to be the result of mutations in regulatory genes that cause an ancestral pattern of development to re-emerge. A physiological step backwards through the path of descent is often easy to recognize, because many of the evolutionary relationships are known. It should also be possible to identify atavistic events in particular molecules. For instance, one can imagine that a mutation to CLC-0 might result in a reversion to the ancestral transporter function. In a recent article in PLoS Biology, researchers from Florida State University and Brandeis University identify just such a relationship in the bi-functional enzyme inosine monophosphate dehydrogenase (IMPDH). PLoS Biology is an open-access journal, so open it up and follow along.

IMPDH plays a critical role in the synthesis of guanine nucleotides, an essential component of DNA. Two reactions take place in the active site — first, the inosine ring is oxidized to xanthosine, forming a covalent linkage with the enzyme, and then this bond is broken by a hydrolysis. The enzyme active site changes shape to carry out the reaction, bringing a catalytic arginine (R418) into position to activate the water for nucleophilic attack. Any time you see a complicated mechanism like this, it’s natural to wonder how such a system could have evolved. Min et al. performed simulations and experiments to find out.

Using a crystal structure of IMPDH as a starting point, Min et al. performed hybrid QM/MM simulations in which the atoms taking direct part in the reaction were treated with quantum mechanics, and the rest of the protein was simulated using molecular mechanics. As one would expect given the enormous reduction in catalytic rate that occurs when R418 is mutated, the reaction proceeded through the arginine when the simulation had a neutral R418 side chain. The water is stabilized by two additional side chains from T321 and Y419, and reacts almost instantaneously, without the formation of a stable hydroxide intermediate. Although this is unusual, this prediction of the simulation is consistent with isotope effect experiments.

When the arginine was replaced by a glutamine in the simulation, the mechanism changed, naturally. Under these conditions, it was Y419 that activated the water for the hydrolysis, although the energy barrier was much higher (leading to a slower reaction). Again, the characteristics of the reaction indicated by the simulation line up pretty well with the results of biochemical experiments. Of course, Y419 enters the active site the same way R418 does, so the question of how the hydrolase activity could have evolved remains open.

Something very interesting, however, happens when the simulation is performed with R418 in a charged state. A fully protonated arginine will have a very hard time activating water for a nucleophilic attack. The simulation indicated that under these conditions, T321 performed this role, after being activated by a nearby glutamate (E431). T321 is adjacent to cysteine 319, which is essential for the oxidation reaction, and is not located on the mobile flap. If T321 really can catalyze hydrolysis, this would mean that it is possible that IMPDH possessed an (inefficient) hydrolysis activity before it evolved the mobile flap.

Because T321 only plays a signficant role in catalysis when R418 is protonated, blocking this pathway should result in decreased IMPDH activity at low pH. This is precisely what Min et al. observe in enzymatic assays (Figure 5) on a mutant in which E431 is mutated to glutamine. There is other experimental support as well: IMPDH enzymes that have been mutated at R418 usually have large isotope effects, which makes sense in light of the fact that the alternative T321 pathway involves the simultaneous transfer of two protons (rather than just one).

Things get even more interesting when IMPDH is compared to one of its cousins, GMP reductase. Although GMPR catalyzes a very different reaction, the C319/T321/E431 triad is also present there. This, along with other data from sequence alignment, suggests that these three residues were also present in a similar configuration in the ancestor of these modern proteins. Over time, progressive optimization of the two proteins resulted in the T321 pathway being supplanted by the more effective R418 in IMPDH, while remaining essential in GMPR.

If T321 really is a remnant of an earlier water-activating pathway, why is it conserved now that IMPDH has a much more efficient catalytic residue available? T321 is probably preserved because it stabilizes the water while it is being activated by R418. However, the other essential residue of that activating pathway (E431) is usually an inactive glutamine in eukaryotic forms of IMPDH (and some prokaryotes, as well). In these species the T321 activation pathway has been completely supplanted by the arginine pathway. Yet in the other forms of IMPDH this alternative mechanism still lingers, perhaps because of the additional activity it affords at low pH, or because it confers resistance to a particular inhibitor of the enzyme. In that sense, IMPDH’s “tail” might provide an adaptive advantage quite different from that which gave rise to hydrolytic activity in the first place.

Donghong Min, Helen R. Josephine, Hongzhi Li, Clemens Lakner, Iain S. MacPherson, Gavin J. P. Naylor, David Swofford, Lizbeth Hedstrom, Wei Yang, Daniel Herschlag (2008). An Enzymatic Atavist Revealed in Dual Pathways for Water Activation PLoS Biology, 6 (8) DOI: 10.1371/journal.pbio.0060206 OPEN ACCESS
Disclaimer: Although I have little contact with Dr. Hedstrom’s group, I am also working at Brandeis.

