The Curious Wavefunction

The Sheri Sangji accident: The experimental details

noreply@blogger.com (Wavefunction) — Wed, 25 Jan 2012 19:18:00 +0000

Science has just published a summary of the report by California's Division of Occupational Safety and Health about the tragic accident involving Sheri Sangji and tert-butyl lithium. The summary is the most detailed description of the accident that I have seen so far and it makes it clear that there were at least four very significant violations of protocol during the experiment that Sangji was performing. These included:

1. Not wearing a lab coat and other appropriate safety gear.

2. Using a plastic syringe that by definition cannot be oven-baked to remove traces of moisture.

3. Using a syringe with a 2-inch needle that was about an order of magnitude shorter than the recommended length (1-2 ft.). This was a very significant safety breach since it would have required Sangji to tilt the bottle to extract the liquid, thus not only increasing the chances of a spill but also diminishing her general degree of control over the whole procedure.

4. Actually pulling the plunger back rather than let it be pushed by the inert nitrogen pressure from the bottle.

Of these four violations, only the first one can be easily assigned to Sangji herself since lab coats constitute a very general and well-known part of safety equipment. The others are specialized and specific to hazardous substances and their assignment is going to be much more ambiguous. The rub of the matter is going to be in finding out if these violations were the result of inappropriate or insufficient communication by the PI or an oversight on the part of Sangji herself.

From what I can tell, the report seems to lean toward the former possibility. One of the statements I found disturbing was Prof. Harran's admission that he "never discussed with Victim Sangji the risks associated with the tasks she was undertaking". Another important matter which I alluded to in a previous post was the responsibility of senior postdocs and graduate students in the lab, and the report provides a new twist to the issue that I hadn't seen before. Harran says that a postdoc in his group was supposed to train Sangji in the specifics of handling t-BuLi. The postdoc himself admits that he does not have specific recollection of providing "formal" training to Sangji. In addition Harran admits that he never confirmed whether the postdoc had in fact properly instructed Sangji in the use of the hazardous reagent. I would think that the relative apportioning of the blame between Harran and the postdoc is almost certainly going to be a focus during the trial.

None of this is too comforting and it certainly does not sound like it would make it easier for Harran to defend himself. And yet the sad fact of the matter is that this is how many labs around the world probably operate. The PI does not immerse himself in the minutiae of handling specific reagents and leaves it to the postdocs in the group. The postdoc or senior students in turn gingerly step into that notoriously gray area where it becomes difficult to say whether a particular degree of instruction was "sufficient" or not; for instance, was it enough for the postdoc to demonstrate the protocol once? How about twice? How about one actual demonstration followed by two pointed reminders?

These and other questions are almost certainly going to come up during the proceedings and their fuzzy, gray nature is going to make it difficult to assign blame. But the details of the report make it clear that somewhere, sometime, the crucial information undoubtedly slipped through the cracks. And even a clear admission of this fact may make practitioners around the world more vigilant and, one hopes, more humble.

Will quantum physics help us cure Alzheimer's disease?

noreply@blogger.com (Wavefunction) — Tue, 24 Jan 2012 21:18:00 +0000

There's an interesting bit of writing out in the journal ChemMedChem by Jean-Louis Kraus, a medicinal chemist in France who has worked on drug discovery for Alzheimer's disease. The article is essentially a summary of Kraus's pessimistic outlook towards current therapies and approaches addressing Alzheimer's disease. Kraus has worked for a long time in medicinal chemistry and his words reflect experience and not just opinion. The article starts off with some well-founded skepticism but ends up sounding...let's say a little questionable.

For the most part I agree with Kraus that Alzheimer's research in the last decade has seen one disappointment after another and that we are still largely groping in the dark. He refers to the fact that all the concerted research into the disease and the billions of dollars in funding have resulted in only a handful of drugs and a handful of protein targets. None of the drugs even partially repair the damage and none of the proteins have been shown to be decisive as targets for treatment. Beta-secretase, gamma-secretase, the NMDA receptor, acetylcholine esterase; all of these have seen their day in the sun, followed by a disappointing set of results usurping them from front stage. That doesn't mean that none of them are important, it's just that targeting them with therapies seems to have no direct causal connection with treating the disease.

But what is even more disappointing is that the basic hypothesis driving the field - the amyloid hypothesis - has now been seriously questioned. A series of high-profile clinical trial failures have sent researchers scurrying back to their benches and while amyloid almost certainly has an important connection with the disease, it's now not clear at all whether actually targeting the infamous protein aggregates will bring any benefits. What seemed like a promising and rather direct direction of research has devolved into a scientific mess that will need at least a few years to be sorted out. As far as Alzheimer's disease goes, we are still fighting with sticks and stones.

As Kraus says, much of this failure eventually is a failure to understand the basic biology of the disease, which in turn entails a failure to understand the brain on a more general level, including mechanisms of memory generation and storage. Even now, much of drug discovery fails because of ignorance of the detailed biology of the disease and its perturbation by small molecules. What is regarded as a sound mechanistic hypothesis is often thwarted by the complex realities of signal transduction. With Alzheimer's disease we seem to have been biting off more than we could chew, and we need to keep untangling the complex interplay of amyloid, the protein tau, the secretases and a multitude of other biochemical components before we can truly start developing therapies. Thus it's inevitable and essential that in addition to chemists and biologists, we will need crucial input from neuroscientists to target the disease.

But will we also need quantum physicists? Kraus's thoughts on the relevance of subatomic science to AD left me slightly nonplussed. He says that:

The theories behind black holes generally suggest that subatomic particles (electrons, protons, neutrons) are themselves black holes, in which time expands in the opposite direction of our proper (perceived) time. Huge amounts of information could be stored by the spin number of photons present in these particle black holes. Could it be possible that the organization of brain matter, in terms of the properties of subatomic particles (quantum mechanics), confers on brain matter the capacities of memory and cognition, and that these phenomena are not encountered in other types of matter structure in the human body?

Come again? I was not familiar with electrons, protons and neutrons being black holes. But even if they are, I fail to see their direct relevance to understanding memory and cognition. Sure, it's a trivial fact that it's a very specific organization of subatomic particles that leads to a brain rather than to a liver or a chair. But the real action all takes place at the level of aggregates of these particles which we call molecules. I get the feeing that Kraus is indulging in a classic reductionist fallacy here. While subatomic particles do constitute the brain, understanding the brain can only come at a higher level, that of rather old-fashioned physics and chemistry involving ionic currents and neurotransmitters.

But Kraus finds a valuable place for quantum physicists in the war on neurodegenerative disorders:

To me it has become mandatory to create an AD scientific community that includes not only medicinal chemists, pharmacologists, biologists, and medical doctors, but also quantum physicists, in order to understand how aging alters the intimate structure of brain matter, where memory and cognition are located, with the hope of finding new AD treatment research orientations.

To me this sounds suspiciously like Roger Penrose's argument in his rather startling book "Shadows of the Mind" in which he postulated a relationship between wavefunction superposition in quantum mechanics and the growth and shrinkage of microtubules as significantly contributing to consciousness. Even a cursory look at that argument raised serious doubts about the relevance of quantum behavior in microtubules and more formal analysis seemed to confirm these doubts. I am not saying that physicists won't be a valuable asset on a drug discovery team, it's just that they are probably not going to use the tools of quantum gravity to map out cognitive pathways anytime soon.

Somewhat ironically, Kraus ends his piece by extolling the role of a systems biology approach in addressing a problem as complex as Alzheimer's disease. With this I wholeheartedly agree, but systems biology is the opposite of reductionism, where new emergent phenomena provide causal explanations that cannot be reduced to the laws underlying their substrates. We do need a suite of analytical tools operating at various hierarchical levels to address the issue, but given enough time and smart people, we should be able to do the job using standard chemistry and biology, albeit at a more sophisticated level. No fancy biophoton entanglement may be necessary.

Kraus, J. (2011). Why as a Medicinal Chemist I Am Not Optimistic about the Possibility of Finding, in a Reasonable Timeframe, Small-Molecule Drugs Capable of Curing the Evolution of Alzheimer’s Disease ChemMedChem DOI: 10.1002/cmdc.201100431

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Introverts, extroverts and modern science

noreply@blogger.com (Wavefunction) — Tue, 24 Jan 2012 02:29:00 +0000

Over at "In the Pipeline", Derek has a post that indirectly asks the following question; all other factors being the same, is modern scientific research more conducive toward introverts or extroverts?

The post was inspired by an Op-Ed in today's New York Times by Susan Cain, a social scientist who has an interesting book arguing that modern professional life's bias toward extroverted behavior may be stifling the kind of creativity engendered by introverts. Whether at home or at work, we are expected to constantly collaborate, interact, email and reach out and perhaps all this is turning a little obsessive. I haven't read the book yet but the author is suggesting that much of the modern workplace not only encourages but often demands constant collaborations, meetings (both offline and online) and social networking in ways that are stacked against quiet introverts whose valuable skills may be lost in the din.

It may be worth noting first that both introverts and extroverts have made monumental contributions to scientific research, although there could be an argument for certain types of research favoring one or the other kind of personality. If most of your research involves working out dense mathematical theorems or tracing the life histories of ants in the Amazonian rainforest for that matter, an introverted personality that isn't too fond of social interaction may be best for you. On the other hand, if your research involves tracking consumer behavior in supermarkets or evaluating the effects of political bias on brains using fMRI, you may be best off being an extrovert who likes to interact with other social creatures.

Notwithstanding this tailoring of certain kinds of personalities for certain kinds of research, it's pretty clear that there are lots of exceptions and that people with diverse temperaments have flourished in scientific research. Henry Cavendish who would probably have received two or three Nobel Prizes had his discoveries been made in recent times was pathologically shy and would fire his maid if she stepped in the same room with him. As a contrast, among modern scientists, Niels Bohr was famous for being someone who thought best by talking to people. In the words of his protege, the physicist John Wheeler, talking physics with Bohr was like playing a one-man tennis match, with the other person serving as a wall to relentlessly bounce off ideas. Bohr would often take his students and collaborators for long walks, sometimes for circular walks around the building with the intent of hammering out his thoughts. The mumbling and the perpetual refinements and revisions of his sentence constructions made the experience a little harrowing for the listener. As Bohr and his victim went round and round, ideas would spin off. In sharp contrast to Bohr, Paul Dirac was equally famous for being taciturn to an almost pathological degree, mostly remaining silent even when questions were asked. Another example is Fred Sanger, the two-time Nobel Prize winner who is one of the most self-effacing and introverted scientists that you can find. In his case his inward-looking personality led to a career-long life in the lab doing technical work with scant regard for interviews, publicity or even scientific writing. Sanger who faithfully retired at the relevant age and now tends roses in his garden says that he has always been good at doing, listening and talking, in that order.

