GPCR modeling: The devil hasn't left the details

The last decade has been a bonanza decade for the elucidation of structures of G Protein-Coupled Receptors (GPCRs), culminating with the landmark structure of the first GPCR-G protein complex published a few weeks ago. With 30% of all drugs targeting these proteins and their involvement in virtually every key aspect of health and disease, GPCRs remain glowingly important targets for pure and applied science.

Yet there are miles to go before we sleep. Although we now have more than a dozen structures of half a dozen GPCRs in various states (inactive, active, G-protein coupled), there are still hundreds of GPCRs whose structures are not known. The existing GPCRs all fall into the 'Class A' GPCRs. We still have to mine the vast body of Class B and C GPCRs which comprise a huge number of functionally relevant proteins. The crystal structures which we do have comprise an invaluable resource but from the point of view of drug discovery, we still don't have enough.

In the absence of crystal structures, homology modeling wherein a protein of high sequence homology is used to build a computational model for an unknown structure has been the favorite tool of modelers and structural biologists. Homology modelers were recently provided an opportunity to pit their skills against nature when a contest asked them to predict the structures of the D3 and CXCR4 receptors just before the real x-ray structures came out. Both proteins are important targets involved in multiple processes like neurotransmission, depression, psychoses, cancer and HIV infection. The D3 structure prediction involved predicting the ligand-bound structure of the protein complexed with eticlopride, a D3 antagonist.

The results of the contest have been published before, but in a recent Nature Chemical Biology paper, a team led by Brian Shoichet (UCSF) and Bryan Roth (UNC-Chapel Hill) perform another test of homology modeling, this time connected to the ability to virtually screen potential D3 receptor ligands and discover novel active molecules with interesting chemotypes.

Two experiments provided the comparison. One protocol used the D3 homology model to screen about 3 million compounds by docking, out of which about 20 were picked and tested in assays based on docking scores and inspection. The homology model was built on the basis of the published structure of the ß2 adrenergic receptor which has been structurally heavily studied. Then, after the x-ray structure of the D3 was released, they repeated the virtual screening protocol with the crystal structure; again, 3 million compounds out of which roughly 20 were picked and tested.

First the somewhat surprising and heartening result; both homology model and crystal structure demonstrated similar hit rates- about 20%. In both the cases the actual affinity of the ligands ranged from about 200 nM - 3 µM. In addition, the screen revealed some novel chemotypes that did not resemble known D3 antagonists (although not surprisingly, some hits were similar to eticlopride). As an added bonus, the top ranked ligands using the homology model did not measurably inhibit the template ß2 adrenergic receptor, which means that the homology model probably did not retain the "memory" of the original template.

Now for the bee in the bonnet. The very fact that the homology model and the crystal structure produced different hits means that the two models were not identical (only one hit overlapped between the two). Of course, it's too much to expect a model of a protein with thousands of moving parts to be identical to the experimental structure, but it goes to show how careful homology modeling has to be performed and how it can still be imperfect. What is more disturbing is that the differences between the model and the crystal structure responsible for the different hits were small; in one case the difference between two carbons was only 1 Å between the two models. Other amino acids differed by less than that.

And all this even after generating a stupendous number of models of unbound and ligand-bound protein. As the paper says, the team generated about 98 million initial ligand-bound homology models. Screening the top models among these involved generating multiple conformations and binding modes of the 3 million compounds; the total number of discrete protein-ligand complexes resulting from this exercise numbered about 2 trillion. That such kind of evaluation is possible is a tribute to the enormous computing power we have at our fingertips. But it's also a commentary on how relatively primitive our models are so that we are still at a loss to predict minute structural differences with significant consequences in finding new active molecules.

So where does this lead us? I think it's really useful to be able to perform such comparisons between homology models and crystal structures and we can only hope more such comparisons will be possible by virtue of an increasing pipeline of GPCR structures. Yet these exercises demonstrate how challenging it is to generate a truly accurate homology model. A few years ago a similar study demonstrated that a difference in a single torsional angle of a phenylalanine residue (and that too resulting in a counter-intuitive gauche conformation) affected the binding of a ligand to a homology model of the ß2 adrenergic receptor. Our ability to pinpoint such tiny differences in homology models is still in its infancy. And this is just for Class A GPCRs for which relatively accurate templates are available. Get into Class B and Class C territory and you start looking for the proverbial black cat in the dark.

Now throw in the fascinating phenomenon of functional selectivity and you have a real wrench in the works. Functional selectivity, whereby different conformations of a GPCR binding to the same ligand modulate different signal transduction pathways and cause the ligand to change its mode of action (agonist, inverse agonist etc.) takes modeling of GPCRs to unknown levels of difficulty. Most modeling currently being done does not even attempt to consider protein flexibility which is at the heart of functional selectivity. Routinely including protein flexibility in GPCR modeling has some way to go.

That is why I think that, as much as we will continue to learn from GPCR homology modeling, it's not going to contribute massively to GPCR drug discovery anytime soon. Constructing accurate homology models of even a fraction of the GPCR universe will take a long time. Using such models would be like throwing darts at a board for which the center is unknown. Until we can locate the center and are plagued with the complexities of functional selectivity, we may be better off pursuing experimental approaches that that can map the effect of ligands on a particular GPCR using multifunctional assays. Fortunately, such approaches are definitely seeing the light of day.

Carlsson, J., Coleman, R., Setola, V., Irwin, J., Fan, H., Schlessinger, A., Sali, A., Roth, B., & Shoichet, B. (2011). Ligand discovery from a dopamine D3 receptor homology model and crystal structure Nature Chemical Biology DOI: 10.1038/nchembio.662

On being a computational chemist in industry

In a recent post on Chemjobber, Lisa Balbes interviewed a computational chemist in the pharmaceutical industry about his job description and the skills that are needed to work as a modeler in industry. And as a computational chemist working on applied problems for almost a decade now (goodness gracious), this gives me the perfect reason to hold forth a little on this topic. I may do a series of posts later, but for now here's what I think is the low down.

Let's get the most important thing out of the way first. It is absolutely important for a modeler to speak the language of the medicinal chemist and biologist. Personally, in spite of being a computational chemist, I always consider myself first and foremost an organic chemist (and I did go to graduate school in organic chemistry before specializing in modeling), using modeling only as a set of tools to shed light on interesting chemical problems. In fact I find myself spending as much time studying the literature on synthesis, physical chemistry, biological assays and protein structure as on modeling.

