Field of Science

Why the free market is like quantum mechanics

If we were all omniscient and had infinitely fast and perfect computers, maybe we could use quantum mechanics to explain chemistry and biology. In reality, no amount of quantum mechanics can completely explain the chemistry that goes into treating a disease with a drug, baking a cake or making a baby.

Now imagine someone who has started out with the honest and admirable goal of trying to apply quantum mechanics to understand the behavior of a biological system like a protein. He knows for a fact that quantum mechanics can account for (a far better word than explain) all of chemistry- the great physicist Paul Dirac himself said that. He has complete confidence that quantum mechanics is really the best way to get the most accurate estimates of free energy, dipole moments, molecular charges and a variety of other chemical properties for his system.

But as our brave protagonist actually starts working out the equations, he starts struggling. After all, the Schrodinger equation can be solved exactly only for the hydrogen atom. We are dealing with a system of solute and solvent that’s infinitely more complex. The complexity forces our embattled savant to make cruel approximations at every stage. At some point, not only is he forced to commit the blasphemy of using classical mechanics for simulating the dynamics of the protein, but he has to stoop to using empirical data for parameterizing many of his models. At one point he finds himself fighting against the Uncertainty Principle itself!

In the end our hero is chagrined. He started out with the lofty dream of using quantum mechanics to capture the essence of his beloved protein. He ended instead with a set of approximations, parameters from experiments, and classical mechanics-derived quantities just for explaining his system. Prediction was not even an option at this point.

But his colleagues were delighted. This patchwork model actually gave fairly useful answers. Like most models in chemistry, it had some explanatory and predictive value. Even though the model was imperfect and they did not completely understand why it worked, it worked well enough for practical purposes. But this modest degree of success held no sway for our bright young scientist. He stubbornly insisted that if, just if, we had a perfectly fast computer with unlimited accuracy and an infinite amount of time, quantum mechanics indeed would have been spectacularly successful at predicting every property of this system with one hundred percent accuracy. Maybe next time he should just wait until he gets a perfectly accurate computer and has an infinite amount of time.

Now I usually don’t hold forth on economics on this blog, but the following parable was narrated to describe what I think is a rather unwarranted swathe of criticism coming from libertarians about the financial crisis during the last few years. The reasons for the financial crisis are many, probably more complex than quantum mechanics, and society will surely keep on debating them for years. But one of the most common reasons cited by libertarians (usually in the form of a complaint) for the failure of the economy is that we should not blame the free market for what happened because we never got a chance to actually have a free market. If only we got a chance to have a perfect free market, things would be lovely.

Notwithstanding the fact that this argument inches uncomfortably close to arguments made by the most vocal proponents of socialism in the twentieth century (“There was nothing wrong with the system per se, only with its implementation”), I think it’s a little nutty. Maybe a perfect free market wouldn’t have led to the crisis, but that’s like our young chemist saying that infinitely accurate computers and quantum mechanics would not have led to the kind of imperfect models that we get. The problem is really that there are so many practical obstacles in the application of quantum mechanics to a real-life chemical system, that we are simply forced to abandon the dream of using it for explaining such systems. Unless we come up with a practical prescription for how quantum mechanics is going to address these real-life obstacles without making approximations, it seems futile to argue that it can really take us to heaven.

To me it seems that libertarians are ignoring similar obstacles in the way of implementing a perfect free market. What are these obstacles? Most of them are well known. There’s imperfect competition because of the existence of inherent inequalities, leading to monopolies. There’s all that special interest lobbying, encouraged by politicians, which discourages true competition and allows monopolies to get a head start. There’s information asymmetry, which simply keeps people from knowing all the facts.

But all these problems are really part of a great problem- human nature itself. All the obstacles described above are basically the consequence of ingrained, rather unseemly human qualities- greed, the lust for power, the temptation to deceive, and a relentless focus on short term goals at long term expense. I don’t see these qualities disappearing from our noble race anytime soon.

Now sure, I think we can completely agree that the free market was invented to curb some of the worst manifestations of these qualities, and it has worked remarkably well in this regard. Remarkably well, but not perfectly so. Maybe libertarians need to understand that the last vestiges of the dark side of humanity cannot be done away with, since they are an indelible part of what makes us human. So unless they come up with practical ways in which they can surmount these obstacles, in which they can solve the problem of human nature itself- a difficult goal to put it mildly- it’s rather futile to keep on chanting that all our problems would be solved if only we could somehow make these inherently human qualities disappear.

The final argument that libertarians usually make is; just because there are obstacles in the way of a goal (the perfect free market) that may seem even insurmountable, that does not mean we should not keep on striving towards the goal. I think that’s perfectly laudable. But the problem is, unless you come up with a practical solution for all the problems that you face on the way, your goal is just going to remain an abstract and unworkable ideal, not exactly the kind of solution that's desirable in the practical fields of politics and economics. More importantly, all this striving towards the goal may create problems of its own (the science analogy would be unimaginably expensive calculations, scientists laid-off because of the lack of results, overheating of the computers leading to fires etc.). We have all seen these problems. There’s the well-known problem of externalities, there’s the problem of unregulated firms getting ‘too big to fail’, there’s the problem of growing income inequality. Surely we have to admit that these are real problems too.