But as far as the trend of modern research and the current emphasis on open science goes, James Watson and Francis Crick's collaboration may be a better role model than Bohr's peripatetic thinking-out-loud style. The two hit it off almost instantly at the Cavendish Laboratories. Watson was irreverent and not afraid to walk up to anyone and ask any question that came to him. Crick was notoriously talkative and loud to the point of being irritating. The two were ideally suited to constantly bounce ideas off each other and rattle off their latest brainwave without any thought of politeness or social etiquette. But the real reason their collaboration serves as an inspiration for modern research is because of their completely open style of approaching the DNA problem. The two would ask anybody, learn any technique, build any model, perform any calculation, read any textbook and consult any reference, all without abandon. Any person, printed source, experimental or theoretical technique was meat and drink to them, even if in one infamous case Watson used Rosalind Franklin's x-ray data without her knowledge. This practice of doing whatever it takes to solve a problem is cardinal to today's multidisciplinary scientific research and it will continue to serve us well.

Open science will indeed bring us enormous benefits but it may also inevitably cast out the valuable outliers. The book by Cain seems to argue that there can be such a thing as too much collaboration if it becomes enshrined in scientific practice as an inviolable rule, leading to those who don't fit the mold being ignored or even ostracized. The real problem is with implicitly or explicitly penalizing introverts who would rather work by themselves. I myself am someone who prefers a balance; I don't like to be secluded in a private office (as I was in a previous job) and certainly prefer an open work space with desks and cubicles. But I would also like to work on ideas in solitude both before and after I discuss them with others. If I were to criticize modern collaborative science, it would be in its tendency to convene meetings or telephone conferences to sometimes discuss even trivial matters. It would be in emphasizing a talent for teamwork so much that not perceiving someone as an extroverted team player almost automatically ejects him from the pool of job applicants in an interview. And it would be in criticizing or even bullying up on someone who does not instantly subscribe to the latest technological breakthrough in online collaboration and data sharing.

There's one more drawback which I think is inherent in this era of constantly collaborative science. It offers little opportunity to work for some time without preconceived notions. Sometimes the best ideas come from people who can think outside the box precisely because they are not already exposed to the conventional wisdom in the field. In some ways, the first few days in a new position offer the best chance to do independent thinking that is unencumbered by groupthink. Bringing a new researcher in your organization or group up to speed by instantly steeping him or her in the group's research philosophy may seem like a good idea, but it may deny you the chance to tap into fresh and original insight. It is important in my opinion to let a newcomer in a research organization take some time and offer his own way of thinking to others rather than have him fall in line with your preferred mindset right away. And then there's the currently unanswered question of how much a world full of distractions that include the internet, phones, email and co-workers is affecting productivity.

Part of the reason why Cain's outlook strikes a chord with me is because of the realization that scientific research has always benefited from personalities, working styles and mindsets that are as diverse as the discoveries they make. Science has had room for all of them; the introverts, the extroverts, the evangelists, the prophets, the quiet lab technicians, the soap box enthusiasts and the madmen. All of these have made modern science, and all of them deserve a place at the table, no matter how eager its inexorable march.

Fraud in a glass of wine

noreply@blogger.com (Wavefunction) — Wed, 18 Jan 2012 13:27:00 +0000

One of the biggest stories in biomedical research during the last decade has been the discovery that certain molecules can mimic the effect of what's called caloric restriction, the reduced consumption of calories, either by starvation or by deliberation. Caloric restriction in turn has been linked quite reliably to a slowdown in aging and a general improvement in metabolism in lower animals like, yeast, fruit flies and certain worms. What was particularly alluring was that these effects seemed to be mediated by a single family of genes through proteins called sirtuins. The implication was clear; not only did we have a handle on a significant component of the genetic basis of aging but we could also potentially mimic the effects of anti-aging genes by drugs that targeted sirtuins.

But what really catapulted the story to public attention was the finding that resveratrol, a molecule found in red wine, might do this. The presence of a (relatively) cheap edible substance, universally consumed, savored and culturally revered that might slow down aging naturally led to unprecedented public attention. The French and Italians might say "I told you so", but suddenly the holy grail of medical science seemed to be within reach. As usual though, the initial euphoria gradually gave way to a more cautious and tempered belief in the benefits of red wine in mitigating the ill effects of age, and indeed in the whole field of caloric restriction itself. The complete story is fascinating and too convoluted to recount here, but the simple fact of the matter is that the biology of aging is much more complex than we imagined and the initial breakthroughs have not been as unambiguous as they seemed. Not surprisingly, ascribing something as complex as aging and its attendant physiological changes to the action of a single family of genes and proteins has turned out to be simplistic at best (as a comparison, even obesity is thought to be caused by dozens of genes with overlapping effects). In addition, anti-aging effects that got the attention of the New York Times turned out to be significant only in "lower" animals and not in mammals. As it stands today, while research on caloric restriction undoubtedly has great potential, many complications need to be ironed out before the initial optimism can be justified. Curiously, much of the high-profile work in the area can be traced in various forms to a single laboratory at MIT. A recent article in Science does a great job detailing the personalities, the findings and the controversies that sprang from this and other laboratories' work; the entire saga seems fit for a Sinclair Lewis novel.

But whatever the scientific status of the field, its high-profile nature and its potentially revolutionary implications promised ample funding for interested researchers, and over the years it has attracted both highly visible as well as lesser known scientists. One of the individuals who waded into resveratrol territory was Dipak Das of the University of Connecticut Medical School. Over the last few years Das published several papers detailing the beneficial effects of resveratrol in possibly preventing or mitigating oxidative damage caused in cardiovascular and neurological diseases. While most of his research has been published in low-impact journals, it seems that Das was on his way to a lucrative research career involving resveratrol and its role in health and disease.

Until now . It seems that somewhere along the road, he started committing fraud on a massive scale, the likes of which haven't been seen in some time in biomedical research. It started when an anonymous tipster tipped off the university about fabrication in some of Das's papers. The university then launched its own probe and formed a review committee. For the past two years the committee has been working in the shadows with the Office of Research Integrity (ORI) and last week they released their findings in a 50-page document. The findings indicate wholesale fraud, manipulation of results and deliberate doctoring of critical data on a shockingly regular basis between at least 2002 to 2009.

Much of the fabrication centers around Western blots, a research tool that's universally used in all aspects of biological research. The Western blot is to biology what spectroscopy is to chemistry and physics. It essentially detects the presence and identity of specific biomolecules, especially proteins. The proteins appear as dark bands on a white background with different lanes representing different proteins. Western blots are absolutely indispensable in confirming the identity of a novel or unknown protein, and I think it's safe to say that most biomedical researchers who have won Nobel Prizes have used these tools in their research. In case of Das, the report has found dozens of Western blots to be grossly manipulated using image manipulation software.

There are two aspects of the report that bear closer scrutiny. One is the sheer number of Western blots found to have been doctored. The committee examined 26 papers and cited no less than 88 figures which appear to be manipulated (there were also several that appeared normal). This is a staggering amount of manipulation and rules out accidental oversight. Das would have to be involved in a conscious, deliberate and extended effort to tamper with so much data. It's quite clear that the magnitude of the manipulation alone points strongly to purposeful fraud.

The second aspect of the report concerns the great difficulty of detecting the fraud. Western blots seem to be notoriously amenable to manipulation; for instance they prominently featured in another recent high-profile case of fraud in India involving a researcher at the National Center for Cell Science (NCCS). In the report on Das's work, single bands of proteins in Western blots have been enlarged and their borders further magnified to show the contrast between the background for that particular band and for others, indicating that the band in question was copied and pasted. Image manipulation software can sometimes produce such artifacts and some of the data appears like it could also have been the result of negligence or sloppy editing, but the number of such instances again rules out merely these possibilities.

But the difficulty of discerning the doctored data also indicates more generally how difficult it can be to detect fraud in science. Even experienced specialists, let alone laymen or researchers from other fields, wouldn't have thought to look for these minute differences which are evident only in retrospect. To demonstrate the process, at one point in the report the committee actually depicts a mock manipulation protocol where bands are edited or appended to other bands from totally different experiments. The subtleties in the data make it clear that even in the future it would be relatively easy for researchers to get away with this kind of manipulation. What these cases point to is the need for automated systems ("counter-software", if you will) that could detect such fine anomalies in submitted Western blots or other presentations and try to separate artifacts from real red flags. In this case of course, the verdict is unanimous. At the end of the report, the committee finds unambiguous evidence of "unequivocal image manipulation, splicing, background erase and duplication" in a vast number of cases resulting in both data fabrication and falsification.

The debacle is ending in ways that such unfortunate scenarios usually end. The university has already begun proceedings to fire Das from his position. It is very likely that he will never be able to do research again, and that's probably the way it should be given the extent of his fraud. Sadly, Das has not made things any easier by accusing university and department officials of racist prejudice. When you have to resort to such allegations in the face of massive evidence detailing your dishonesty, you only make your guilt seem more likely.

Ultimately this episode speaks as much about the culture of scientific research as it does about the transgressions of a particular researcher. We may not know for some time why Das felt like committing fraud on such a massive scale, but I suspect that the high-profile nature of anti-aging research and the funding that such research commands may have had at least something to do with it. In the last few years, resveratrol, caloric restriction and sirtuins have made it into the public discourse about science like few other topics. The possibility of harnessing all this data to solve the ultimate mystery of aging has ensured both sensationalist news items and eager funding agencies wanting to enable the next breakthrough. When you work in such high-profile fields, it is more tempting to fabricate your results to snare more funding. In this particular case, Das's work was deemed to be low-impact and peripheral to the field and so the damage might be negligible, but in someone else's hands it could well be extensive. The case of Jan Schon immediately comes to mind.

The other reason underlying Das's behavior might simply have been the complexities of biology. Das has been at the university since 1984 and got tenure in 1993, so it's curious why he decided to fabricate results in the last decade or so. I am quite ready to believe that his work on the complex effects of resveratrol on disease may have run into roadblocks, prompting him to start making up results that he wanted to see but which he didn't. Most research these days and especially biomedical research is a complex game. In some ways we are trying to bite off more than we can chew. In such cases wishful thinking can dominate, and when expected results don't pan out because of the complexity of the system under consideration, it becomes easier to succumb to desperation and temptation. The resveratrol story may fit into this paradigm, with initial reports suggesting a tantalizingly simple connection between the drugs and aging and more recent reports questioning this connection. Yet again, nature is not just more complex than we imagine, but it is more complex than we can imagine.

The only remedy for avoiding such debacles may be more acute vigilance, self-policing and an honest willingness to accept failures. And some modesty before nature may be in order here.

Other coverage: Derek Lowe (1, 2), San Francisco Chronicle

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What is your favorite deep, elegant or beautiful explanation (in chemistry)?

noreply@blogger.com (Wavefunction) — Mon, 16 Jan 2012 15:50:00 +0000

Over at Edge, they have a survey asking leading scientists, thinkers and writers about what they think is their favorite "elegant, deep or beautiful explanation". This is meant to be a very general question, not even limited to science and includes responses from people as diverse as the economist Richard Thaler, complexity theorist Stuart Kauffman and Stewart Brand (founder of the Whole Earth Catalog). The answers include ideas, explanations, experiments and entities as general and diverse as the scientific method, genes, Pascal's wager, bounded rationality, relativity and the limits of intuition.