Computational chemistry is certainly a bonafide field of chemistry in itself now, but especially in industry it's primarily the means to an end. It doesn't matter how well versed you are with a particular technique like molecular dynamics or quantum chemistry, what matters the most is how well you understand the strengths and limitations of these methodologies. Understanding the limitations is as important since only this can help you decide in the end how much you can trust your results - a prerequisite for any scientist. What is key is your knowledge of the chemical system under consideration that will allow you to best choose a judicious combination of relevant techniques. And even this is not as important as the final goal: being able to interpret the results in the language of chemistry that everyone understands, telling your colleagues what it means and how they should now proceed, with all the appropriate caveats and optimism that apply. Understanding and conveying the uncertainty in your methods is as important as anything else since your colleagues need to hear an informed viewpoint that tells them what they are in for rather than a blind prediction.

Unfortunately I have met my share of modelers who think that their expertise in programming or in the intimate working details of one particular method automatically qualifies them to shed light on the details of an interesting medicinal system. Broadly speaking, modelers can be categorized between method developers and application scientists. There is of course considerable overlap between the two and both are valuable but let's make no mistake; in industry the ones who can directly contribute to a project the most are the latter, using tools developed by the former. No amount of training in C++ or in the mathematical wizardry behind a quantum chemical method can prepare you for intuiting the subtle interplay between electrostatic, steric, polar and nonpolar interactions that cause a ligand to bind to a protein with high affinity and selectivity. Much of this comes from experience of course, but it also develops from being able to constantly appreciate the basic chemical features of a system rather than getting hung up on the details of the method.

As we have seen in other posts, a lot of chemical problem solving depends on intuition, an almost tactile feel for how atoms and molecules interact with each other. This falls squarely within the purview of basic chemistry, most of the kind that we learnt in college and graduate school. An ideal computational chemist in industry should first and foremost be a chemist; the "computational" part of the title describes the means to the end. There is no substitute for basic familiarity with the principles of conformational analysis, acid-base equilibria, physical organic chemistry, protein structure, thermodynamics and stereochemistry. Nobody can be good computational chemists if they are not good chemists to begin with.

Apart from these skills, modelers can also bring some more under-appreciated skills to the table. Those who look at protein and ligand structures on the screen all day long usually have a much better sense of molecular sizes and volumes compared to bench chemists. A medicinal chemist might look at a protein cavity and conclude that it's big enough to fit a cyclohexyl group, but a modeler might display the cavity in space-filling interactions and doom any such idea to the realm of steric hell. Unfortunately the kind of line drawings that chemists are accustomed to give a false impression of size and shape, and sometimes simply looking at structures in space-filling mode on a screen can do wonders for deciding whether a particular group will fit into a particular part of a protein. This also makes modelers responsible for something that may need awesome powers of persuasion; convincing your experimental colleagues to regularly come to your desk and look at some pretty pictures (as an aside, modelers may have to play especially nice with their colleagues). Looking at protein structures and molecules all the time should ideally also make a modeler something of an informal expert in structural biology and physical chemistry. Thermodynamics especially is one area where modelers might know more than their organic colleagues because of their focus on the free energy of binding, and I have occasionally productively contributed to discussions about enthalpy, entropy and isothermal titration calorimetry (ITC). In addition, doing structure-based design is always a good opportunity to learn about x-ray crystallography and NMR spectroscopy. You may increasingly find that your colleagues come to you for advice on many structural aspects of their disciplines.

Ultimately, modelers' value to an organization is going to be judged on the basis of their abilities to offer practical suggestions to their colleagues in the language of their own disciplines (as well as the shared language of basic chemistry). The more organic chemistry and biology they know, the more they will be cherished. The more they empathize with the particular intricacies of their colleagues' disciplines, the more they will be regarded as an asset. As just an illustration, let me recount a personal anecdote.

I was collaborating with some chemists on a kinase inhibitor project. At one point I thought of a modification to our compound that looked very promising. At the next meeting, here's what I said to my medicinal chemistry colleague: "Jim, there are two modifications that I thought might improve the potency of our hits. One looks very promising, but I have studied your synthetic scheme and I think this modification might be a little intractable, especially considering the cost of your building blocks. On the other hand, here's this other modification which would be my second-best choice, but which you can probably easily install using a Buchwald-Hartwig coupling reaction."

Both me and my colleague were whistling all day long.

Image source

The future of science: Will models usurp theories?

This year's Nobel Prize for physics was awarded to Saul Perlmutter, Brian Schmidt and Adam Riess for their discovery of an accelerating universe, a finding leading to the startling postulate that 75% of our universe contains a hitherto unknown entity called dark energy. All three were considered favorite candidates for a long time so this is not surprising at all. The prize also underscores the continuing importance of cosmology since it had been awarded in 2o06 to George Smoot and John Mather, again for confirming the Big Bang and the universe's expansion.

This is an important discovery which stands on the shoulders of august minds and an exciting history. It continues a grand narrative that starts from Henrietta Swan Leavitt (who established a standard reference for calculating astronomical distances) through Albert Einstein (whose despised cosmological constant was resurrected by these findings) and Edwin Hubble, continuing through George Lemaitre and George Gamow (with their ideas about the Big Bang) and finally culminating in our current sophisticated understanding of the expanding universe. Anyone who wants to know more about the personalities and developments leading to today's event should read Richard Panek's excellent book "The 4 Percent Universe".

But what is equally interesting is the ignorance that the prizewinning discovery reveals. The prize was really awarded for the observation of an accelerating universe, not the explanation. Nobody really knows why the universe is accelerating. The current explanation for the acceleration consists of a set of different models, none of which has been definitively proven to explain the facts well enough. And this makes me wonder if such a proliferation of models without accompanying concrete theories is going to embody science in the future.

The twentieth century saw theoretical advances in physics that agreed with experiment to an astonishing degree of accuracy. The culmination of achievement in modern physics was surely quantum electrodynamics (QED) which is supposed to be the most accurate theory of physics we have. Since then we have had some successes in quantitatively correlating theory to experiment, most notably in the work on validating the Big Bang and the development of the standard model of particle physics. But dark energy- there's no theory for it that remotely approaches the rigor of QED when it comes to comparison with experiment.

