I am full of piety

Or so the Blog Cuss-o-Meter says. Only 0.1% of pages on my blog have "cuss words", less than the average of 10%. I wonder how they are defining these though. Does "creationist" count? How did you do?


OnePlusYou Quizzes and Widgets

Why is body temperature 36 degrees celsius?

While I was doing an unrelated search on the Nature website, I came across this intriguing debate about why body temperature is maintained around 36 degrees and not some other value. (Nature, Vol 324, December 4, 1986, p. 418)

The discussion was initiated by a letter from John Paul, a scientist in Australia who contended that the specific heat capacity of water is lowest at 36 degrees, and therefore heat loss would be minimal at that temperature

But he neglected a fundamental principle of physical chemistry; the rate of heat loss is proportional to the difference between the temperature of the body and that of the surroundings and is independent of the specific heat capacity (remember high school and Newton's Law of Cooling?). More importantly, the specific heat capacity of a body can be thought of as a measure of how well the body offers "resistance" to fluctuations in temperature. The reason why water works so well as an essential life fluid for example is because its specific heat is so high; there is minimal fluctuation in the temperature of water when heat is injected or taken away from it.

Thus, an optimal substance for maintaining a given temperature would be one whose specific heat capacity is as high as possible under the given circumstances, not one whose specific heat capacity is minimum at the given temperature.

These facts were pointed out by William Calder from the University of Arizona and by Steven Benner and Jack Dunitz at the ETH, Zurich. Dunitz as is known is an extremely versatile scientist, a veteran researcher and one of the greatest structural chemists and technical writers of the last century.

Dunitz and Benner make their objections to Paul's explanation clear and offer an alternative partial explanation; that 36 degrees is the optimum compromise between viscosity and hydrophobicity. It's high enough for the viscosity to not become so low as to impede diffusion-limited processes, and low enough that hydrophobic molecules do not "dissolve" too easily.

Natural selection must have taken a remarkable number of factors into account in optimizing this property.

R.I.P

A man who epitomized one of those essential qualities of sound science; eternal skepticism, and a congenital aversion to sacred cows

Aß Dimers- The Long-Sought Minimal Culprit in Alzheimer's Disease?

Following on the heels of the headline-making Nature publication that demonstrated that NSAIDs (Non-steroidal AntiInflammatory Drugs) uniquely targeted a substrate (APP) rather than an active site of the gamma-secretase complex involved in plague formation in Alzheimer's (see Discount Thoughts for a great summary) comes a paper that may turn out to be one of the important papers in the history of Alzheimer's disease (AD) research.

Since 1905 when Alois Alzheimer first detected the symptoms of what we today call AD and identified the characteristic plaques that form in the brains of AD patients, the "amyloid hypothesis" has become almost synonymous with AD. For decades now, insoluble amyloid plaques, later found to consist of 40 (Aß 1-40) and 42 (Aß 1-42) residue oligopeptides, have been thought to be the hallmark of AD. Indeed, amyloid has become the poster boy for diseases caused by protein misfolding. Say "protein misfolding", and college students will pipe up and say "Alzheimer's"

However, the truth as usual has been complicated. In the last few years, attention has been shifting from the insoluble Aß to soluble forms of the peptide that are apparently in equilibrium with the aggegated beasts. Many oligomers have been isolated through antibody labeling and their toxicity has been demonstrated to various extents under various conditions. The "amyloid hypothesis" has become much more complex than before, and one of the original questions- whether these insoluble plaques are really the cause or just a manifestation of AD- has raised its head even more.

Recently exciting progress has been made in the field, with everything from metals to free radicals being implicated in the dementia and neuronal death that AD causes. On a wall in my room I have a Sigma Aldrich poster displaying a huge schematic of the principal species and pathways involved in AD, and one look at the poster clearly indicates how convoluted the whole scenario is. One of the continuing main reasons for slow progress has been the lack of structural information, with amyloid itself not being crystallizable and soluble species by definition being hard to structurally nail down.

But in light of the connection to soluble oligomers unearthed for AD, one lingering question has been foremost on everyone's minds- What is the minimal soluble species responsible for the symptoms of AD? Now it seems that a paper might go a long way in answering this question.