The explanations run across the gamut of the sciences and the humanities including physics, biology, economics, neuroscience, politics and business. But conspicuously absent is chemistry, except for a few peripheral references like Charles Simonyi's listing of Besicovitch's theory of atomic forces. And this is in spite of our friend Derek Lowe of "In the Pipeline" being included in this august list. I was gratified to see a chemist being asked for his opinion, and was somewhat disappointed that Derek's favorite explanation was not chemical (his favorite is the rather deceptively simple notion of "freefall"). I of course don't blame Derek for his choice since there is no law dictating that a chemist's favorite explanation should be from chemistry just because he or she is a chemist. My own favorite beautiful explanation is probably Cantor's notion of multiple infinities.

But I did regret the striking omission of chemistry from the list. Sometime back I had a whole post on elegance in chemistry. And I certainly don't want people to think that deep and elegant explanations are limited to physics and biology, because they are not. Chemistry may not boast of profound philosophical explanatory frameworks like the Big Bang or evolution by natural selection. But it makes up for this fact by creating paradigms that directly touch the lives of millions of human beings in ways much more palpable than the Big Bang and evolution. So I thought I would add my own modest thoughts on my favorite deep idea in chemistry.

There's actually a few things at the top of my list; you certainly don't have to think hard to come up with several foundational chemical ideas. But if you really asked for my absolute favorite deep and elegant explanation, it is the shared-electron chemical bond. That's it. Right there is the simple concept that is at the heart of the material world, a concept that if you think about it has had a staggering impact on our quality of life, our relationships with other nations, our notion of prosperity itself. Chemical bonds as manifested in the foundations of modern civilization have certainly contributed as much to life, liberty and the pursuit of happiness as any scientific idea.

The idea itself as formulated by the great Gilbert Newton Lewis and comprehensible to any high-school student is simplicity incarnated; atoms combine into molecules and form a bond when electrons are shared. Everything that comes after the stating of this fact, important as it is, is details. All the quantum chemical wizardry, the thinking-in-orbitals, the great Gaussian simplification, it's after this basic groundwork has been laid. Heitler and London, Pauling, Slater, Mulliken, Pople, all of them made critical contributions to chemical bonding, but they all stood on Lewis's shoulders and built up from his landscape of the shared electron chemical bond.

Given the absolutely foundational role that the chemical bond plays in the thinking of chemists, it may be both ironic and a tad disturbing that chemists still cannot completely agree on the precise definition of every molecular bond out there. But that's not because the basic framework underlying bonding is uncertain. Part of the reason is simply because there is no such thing as "the" chemical bond. The bonding zoo sports a bewildering variety of animals, from the upstanding "normal" chemical bonds in, say the hydrogen or methane molecules, to the (literally) ready-to-snap pressure cooker entities in strained organic compounds, from the wily, shape-shifting bonds between metals and organic compounds to the ephemeral but biologically vital hydrogen bonds. Although the basic theory of the chemical bond is securely in place, it's going to take some time to craft a net wide and yet rigorous enough to snare the unruly and colorful creatures dotting the chemical landscape.

Now physicists may try to appropriate the chemical bond as their own, but they are out of luck. No explanation based purely on physics can truly impart a feel for the sheer diversity of bonds quoted above and their context-specific personalities. Just one bond serves to create a nightmare for purely reductionist approaches to defining chemical bonding- the hydrogen bond. Last year chemists convened at a meeting with the express purpose of tweaking their description of this all-important biological mediator, the glue that holds life together. Several questions were bandied about, but none more important than the very definition of a hydrogen bond. The problem was simple; hydrogen bonds can be weak or strong, sometimes so weak as to strain the definition of a bond, sometimes strong enough to suspiciously qualify as a covalent bond. How much of hydrogen bonding is electrostatic and how much is covalent? Is "bond" even the right term, or would "bridge" be more accurate? How do you define hydrogen bonds to metals? A consensus was finally reached on a new definition, but not even Linus Pauling could say that the definition would hold for all of eternity. Defining a hydrogen bond would give every physicist out there a run for his money. I find the concept of the chemical bond so enticing and elegant partly because even a single kind of bond like the hydrogen bond can hide a richly textured world of possibilities lurking behind its surface.

So there it is, why the concept of the chemical bond is my favorite idea, certainly in chemistry. It is deep because it underlies the making of the material universe, explaining the stuff that everything from crab shells to the Crab Nebula is made of. It is elegant because of the virtually unlimited amount of explanatory power that it hides in a simple statement of definition. And it is beautiful because of the sheer diversity of materials and structures that are created from a simple law of attraction. A lot of the thinkers in the Edge survey quoted evolution as their favorite deep idea. It certainly is beautiful. But Darwin could well have slightly paraphrased his words to apply to Lewis's shared-electron chemical bond:

"There is grandeur in this view of the material world, with its several powers, having been originally breathed into a single bond; and that, whilst this planet has gone cycling on according to the fixed law of gravity, from so simple a bond endless forms most beautiful and most wonderful have been, and are being, evolved."

Molecular modeling: How far can physics take us?

noreply@blogger.com (Wavefunction) — Fri, 06 Jan 2012 01:30:00 +0000

Of all the scientists writing about modeling and simulation in drug discovery in the last decade or so, I have found Anthony Nicholls of OpenEye Scientific Software to be one of the most insightful. Not only has he written important papers emphasizing the role of rigorous statistics in generating and communicating modeling results, but he has also been a relentless proponent of the need for rigorous, unglamorous but essential experimental data to validate modeling protocols. In the phalanx of modelers pointing to a better future for their field, Anthony has been one of the torchbearers. I usually pay close attention when he writes so I think it's worth noting what he has to say in a recent article titled "The character of molecular modeling".

He starts by asking what the real advances in the field have been in the past 25 years and by observing an apparently rather disconcerting fact about modeling and especially structure-based modeling - successes are still mainly anecdotal. He expresses his disappointment while noting that most of the successful results in modeling are still mainly of the "find protein pocket, fill pocket" type. The chief role of the crystallographer, it seems, is to supply pockets that the computational chemist can then fill. The problem according to Anthony is that chemists are not "abstracting principles of wide applicability; they are recognizing domains of expertise".

At this point let me interject and say that while Anthony's gloomy prognosis might be true, it's also true that "find pocket, fill pocket" (or "find pocket, kill pocket" if you are in a hunter-gatherer mood) campaigns are not as straightforward as we think. There can be unexpected effects on both protein conformation and ligand conformation, similar to the "activity cliffs" witnessed by medicinal chemists. Even if the binding orientation of the ligand stays constant upon small changes, the distribution of solution conformations of the modified ligand is likely quite different, leading to differing energetic penalties that the protein has to pay for binding. I am sure I am not alone in saying that small changes in ligand structure leading to changes in binding affinity enforced by ligand strain and conformation are uncomfortably frequent. But there's another dimension to the "find pocket, fill pocket" campaign; it can actually be quite satisfying to suggest changes to a medicinal chemist for filling the pocket that are borne out by further crystallography. Finding pockets may generate anecdotes, but chemistry is a more anecdotal science than say physics, and chemists often revel in these little successes and failures. Chemists are more frogs than eagles.

But the real sticking point for Anthony is not really the anecdotal success of structure-based modeling but the lack of general physics-based principles and laws for doing molecular modeling. Docking is an example. In the last several years there have been many attempts to use physics-based "scoring functions" - essentially ways to sum up different protein-ligand interactions to a number - for calculating the binding affinity of a ligand. Programs for docking have evolved to a stage where ligands can be docked in the correct orientation with a roughly 30% success rate, depending on how similar the docked ligands are to a reference co-crystallized ligand. But the truth of the matter is that we still fail miserably when trying to dock an arbitrary ligand to an arbitrary protein in an arbitrary conformation. And of course, we are light years away from predicting free energies of binding for the general case. There have been cases in which physics in the form of electrostatics and quantum mechanics (more on this later) has significantly accelerated the search for similar molecules, but the promised land still seems far.

Does this failure reflect an absence of general principles of physics for computing protein-ligand interactions? Paraphrasing Rutherford (not Niels Bohr), in the next few decades will we do more physics or simply collect more stamps? Is this concern even warranted? To some extent, yes. It would certainly be very satisfying to have a general explanatory framework, a pool of more or less universal laws that explained the wide variety of protein-ligand complexes as completely as Newton's laws explain the behavior of an astonishingly diverse set of particle interactions in the classical world. Curiously, such a general framework does exist in the form of statistical mechanics and quantum mechanics. In theory, both these disciplines encompass the binding of every single protein to every single drug. So does that mean we can look forward to a time when every modeler can "abstract these principles of wide applicability" and use them to solve the particular case of his or her protein and ligand?

Here is where I part ways with Anthony at least partly. The reason in my mind is not too hard to discern. Think about how far we have come in explaining protein-ligand binding using the rather extensive developments in either quantum or statistical mechanics over the past five decades. The answer is, not as far as we would have liked to. While we have indeed made great advances in understanding the basic thermodynamics of protein-ligand binding, we have not been very successful in incorporating these principles into predictive computational models. Why so? For the same reason that we have not been successful in using physics to explain "all of chemistry", in Paul Dirac's words. Quantum mechanics has been applied to chemistry for fifty years and exponentially increasing computational power has significantly furthered its application, but even now, for most practical systems chemists use a variety of empirical models to understand and predict. That's partly because most real systems are too complex for the direct use of quantum mechanics, and an imperfectly understood protein and ligand immersed in an imperfectly understood solvent certainly belong to this category. It's also because we are still far from calculating things like entropy and being able to model the differential behavior of water at interfaces and in the bulk.

But even more importantly, physics may not solve our problems because chemists need to abstract general principles at the level of chemistry to ply their trade. Thus, in expressing doubts about the utility of general physics-based principles, I am appealing to the strong sense of non-reductionism that permeates chemistry and separates it from physics. The same principle applies to biology and I have written about this often. Principles drawn from physics have always been very useful in gaining insights into molecular interactions and they will continue to be an essential part of the mix. But unlike Anthony, I see a far smaller role that pure physics can truly make in enabling a general, practical predictive approach to modeling that's "chemical" enough to be widely used by chemists.

So are there cases in which physics can make a contribution? Here I actually do agree with Anthony when he mentions two areas where physics really promises to have a substantial impact, both conceptually and practically. The first is crystal structure prediction for organic molecules which is a notoriously fickle problem (a measure of the difficulty can be gleaned by the fact that even the simple benzene can crystallize in more than 30 different geometries), essentially one of being able to predict fine energy differences between almost equienergetic arrangements. Yet I see this problem as one of the more reductionist problems in chemistry, and as Anthony notes, it is conceivable that it will yield to physics-based approaches in the near future.

The other problem is one of the holy grails of chemistry and biology - protein structure prediction. In various guises, the last few years have seen a startlingly impressive set of cases where protein structures of small and (some) medium-sized proteins were predicted with atomic level accuracy. Protein structure prediction has to overcome the twin challenges of sampling and energy estimation that are a mainstay of almost every other modeling method. In this case Anthony thinks that we will have to get the physics right to address this issue.