Of course it's unfair to criticize dark energy since we are just getting started on tackling its mysteries. Maybe someday a comprehensive theory will be found, but given the complexity of what we are trying to achieve (essentially explain the nature of all the matter and energy in the universe) it seems likely that we may always be stuck with models, not actual theories. And this may be the case not just with cosmology but with other sciences. The fact is that the kinds of phenomena that science has been dealing with recently have been multifactorial, complex and emergent. The kind of mechanical, reductionist approaches that worked so well for atomic physics and molecular biology may turn out to be too impoverished for taking apart these phenomena. Take biology for instance. Do you think we could have a complete "theory" for the human brain that can quantitatively calculate all brain states leading to consciousness and our reaction to the external world? How about trying to build a "theory" for signal transduction that would allow us to not just predict but truly understand (in a holistic way) all the interactions with drugs and biomolecules that living organisms undergo? And then there's other complex phenomena like the economy, the weather and social networks. It seems wise to say that we don't anticipate real overarching theories for these phenomena anytime soon.

On the other hand, I think it's a sign of things to come that most of these fields are rife with explanatory models of varying accuracy and validity. Most importantly, modeling and simulation are starting to be considered as a respectable "third leg" of science, in addition to theory and experiment. One simple reason for this is the recognition that many of science's greatest current challenges may not be amenable to quantitative theorizing, and we may have to treat models of phenomena as independent, authoritative explanatory entities in their own right. We are already seeing this happen in chemistry, biology, climate science and social science, and I have been told that even cosmologists are now extensively relying on computational models of the universe. Admittedly these models are still far behind theory and experiment which have had head starts of about a thousand years. But there can be little doubt that such models can only become more accurate with increasing computational firepower. How accurate remains to be seen, but it's worth noting that there are already books that make a case for an independent, study-worthy philosophy of modeling and simulation. These books extol philosophers of science to treat models not just as convenient applications and representations of theories (which are then the only fundamental things worth studying) but as ultimate independent explanatory devices in themselves that deserve separate philosophical consideration.

Could this then be at least part of the future of science? A future where robust experimental observations are encompassed not by beautifully rigorous and complete theories like general relativity or QED but only by different models which are patched together through a combination of rigor, empirical data, fudge factors and plain old intuition? This would be a new kind of science, as useful in its applications as its old counterpart but rooting itself only in models and not in complete theories. Given the history of theoretical science, such a future may seem dark and depressing. That is because as the statistician George Box famously quipped, although some models are useful, all models are wrong. What Box meant was that models often feature unrealistic assumptions about all kinds of details that nonetheless allow us to reproduce the essential features of reality. Thus they can never provide the sure connection to "reality" that theories seem to. This is especially a problem when disparate models give the same answer to a question. In the absence of discriminating ideas, which model is then the "correct" one? The usual answer is "none of them", since they all do an equally good job of explaining the facts. But this view of science, where models that can be judged only on the basis of their utility are the ultimate arbiters of reality and where there is thus no sense of a unified theoretical framework, feels deeply unsettling. In this universe the "real" theory will always remain hidden behind a facade of models, much as reality is always hidden behind the event horizon of a black hole. Such a universe can hardly warm the cockles of the heart of those who are used to crafting grand narratives for life and the universe. However it may be the price we pay for more comprehensive understanding. In the future, Nobel Prizes may be frequently awarded for important observations for which there are no real theories, only models. The discovery of dark matter and energy and our current attempts to understand the brain and signal transduction could well be the harbingers of this new kind of science.

Should we worry about such a world rife with models and devoid of theories? Not necessarily. If there's one thing about science that we know, it's that it evolves. Grand explanatory theories have traditionally been supposed to be a key part- probably the key part- of the scientific enterprise. But this is mostly because of historical precedent as well a psychological urge for seeking elegance and unification. Such belief has been resoundingly validated in the past but it's utility may well have plateaued. I am not advocating some "end of science" scenario here - far from it - but as the recent history of string theory and theoretical physics in general demonstrates, even the most mathematically elegant and psychologically pleasing theories may have scant connection to reality. Because of the sheer scale and complexity of what we are trying to currently explain, we may have hit a roadblock in the application of the largely reductionist traditional scientific thinking which has served us so well for half a millennium

Ultimately what matters though is whether our constructs- theories, models, rules of thumb or heuristic pattern recognition- are up to the task of constructing consistent explanations of complex phenomena. The business of science is explanation, whether through unified narratives or piecemeal explanation is secondary. Although the former sounds more psychologically satisfying, science does not really care about stoking our egos. What is out there exists, and we do whatever's necessary and sufficient to unravel it.

Chemistry Nobel Prizes redux

In tribute to tomorrow's impending chemistry Nobel Prize, I thought I would repost a slightly updated list of predictions.

1. Computational chemistry and biochemistry (Difficult):
Pros: Computational chemistry as a field has not been recognized since 1999 so the time seems due. One obvious candidate would be Martin Karplus. Another would be Norman Allinger, the pioneer of molecular mechanics.
Cons: This would definitely be a lifetime achievement award. Karplus did do the first MD simulation of a protein ever but that by itself wouldn’t command a Nobel Prize. The other question is regarding what field exactly the prize would honor. If it’s specifically applications to biochemistry, then Karplus alone would probably suffice. But if the prize is for computational methods and applications in general, then others would also have to be considered, most notably Allinger but perhaps also Ken Houk who has been foremost in applying such methods to organic chemistry. Another interesting candidate is David Baker whose program Rosetta has really produced some fantastic results in predicting protein structure and folding. It even spawned a cool game. But the field is probably too new for a prize and would have to be further validated by other people before it's recognized.

2. Chemical biology and chemical genetics (Easy)
Another favorite for years, with Stuart Schreiber and Peter Schultz being touted as leading candidates.
Pros: The general field has had a significant impact on basic and applied science
Cons: This again would be more of a lifetime achievement award which is rare. Plus, there are several individuals in recent years (Cravatt, Bertozzi, Shokat) who have contributed to the field. It may make some sense to award Schreiber a ‘pioneer’ award for raising ‘awareness’ but that’s sure going to make a lot of people unhappy. Also, a prize for chemical biology might be yet another one whose time has just passed.