The short answer is "dimers dimers dimers". For the long answer, read the Nature Medicine paper. Charles Selkoe, Ganesh Shankar and others at Harvard separated different Aß species, insoluble and soluble, from the brains of AD patients. They then performed detailed characterization through immunoprecipitation, Western blots and other techniques, and then injected these fractions into rats, documenting which species can be identified as being the minimal as well as dominant contributors to the pathophysiology of AD.

I am no neurologist (paging Retread) but the researchers seem to have focused on three indicators of "brain damage"- an adverse effect on LTP (long-term potentiation); Wikipedia defines this as "the persistent increase in synaptic strength following high-frequency stimulation of a chemical synapse" which seems to indicate the fidelity of synaptic communication and a contributor to memory, LTD (long-term depression) which is the weakening of a synapse, and a decrease in dendritic spine density.

The researchers clearly find that dimers displaying a mass band of 8kD (confirmed by mass spectrometry) provide the greatest effect on these three parameters. Monomers and other soluble oligomers were not just less toxic but inactive. They also performed the interesting experiment of treating insoluble Aß cores with formic acid, this causing some of it to dissociate into dimers. This concoction proved deadly for rat brains, while the original untreated assembly did not prove as toxic. To make sure that the dimers were pure, they also used synthetic Aß dimers and obtained the same results. These set of results are pretty conclusive in demonstrating the toxicity of dimers.

As an interesting sidepoint, the authors also demonstrate the role of the metabotropic glutamate and NMDA receptors in facilitating the symptoms.

The significance of these results are clear. The authors themselves say "Our findings fulfill an essential requirement for establishing disease causation in Alzheimer’s disease". Many questions still remain though. We still don't know the molecular mechanism through which these dimers finally lead to neuronal death. Do they exert their effects by binding to metals like copper or iron? Do they slide into neuronal membranes and cause them to disintegrate? What other species do they actually go through before they cause harm? All these effects have been suggested as part of the list of effects responsible for neuronal damage. Which effects do Aß dimers fit into?

But all this is later. For now it's a significant achievement that we seem to have a handle on the minimal species responsible for AD. It's a staggeringly simple (or not...) structure involved in the progression of a set of maddeningly complex events. This finding seems to open a whole new window of experiments, conjectures and principles related to Aß dimers and AD in general.

My compliments to the team.

1. Shankar, G.M., Li, S., Mehta, T.H., Garcia-Munoz, A., Shepardson, N.E., Smith, I., Brett, F.M., Farrell, M.A., Rowan, M.J., Lemere, C.A., Regan, C.M., Walsh, D.M., Sabatini, B.L., Selkoe, D.J. (2008). Amyloid-ß protein dimers isolated directly from Alzheimer's brains impair synaptic plasticity and memory. Nature Medicine DOI: 10.1038/nm1782

2. Halliwell, B. (2006). Oxidative stress and neurodegeneration: where are we now?. Journal of Neurochemistry, 97(6), 1634-1658. DOI: 10.1111/j.1471-4159.2006.03907.x

3. Bush, A.I. (2003). Copper, ß -amyloid, and Alzheimer's disease: Tapping a sensitive connection. Proceedings of the National Academy of Sciences, 100(20), 11193-11194. DOI: 10.1073/pnas.2135061100

4. 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 gamma-secretase modulators. Nature, 453(7197), 925-929. DOI: 10.1038/nature07055

ADMET@TheSpeedOfThought

Accurate estimation of ADMET effects still remains the Waterloo of drug development, with up-to 40% of promising candidates failing in clinical trials because of unfavorable pharmacological properties. Over the last few years many algorithms and descriptors have been developed and implemented into programs- many of them proprietary- to predict ADMET. However at some point these programs become far from intuitive, being of no real help to medicinal chemists engaged with actual lead design.

Paul Gleeson from GSK has come up with a set of rules of thumb for remembering the different properties of drugs that influence their ADMET behavior. He has used large databases of molecules, sound statistics and confidence intervals and GSK's internal studies to derive these rules. In my opinion he has done us all a service, because most pharmaceutical companies would want to and in fact do keep such data proprietary. He narrows down these properties to three important ones which medicinal chemists are comfortable and familiar with- molecular weight, clogP (lipophilicity) and ionization state (+ve, -ve or neutral). The major ADMET parameters he looks at are:

1. For Absorption:solubility, permeability and bioavailability
2. For Distribution: volume of distribution, blood-brain barrier penetration and plasma protein binding
3. For Metabolism: clearance, half life, P450 metabolism, hERG inhibition, P-glycoprotein efflux