But we have to be careful to distinguish between two cases here. The first case is where we get the right structure even if we have no idea how we got there. This is the field of empirical (non-physics based) protein fold prediction and the biggest success in this area has been the ROSETTA suite of programs. ROSETTA has definitely turned heads within the community by its ability to generate accurate structures for hundreds of proteins, but the big drawback of the approach is that it only generates the end result. Curiously Anthony does not mention ROSETTA, but I am also surprised that he does not mention in detail another significant development that does fit into the physics-based paradigm. This is the molecular dynamics approach developed by David Shaw, Vijay Pande and others. Unlike ROSETTA, MD can actually shed light on the process leading to a correct structure, although the details of the process are subject to errors, most notably in the force fields that underlie the simulation. It's quite clear that with all their limitations, ROSETTA and MD have been the biggest contributors to successful protein folding simulations over the last decade.

And yet as Anthony rightly says, their success seems almost like a miracle. This becomes clear when we realize that even now we have trouble predicting something as simple as the solvation energy of a simple organic molecule or the interaction energy of two simple molecules using even sophisticated quantum mechanics calculations. If our ability to predict even such simple scenarios is dismal, how on earth are we getting the structures of all those complex proteins right? The answer deserves as much scrutiny as the solution to these problems, scrutiny that is severely lacking. Anthony's answer (and mine) is "cancellation of errors along with a need to calculate only relative, not absolute, energies" (it's well known that force fields are virtually worthless for the calculation of absolute energies). It still strains my mind to think that these two factors could contribute to so many successful predictions published in the likes of Nature and Science. Cancellation of errors was partly made famous by Enrico Fermi. If that's really what's happening in all these cases, then the entire field needs to start celebrating Fermi as their guardian angel.

Ultimately, there is no doubt that advances will continue to be made with increasing computational firepower, but the foundations of the field will stay brittle unless these fundamental issues are addressed. Anthony ends with something he has been doing for a long time now - appealing to experimentalists, industry and government to contribute a small part of their funds to the kind of basic experiments that can further the field of modeling. This especially involves experiments that can refute an idea, a philosophy that has been dominant in the practice of science since its modern conception but one which seems to be unusually neglected in drug discovery because of the emphasis on positive data gathering. Science has always progressed by the testing of ideas that have no immediate practical bearing, except that they perform the invaluable function of making future scientific research worthwhile. It would be fundamentally unscientific if such ideas are not supported. Anthony puts it well:

"The simple commitment to spend a small percentage of the science budget at the NIH or at pharmaceutical companies on nontranslational work, providing support for the small cabals of scientists actually interested in making fundamental progress would be enormous. Reestablishing the contact between theorists and experimentalists, the publishing of high quality data, conferences devoted to the actual testing of ideas—in 25 years we might hope molecular modeling could become a real scientific discipline."

Is it too early to ask for automation in lab safety?

noreply@blogger.com (Wavefunction) — Fri, 30 Dec 2011 17:33:00 +0000

Let's face it. Graduate students do work occasionally without lab coats. Sometimes you have to do a quick procedure and sometimes you can just get bored of wearing the coat. I don't recall a time when I have walked into a lab and not seen someone not wearing a coat. Most of these individuals were hopefully not working with dangerous reagents, but the point is that it's human nature to be occasionally negligent. While Sheri Sangji's not wearing a coat was a breach of basic safety protocol (although it's not clear how far the coat would have gone in lessening her injuries), she was no more guilty of violating this safety norm than many other graduate students around the world. It's certainly not right but I don't think the practice is going to disappear anytime soon.

Then there's the question of Prof. Harran's responsibility in enforcing safety standards which I have talked about in a previous post. Even if a professor constantly monitors lab coat violation, he is naturally not going to patrol his lab 24 hours a day during each and every experiment. In addition, even the most diligent professor who is straggled with multiple responsibilities (research, grant writing, teaching, mentoring, administrative work) is going to have an occasional lapse of safety.

The reason I was mulling over these two points was to remind myself of something we all know about; you can't fight human nature. And since human nature is not going to go away, it seems odd to depend purely on human beings to enforce safety standards in a lab. The obvious question that then came to my mind was; why aren't automated systems employed for enforcing at least some safety standards in chemistry laboratories? Why do we still mainly depend on human beings to make sure everyone obeys safety protocols?

This seems especially pertinent if we think of the many other industries and research environments where technology reduces our dependence on the whims and uncertainties of human nature. In industries ranging from the nuclear to the aerospace to the automobile industries, multiple primary and backup systems are in place to kick in during those sadly too frequent occasions when human negligence, error and indifference endanger property and lives. It seems odd to me that in an age when technology is extensively used to deploy automated safety systems in multiple spheres of life, we are still depending on humans to constantly enforce basic and essential safety rules like the wearing of lab coats, glasses and gloves.

Automated systems would of course not protect lab personnel against every accident and it goes without saying that human review would still be necessary, but I don't see why relatively simple systems could not lead to a safer chemical workplace.

Two such simple systems come to my mind. In most current cars, you can open the door only when your keys are closer to it than a certain distance. There is clearly a proximity sensor in the car which detects the keys. A similar system could be used in a lab that would allow a chemical hood to function only when it detects a lab coat. A simple RFID tag embedded in the coat would activate a complementary sensor in the hood. So unless the person who approaches the hood has his or her lab coat on all the time, the hood would essentially go into lock down mode or at least activate an annoying alarm that can be turned off only when the coat is worn (similar to the beeping that results from not wearing a seat belt in a car). The proximity sensor system could hinge on RFID, infrared or optical sensors and the exact details would be dictated by cost, efficiency and mass deployment. But the technology certainly seems to exist and it should not be too expensive or difficult to install such a system in place. The system could of course also detect other safety gear like lab goggles and gloves.

As useful as such techniques for detecting lab gear could be, they would not stop an accident after it happens. A comprehensive automated safety framework needs provisions for both prevention and cure. These systems should especially be viable in the presence of a human being who is unable to take care of himself or herself. Although interfering with a runaway accident after it happens is difficult, there could be a few options. In case of Sheri Sangji, a violently flammable chemical spilled on her lab coat and caught fire, spreading to her sweater. For the next few minutes there was an intense cluster of "hot spots" in the room which she worked in. One could have a fairly simple infrared scanning system which sweeps the room and activates an alarm when it detects such a swarm of high-temperature spots, especially when they are moving. Implementing the condition of motion could help prevent the system from being set off by false positives such as hot flasks and beakers.

These are just a few thoughts. Naturally any such system would have to be refined, tuned and tested and would be subject to emergency human overrides. But it just seems to me that we should be able to implement at least a few robust automated safety systems for preventing lab tragedies when we take their existence in virtually every other aspect of our modern industrial lifestyle for granted.

Professorial oversight, availability bias and the Sheri Sangji case

noreply@blogger.com (Wavefunction) — Wed, 28 Dec 2011 23:56:00 +0000

There's a new twist on the tragic case of Sheri Sangji, a UCLA student working in the lab of Prof. Patrick Harran who died from burns resulting from her handling of tert-Butyllithium, a notoriously and violently flammable substance which has to be handled with the utmost case. This is a horrific example that reminds us of the perpetual and always potentially fatal dangers lurking in every corner of the lab. Our heart goes out to the Sangji family whose rage, grief and frustration are understandable.

But the issue gets murkier. It seems that criminal charges have now been brought against UCLA and Harran by the Los Angeles district attorney. Harran is going to surrender to the authorities when he comes back from what I am assuming is a holiday vacation.

I feel extremely doubtful that the charges would hold up, but I also think that these kinds of debates are generally conducive to maintaining a healthy safety culture. Something about Jefferson's quote about the price of democracy being eternal vigilance comes to mind. It's clear that the lab in which Sangji was working was found to violate safety standards, but I am sure that's probably the case for several other labs across the country. This does not excuse the lack of standards, but it makes one wonder if focusing on such stories leads to the typical situation where certain "rare events" seem to dictate our feelings and opinions on a more general issue because of their graphic nature and the emphasis that the media puts on them. More on this later.

The other reason the charges may not hold up is that the culpability of the institution and Prof. Harran, if it exists at all, is likely to be very fuzzy. Unfortunately Sangji was not wearing a lab coat, and I am guessing it would be very difficult, if not impossible, to find demonstrable evidence that she had not been told to constantly use this most basic of lab safety measures. In addition she was also wearing a sweater and was syringing out a rather large amount of the inflammable substance, and the prosecution will also have to find evidence that she was not warned against either of those practices. In addition Sangji was considered fairly well-versed in the hazards of chemical experimentation so she was expected to have known about basic lab protocols. None of this is to lay blame at her feet, but only to note that it muddies the legal aspect of the case.

But I think the greater issue deals with the amount of involvement that a professor should have in the safety of his students. I don't know of any faculty member (although I am sure there are a few) who schedules individual sessions with each of his or her students and instructs them in the minutiae of lab safety. Nor does every professor step into lab several times a day looking for every safety violation and I don't think it's realistic to expect them to. I don't know if it's legally required for any professor to specifically warn their students about the danger of handling t-BuLi. At most professors should periodically (but regularly) remind their students about safety standards, loudly denounce blatant violations and then expect senior graduate students and postdocs to enforce standards. If Prof. Harran is indeed guilty of transgressing safety norms, then it seems that the senior students and postdocs in his lab should share this blame even more. I am not saying either of them should, but it's hard for me to see how the responsibility for safety violations should fall squarely on the shoulders of Prof. Harran and not on his lab personnel.

Coming back to the highlighting of the issue as an indictment of lab safety, I am reminded of the always controversial issue of safety in the nuclear industry. We have constantly lived in times when the graphic, the dramatic and the most sensationalized events have dictated our opinions, no matter how rare they are. In case of the nuclear industry for instance, the occasional Chernobyl and Fukushima color our opinions of nuclear power for decades, even if thousands of nuclear reactors have been humming along for decades without major incidents. The safety record in the nuclear industry is way better than that in the chemical, coal or automobile industries, yet the nuclear industry gets an outrageous share of our derision and disapproval. The result? The distinct censure and under-utilization of nuclear power which has held its widespread deployment back for decades.

An undue focus on the perils of chemical research may similarly detract from the decades of productive chemical research and the education of promising chemists that has largely transpired without incident. I fear that bringing charges against UCLA and Prof. Harran will set a troubling precedent and may result in similar under-utilization of the benefits of chemical education. For instance I can see professors at other institutions holding back and being more reluctant to let undergraduates or technical assistants indulge in research involving common but potentially dangerous chemicals. We are already seeing the consequences of a disproportionate preoccupation with chemical safety in the lack of interesting experiments in chemistry sets for teenagers (presumably because most interesting experiments involve dangerous chemicals). Students themselves might be less eager to burnish their research credentials by working in a chemistry lab. Universities may enforce stricter rules restricting the availability of research opportunities for undergraduates on the grounds that they may lead to potential accidents.