3. Single-molecule spectroscopy (Easy)
Pros: The field has obviously matured and is now a powerful tool for exploring everything from nanoparticles to DNA. It’s been touted as a candidate for years. The frontrunners seem to be W E Moerner and M Orrit, although Richard Zare has also been floated often.
Cons: The only con I can think of is that the field might yet be too new for a prize

4. Electron transfer in biological systems (Easy)
Pros: Another field which has matured and has been well-validated. Gray and Bard seem to be leading candidates.
Cons: Although electron transfer in biological systems is important, Gray and Bard's discoveries don't seem to have the ring of fundamental importance that, say, Marcus's electron transfer theory has, nor do they seem to be widely utilized by other chemists in the way that, say, palladium catalyzed reactions are.

Among other fields, I don’t really see a prize for the long lionized birth pill and Carl Djerassi; although we might yet be surprised, the time just seems to have passed. Then there are fields which seem too immature for the prize; among these are molecular machines (Stoddart et al.) and solar cells (Gratzel).

5. Statins (Difficult)
Akira Endo’s name does not seem to have been discussed much. Endo discovered the first statin. Although this particular compound was not a blockbuster drug, since then statins have revolutionized the treatment of heart disease.
Pros: The “importance” as described in Nobel’s will is obvious since statins have become the best-selling drugs in history. It also might be a nice statement to award the prize to the discovery of a drug for a change. Who knows, it might even boost the image of a much maligned pharmaceutical industry...
Cons: The committee is not really known for awarding actual drug discovery. Precedents like Alexander Fleming (antibiotics), James Black (beta blockers, antiulcer drugs) and Gertrude Elion (immunosuppresants, anticancer agents) exist but are far and few in between. On the other hand this fact might make a prize for drug discovery overdue.

6. DNA fingerprinting and synthesis (Easy)
Now this seems to me to be very much a field from the "obvious" category. The impact of DNA fingerprinting and Western and Southern Blots on pure and applied science- everything from discovering new drugs to hunting down serial killers- is at least as big as the prizeworthy PCR. I think the committee would be doing itself a favor by honoring Jeffreys, Stark, Burnette and Southern.

And while we are on DNA, I think it's also worth throwing in Marvin Caruthers whose technique for DNA synthesis really transformed the field. In fact it would be nice to award a dual kind of prize for DNA- for both synthesis and diagnosis.

Cons: Picking three might be tricky.

7. GPCR structures (Difficult)
When the latest GPCR structure (the first one of a GPCR bound to a G protein) came out I remember remarking that Kobilka, Stevens and Palczewski are probably up for a prize sometime. Palczewski solved the first structure of rhodopsin and Stevens and Kobilka have been churning out structure after important structure over the last decade, including the first structure of an active receptor along with several medicinally important ones including the dopamine D3 and CXCR4 receptors. These feats are definitely technical tour de forces.
Pros: GPCR's are clearly important for basic and applied science, especially drug discovery where 30% of drugs already target these proteins. Plus, structural biology has often been awarded a Nobel so there's lots of precedents (hemoglobin, potassium channel, ATPase etc.)
Cons: Probably too early.

Other predictions: Canine Ed, Sam@EverydayScientist

A posthumous Nobel Prize

The Nobel Prize for Medicine was announced today and it went to Bruce Beutler, Jules Hoffmann and Ralph Steinman for their discoveries concerning innate immunity. More specifically the prize was awarded to the discovery of tumor necrosis factor (TNF), toll-like receptors (TLRs) and dendritic cells. All three are undoubtedly key components of the immune system so the prize is well deserved.

In a tragic twist of fate, Ralph Steinman of the the Rockefeller University (who discovered dendritic cells) died only two days ago after fighting pancreatic cancer. Apparently the committee was not aware of this so it makes the prize a posthumous one. Has this happened before? The rules do seem to stipulate that someone who dies after the announcement is still a legitimate candidate, and it would of course be cruel to withdraw the prize now so they probably won't court controversy (when it comes to science prizes the committee is considered pretty conservative).

Book review: Robert Laughlin's "Powering the Future"

In the tradition of physicists writing for the layman, Robert Laughlin has emerged as a writer who pens unusually insightful and thought-provoking books. In his "A Different Universe" he explored the consequences and limitations of reductionism-based physics for our world. In this book he takes an equally fresh look at the future of energy. The book is not meant to be a comprehensive survey of existing and upcoming technologies; instead it's more like an assortment of appetizers designed to stimulate our thinking. For those who want to know more, it offers an impressive bibliography and list of calculations which is almost as long as the book itself.

Laughlin's thinking is predicated on two main premises. The first is that carbon sources are going to eventually run out or become inaccessible (either because of availability or because of legislation). However we will still largely depend on carbon because of its extraordinarily fortuitous properties like high energy density, safety and ease of transportation. But even in this scenario, simple rules of economics will trump most other considerations for a variety of different energy sources. The second premise which I found very intriguing is that we need to uncouple our thinking on climate change from that on energy instead of letting concerns about the former dictate policy about the latter. The reason is that planetary-level changes in the environment are so vast and beyond the ability of humans to control that driving a few more hybrids or curbing carbon emissions will have little effect on millennial events like the freezing or flooding of major continents. It's worth noting here that Laughlin (who has been called a climate change skeptic lately) is not denying global warming or its consequences here; it's just that he thinks that it's sort of beside the point when it comes to thinking about future energy, which will be mainly dictated by economics and prices more than anything else. I found this to be a commonsense approach based on an appreciation of human nature.

With this background Laughlin takes a sweeping and eclectic look at several interesting technologies and energy sources including nuclear energy, biofuels, energy from trash, wind and solar power and energy stored beneath the sea. In each case Laughlin explores a variety of problems and promises associated with these sources.

Because of dwindling uranium resources, the truly useful form of nuclear energy for instance will come from fast breeder reactors which produce their own plutonium fuel. However these reactors are more susceptible to concerns about proliferation and theft. Laughlin thinks that a worldwide, tightly controlled system of providing fuel rods to nations would allow us to fruitfully deploy nuclear power. One of his startling predictions is the possibility that we may put up with occasional Chernobyl-like events if nuclear power truly becomes cheap and we don't have any other alternatives.