While some of the rules are what they are supposed to be- intuitive- there are also some revelations. I will let the reader muse over the details of how the three aforementioned properties affect the ADME parameters. The author also interestingly considered the effect of changes in clogP- important for medicinal chemists- on the properties. For now I will list a few effects I personally found most interesting:

1. Basic compounds have a larger volume of distribution (which means a larger half-life for a given clearance rate) than acidic compounds. That's because basic compounds can interact with the negatively charged head groups of phospholipid membranes and get distributed easily throughout the body. Acidic compounds in contrast bind to positively charged residues on ubiquitous albumin, which limits their volume of distribution. Thus acidics show greater plasma protein binding.
2. Because of the same property basics also can pass more easily through the gut wall membrane (permeability). However, lesser plasma protein binding can also increase the clearance rate for basics. Thus there's a balance between volume of distribution and clearance, and since half life is given by

half-life = 0.693 * Volume of distribution/Clearance

one often has to face a compromise. In general having basic side-chains can be advantageous (also see effects on CNS penetration below), notwithstanding higher clearance rates. There is an instructive example that comes to my mind; the anti-TB antibiotic rifamycin has an unfavorable Vd which can be improved by adding a basic side chain, thus converting it to rifampicin with a radically better Vd and half-life.

3. As for CNS penetration, it's well-known that small, non-polar molecules easily make their way across the blood brain barrier (ask any druggie). However the study shows that again, basic molecules are on average more CNS permeable than neutrals, followed by acidics. This trend mirrors the permeability trend above. CNS penetration may also be complicated by active transport mechanisms that are hard to predict. For non-CNS drugs, preventing CNS penetration is what's most important. In general I would stay away from making predictions about CNS effects of drugs.

4. hERG inhibition: The human-ether-a-go-go-related-gene ion channel is notorious for flagging toxic molecules in studies. Excessive hERG inhibition leading to QT interval prolongation of heartbeat can assuredly be a death warrant for your molecule, not to mention for yourself. For some years now people have been trying to figure out pharmacophores for hERG inhibition that would enable them to find common features among hERG-unfriendly molecules. To my knowledge nobody has been spectacularly successful although there are have been some interesting results. It is generally accepted now that basic molecules tend to block hERG more than neutrals or acidics. Interestingly as this study shows, effects of clogP on hERG inhibition are also the greatest for basic molecules.

5. Finally, there is no meaningful relationship between molecular weight and many of these factors. Parameters like permeability will naturally be greatly influenced by Mol. Wt. I think it's more accurate to say that relationships between MW and these parameters will be masked by factors like clogP and ionization state.

Readers are urged to go through the article for more details. The sections on PgP and P450 inhibition are interesting and these properties are extremely important (Taxol for example is pumped out by PgP in resistant cells) but it's also difficult as of now to come up with predictive models for these. It's still very difficult to predict ADMET for budding clinical candidates, mainly simply because the body is still too complex a creature for us mortals to ponder. But medicinal chemists will greatly benefit from intuitive rules, which even if they break down in certain scenarios, do provide a rough and ready guide for checking off factors from your list of adverse ADMET effects. Such studies would help.

Gleeson, M.P. (2008). Generation of a Set of Simple, Interpretable ADMET Rules of Thumb. Journal of Medicinal Chemistry, 51(4), 817-834. DOI: 10.1021/jm701122q

Multiconformational MMGBSA Rescoring; Advancing On Mount Free Energy

Blogging has been a little slow lately mainly because there have been exciting new developments with one of the projects I have been involved in and I was in meetings related to this. One of the topics that was discussed at the conference I was at last week was the accurate prediction of free energies of binding, one of the holy grails of drug discovery. Free-energy perturbation (FEP) still remains the gold standard to get relative free energies of binding, but the procedure is very computer intensive and therefore can be carried out only with small changes in congeneric series of inhibitors. The goal remains elusive and extremely challenging.

A poor man's way of quickly obtaining such ∆Gs is MMGBSA (Molecular Mechanics Generalized Born Surface Area). The GBSA model is well-established as a continuum solvation model for taking solvation into account. What MMGBSA does is take a docked ligand structure and then calculate the free energy of binding as the difference between the bound and unbound states using a force field, including implicit solvation.