Finally, the undue emphasis on safety and the resulting media circus may simply make worse what has been a perpetual headache for the proponents of chemistry - the public image of the discipline. The media has always been adept at exploiting a version of availability bias, a phenomenon delineated by psychologists Daniel Kahneman and Amos Tversky in which our perceptions of a phenomenon are shaped by what's easily remembered rather than what's the norm. One can be assured that the media will be far more eager to write about the occasional chemical tragedy than the countless number of times when the system actually worked and nobody was harmed. The Sangji case and the current charges against UCLA will do nothing to quell public fears about the dangers of chemical research. The public perception of working in a chemical laboratory will relate to what's "newsworthy" (deaths and fires) rather than what the facts are (thousands of safe experiments resulting in no harm). Ironically these dangers have always been there, but the countless number of times when they have caused no harm and in fact have led to great advances has gone unheeded.

Of course, none of this backlash may occur and certainly none of the ensuing discussion implies that we should be lackadaisical in the implementation and review of safety standards. Safety reviews should be second nature to lab personnel irrespective of tragedies like this one. Whenever they can professors should always remind every student under their wing of the ever-present dangers lurking in their laboratory. Senior graduate students and postdocs should consider the enforcing of lab safety their special responsibility since only a palpable safety-conscious culture could lead to an unconscious regard for safety. And universities should spare no effort in carrying out regular safety assessments.

But none of this should distract us from the very real benefits that chemical research and education have brought to countless young researchers whose time in the lab has inspired them to contribute to the advancement of chemical knowledge. It should not make us ignore the commendable tradition of chemical research in which professors and their students have carried out safe and illuminating chemical experiments in the presence of thousands of potentially fatal chemicals. Yes, students in labs are surrounded by chemical perils. But so are most of us in virtually every sphere of life. In the face of risks we do what we have always done, assess the dangers and constantly review, revise and research. And carry on.

What happened to Sheri Sangji was a tragedy, and sadly a preventable one at that. Yet if we overstep our boundaries of response and reaction, Sangji will not be the only victim. The real tragedy will be the discipline of chemistry itself.

A Christmas message from Steve Jobs for our friends in pharma

noreply@blogger.com (Wavefunction) — Sun, 25 Dec 2011 18:48:00 +0000

I am at the end of Walter Isaacson's excellent biography of Steve Jobs and it's worth a read even if you think you know a lot about the man. Love him or hate him, it's hard to deny that Jobs was one of those who disturbed our universe in the last few decades. You can accuse him of a lot of things, but not of being a lackluster innovator or product designer.

The last chapter titled "Legacy" has a distillation of Jobs's words about innovation, creativity and the key to productive, sustainable companies. In that chapter I found this:

"I have my own theory about why decline happens at companies like IBM or Microsoft. The company does a great job, innovates and becomes a monopoly or close to it in some field, and then the quality of product becomes less important. The company starts valuing the great salesmen, because they're the ones who can move the needle on revenues, not the product engineers and designers. So the salespeople end up running the company. John Akers at IBM was a smart, eloquent, fantastic salesperson but he didn't know anything about product. The same thing happened at Xerox. When the sales guys run the company, the product guys don't matter so much, and a lot of them just turn off."

Jobs could be speaking about the modern pharmaceutical industry. There the "product designers" are the scientists of course. Although many factors have been responsible for the decline of innovation in modern pharma, one of the variables that strongly correlates is the replacement of product designers at the helm by salespeople and lawyers beginning roughly in the early 90s.

There's a profound lesson in there somewhere. Not that wishes come true, but it's Christmas, and while we don't have the freedom to innovate, hold a stable job and work on what really matters, we do have the freedom to wish. So with this generous dose of wishful thinking, I wish you all a Merry Christmas.

Unruly beasts in the jungle of molecular modeling

noreply@blogger.com (Wavefunction) — Thu, 22 Dec 2011 22:42:00 +0000

The Journal of Computer-Aided Molecular Design is having a smorgasbord of accomplished modelers reflecting upon the state and future of modeling in drug discovery research and I would definitely recommend anyone - and especially experimentalists - interested in the role of modeling to take a look at the articles. Many of the articles are extremely thoughtful and balanced and take a hard look at the lack of rigorous studies and results in the field; if there was ever a need to make journal articles freely available it was for these kinds, and it's a pity they aren't. But here's one that is open access, and it's by some researchers from Simulations Inc. who talk about three beasts (or in the authors' words, "Lions and tigers and bears, oh my!") in the field that are either unsolved or ignored or both.

1. Entropy: As they say, entropy, taxes and death (entropy) are the three constant things in life. In modeling both small molecules and proteins, entropy has always been the elephant in the room, blithely ignored in most simulations. At the beginning there was no entropy. Early modeling programs then started extracting a rough entropic penalty for freezing certain bonds in the molecule. While this approximated the loss of ligand entropy in binding, it did nothing to take care of the conformational entropy loss that resulted in the compression of a panoply of diverse conformations in solution to a single bound conformation.

But we were just getting started. A very large part of the entropy of binding a ligand by a protein comes from the displacement of water molecules in the active site, essentially their liberation from being constrained prisoners of the protein to free-floating entities in the bulk. A significant advance in trying to take this factor into account was an approach that explicitly and dynamically calculated the enthalpy, entropy and therefore the free energy of bound waters in proteins. We have now reached the point where we can at least think of doing a reasonable calculation on such water molecules. But water molecules are often ill-localized in protein crystal structures because of low-resolution, inadequate refinement and other reasons. It's not easy to perform such calculations for arbitrary proteins without crystal structures.

However, a large piece of the puzzle that's still missing is the entropy of the protein which is extremely difficult to calculate on many fronts. Firstly, the dynamics of the protein is often not captured by a static x-ray structure so any attempts to calculate protein entropy in the presence and absence of ligands would have to shake the protein around. Currently the favored process for doing this is molecular dynamics (MD) which suffers from its own problems, most notably the accuracy of what's under the hood- namely force fields. Secondly, even if we can calculate the total entropy changes, what we really need to know is how the entropy is distributed between various modes since only some of these modes are affected upon ligand binding. An example of the kind of situation in which such details would be important is the case of slow, tight-binding inhibitors illustrated in the paper. The example is of two different prostaglandin synthase inhibitors which demonstrate almost identical binding orientations in the crystal structure. Yet one is a weak binding inhibitor which dissociates rapidly and the other is slow, tight-binding. Only a dynamic treatment of entropy can explain such differences, and we are still quite far from being able to do this in the general case.

2. Uncertainty: Out of all the hurdles facing the successful application and development of modeling in any field, this might be the most fundamental. To reiterate, almost every kind of modeling starts by using a training set of molecules for which the data is known and then proceeds to apply the results from this training set to a test set for which the results are unknown. Successful modeling hinges on the expectation that the data in the test set is sufficiently similar to that in the training set. But problems abound. For one thing, similarity is the eye of the beholder and what seems to be a reasonable criterion for assuming similarity may turn out to be irrelevant in the real world. Secondly, overfitting is a constant issue and results that look perfect for the training set can fail abysmally on the test set.

But as the article notes, the problems go further and the devil's in the details. Modeling studies very rarely try to quantify the exact differences between the two sets and the error resulting from that difference. What's needed is an estimate of predictive uncertainty for single data points, something which is virtually non-existent. The article notes the seemingly obvious but often ignored fact when it says that "there must be something that distinguishes a new candidate compound from the molecules in the training set". This 'something' will often be a function of the data that was ignored when fitting the model to the training set. Outliers which were thrown out because they were...outliers might return with a vengeance in the form of a new set of compounds that are enriched in their particular properties which were ignored.

But more fundamentally, the very nature of the model used to fit the training set may be severely compromised. In its simplest incarnation for instance, linear regression may be used to fit data points to a set of relationships that are inherently non-linear. In addition, descriptors (such as molecular properties supposedly related to biological activity) may not be independent. As the paper notes, "The tools are inadequate when the model is non-linear or the descriptors are correlated, and one of these conditions always holds when drug responses and biological activity are involved". This problem penetrates into every level of drug discovery modeling, from basic molecular level QSAR to higher-level clinical or toxicological modeling. Only a judicious and high-quality application of statistics, constant validation, and a willingness to wait (for publication, press releases etc.) before the entire analysis is available will preclude erroneous results from seeing the light of day.

3. Data Curation: This is an issue that should be of enormous interest to not just modelers but to all kinds of chemical and biological scientists concerned about information accuracy. The well-known principle of Garbage-In Garbage Out (GIGO) is at work here. The bottom line is that there is an enormous amount of chemical data on the internet that is flawed. For instance there are cases where incorrect structures were inferred from correct names of compounds:

"The structure of gallamine triethiodide is a good illustrative example where many major databases ended up containing the same mistaken datum. Until mid-2011, anyone relying on an internet search would have erroneously concluded that gallamine triethiodide is a tribasic amine. The error resulted from mis-parsing the common name at some point as meaning that the compound is a salt of gallamine and ‘‘ethiodidic acid,’’ identifying gallamine as the active component and retrieving the relevant structure. In fact, gallamine triethiodide is what you get when you react gallamine with three equivalents of ethyl iodide"

So gallamine triethiodide is the triply protonated salt, not the tribasic amine. Assuming otherwise can only lead to chemical mutilation and death. And this case is hardly unique. An equally common problem is simply assigning the wrong ionization state for chemical compounds as illustrated at the beginning of the post. I have already mentioned this as a rookie mistake, but nobody is immune to it. It should hardly be mentioned that any attempt to model an incorrect structure will result in completely wrong results. The bigger problem of course is when the results seem right and prevent us from locating the error; for example, docking an incorrectly positively charged structure into a negative binding site will result in very promising but completely spurious results.

It's hard to see how exactly the entire modeling community can rally together, collectively rectify these errors and establish a common and inviolable standard for performing studies and communicating their results. Until then all we can do is point out the pitfalls, the possibilities, the promises and the perils.

Clark, R., & Waldman, M. (2011). Lions and tigers and bears, oh my! Three barriers to progress in computer-aided molecular design Journal of Computer-Aided Molecular Design DOI: 10.1007/s10822-011-9504-3

On reproducibility in modeling

noreply@blogger.com (Wavefunction) — Tue, 13 Dec 2011 15:36:00 +0000

A recent issue of Science has an article discussing an issue that has been a constant headache for anyone involved with any kind of modeling in drug discovery - the lack of reproducibility in computational science. The author Roger Peng who is a biostatistician at Johns Hopkins talks about modeling standards in general but I think many of his caveats could apply to drug discovery modeling. The problem has been recognized for a few years now but there have been very few concerted efforts to address it.

An old anecdote from my graduate advisor's research drives the point home. He wanted to replicate a protein-ligand docking study done with a compound so he contacted the scientist who had performed the study and processed the protein and ligand according to the former's protocol. He appropriately adjusted the parameters and ran the experiment. To his surprise he got a very different result. He repeated the protocol several times but consistently saw the wrong result. Finally he called up the original researcher. The two went over the protocol a few times and finally realized that the problem lay in a minor but overlooked detail - the two scientists were using slightly different versions of the modeling software. This wasn't even a new version, just an update, but for some reason it was enough to significantly change the results.