Laughlin also finds promises and pitfalls in solar energy. The basic problem with solar energy is its irregular availability and problems with storage. Backup power inevitably depends on fossil fuel sources which sort of defeats the purpose. Laughlin sees a bright future for molten salt tanks which can very efficiently store solar energy as heat and which can be used when the sun is not shining. These salts are simple eutectic mixtures of potassium and sodium nitrates with melting points that are conveniently lowered even more by the salts' decomposition products. Biofuels also get an interesting treatment in the book. One big advantage of biofuels is that they are both sources and sinks of carbon. Laughlin talks about some recent promising work with algae but cautions that meeting the sheer worldwide demand for energy with biofuels that don't divert resources away from food is very challenging. Further on there's a very intriguing chapter on energy stored under the sea. The sea provides a stupendous amount of land beneath it and could be used for energy storage through novel sources like high-density brine pools and compressed natural gas tanks. Finally, burning trash which has a lot of carbon might appear like a useful source of energy but as Laughlin explains, the actual energy in trash will provide only a fraction of our needs.

Overall the book presents a very thought-provoking treatment of the nature and economics of possible future energy sources in a carbon-strapped world. In these discussions Laughlin wisely avoids taking sides, realizing how fraught with complexity and ambiguity future energy production is. Instead he simply offers his own eclectic thoughts on the pros and cons of energy-related topics which may (or may not) prove important in the future. Of the minor gripes I have with the volume is the lack of discussion of promising recent advances in solar cell design, thorium-based fuels and next generation nuclear reactor technology. Laughlin's focus is also sometimes a little odd and meandering; for instance at one point he spends an inordinate amount of time talking about interesting aspects of robotic technology that may make deep sea energy sequestration possible. But these gripes detract little from the volume which is not really supposed to be an exhaustive survey of alternative energy technologies.

Instead it offers us a very smart scientist's miscellaneous musings on energy dictated by commonsense assumptions based on the simple laws of demand and supply and of human nature. As responsible citizens we need to be informed on our energy choices which are almost certainly going to become more difficult and constrained in the future. Laughlin's book along with others will stimulate our thinking and help us pick our options and chart our direction.

The flame of life and death: My favorite (insufferable) chemical reaction

For me, the most astounding thing about science has always been the almost unimaginably far-reaching and profound influence that the most trite truths about the universe can have on our existence. We may think that we are in charge of our lives through our seemingly sure control of things like food, water, energy and material substances and we pride the ability of our species to stave off the worst ravages of the natural environment such as disease, starvation and environmental catastrophe. We have done such a good job of sequestering ourselves from the raw power of nature that it's all too easy to take our apparent triumph over the elements for granted. But the truth is that we are all without exception critically and pitifully beholden to a few numbers and a few laws of physics.

And a few simple chemical reactions. Which brings me to my favorite reaction for this month's blog carnival. It's a reaction so elementary that it will occupy barely a tenth of the space on a napkin or t-shirt and which could (and should) be productively explained to every human being on the planet. And it's a reaction so important that it both sustains life and very much has the potential to end it.

By now you might have guessed it. It's the humble combination of hydrocarbons with oxygen, known to all of us as combustion.

First the reaction itself which is bleedingly simple:

C_nH_2n+2 + (3n+1)/2 O₂ → (n+1) H₂O + n CO₂ + Energy

That's all there is to it. There, in one line, is a statement about our world that packs at least as much information into itself as all of humanity's accumulated wisdom and follies. A hydrocarbon with a general formula C_nH_2n+2 reacts with oxygen to produce carbon dioxide, water and energy. That's it. You want a pithy, multifaceted (or two-faced, take your pick) take on the human condition, there you have it. While serving as the fundamental energy source for life and all the glory of evolution, it's also one that drives wars, makes enemies out of friends, divides and builds ties between nations and will without a doubt be responsible for the rise, fall and future of human civilization. Faust himself could have appeared in Goethe's dream and begged him to use this reaction in his great work.

First, the hydrocarbon itself. Humanity launched itself onto a momentous trajectory when it learnt how to dig carbon out of the ground and use it as fuel. Since then we have been biding our time for better or worse. The laws of quantum mechanics could not have supplied us with a more appropriate substance. Carbon in stable hydrocarbons is in its most reduced state, which means that you can get a bigger bang out of your buck by oxidizing it compared to almost any other substance. What billions of controlled experiments over the years in oil and natural gas refineries and coal plants have proven is that you really can't do better than carbon when it comes to balancing energy density against availability, cost, ease of handling and transportation and safety. In its solid form you can burn it to stay warm and to produce electricity, in its liquid form you can pump it into an incredibly efficient and compact gas tank. For better or worse we are probably going to be stuck with carbon as a fuel (although the energy source can wildly differ).

The second component of the chemical equation is oxygen. Carbon is very fortunate in not requiring a pure source of oxygen to burn; if it burned, say, only in an environment with 70% or more oxygen that would have been the end of modern civilization as we know it. Air is good enough for combusting carbon. In fact the element can burn under a wide range of oxygen concentrations, which is a blessing because it means that we can safely burn it in a very controlled manner. Varying the amount of oxygen can also lead to different products and can minimize the amount of soot and toxic byproducts. The marriage of carbon and oxygen is a wonderfully tolerant and productive one and we have gained enormously from this union

The right side of the combustion equation is where our troubles begin. First off, water. It may seem like a trivial, harmless byproduct of the reaction but it's precisely its benign nature that allows us to use combustion so widely. Just imagine if the combustion of carbon had produced some godforsaken toxic substance in addition to carbon dioxide as a byproduct. Making energy from combustion would then have turned into a woefully expensive activity, with special facilities required to sequester the poisonous waste. This would likely have radically altered the global production and distribution of energy and human development would have been decidedly hampered. We may then have been forced to pick alternative sources of energy early on in our history, and the face of politics, economics and technology would consequently have been very different.