So what should libertarians do? Well, didn’t our intrepid quantum mechanic grudgingly accept the intervention of approximations and parametrizations? These seemed ugly, but he had no option but to use them, since quantum mechanics simply could not solve all the obstacles in his way. Similarly, perhaps free marketers could realize that at least in some cases, government intervention, no matter how ugly it may seem, may be the only way to reach a workable goal. Sure, it may not be the best of all goods, but it could be the least of all evils. What would have happened if our bright young scientist had kept on insisting that he wouldn’t budge an inch if he is forced to use anything other than quantum mechanics? He would have ended up with nothing.

And in economics even more than in chemistry, a model that partly works is better than a model that does not exist. “Sometimes it’s not enough to do our best; we need to do what’s necessary” (W. Churchill)...

But Bohr never said anything about Nobel Prizes








2. Computational chemistry and biochemistry (Difficult):

3. Chemical biology and chemical genetics (Easy)

4. Single-molecule spectroscopy (Easy)

5. Electron transfer in biological systems (Easy)



1. Nuclear receptors (Easy)

2. Statins (Difficult)

2. Genomics (Difficult)

3. DNA diagnostics (Difficult)

4. Stem Cells (Easy)

4. Membrane vesicle trafficking (Easy)
Rothman and Schekman


I am not a physicist
But if I were
I would dare
To shout from my lair
“Give Hawking and Penrose the Prize!”
For being rock stars of humungous size





In any case,

So much in human affairs
Is open to fate and chance
Thanks, but I will stick to science
And watch the molecules dance...

Previous predictions: 2009, 2008, 2007, 2006

Does bond cleavage require or release energy?

Back from a severe flu spell with a question worth pondering. A commenter on Derek's blog posed the following question, which I think could be a nice trick question for undergrads (or grads for that matter).

Question: "Looks like a bunch of chemists are pooling here. Let me ask an undergrad question as to why bond cleavage in chemistry requires energy whereas bond cleavage in biology releases energy, i.e. from ATP to ADP."

Here's the answer which I could think of on the spur of the moment:

Answer: "Bond cleavage always requires energy, even in biology. The question is whether the net reaction is energetically favorable, which it is in case of ATP. That gives the illusion of energy 'release' by bond cleavage. There are such cases in organic chemistry too; for instance the cleavage of a bond that expels nitrogen as a leaving group is usually very favorable (nitrogen is sometimes called "the world's best leaving group"). That does not mean that you don't need energy to cleave this bond, it's just that the unusual stability of nitrogen makes the net reaction favorable.

Fair enough?

'SAR by C13 NMR'
The biggest utility of NMR spectroscopy in drug discovery is in assessing three things; whether a particular ligand binds to a protein, what site on the protein it binds, and what parts of the ligand interact with the protein. Over the last few years a powerful technique named ‘SAR by NMR’ has emerged which is now widely used in ligand screening. In this technique, changes in the resonances of ligand and protein protons are observed to pinpoint the ligand binding site and corresponding residues. Generally when a ligand binds to a protein, both its and the protein’s rotational correlation time decreases; the result is a broadening of signals in the spectrum which can be used to detect ligand binding. One of the most effective methods in this general area is Saturation Transfer Difference (STD) spectroscopy. As the name indicates, it hinges on the transfer of magnetization between protein and ligand; the resulting decrease in intensity of ligand signals can provide valuable information about proximity of ligand protons with specific protein residues.

But these kinds of techniques suffer from some drawbacks. One straightforward drawback is that signals from protein and ligand may simply overlap. Secondly, the broadening may be so much as to virtually make the signals disappear. Thirdly from a practical perspective, it is hard to get sufficient amounts of N15-labeled protein (usually obtained by growing bacteria on a N15-rich source and then purifying the proteins of interest).

To circumvent some of these problems, a team at Abbott Laboratories has come up with a neat and relatively simple method which they call ‘labeled ligand displacement’. The method involves synthesizing a protein-binding probe that has been selectively labeled with C13. Protein binding broadens and diminishes the signals of this probe. However, when a high-affinity ligand is then added, it displaces the probe and we get recovery of the C13 signals. The authors illustrate this paradigm with several proteins of pharmaceutical interest, including heat-shock protein and carbonic anhydrase.

The method is relatively simple. For one thing, using a commercially available C13-labeled building block for synthesizing a ligand is easier than obtaining a N15-labeled protein. The biggest merit of the method though is the fact that it hinges on C13 signals very specific to the probe; thus there is no complicating overlap of signals. And finally, the ligand seems to be general enough to be applied to any protein. Only time will tell how much it is utilized, but for now it seems like a neat addition to the arsenal of NMR methods for studying protein-ligand interactions.