These and other problems dot the landscape of modeling in drug discovery. The biggest problem to begin with is of course the sheer lack of reporting of details in modeling studies. I have seen more than my share of papers where the authors find it enough to simply state the name of the software used for modeling. No mention of parameters, versions, inputs, "pre-processing" steps, hardware, operating system, computer time or "expert" tweaking. The latter factor is crucial and I will come back to it. In any case, it's quite obvious that no modeling study can be reproducible without these details. Ironically, the same process that made modeling more accessible to the experimental masses has also encouraged the reporting of incomplete results; the incarnation of simulation as black-box technology has inspired experimentalists to widely use it, but on the flip side it has also discouraged many from being concerned about communicating under-the-hood details.

A related problem is the lack of objective statistical validation in reporting modeling results, a very important topic that has been highlighted recently. Even when protocols are supposedly accurately described, the absence of error bars or statistical variation means that one can get a different result even if the original recipe is meticulously followed. Even simple things like docking runs can give slightly different numbers on the same system, so it's important to be mindful of variation in the results along with their probable causes. Feynman talked about the irreproducibility of individual experiments in quantum mechanics, and while it's not quite that bad in modeling, it's still not irrelevant.

This brings us to one of those important but often unquantifiable factors in successful modeling campaigns - the role of expert knowledge and intuition. Since modeling is still an inexact science (and will probably remain so for the foreseeable future), intuition, gut feelings and a "feel" for the particular system under consideration based on experience can often be an important part of massaging the protocol to deliver the desired results. At least in some cases these intangibles are captured in any number of little tweaks, from constraining the geometry of certain parts of a molecule based on past knowledge to suddenly using a previously unexpected technique to improve the clarity of the data. A lot of this is never reported in papers and some of it probably can't be. But is there a way to capture and communicate at least the tangible part of this kind of thinking?

The paper alludes to a possible simple solution and this solution will have to be implemented by journals. Any modeling protocol generates a log file which can be easily interpreted by the relevant program. In case of some modeling software like Schrodinger, there's also a script that records every step in a format comprehensible to the program. Almost any little tweak that you make is usually recorded in these files or scripts. A log file is more accurate than an English language description at documenting concrete steps. One can imagine a generic log file- generating program which can record the steps across different modeling programs. This kind of venture will need collaboration between different software companies but it could be very useful in providing a single log file that captures as much of both the tangible and intangible thought processes of the modeler as possible. Journals could insist that authors upload these log files and make them available to the community.

Ultimately it's journals which can play the biggest role in the implementation of rigorous and useful modeling standards. In the Science article the author describes a very useful system of communicating modeling results used by the journal Biostatistics. Under this system authors doing simulation can request a "reproducibility review" in which one of the associate editors runs the protocols using the code supplied by the authors. Papers which pass this test are clearly flagged as "R" - reviewed for reproducibility. At the very least, this system gives readers a way to distinguish rigorously validated papers from others so that they know which ones to trust more. You would think that there would be backlash against the system from those who don't want to explicitly display the lack of verification of their protocols, but the fact that it's working seems to indicate its value to the community at large.

Unfortunately in case of drug discovery, any such system will have to deal with the problem of proprietary data. There are several papers without such data which could also benefit from this system, but there can be ways to handle proprietary data. Even proprietary data can be amenable to partial reproducibility. In a typical example for instance, molecular structures which are proprietary could be encoded into special organization-specific formats that are hard to decode (an example would be formats used by OpenEye or Rosetta). One could still run a set of modeling protocols on this cryptic data set and generate statistics without revealing the identity of the structures. Naturally there will have to be safeguards against the misuse of any such evaluation but it's hard to see why they would be difficult to institute.

Finally, it's only a community-wide effort equally comprised of industry and academia which can lead to the validation and use of successful modeling protocols. The article suggests creating a kind of "CodeMed Central" repository akin to PubMed Central, and I think modeling could greatly benefit from such a central data source. Code for successful protocols in virtual screening or homology modeling or molecular dynamics or what have you can be uploaded to a site (along with the log files of course). Not only would these protocols be used to verify their reproducibility, but they could also be used to practically aid data extraction from similar systems. The community as a whole would benefit.

Before there's any data generation or sharing, before there's any drawing of conclusions, before there's any advancement of scientific knowledge, there's reproducibility, a scientific virtue that has guided every field of science since its modern origin. Sadly this virtue has been neglected in modeling, so it's about time that we pay more attention to it.

Peng, R. (2011). Reproducible Research in Computational Science Science, 334 (6060), 1226-1227 DOI: 10.1126/science.1213847

Why drug design is like airplane design. And why it isn't.

noreply@blogger.com (Wavefunction) — Thu, 08 Dec 2011 01:50:00 +0000

Air travel constitutes the safest mode of travel in the world today. What is even more impressive is the way airplanes are designed by modeling and simulation, sometimes before the actual prototype is built. In fact simulation has been a mainstay in the aeronautical industry for a long time and what seems like a tremendously complex interaction of metal, plastic and the unpredictable movements of air flow can now be reasonably captured in a computer model.

In a recent paper, Walter Woltosz of Simulations Plus Inc. asks an interesting question: compared to the aeronautical industry where modeling has been applied to airplane design for decades, why has it taken so long for modeling to catch on in the pharmaceutical industry? In contrast to airplane design which is now a well-accepted and widely used tool, why is simulation of drugs and proteins still (relatively) in the doldrums? Much progress has surely been made in the field during the last thirty years or so, but modeling is nowhere as integrated in the drug discovery process as computational fluid dynamics is in the airplane design process.

Woltosz has an interesting perspective on the topic since he himself was involved in modeling the early Space Shuttles. As he recounts, what's interesting about modeling in the aeronautical field is that NASA was extensively using primitive 70s computers to do it even before they built the real thing. A lot of modeling in aeronautics involves figuring out the right sequence of movements an aircraft should take in order to keep itself from breaking apart. Some of it involves solving the Navier-Stokes equations that dictate the complicated air flow around the plane, some of it involves studying the structural and directional effects of different kinds of loads on materials used for construction. The system may seem complicated but as Woltosz tells it, simulation is now used ubiquitously in the industry to discard bad models and tweak good ones.

Compare that to the drug discovery field. The first simulations of pharmaceutically relevant systems started in the early 80s. Since then the field has progressed in fits and starts and while many advances have come in the last two decades, modeling approaches are not a seamless part of the process. Why the difference? Woltosz comes up with some intriguing reasons, some obvious and others more thought-provoking.

1. First and foremost of course, biological systems are vastly more complicated than aeronautical systems. Derek has already written at length about the fallacy of applying engineering analogies to drug discovery and I would definitely recommend his thoughts on the topic. In case of modeling, I have already mentioned that the modeling community is getting ahead of itself by trying to chew on more complexity than it can bite. Firstly you need to have a list of parts to simulate and we are still very much in the process of putting together this list. Secondly, having the list will tell us little about how the parts interact. Biological systems display complex feedback loops, non-linear signal-response features and functional "cliffs" where a small change in the input can lead to a big change in the output. As Woltosz notes, while aeronautical systems can also be complex, their inputs are much more well-defined.

But the real difference is that we can actually build an airplane to test our theories and simulations. The chemical analogy would be the synthesis of a complex molecule like a natural product to test the principles that went into planning its construction. In the golden age of organic synthesis, synthetic feats were undertaken for structure confirmation but also to validate our understanding of the principles of physical organic chemistry, conformational analysis and molecular reactivity. Even if we get to a point where we think we have a sound grounding of the principles governing the construction and workings of a cell, it's going to be a while before we can truly confirm those principles by building a working cell from scratch.

2. Another interesting point concerns the training of drug discovery researchers. Woltosz is probably right that engineers are much more of generalists than pharmaceutical scientists who are usually rigidly divided into synthetic chemists, biologists, pharmacologists, modelers, process engineers etc. The drawback of this compartmentalization is something I have experienced myself as a modeler; scientists from different disciplines can mistrust each other and downplay the value of other disciplines in the discovery of a new drug. This is in spite of the fact that drug discovery is an inherently complex and multidisciplinary process which can only benefit from an eclectic mix of backgrounds and approaches. A related problem is that some bench chemists, even those who respect modeling, want modeling to provide answers, but they don't want to run experiments (such as negative controls) which can advance the state of the field. They are reluctant to carry out the kind of basic measurements (such as measuring solvation energies of simple organic molecules) which would be enormously valuable in benchmarking modeling techniques. A lot of this is unfortunate since it's experimentalists themselves who are going to ultimately benefit from highly validated computational approaches.

There's another point which Woltosz does not mention but which I think is quite important. Unlike chemists, engineers are usually more naturally inclined to learn programming and mathematical modeling. Most engineers I know know at least some programming. Even if they don't extensively write code they can still use Matlab or Mathematica, and this is independent of their specialty (mechanical, civil, electrical etc.). But you would be hard-pressed to find a synthetic organic chemist with programming skills. Also, since engineering is inherently a more mathematically oriented discipline, you would expect an engineer to be more open to exploring simulation even if he doesn't do it himself. It's more about the culture than anything else. That might explain the enthusiasm of early NASA engineers to plunge readily into simulation. The closest chemical analog to a NASA engineer would be a physical chemist, especially a mathematically inclined quantum chemist who may have used computational techniques even in the 70s, but how many quantum chemists (as compared to synthetic chemists for instance) work in the pharmaceutical industry? The lesson to be drawn here is that programming, simulation and better mathematical grounding need to be more widely integrated in the traditional education of chemists of all stripes, especially those inclined toward the life sciences.

3. The third point that Woltosz makes concerns the existence of a comprehensive knowledge base for validating modeling techniques and he thinks that a pretty good knowledge base exists today upon which we can build useful modeling tools. I am not so sure. Woltosz is mainly talking about physiological data and while that's certainly valuable, the problem exists even at much simpler levels. I would like to stress again that even simple physicochemical measurements of parameters such as solvation energies which can contribute to benchmarking modeling algorithms are largely missing, mainly because they are unglamorous and underfunded. On the bright side, there have been at least some areas like virtual screening where researchers have judiciously put together robust datasets for testing their methods. But there's a long way to go and much robust basic scientific experimental data needs to be gathered. Again, this can come about only if scientists from other fields recognize the potential long-term value that modeling can bring to drug discovery and contribute to its advancement.

Woltosz's analogy of drug design and airplane design also reminds me of something that Freeman Dyson once wrote about the history of flight. In "Imagined Worlds", Dyson described the whole history of flight as a process of Darwinian evolution in which many designs (and lives) were destroyed in the service of better ones. Perhaps we also need a merciless process of Darwinian evaluation in modeling. Some of this is already taking place in the field of protein modeling field with CASP and in protein-ligand modeling with SAMPL, but the fact remains that the drug discovery community as a whole (and not just modelers) will have to descend on the existing armamentarium of modeling tools and efficiently and ruthlessly evaluate them to pick out the ones that work. This has not happened yet.