Moving on we come to what's almost ubiquitously regarded as a villain these days- carbon dioxide. If carbon dioxide were as harmless as water we would live in a very different world. Sadly it's not and its properties again underscore the profound influence that a few elementary facts of physics and chemistry can have on our fate. The one property of CO2 that causes us so much agony is the fact that it's opaque to long-wavelength infrared radiation and absorbs it, thus warming the surroundings. This is not a post to discuss global warming but it's obvious to anyone not living in a cave that the issue has divided the world like no other. We still don't know for sure what it will do, either by itself or because of the actions taken by human beings from merely perceiving its effects. But whatever it is, it will profoundly alter the landscape of human civilization for better or worse. We can all collectively curse the day that the laws of physics and chemistry decided to produce carbon dioxide as a product of combustion.

Finally we come to the piece de resistance. None of this would have mattered if it weren't for the most important thing combustion produces- energy (in fact we wouldn't have been around to give a fig). In this context combustion is exactly like nuclear fission; twentieth-century history would have been very different if all uranium did was break up into two pieces. Energy production from combustion is what drives life and human greed. We stay alive by eating carbon-rich compounds - especially glucose - which are then burned in a spectacularly controlled manner to provide us with energy. The energy liberated cannot be used directly for our actions and thoughts. Instead it is used to construct devilishly clever chemical packages of ATP (adenosine triphosphate) which then serves as the energy currency.

Our bodies (and those of other creatures) are staggeringly efficient at squeezing oxidation-derived energy out of compounds like glucose; for instance in the aerobic oxidation of glucose, a single glucose molecule can generate 32 molecules of ATP. Put another way, the oxidation of a gram of glucose yields about 4 kilocalories of energy. This may not seem like a lot until we realize that the detonation of a gram of TNT yields only about 1 kilocalorie (the reason the latter seems so violent is because all the energy is liberated almost instantaneously). Clearly it is the all-important energy term in the combustion equation that has made life on earth possible. We are generously contributing to this term these days by virtue of quarter pounders and supersizing but our abuse does not diminish its importance.

The same term of course is responsible for our energy triumphs and problems. Fossil fuel plants are nowhere as efficient in extracting energy from carbon-rich hydrocarbons as our bodies, but what matters is whether they are cheap enough. It's primarily the cost of digging, transporting, storing and burning carbon that has dictated the calculus of energy. Whatever climate change does, of one thing we can be sure; we will continue to pay the cheapest price for our fuel. Considering the many advantages of carbon, it doesn't seem like anything is going to substitute its extraordinarily fortuitous properties anytime soon. We will simply have to find some way to work around, over or through its abundance and advantages.

If we think about it then, the implications of combustion for our little planet and its denizens are overwhelming and sometimes it's hard to take it all in. At such times we only need to take a deep breath and remember the last words spoken by Kevin Spacey's character from "American Beauty":

"Sometimes I feel like I'm seeing it all at once, and it's too much, my heart fills up like a balloon that's about to burst... And then I remember to relax, and stop trying to hold on to it, and then it flows through me like rain and I can't feel anything but gratitude for every single moment of my stupid little life..."

That's right. Let's have it flow through us like rain. And watch it burn.

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Rookie mistakes in molecular modeling: Part 1

Molecular modeling as a general approach is no longer utilized only by experts but has reached the masses. Improved hardware and software capabilities combined with easy-to-use graphical user interfaces have enabled experimental chemists of all kinds to build models of molecules and perform relatively sophisticated calculations on them. Calculations which once required supercomputers can now be routinely done on desktops by organic, inorganic and biological chemists who can use the results to explain, support and predict chemical phenomena. In the coming years we can be confident that we will witness an increasing use of modeling techniques by experimentalists.

An unfortunate (but probably not unexpected) consequence of this ease of use is that it has also become easier to make mistakes while building molecular structures. The main source of errors arises during the translation of 2D chemical structures to their 3D counterparts using some energy minimization protocol. The apparently simple process of drawing a 3D-worthy 2D structure is trickier than it sounds and is therefore quite prone to error. Conformation which was not as important when drawing in 2D is suddenly of overriding importance and it's relatively easy to get it wrong.

As a modeler who has interacted closely with experiment, I have come across a number of rookie mistakes which I have seen myself and others make over the years. Sometimes these mistakes don't matter too much for the final result but sometimes they can completely change it. So I thought I would make a short list of easy-to-avoid errors which may provide checks on modeling structures. In part 1 I will describe mistakes commonly seen during the simple building of structures. Part 2 will deal with interactions with experimentalists.

1. Getting the ionization state wrong: I put this rookie mistake at the top because it's remarkable how many times I have seen even experienced modelers make it. Always remember; amines are protonated at physiological pH while carboxylic acids are deprotonated. The reason why getting this right is important is because it can completely change results from protocols like docking. Just think of the difference a protonated vs unprotonated carboxylate makes for binding to a protein. Also, many modeling algorithms use force fields which are dominated by electrostatic interactions; the wrong protonation state can therefore make a world of difference. A corollary of the ionization state problem results when replacing atoms. For instance you may have a protonated amine which you then want to turn into an alcohol by replacing the N with a O. Unfortunately the atom does change but not the ionization state, and you end up with a weird positively charged doubly bonded oxygen. On a related note, it goes without saying that you shouldn't charge up inappropriate atoms such as those which are conjugated to aromatic systems. The best way to overcome these issues is to simply display charges for all heteroatoms in your final structure.

2. Getting the stereochemistry wrong: The CIP rules were taught to us because they really matter. Here's a typical stereochemical mistake: You construct a structure in 2D and come across a stereocenter. You may even build that stereocenter with the right absolute (R or S) stereochemistry. And then you attach something else to that center and forget to recheck the stereochemistry which may have changed because of the change in CIP priority. The simplest way to make sure about stereochemistry is to always have the program display all absolute stereochemistry for the final structure.

2. Forgetting basic conformational rules: This mistake is most commonly made when converting a 2D structure into a 3D structure. The problem is that when you build a 2D structure, your placement of bonds and angles is somewhat ad hoc based on the rather random way in which you are conveniently rotating and viewing the structure. When you then suddenly convert 2D to 3D, you may end up with axial substituents on six-membered rings, syn-pentane or eclipsing interactions between substituents, funky substructures like non-planar aromatic rings resulting from strain or in the worst cases, even boats for cyclohexanes. Here's another common pitfall: You may try to close a ring by building an unrealistic long bond between two initially separated atoms, thinking that when you then minimize this structure the program will take care of the bond by shortening it to its standard length. This usually happens, but in the process some other parts of your molecule gets messed up. Again, judicious inspection can avoid most of these issues.