Swann, S., Song, D., Sun, C., Hajduk, P., & Petros, A. (2010). Labeled Ligand Displacement: Extending NMR-Based Screening of Protein Targets ACS Medicinal Chemistry Letters, 1 (6), 295-299 DOI: 10.1021/ml1000849

Drugging the cell's best friend










Fishin' in the membrane







Why modeling GPCRs is (still) hard
Well, it's hard for several reasons which I have discussed in previous posts, but here's one reason demonstrated by a recent paper. In this paper they crystallized the ß2 adrenergic receptor with an antagonist. Previously, in the landmark publication of the ß2 structure in 2007, the protein had been crystallized with an inverse agonist. Recall that an inverse agonist inhibits the basal activity of the GPCR whereas an antagonist stabilizes both active and inactive states but does not affect the basal activity.

In this case they crystallized the ß2 with an antagonist and compared the resulting structure to that of the agonist-GPCR complex. And they saw...nothing in particular. The protein backbone and side-chain locations are very similar for the antagonist (compound 3) and inverse agonist (compound 2) shown in the figure below.

As we can see in the figure, about the only side-chain that shows any movement is the tyrosine on the left (Y316). No wonder that cross-docking the two ligands (that is, docking one ligand into the other ligand's protein conformation) gave very accurate ligand orientations; this was essentially a softball problem for a docking program since the antagonist was being docked into a protein conformation that was virtually identical to its own.

But of course, we know that antagonists and agonists affect GPCR function quite differently. As this study shows, clearly the action is not taking place in the ligand-binding pocket where things aren't really moving. So where is the real action? It's naturally taking place on the intracellular side, where the GPCR interacts with a medley of other proteins. And as the paper accurately notes, the difference between antagonist and inverse agonist binding is probably also reflected in the protein dynamics corresponding to the two ligands.

Good luck modeling that. That's the whole deal with modeling GPCRs. Simply modeling the ligand-binding pocket is not going to help us understand the differences between the binding of various ligands; one has to model multiprotein interactions and subtle effects on dynamics that are relayed through the helices. The program Desmond which I described in a earlier post is a powerful MD program, but even MD is going to really turn heads when it can take account of multiprotein interactions, and such interactions happen on a time-scale much longer than what even Desmond can access. We have a long way to go before we can do all this. But please, don't stop.

Wacker, D., Fenalti, G., Brown, M., Katritch, V., Abagyan, R., Cherezov, V., & Stevens, R. (2010). Conserved Binding Mode of Human β-2 Adrenergic Receptor Inverse Agonists and Antagonist Revealed by X-ray Crystallography Journal of the American Chemical Society, 132 (33), 11443-11445 DOI: 10.1021/ja105108q

The price of teaching

Why spreadsheets and university research don't gel

I have always been wary of evaluating faculty members based on the amount of money they bring in. One of the casualties of American academic science in the latter half of the twentieth century was that it commodified research, and money became a much bigger part of the equation. Research groups started to bear a striking resemblance to corporate outfits. Undoubtedly there were benefits to this practice since it brought in valuable funding, but it also tended to put a price on the generation of knowledge, which seems inherently wrong.

Now it seems that Texas A & M is thinking of turning this kind of valuation into official policy. As C & EN reports, TAMU is planning to rate its faculty based on their "net worth". This would be calculated based on the faculty member's salary, the funding that he or she can generate, and teaching (how on earth are they going to financially evaluate that?)

Sorry, but I think this is hogwash, and others seem to agree with me. The "worth" of faculty members goes way beyond the funding they can procure. There may be professors who bring in modest amounts of money but who inspire generations of students through their teaching, who significantly contribute to the public perception of science through science communication, and who generally contribute to the academic environment in a department simply through their passion and strong advocacy of science. Even from the point of view of research, there are faculty members who publish relatively less, do research on the cheap, and yet steer their respective fields in new directions simply by generating interesting ideas. Very few of these qualities lend themselves to spreadsheet analysis.

In fact, I will go a step further. If a faculty member does little more than inspire generations of students to pursue careers in science research, education and policy, there is no metric that can financially measure the worth of such contributions. Simply put, such contributions may well be priceless. That should easily satisfy Texas A & M's criteria for high-value "assets".

Louisa Gilder and Robert Oppenheimer







This is well-known; Robert Oppenheimer was a complex and flawed character. But did Gilder's personal dislike of Oppenheimer in the context of Bohm color her attitude toward him and cause her to casually toss out a tantalizing allegation which she must have known is not substantiated? I sure hope not.

The difference between chemistry and physics



My E. coli brother's keeper
Would an anti-indole work?









Stimulating quasi-erotic excitement through organic structure determination