Ultimately I like the fact that Woltosz is upbeat, and while the real benefits coming out of the process are uncertain, I definitely do agree with him that that we will know the answer only if the pharmaceutical industry makes a concerted effort to test, refine, retain and discard modeling approaches to drug design at all levels. That's the only way we will know what works. Sadly, one of the problems is that it will necessarily be a slow, long-term validation and development effort that will need the constant engagement of the global drug discovery community as a whole. It may be too much to ask in this era of quick profits and five-year exit strategies. On the other hand, we are all in this together, and we do want to have our chance at the drug discovery equivalent of the moon shot.

References:

Woltosz, W. (2011). If we designed airplanes like we design drugs… Journal of Computer-Aided Molecular Design DOI: 10.1007/s10822-011-9490-5

Truth and beauty in chemistry

noreply@blogger.com (Wavefunction) — Sat, 03 Dec 2011 20:38:00 +0000

The mathematician Hermann Weyl who made many diverse contributions to his discipline once made the startling assertion that whenever he had to choose between truth and beauty in his works, he usually chose beauty. Mathematicians and theoretical physicists are finely attuned to the notion of beauty. They certainly have history on their side; some of the greatest equations of physics and theories of mathematics sparkle with economy, elegance and surprising universality, qualities which make them beautiful. Like Weyl, Paul Dirac was famously known to extol beauty in his creations and once said that there is no place in the world for ugly mathematics; the equation named after him is a testament to his faith in the harmony of things.

How do you define and reconcile truth and beauty in chemistry? And is chemical truth chemical beauty? In chemistry the situation is trickier since chemistry much more than physics is an experimental science based on models rather than universal overarching theories. Chemists more than physicists revel in the details of their subject. Perhaps the succinct equations of thermodynamics come closest in chemistry to defining beauty, but physics can equally lay claim to these equations. Is there a quintessentially chemical notion of beauty and how does it relate to any definition of truth? Keats famously said, “Beauty is truth, truth beauty”. Is this true in chemistry?

At this point it’s fruitful to compare any description of beauty in chemistry with that in science in general. Although scientific beauty can be notoriously subjective, many explanatory scientific frameworks deemed beautiful seem to share certain qualities. Foremost among these qualities are universality and economy; more specifically, the ability to explain the creation of complexity from simplicity. In physics for instance, the Dirac equation is considered a supreme example of beauty since in half a dozen symbols it essentially explains all the properties of the electron and also unifies it with the special theory of relativity. In mathematics, a proof – Euclid’s proof of the infinitude of prime numbers for instance – is thought to be beautiful if it combines the qualities of economy, generality and surprise. Beauty is inherent in biology too. Darwin’s theory of natural selection is considered to be especially elegant because just like equations in physics or theorems in mathematics, it explains an extraordinary diversity of phenomena using a principle which can be stated in a few simple words.

It is not easy to find similar notions of beauty in chemistry, but if we look around carefully we do find examples, even if they may not sound as profound or universal as those in chemistry’s sister disciplines. Perhaps not surprisingly, many of these examples are most manifest in theories of chemical bonding, since these theories underlie all of chemistry in principle. I certainly saw elegance when I studied crystal field theory. Crystal field theory uses a few simple notions of the splitting of energies of molecular orbitals to explain the color, magnetic and electric properties of thousands of compounds. It’s not a quantitative framework and it’s not perfect, but it can be taught to a high school student and has ample qualitative explanatory power. Another minor chemical concept which impressed me with its sheer simplicity was VSEPR (Valence Shell Electron Pair Repulsion). VSEPR predicts the shape of simple molecules based on the number of their valence electrons. Working out the consequences for a molecule’s geometry using VSEPR is literally a back of the envelope exercise. It’s the kind of idea one may call “cute”, but in its own limited way it’s certainly elegant. Yet another paradigm from the field of bonding is Hückel theory. Hückel theory seeks to predict the orbital energies and properties of unsaturated molecules like ethylene and benzene. It will tell you for instance why tomatoes are red and what happens when a photon of light strikes your retina. Again, the theory is not as rigorous as some of the advanced methods that followed it, but for its simplicity it is both elegant and remarkable useful.

As an aside, anyone who wants to get an idea of beauty in chemistry should read Linus Pauling’s landmark book “The Nature of the Chemical Bond”. The volume still stands as the ultimate example of how an untold variety of phenomena and molecular structures can be understood through the application of a few simple, elegant rules. The rules are derived through a combination of empirical data and rigorous quantum mechanics calculations. This fact may immediately lead purist physicists to denounce any inkling of beauty in chemistry, but they would be wrong. Chemistry is not applied physics, and its unique mix of empiricism and theory constitutes its own set of explanatory fundamental principles, in every way as foundational as the Dirac equation or Einstein’s field equations are to physics.

This mention of the difference between empiricism and theory reminds me of a conversation I once had with a colleague that bears on our discussion of elegance and beauty in chemistry. We were arguing the merits of using molecular mechanics and quantum mechanics for calculating the properties of molecules. Molecular mechanics is a simple method that can give accurate results when parameterized using empirical experimental information. Quantum mechanics is a complicated method that gives rigorous, first-principle results without needing any parameterization. The question was, is quantum mechanics or molecular mechanics more “elegant”? Quantum mechanics does calculate everything from scratch and in principle is a perfect theory of chemistry, but for a truly rigorous and accurate calculation of a realistic molecular system, its equations can become complicated, unwieldy and can take up several pages. Molecular mechanics on the other hand can be represented using a few simple mathematical terms which can be scribbled on the back of a cocktail napkin. Unlike quantum mechanics, molecular mechanics calculations on well-parameterized molecules take a few minutes and can give results comparable in accuracy to those of its more rigorous counterpart. The method needs to be extensively parameterized of course, but one could argue that its simple representation makes it more “elegant” than quantum mechanics. In addition, on a practical basis one may not even need the accuracy of quantum mechanics for their research. Depending on the context and need, different degrees of accuracy may be sufficient for the chemical practitioner; for instance, calculation of relative energies may not be affected by a constant error in each of the calculations, but that of absolute energy will not tolerate such an error. The discussion makes it clear than, while definitions of elegance are beyond a point subjective and philosophical, in chemistry elegance can be defined as much by practical accessibility and convenience as by perfect theoretical frameworks and extreme rigor. In chemistry “truth” can be tantamount to “utility”. In this sense the chemist is akin to the carpenter who judges the “truth” of his chair based on whether someone can comfortably sit on it.

While these expositions of beauty in theories of chemical bonding are abstract, there is a much starker and obvious manifestation of chemical pulchritude, in the marvelous little molecular machines that nature has exquisitely crafted through evolution. This is true of crystal structures in general but especially of protein structures. X-ray crystallographers who have cracked open the secrets of key proteins are all too aware of this beauty. Consider almost any structure of a protein deposited in the Protein Data Bank (PDB) – the world’s largest protein structure repository – and one immediately becomes aware of the sheer variety and awe-inspiring spatial disposition of nature’s building blocks. As someone who looks at protein and ligand structures for a living, I could spend days staring at the regularity and precise architecture of these entities. The structure of a profoundly important biochemical object like the ribosome is certainly pleasing to the eye, but more importantly, it contains very few superfluous elements and is composed of exactly the right number of constituent parts necessary for it to carry out its function. It is like a Mozart opera, containing only that which is necessary. In addition these structures often display elements of symmetry, always an important criterion for considerations of beauty in any field. Thus an elegant molecular structure in general and protein structure in particular straddles both the mathematician’s and biologist’s conception of beauty; it is a resounding example of economy and it resembles the biologist’s idea of geometric harmony as found in creatures like crustaceans and diatoms.

The ensuing discussion may make it sound like chemistry lacks the pervasive beauty of grand explanatory theories and must relegate itself to limited displays of elegance and beauty through specific models. But chemistry also has trappings of beauty which have no counterpart in physics, mathematics or biology. This is most manifest through the drawing of molecular structures which are an inseparable part of the chemist’s everyday trade. These displays of penmanship put chemistry in the same league as the visual arts and architecture and impart to it a unique element of art which almost no other science can claim. They constitute acts of creation and not just appreciation of existing phenomena. What other kind of scientist spends most of his working day observing and manipulating lines, circles, polygons and their intersections? A Robert Burns Woodward who could fill up a whole blackboard with stunningly beautiful colored handrawn structures and make this chemical canvas the primary focus of his three-hour talk can exist only in chemistry.

While contemplating these elegant structures, our original question arises again: is the beauty in these drawings the truth? What is astonishing in this case is that almost all the structures that chemists draw are purely convenient fictions! Consider the quintessential prototype aromatic hydrocarbon, benzene, drawn with its alternating double bonds. In reality there are no double bonds, not even dotted lines representing partial double bonds. All that exists is a fuzzy dance of electrons and nuclei which cannot be imagined, let alone drawn on paper. The same goes for every other molecule that we draw on paper in which one-dimensional geometric representations completely fail to live up to the task of corresponding to real entities. Like almost everything else in chemistry, these are models. And yet, think about how stupendously useful these models are. They have made their way into the textbooks of every budding student of chemistry and constitute the principal tools whereby chemists around the world turn the chaff of raw materials like hydrocarbons from crude oil into the gold of immensely useful products like pharmaceuticals, plastics and catalysts. The great physicist Eugene Wigner once wrote an influential article titled “The Unreasonable Effectiveness of Mathematics in the Natural Sciences”. Wigner was expressing awe at the uncanny correspondence between artificial squiggles of mathematical symbols on paper and the real fundamental building blocks of the natural world like elementary particles. Chemists need to express similar awe at the correspondence between their arrow pushing, molecular chairs and boats and the manifestation of these manipulations as the real solids, liquids and gases in their beakers. One kind of arrow pushing leads to the creation of a cancer drug, another kind leads to a better catalyst for petroleum refining. In this instance, the beauty of molecular structures quite spectacularly corresponds to the truth.

Finally, are their cases where chemists have to sacrifice truth for beauty just like Weyl did? Unlike mathematics and physics where equations can be unusually powerful in explaining the world, such a sacrifice would probably be far more wasteful and risky in the messy world of chemistry. In his Cope Lecture, Woodward said it best when he acknowledged the special challenge of chemistry compared to mathematics:

“While in mathematics, presumably one's imagination may run riot without limit, in chemistry, one's ideas, however beautiful, logical, elegant, imaginative they may be in their own right, are simply without value unless they are actually applicable to the one physical environment we have- in short, they are only good if they work! I personally very much enjoy the very special challenge which this physical restraint on fantasy presents."

The “physical restraint on fantasy” that Woodward talked about keeps every chemist from being seduced by beauty at the expense of truth. Beauty still reigns and is a guiding force for the chemist whenever he or she plans a synthesis, solves an x-ray structure, computes a molecular property or mixes together two simple chemicals with the expectation that they will form a wondrous, intricate lattice. But unlike Keats, the chemist knows that truth can be beauty but beauty may not be truth. As Woodward quipped, “In chemistry, ideas have to answer to reality”. And reality tends to define beauty in its own terms.