Therefore, it calculates

∆G (binding) = ∆G (protein-ligand complex) - ∆G (protein) - ∆G (ligand)

Clearly it has to calculate the energies of the free ligand and free protein. Much of the challenge lies in these two terms. For starters, one has to calculate the strain energy penalty that the protein has to pay in order to bind the ligand. The binding energy that we see experimentally emerges after the protein has paid this strain penalty. How much this strain energy can be has been a controversial topic recently and I will get into it in another post. Suffice it to say that it's a challenging calculation that is not always handled well by MMGBSA. This is because in calculating the ligand free energy, MMGBSA essentially uses a force field to relax the ligand from the bound conformation to the nearest local energy minimum. However, a complex ligand exists in several local energy minima in solution and this force field local minimum may not correspond to any of them. Thus, one has to consider the global strain penalty that the protein has to pay. For this the method also has to consider the multiple conformations that a ligand adopts in solution. Sadly there are very few techniques that will deconvolute the Boltzmann population of a ligand's real conformations in solution and give us the global minimum. This problem in calculating strain energies remains an important drawback of the method.

Calculating ∆G (protein) is also not a trivial matter. We need to consider the entropy of the protein. One can get this from time-consuming MD simulations but it's not certain if the force field is parametrized well and if conformational space has been sampled comprehensively. Another uncertain factor is the induced fit effects involved in binding. A lot of these effects can be subtle and may extend to second shell amino acid residues.

Given these drawbacks, MMGBSA has nonetheless been quite successful in improving agreement with experiment. One of the reasons it works so well is that when you are dealing with congeneric series of ligands for a given target, many of the terms like conformational entropy and protein reorganization energy are the same or very similar and cancel, although there can be surprises. It seems now that at least one of the problems in MMGBSA- not considering the multiple conformations of the ligand in solution- can be tackled. A simple way to get multiple conformations of a ligand in solution is to do a conformational search. Assuming that the search is "complete", one can then calculate the conformational entropy penalty that the ligand has to pay in order to sacrifice all conformations except one in which it binds to the protein. There has been an implicit way to take this into account- many docking programs include a penalty of 0.65 kcal/mol per frozen rotatable bond. But clearly this penalty may be quite less if there are hundreds of conformations in solution that would lead to a large conformational penalty.

Now a group from Amgen has done such multi-conformational MMGBSA rescoring for four important targets and their ligands- CDK2, Thrombin, Factor Xa and HIV-RT. They compare scores obtained with Schrodinger's GlideXP routine with experimental binding affinities. Then they compare scores obtained with MMGBSA rescoring either with a single ligand conformer representation or with a multiple conformer representation that takes ligand conformational entropy into account. The comparison between single and multiple conformers gives somewhat mixed results and sometimes the single conformer representation also does fairly well; however, one thing is strikingly clear, that MMGBSA rescoring can radically improve correlation with experimental affinities compared to simple GlideXP scoring. In some cases the correlation coefficient jumps from essentially 0.00 to a whopping (by current standards) 0.75. There is a lot of interesting methodology described in the paper worth taking a look at. But it's quite clear how including some of the explicit physical effects involved in protein-ligand binding can substantially improve correlation with experiment. In this case the extra effort expended is a fraction of the cost involved in FEP calculations and the methods can also tackle more diverse ligands.

Even if we are not close to conquering the free energy fort, at least we seem to be getting concrete footholds on it.

Guimaraes, C.R., Cardozo, M. (2008). MM-GB/SA Rescoring of Docking Poses in Structure-Based Lead Optimization. Journal of Chemical Information and Modeling, 48(5), 958-970. DOI: 10.1021/ci800004w

Glad to be away from home

I am lounging around in Seattle, WA after a fantastic excursion to Portland, OR. This is one of those moments when I am truly glad to be away from Atlanta. Simple reason- Right now, Seattle is 54 degrees. Atlanta, 97 degrees. Even if this place is cloudy, I prefer it any day to that monstrous heat. This is the first time in 5 years that I am seeing so many 90 degree+ days back home. Hello to my friends there. Hope you had fun playing volleyball at the department picnic...

Portland, Powell's

I am off to a conference in Portland, OR and while I have heard many tales of its beauty, about the only thing I am looking forward to even more than the conference and place is the delicious-looking and famous Powell's Books store, supposed to be the largest in the world. Unfortunately and sadly it seems I will miss Fup the technical store cat who passed away at the ripe old age of 19 last year.