3. Cis and trans: The process of building unrealistic bonds between distant atoms and then simply minimizing a structure that I just mentioned can sometimes result in amide bonds becoming cis and this is important enough to be listed as a separate point. This is also a common consequence of importing 2D files in SDF format (which lack hydrogens) and asking a program to add hydrogens. The same thing can happen with double bonds.

4. Forgetting basic chemistry: This mistake has more to do with forgetting basic rules of bonding and chemistry than with modeling. Occasionally you may do things like exceeding the allowed valency of an atom, putting a double bond at a bridgehead carbon (violating Bredt's rule), generating antiaromatic rings, forgetting Baldwin's rules for ring closure, building a vinyl amine or a geminal amino alcohol...and generally creating all sorts of unstable and "impossible" molecules. The problem is that your program won't always raise red flags notifying you about these errors so you need to remember your chemistry and make sure you don't recommend some wacky molecules to make to the synthetic chemist (one of the constant sources of friction between experimentalists and modelers arises from the latter forgetting what's synthetically feasible and stable).

Ultimately, the path to a well-constructed molecule simply depends on being vigilant and judiciously checking your final structure. Remember the well-worn adage; computers don't know any chemistry whatever and they are only as good as the code that goes into them. Nothing can trump a sound knowledge of basic chemical principles.

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In which creationists' understanding of amyloid appears...tangled

The biophysicist David Eisenberg of UCLA recently published a paper in which his group surveyed what they called the "amylome", the set of all possible proteins that can potentially form the deadly amyloid aggregate implicated in diseases like Alzheimer's. I haven't read the whole paper yet and will describe it in another post but it has some very intriguing conclusions (see the Nature News piece).

Eisenberg's group end up finding common segments possessing amyloid-forming propensity in pretty much every protein, given the right conditions. Not surprisingly, these segments are mostly kept tucked inside protein cores; if exposed the proteins are refolded with chaperones or eliminated as non-functional. But this ties in with Chris Dobson's work which I described in a recent post. Dobson's most recent paper seemed to conclude that amyloid is actually the most thermodynamically stable state of a protein, with "normal" protein states being metastable.

All this is fascinating stuff, but you can always trust creationists to put an anti-evolutionist spin on almost any scientific funding. Someone named Cornelius Hunter asserts on his blog that the fact that the most stable state of proteins seems to be amyloid and that most real proteins don't actually exist in this state seems to be a kind of miracle or at the very least indicates the enormous difficulties attendant in creating complex biomolecular structures.

Mr. Hunter seems to be indulging in a common fallacy, that of assuming that evolution somehow tends to an ideal. This is just not the way the process works. Considering the stringent constraints and time in which evolution has to work, it can only explore the available space of solutions and not the entire possible space. It can never achieve the best possible result in solution space, only one that is good enough under the given circumstances. I don't know if it's really that hard to understand or whether creationists like Mr. Hunter want to deliberately obfuscate the issue. Evolution can only work on what's already available; it can only mix and match existing motifs, and what exists need not be perfect at all. As an aside, this function of evolution reminds me of the concept of "satisficing" or "bounded rationality" in economics; lacking perfect knowledge of all solutions and unlimited time, economic actors like us can only pick solutions optimal within current constraints, not "global" maximums on the solution landscape.

There's therefore no reason to believe that biological evolution should create the most thermodynamically stable state of a protein. Creating a state that is functionally relevant is enough, even if it's thermodynamically metastable. In fact one can even make an argument that a thermodynamically superstable state might lead to an evolutionary dead end since it will be hard to tinker with. Given such constraints it's indeed impressive that evolution creates enzymes speeding up chemical reactions by twelve orders of magnitude, but even these enzymes are few and probably not the best possible in all of enzyme space.

So yes, the fact that it's amyloid and not the normal state of proteins that is the most thermodynamically stable one is fascinating, but in no way does this present a great challenge to evolution. In fact it reinforces evolution's essential character, to hunker down and make do as well as it can under the given circumstances. Don't we all?

2011 Nobel Prizes

So it's that time of the year again, the time when just like Richard Feynman and Paul Dirac, three select individuals get to mull over whether they will incur more publicity by accepting the Nobel Prize or rejecting it.

Predicting the Nobel Prizes gets easier every year ((I said predicting, not getting your predictions right) since there's very little you can add in the previous year's list, although there are a few changes; the Plucky Palladists can now happily be struck off the list. As before, I am dividing categories into 'easy', and 'difficult' and assigning pros and cons to every prediction.

The easy ones are those regarding discoveries whose importance is (now) ‘obvious’; these discoveries inevitably make it to lists everywhere each year and the palladists clearly fell into this category. The difficult predictions would either be discoveries which have been predicted by few others or ones that that are ‘non-obvious’. But what exactly is a discovery of ‘non-obvious’ importance? Well, one of the criteria in my mind for a ‘non-obvious’ Nobel Prize is one that is awarded to an individual for general achievements in a field rather than for specific discoveries, much like the lifetime achievement Academy Awards given out to men and women with canes. Such predictions are somewhat harder to make simply because fields are honored by prizes much less frequently than specific discoveries.

When predicting the Nobel prize it's also prudent to be cognizant of discoveries whose recognition makes you go "Of course! That's obvious". Prizes for the charge-coupled device (CCD) (2009) integrated chip (2000) and in-vitro fertilization (2010) fall into this category.

Anyway, here's the N-list

2. Computational chemistry and biochemistry (Difficult):
Pros: Computational chemistry as a field has not been recognized since 1999 so the time seems due. One obvious candidate would be Martin Karplus. Another would be Norman Allinger, the pioneer of molecular mechanics.
Cons: This would definitely be a lifetime achievement award. Karplus did do the first MD simulation of a protein ever but that by itself wouldn’t command a Nobel Prize. The other question is regarding what field exactly the prize would honor. If it’s specifically applications to biochemistry, then Karplus alone would probably suffice. But if the prize is for computational methods and applications in general, then others would also have to be considered, most notably Allinger but perhaps also Ken Houk who has been foremost in applying such methods to organic chemistry. Another interesting candidate is David Baker whose program Rosetta has really produced some fantastic results in predicting protein structure and folding. It even spawned a cool game. But the field is probably too new for a prize and would have to be further validated.