 Posted by at 7:30 PM
Aug 202008
ResearchBlogging.orgIn addition to the adverse consequences of addiction and the inconvenience of serving several years of jail time for possessing it, cocaine can cause a fatal overdose. Although this condition can be treated, no therapy presently exists that attacks the overdose by removing cocaine from the bloodstream. One possible approach to eliminating cocaine from a patient would be to accelerate the process by which it is degraded. Unfortunately, the enzymes that perform this activity in the human body are not very efficient. In an upcoming article in the Journal of the American Chemical Society, however, a group from the University of Kentucky (assisted by researchers at the University of Michigan) have remodeled the active site of butyrylcholinesterase (BChE) to achieve a 2000-fold increase in rate. This raises the possibility of producing therapeutic enzymes as a treatment for cocaine overdose.

A cocaine overdose typically results in an elevated pulse rate, seizures, and hyperthermia, among other possibilities. The usual course of treatment involves addressing the symptoms — diazepam to reduce the heart rate, cooling protocols to address hyperthermia. These steps are proven to work, but they don’t address the core problem: there’s still a lot of cocaine floating around in the bloodstream. Treating with sedatives amounts to using one giant truck to stop another giant truck… both trucks will probably stop, but there might be a lot of collateral damage. Instead, it would be advantageous to either block the receptors that cocaine binds, or clear cocaine from the bloodstream somehow.

Plasma butylcholinesterase does most of the work in metabolizing cocaine, by cleaving it into two products that no longer exert the same pharmacological effects. If BChE was a highly efficient enzyme it’s unlikely that people would experience cocaine overdoses at all, but it breaks down the main form of cocaine quite slowly, with a catalytic rate (kcat) of 4.1 /min, resulting in a very long half-life for this substrate. The chemical mechanism of BChE (Figure 1) will be familiar to anyone who has taken biochemistry, being basically the same as a serine protease. Instead of a peptide bond, however, it is the ester linkage of cocaine that undergoes nucleophilic attack from an activated serine, while hydrogen bonds stabilize the evolving negative charge in an oxyanion hole.

Previous efforts to optimize the activity of BChE by mutation focused on eliminating steric clashes, but Zheng et al. noted that the hydrogen bond lengths in the oxyanion hole were not optimal for stabilizing the putative transition state. They therefore decided to focus their efforts on improving the energetics of this region. To do so, they used combined quantum mechanics/ molecular mechanics (QM/MM) simulations to determine the energy barriers in simulated reaction coordinates for a number of different mutants. This has the advantage of screening potential mutants for a specific effect, which may be quicker than wet lab work, but it requires the researcher to know the catalytic mechanism and to define a region of interest in advance.

By working through a series of mutations, Zheng et al. arrived at one multiple mutant of BChE that had favorable interaction energy for every residue in the oxyanion hole. When they generated this mutant in the lab, they found that it had a vastly increased catalytic rate towards cocaine, with kcat now about 5700 /min. Based on these in vitro results they decided to test the mutant BChE in vivo using mice. They found that injecting mice with 30 µg of BChE protected them from seizure and death due to cocaine overdose. While the n for this experiment is small, and the BChE was injected prior to cocaine exposure rather than after, these results suggest that the mutant BChE has potential as a therapy for cocaine overdose in humans.

Obviously, further improvement would be needed before these protective effects could be realistically equaled in humans. To match the dose used in this experiment, a 180-pound man would need to be injected with 82 mg of the protein, which is a rather large amount. However, if used in conjunction with existing treatments, the required dose of BChE may be lower. If not, then translating these results into a useful therapy will require either further catalytic optimization or an enormous production effort. A significant amount of additional clinical research is required before this or any other mutant of BChE is introduced as a therapy for overdose or addiction. Nonetheless, these results illustrate the promise of enzyme optimization and design as a tool for medicine in the future.

Fang Zheng, Wenchao Yang, Mei-Chuan Ko, Junjun Liu, Hoon Cho, Daquan Gao, Min Tong, Hsin-Hsiung Tai, James H. Woods, Chang-Guo Zhan (2008). Most Efficient Cocaine Hydrolase Designed by Virtual Screening of Transition States Journal of the American Chemical Society DOI: 10.1021/ja803646t

 Posted by at 1:30 AM
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

Jun 212008
ResearchBlogging.orgIf Michele Vendruscolo were trying to get me to blog about one of his papers, he could hardly have assembled a more perfect lure than his upcoming paper in JACS. It brings together all sorts of things I’ve been talking about on this webpage: NMR dynamics, MD simulations, and dynamics-driven allostery (in the PDZ domain, no less). Previous investigations of this PDZ domain indicated the existence of a network of residues that had a dynamic response to ligand binding. Dhuselia et al. extend this work using molecular dynamics simulations constrained by the existing dynamics results. This leads them to discover not one, but two networks in the PDZ domain, with different properties.