Right now I am licking my lips reading the store description, and later in the evening will be on my way to buy an extra duffel bag that can hold at least a dozen books. Although this does not bode well for moving time after my PhD.; the last time I counted I had three hundred something books stashed in bookshelves, on the carpet and on chairs in my little rented bedroom. Well.

Myths about disrupting protein-protein interactions with small molecules

The first era of medicinal chemistry was finding small molecules to target proteins. It's still going strong and will continue to do so. The second era that promises a treasure trove at least in principle is finding small molecules for disrupting protein-protein (PP) interactions. So many important processes in our body are regulated by these crucial interactions that finding small molecules to modulate them could promise a bonanza of new therapies.

But disrupting PP interactions is fundamentally different from designing a small molecule to bind to a (mostly) well-defined active site on a protein. PP interaction surfaces are rather flat and expansive and depend on subtle interactions between amino acids that add up to provide substantial binding affinity. Designing a small molecule to disrupt such interactions is somewhat like disrupting the sliding of one door hinge against another by lodging a grain of sand between the two. However, the picture has been simplified somewhat in recent years by the identification of "hotspots"- key amino acid epitopes that provide the bulk of binding interaction. If one could design a small molecule that could provide this major contribution to the free energy of binding, one could have an effective drug that could target PP interactions. After all, a grain of sand can indeed inhibit hinge sliding if it's placed in the right position.

One of the big players in investigating small molecule PP interaction agents has been Jim Wells, formerly at Genentech and Sunesis and now at UCSF. He has a nice Nature review that traces recent successful examples of small molecule PP interaction antagonists. Wells considers six or seven successful stories involving important proteins playing key roles in health and disease. For example, disrupting the inhibition of the pro-apoptotic BAD and BAK by the anti-apoptotic Bcl-2 and Bcl-xl, disrupting the binding of interleukin IL-2 to its receptor, and disrupting the binding of HDM2 to the tumour suppressor p53.

But more importantly, Wells then address some widely held myths about PP interactions that seem to drive pessimism in the field. They are worth taking a look at:

Myth 1: It's very difficult to find a small molecule that would lodge between the rather disordered flat surfaces of two proteins.

While this is true, in practice as is exemplified by almost all the examples, the protein surface does not remain flat when a molecule binds to it. Loops and side chain adapt and form shallow pockets and dents to which the molecule can bind. The fallacy in believing the myth is to assume that small-molecule protein surface binding is a rigid body interaction. It's not, and there's a strong element of induced fit in the process. This is probably the single most important thing to keep in mind, that small molecules will form their own small pockets and bind well to initially flat protein surfaces.

Myth 2: Small molecules that disrupt PP interactions are too large to be drugs

This is part of the partly substantiated myth that large molecules usually don't become drugs, because of many factors including ROF problems. But many molecules disrupting PP interactions cited in the review are about 500-700 Da, perhaps a little large but not intractable as drugs. The authors also calculated the ligand efficiency which is the free energy of binding per non-hydrogen (heavy) atom for the ligands, and found that it was comparable to that of kinase or protease inhibitors. Clearly with sound med chem efforts, it won't be too difficult to have such drugs. Interestingly, since the molecules occupy about half the binding site that the parts of the native protein partner do, their ligand efficiency is almost twice.

Myth 3: Small molecules disrupting PP interactions won't be potent

Just not true. Almost all the molecules found in the cited examples had mid to low nanomolar Ki values, almost as good as the binding constants for the partner proteins.

Myth 4: Screening would not help find novel small molecule PP modulators

Again, not true. Most of the cited molecules were found by HTS. Interestingly, there may be even more wealth in HTS than we have now. As the authors explain, HTS hits are fundamentally going to be limited by chemotypes present in the libarries. After all we can do only as well as chemical space in existing libraries. Existing libraries contain many molecules targeted against kinases, GPCRs and other well-known targets for which common privileged structures have been deduced. But because of the diversity of PP interactions, it is improbable that common scaffolds will exist for disrupting them, and libraries will have to contain novel scaffolds to get better hits. Given this fact, it's impressive and encouraging that the existing libraries could come up with such potent structures for disrupting a few PP interactions. Remarkably, even with such different scaffolds, the ligand efficiency remains more or less constant for the cases studied.

Clearly the field of small-molecule-PP interactions is alive and kicking. In the next few years, hopefully computational, screening and NMR approaches will converge to discover novel agents for these important processes.