3. Chemical biology and chemical genetics (Easy)
Another favorite for years, with Stuart Schreiber and Peter Schultz being touted as leading candidates.
Pros: The general field has had a significant impact on basic and applied science
Cons: This again would be more of a lifetime achievement award which is rare. Plus, there are several individuals in recent years (Cravatt, Bertozzi, Shokat) who have contributed to the field. It may make some sense to award Schreiber a ‘pioneer’ award for raising ‘awareness’ but that’s sure going to make a lot of people unhappy. Also, a prize for chemical biology might be yet another one whose time has just passed.

4. Single-molecule spectroscopy (Easy)
Pros: The field has obviously matured and is now a powerful tool for exploring everything from nanoparticles to DNA. It’s been touted as a candidate for years. The frontrunners seem to be W E Moerner and M Orrit, although Richard Zare has also been floated often.
Cons: The only con I can think of is that the field might yet be too new for a prize

5. Electron transfer in biological systems (Easy)
Pros: Another field which has matured and has been well-validated. Gray and Bard seem to be leading candidates.

Among other fields, I don’t really see a prize for the long lionized birth pill and Carl Djerassi; although we might yet be surprised, the time just seems to have passed. Then there are fields which seem too immature for the prize; among these are molecular machines (Stoddart et al.) and solar cells (Gratzel).

MEDICINE:

1. Nuclear receptors (Easy)
Pros: The importance of these proteins is unquestioned. Most predictors seem to converge on the names of Chambon/Jensen/Evans.

2. Chaperones: (Easy)

Arthur Horwich and Franz-Ulrich Hartl just won this year's Lasker Award for their discovery of chaperones. Their names have been high on the list for some time now.

Pros: Clearly important. Chaperones are not only important for studying protein folding on a basic level but in the last few years the malfunctioning of chaperones such as heat-shock proteins has been shown to be very relevant to diseases like cancer.

Cons: Too early? Probably not.

3. Statins (Difficult)
Akira Endo’s name does not seem to have been discussed much. Endo discovered the first statin. Although this particular compound was not a blockbuster drug, since then statins have revolutionized the treatment of heart disease.
Pros: The “importance” as described in Nobel’s will is obvious since statins have become the best-selling drugs in history. It also might be a nice statement to award the prize to the discovery of a drug for a change. Who knows, it might even boost the image of a much maligned pharmaceutical industry...
Cons: The committee is not really known for awarding actual drug discovery. Precedents like Alexander Fleming (antibiotics), James Black (beta blockers, antiulcer drugs) and Gertrude Elion (immunosuppresants, anticancer agents) exist but are far and few in between. On the other hand this fact might make a prize for drug discovery overdue.

4. Genomics (Difficult)
A lot of people say that Venter should get the prize, but it’s not clear exactly for what. Not for the human genome, which others would deserve too. If a prize was to be given out for synthetic biology, it’s almost certainly premature. Venter’s synthetic organisms from last year may rule the world, but for now we humans still prevail. On the other hand, a possible prize for genomics may rope in people like Carruthers and Hood who pioneered methods for DNA synthesis.

5. DNA fingerprinting and synthesis (Easy)
Now this seems to me to be very much a field from the "obvious" category. The impact of DNA fingerprinting and Western and Southern Blots on pure and applied science- everything from discovering new drugs to hunting down serial killers- is at least as big as the prizeworthy PCR. I think the committee would be doing itself a favor by honoring Jeffreys, Stark, Burnette and Southern.

And while we are on DNA, I think it's also worth throwing in Marvin Caruthers whose technique for DNA synthesis really transformed the field. In fact it would be nice to award a dual kind of prize for DNA- for both synthesis and diagnosis.

Cons: Picking three might be tricky.

6. Stem Cells (Easy)
This seems to be yet another favorite. McCulloch and Till are often listed. Unfortunately McCullough died earlier this year so it would be a little unfair to award just Till. However such a thing is not unprecedented. For example, the psychologist Daniel Kahneman shared the 2002 Economics Nobel Prize with Vernon L. Smith. Left out was his long-time collaborator Amos Tversky who had died in the 90s; it's pretty much regarded as a given that Tversky would have shared the prize had he been alive.
Pros: Surely one of the most important biological discoveries of the last 50 years, promising fascinating advances in human health and disease.
Cons: Politically controversial (although we hope the committee can rise above this). Plus, a 2007 Nobel was awarded for work on embryonic stem cells using gene targeting strategies so there’s a recent precedent.

7. Membrane vesicle trafficking (Easy)

Rothman and Schekman

Pros: Clearly important. The last trafficking/transport prize was given out in 1999 (Blobel) so another one is due and Rothman and Schekman seem to be the most likely canidates. Plus, they have already won the Lasker Award which in the past has been a good indicator of the Nobel.

8. GPCR structures (Difficult)
A commenter reminded me of this. When the latest GPCR structure (the first one of a GPCR bound to a G protein) came out I remember remarking that Kobilka, Stevens and Palczewski are probably up for a prize sometime. Palczewski solved the first structure of rhodopsin and Stevens and Kobilka have been churning out structure after important structure over the last decade, including the first structure of an active receptor along with several medicinally important ones including the dopamine D3 and CXCR4 receptors. These feats are definitely technical tour de forces.
Pros: GPCR's are clearly important for basic and applied science, especially drug discovery where 30% of drugs already target these proteins.
Cons: Too early.

PHYSICS

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

Also, Anton Zeilinger, John Clauser and Alain Aspect deserve it for bringing the unbelievably weird phenomenon of quantum entanglement to the masses. Zeilinger's book "Dance of the Photons" presents an informative and revealing account of this work

I have also always wondered whether non-linear dynamics and chaos deserves a prize. The proliferation and importance of the field certainly seems to warrant one; the problem is that there are way too many deserving recipients (and Mandelbrot is dead).

Other predictions: Canine Ed, Sam@EverydayScientist