NMR experiments have enormous power to sensitively detect changes in dynamics resulting from a perturbation, but they are also quite limited. Because of the models we use, the parameters we can fit out of relaxation data only give us information about the magnitude and timescale of fluctuations. Chemical shift overlap and interference caused by nearby dipoles limit the number of probes. Moreover, because NMR can only measure an ensemble, it is practically impossible to extract anything other than the most general information about correlated motions. MD has answers to all of these problems, but as a general rule has done poorly at reproducing NMR data about side-chain motions, calling the validity of the conclusions into question. Vendruscolo has taken some interesting strides in this regard by employing the limited experimental dynamics data as a component of the energy function. By constraining the simulation to mimic the known dynamics, we can hopefully learn more about the sites to which we are blind, as well as what kinds of motions the experiment is sensing and how they are linked.

In this instance, the authors make use of the PDZ domain previously studied by Ernesto Fuentes in Drew Lee’s lab (there was also some hack working there at the time). Ernie’s research followed on previous evolutionary studies indicating a network of communication in PDZ domains (local summary here), and Ernie found, by comparing the dynamics of the free and ligand-bound states, that changes in motions propagated away from the binding site to two distal surfaces. The pathways of communication compared pretty well with the evolutionary results. Dhulesia et al. aim to extend these results by determining which motions are correlated and identifying the mechanisms by which energy is transmitted. They accomplished this by running multiple parallel simulations of the free and ligand-bound states of the PDZ domain constrained by Ernie’s dynamics results, as well as NOE and 3J data.

They find that two regions of the protein have correlated motions internally and move in an anticorrelated fashion relative to each other (Figure 3A). One of these regions consists of part of the binding site and all of distal surface 2 (DS2), while the other includes the other half of the binding site and all of distal surface 1 (DS1). When the ligand binds, something interesting happens. The motions of DS2 become more tightly correlated to the motion of an area around V30. The tight correlation between the motions of DS1 and α2 (an element of the binding cleft) switch to a slight anticorrelation.

When a ligand binds to a protein we expect a broad increase in rigidity of the complex so that the proper orientations of bonding pairs are maintained. For the most part, the simulations affirm this expectation, but not for all regions. For the binding site and DS2, the backbone mobility decreases, as expected, but the backbone mobility of DS1 increases (I am going off the text and Table 3 here, rather than Figure 3). The side chains have a similar response. This agrees with other studies indicating that the change in conformational entropy upon binding a ligand need not be homogeneous. What is more interesting is that these results imply that opposite coherent responses can be induced in a small domain by a single stimulus.

Although (as far as I know) this PDZ domain has no allosteric behavior in vivo, one can imagine that the binding of a ligand at the cleft could alter the binding of other modules to this domain. The entropic penalty for binding to DS2 would be lower in this case, while the penalty for binding to DS1 would be higher. The opposed nature of the dynamic responses may be related to the broad regional anticorrelation of free-state motions; disruption of this mode (by linking the motion of β2 and α2) may shunt that energy into DS1.

The authors also find, using a series of structural parameters, that a set of residues have clear structural changes. Some of them appear to be associated with coupled changes in rotameric states; the authors map out one pathway in Figure 5. Because it is a rotameric pathway, it should be possible to test whether it is essential to communication experimentally—mutation of the intermediary residues should abolish the linkage. The authors also carry out a network analysis to identify the most connected residues, a prediction that may also be testable by mutagenesis. These “structural network” residues overlap only slightly with the dynamic network, and indeed do not generally intersect with the evolutionary network either. In the absence of identified allosteric behaviors or clear energetic connectivities it’s difficult to say what this disjunction means. However, the residues undergoing structural changes surround most of the residues undergoing dynamic changes. It is possible that these changes in structure provide the context that allows the changes in dynamics (or vice-versa); the two properties are inextricably linked.

Although communication between the binding site and distal surfaces is proven in this PDZ domain, and appears to be a general feature of the fold, the absence of a known function for the propagation in this instance makes it tough to assess the quality of these results. However, the findings of Dhulesia et al. make it clear that this approach can produce testable predictions and explanations. Hopefully this approach will be employed in the near future to study PDZ domains known to possess allosteric properties.

1. Dhulesia, A., Gsponer, J., Vendruscolo, M. (2008). Mapping of Two Networks of Residues That Exhibit Structural and Dynamical Changes upon Binding in a PDZ Domain Protein. Journal of the American Chemical Society DOI: 10.1021/ja0752080

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