Wells, J.A., McClendon, C.L. (2007). Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature, 450(7172), 1001-1009. DOI: 10.1038/nature06526

Elegant switches in an E. Coli ion channel

Molecular dynamics simulations comprise one of the most important tools in the armamentarium of chemists and biologists. Initially a curiosity for theoretical scientists, MD is now an explanatory and predictive tool in chemistry, biology, materials science, engineering and even weather prediction. In the field of biology, some masters such as Martin Karplus have honed this tool to the status of an art. While great leaps have been made in the context of methods, hardware, software and applications in this field, much remains to be still done. One of the reasons is that even now, running microsecond MD is computationally quite expensive. But many important biological events involving biomacromolecules take place on this time scale, thus making the achievement of efficient microsecond MD simulations important.

Writing in Science, scientists from D. E. Shaw company, Columbia and the Hebrew University of Jerusalem have a lovely paper documenting the application of the new and innovative MD program Desmond to the dynamics of a bacterial ion channel that transports Na+ ions using the electromotive force generated by proton transport. Desmond is supposed to enable efficient microsecond MD. It is going to be interfaced with the Maestro interface developed by Schrodinger and is due to be released this year I believe. Currently the fastest MD program on a single processor is GROMACS. While head to head comparisons of GROMACS and Desmond have not been reported to my knowledge, Desmond is supposed to be very fast on multiple processors, a facility that many can now afford to have.

In the Science paper, the researchers apply Desmond to understand the transport mechanism of the Na+/H+ antiport ion channel in E. Coli. This protein is crucial for E. Coli to survive harsh conditions of pH, alkalinity, and ionic lithium environments. The authors basically focus on the protonation state of certain key aspartates and find something pretty interesting- two crucial aspartates essentially act as switches that decide whether Na+ ions would be transported to the cytoplasm or to the periplasm. Using many long MD simulations involving different protonation states, the authors discovered that one of the carboxylates always has to be protonated. This acts like a "master aspartate" switch. Once this switch's state is set, it's the state of the other switch that decides the direction of transport- protonated leads to expulsion of the Na+ into the periplasm, while deprotonated leads to expulsion into the cytoplasm.

The observation reminded me of a high-school "staircase lighting" electricity experiment. A master switch had to be always on for the assembly to work. The On/Off state of another switch would then govern whether current flowed or not.

Using this discovery as the basis for exploring further conformational changes related to it, the authors come up with an elegant stepwise mechanism for the transport of Na+ and H+ ions that accounts for the observed stoichiometry of one Na+ ion for every two H+ transported. Using free energy perturbation binding affinity calculations, the authors also rationalize the channel's observed selectivity for Na+ over K+, and slightly for Li+ over Na+.

There is also a a very intriguing explanation for the pH sensitivity of this ion channel. The crystal structure of the channel is solved at pH 4, and it is inactive at this pH. How does the channel get activated at higher pH? To explore this, the authors do something simple but quite clever. They first identify all the key aspartates lining the channel and determine their pKa values. Perhaps not surprisingly, the pKa values of these are abnormally high- not an uncommon observation for amino acids in the unusual environments in protein interiors. They then selectively deprotonate one aspartate keeping all others protonated and do MD on the resulting structures. If there is a key "pH sensing" aspartate, its protonation state will likely govern a conformational change from inactive-active. Indeed, one aspartate, D133, turns out to modulate a conformational change involving two helices when it is deprotonated. Crucially, this results in the "master aspartate" noted above to move away from the Na+ entry/exit pathways. Thus it can no longer bind the ion, resulting in an inactive channel. Mutagenesis studies also support the observations.

A neat conclusion from a beautiful set of experiments. A fascinating example of how nature essentially and surprisingly uses high-school chemistry to modulate movements in complex proteins. And a highly successful and inspiring example of how efficient, long MD simulations can shed light on these crucial processes.

Arkin, I.T., Xu, H., Jensen, M.O., Arbely, E., Bennett, E.R., Bowers, K.J., Chow, E., Dror, R.O., Eastwood, M.P., Flitman-Tene, R., Gregersen, B.A., Klepeis, J.L., Kolossvary, I., Shan, Y., Shaw, D.E. (2007). Mechanism of Na+/H+ Antiporting. Science, 317(5839), 799-803. DOI: 10.1126/science.1142824