The future of computation in drug discovery

noreply@blogger.com (Wavefunction) — Tue, 29 Nov 2011 01:56:00 +0000

Computational chemistry as an independent discipline has its roots in theoretical chemistry, itself an outgrowth of the revolutions in quantum mechanics in the 1920s and 30s. Theoretical and quantum chemistry advanced rapidly in the postwar era and led to many protocols for calculating molecular and electronic properties which became amenable to algorithmic implementation once computers came on the scene. Rapid growth in software and hardware in the 80s and 90s led to the transformation of theoretical chemistry into computational chemistry and to the availability of standardized, relatively easy to use computer programs like GAUSSIAN. By the end of the first decade of the new century, the field had advanced to a stage where key properties of simple molecular systems such as energies, dipole moments and stable geometries could be calculated in many cases from first principles with an accuracy matching experiment. Developments in computational chemistry were recognized by the Nobel Prize for chemistry awarded in 1998 to John Pople and Walter Kohn.

In parallel with these theoretical advances, another thread started developing in the 80s which attempted something much more ambitious- to apply the principles of theoretical and computational chemistry to complex systems like proteins and other biological macromolecules and to study their interactions with drugs. The practitioners of this paradigm wisely realized that it would be futile to calculate properties of such complex systems from first principles, thus leading to the initiation of parametrized approaches in which properties would be "pre-fit" to experiment rather than calculated ab initio. Typically there would be an extensive set of experimental data (the training set) which would be used to parametrize algorithms which would then be applied to unknown systems (the test set). The adoption of this approach led to molecular mechanics and molecular dynamics - both grounded in classical physics- and to quantitative structure activity relationships (QSAR) which sought to correlate molecular descriptors of various kinds to biological activity. The first productive approach to docking a small molecule in a protein active site in its lowest energy configuration was refined by Irwin "Tack" Kuntz at UCSF. And beginning in the 70s, Corwin Hansch at Pomona College had already made remarkable forays into QSAR.

These methods gradually started to be applied to actual drug discovery in the pharmaceutical industry. Yet it was easy to see that the field was getting far ahead of itself and in fact even today it suffers from the same challenges that plagued it thirty years back. Firstly, nobody had solved the twin cardinal problems of modeling protein-ligand interactions. The first one was conformational sampling wherein you had to exhaustively search the conformation space of a ligand or protein. The second one was energetic ranking wherein you had to rank these structures, either in their isolated form or in the context of their interactions with a protein. Both of these problems remain the central problems of computation as applied to drug discovery. In the context of QSAR, spurious correlations based on complex combinations of descriptors can easily befuddle its practitioners and create an illusion of causation. Furthermore, there have been various long-standing problems such as the transferability of parameters from a known training set to an unknown test set, the calculation of solvation energies for even the simplest molecules and the estimation of entropies. And finally, it's all too easy to forget the sheer complexity of the protein systems we are trying to address which display a stunning variety of behaviors, from large conformational changes to allosteric binding to complicated changes in ionization states and interactions with water. The bottom line is that in many cases we just don't understand the system which we are trying to model well enough.

Not surprisingly, a young field still plagued with multiple problems could be relied upon as no more than a guide when it came to solving practical problems in drug design. Yet the discipline saw unfortunate failures in PR as it was periodically hyped. Even in the 80s there were murmurs about designing drugs using computers alone. Part of the hype unfortunately came from the practitioners themselves who were less than cautious about announcing the strengths and limitations of their approaches. The consequence was that although there continued to be significant advances in both computing power and algorithms, many in the drug discovery community looked at the discipline with a jaundiced eye.

Yet the significance of the problems that the field is trying to address means that it will continue to be promising. What's its future and what would be the most productive direction in which it could be steered? An interesting set of thoughts is offered in a set of articles published in the Journal of Computer-Aided molecular design. The articles are written by experienced practitioners in the field and offer a variety of opinions, critiques and analyses which should be read by all those interested in the future of modeling in the life sciences.

Jurgen Bajorath from the University of Bonn along with his fellow modelers from Novartis laments the fact that studies in the field have not aspired to a high standard of validation, presentation and reproducibility. This is an important point. No scientific field can advance if there is wide variation in the presentation of the quality of its results. When it comes to modeling in drug discovery, the proper use of statistics and well-defined metrics has been highly subjective, leading to great difficulty in separating the wheat from the chaff and honestly assessing the impact of specific techniques. Rigorous statistical validation in particular has been virtually non-existent, with the highly suspect correlation coefficients being the most refined weapon of choice for many scientists in the field. An important step in emphasizing the virtue of objective statistical methods in modeling was taken by Anthony Nicholls of OpenEye Software who in a series of important articles laid out the statistical standards and sensible metrics that any well-validated molecular modeling study should aspire to. I suspect that these articles will go down in the annals of the field as key documents.

In addition, as MIT physics professor Walter Lewin is fond of constantly emphasizing in his popular lectures, any measurement you make without knowledge of its uncertainty is meaningless. It is remarkable that in a field as fraught with complexity as modeling, there has been a rather insouciant indifference to the estimation of error and uncertainty. Modelers egregiously quote numbers involving protein-ligand energies, dipole moments and other properties to four or six figures of significance when ideally those numbers are suspect even to one decimal point. Part of the problem has simply been an insufficient grounding in statistics. Tying every number to its estimated error margin (if it can be estimated at all) will not only give experimentalists and other modelers an accurate feel for the validity of the analysis and the ensuing improvement of methods but will also keep semi-naive interpreters from being overly impressed by the numbers. Whether it's finance or pharmaceutical modeling, it's always a bad idea to get swayed by figures.

Then there's the whole issue, as the modelers from Novartis emphasize, of spreading the love. The past few years have seen the emergence of several rigorously constructed datasets carefully designed to test and benchmark different modeling algorithms. The problem is that these datasets have been most often validated in an industry that's famous for its secrecy. Until the pharmaceutical industry makes at least some efforts to divulge the results of its studies, a true assessment of the value of modeling methods will always come in fits and starts. I have been recently reading Michael Nielsen's eye-opening book on open science, and it's startling to realize the gains in advancement of knowledge that can result from sharing of problems, solutions and ideas. If modeling is to advance and practically contribute to drug discovery, it's imperative for industry - historically the most valuable generator of any kind of data in drug discovery - to open its vaults and allow scientists to use its wisdom to perfect fruitful techniques and discard unproductive ones.

Perhaps the most interesting article on the future of modeling in drug discovery comes from Arizona State University's Gerald Maggiora who writes about a topic close to my heart - the limitations of reductionist science in computational drug design. Remember the physics joke about the physicist suggesting a viable solution to the dairy farmer, but one that applies only to spherical cows in a vacuum? Maggiora describes a similar situation in computational chemistry which has focused on rather unrealistic and high simplified representations of the real world. Much of modeling of protein and ligands focuses on single proteins and single ligands in an implicitly represented solvent. Reality is of course far different, with highly crowded cells constantly buffeting thousands of small molecules, proteins, lipids, metals and water in an unending dance of chemistry and electricity. Molecules interact constantly with multiple proteins, proteins interact with others proteins, changes in local environments significantly impact structure and function, water adopts different guises depending on its location and controlled chaos generally reigns everywhere. As we move from simple molecules to societies of molecules, unexpected emergent properties may kick in which may be invisible at the single molecule level. We haven't even started to tackle this level of complexity in our models and it's a wonder they work at all, but it's clear that any attempts to fruitfully apply computational science to the behavior of molecules in the real world can only be possible when our models include these multiple levels of complexity. As Maggiora says, part of the solution can come from interfacing computational chemistry with other branches of science including biology and engineering. Maggiora's version of computation in drug discovery thus involves seamlessly integrating computational chemistry, biology, fluid dynamics and other disciplines into an integrated model-building paradigm which bravely crisscrosses problems at all levels of molecular and cellular complexity, unifying techniques from any field it chooses to address the most important problems in drug design and discovery.

Maggiora does not discuss all the details of effort that would lead to this computational utopia, but I see at least two tantalizing signs around. Until now we have focused on the thermodynamic aspects of molecular design (more specifically, being able to calculate free energies of binding) and while this will remain a challenge in the foreseeable future, it's quite clear than any merging of traditional computational chemistry with higher-order phenomena will involve accurately modeling the kinetics of interactions between proteins and small ligands and between proteins themselves. Modeling kinetics can pave the way to understanding fluxes between various components in biochemical networks, thus leading directly to the interfacing of computational chemistry with network biology. Such advances could provide essential understanding of the non-linear mechanisms that control the production, reactions, down-regulation and turnover of key proteins, a process that itself is at the heart of most higher-order physiological processes.

How can such an understanding come about? Earlier this year, scientists at D. E. Shaw Research performed a study in which they ran a completely blind molecular dynamics simulation of a drug molecule placed outside a kinase protein. By running the simulation for a sufficiently long time, the drug could efficiently sample all configurations and finally lock into its binding site where it stayed put. The model system was simple and we don't know yet how generalizable such a simulation is, but one tantalizing possibility such simulations offer is to be able to correlate calculated protein-drug kinetic binding data to experimental on and off binding rates. The D. E. Shaw method certainly won't be the only computational method to estimate such data, but it offers a glimpse of a future in which accurate computed kinetic data will be available to estimate higher-order fluxes between biomolecules. Such approaches promise revealing insights

The other auspicious development concerns the modeling of crowded environments. As mentioned above, no simulation of molecular structure and function can be considered truly realistic until it takes into account all the myriad molecular partners that populate a cell's interior. Until now our methods have been too primitive to take these crowded conditions into account. But recent proof-of-principle studies have provided the first glimpse into how protein folding under crowded conditions can be predicted. Hopefully at some point, improved computing power will afford us the capability to simulate multiple small and large molecules in proximity to each other. It's only when we can do this can we claim to have taken the first steps in moving from one level of complexity to another.

The future abounds with these possibilities. Whatever the hype, promises and current status of modeling in drug discovery, one thing is clear: the problems are tough, many of them have been well-defined for a long time and all of them without exception are important. In the sense of the significance of the problems themselves, computational chemistry remains a field pregnant with possibilities and enormous score. Calculating the free energy of binding of an arbitrary small molecule to an arbitrary protein still remains a foundational goal. Honing in on the correct, minimal set of molecular descriptors that would allow is to predict the biological activity of any newly synthesized molecule is another. So is being able to simulate protein folding, or calculate the interaction of a drug with multiple protein targets, or model what happens to the concentration of a protein in a cell when a drug is introduced. All these problems really present different sides of the same general challenge of modeling biomolecular systems over an expansive set of levels of complexity. And all of them are tough, involved, multifaceted and essentially interdisciplinary problems that will keep the midnight oil burning in the labs of experimental and computational scientists alike.

But as Jack Kennedy would say, that's precisely why we need to tackle them, not because they are easy but because they are difficult. That's what we have done. That's what we will continue to do.

References:

Bajorath, J. (2011). Computational chemistry in pharmaceutical research: at the crossroads Journal of Computer-Aided Molecular Design DOI: 10.1007/s10822-011-9488-z

Martin, E., Ertl, P., Hunt, P., Duca, J., & Lewis, R. (2011). Gazing into the crystal ball; the future of computer-aided drug design Journal of Computer-Aided Molecular Design DOI: 10.1007/s10822-011-9487-0

Maggiora, G. (2011). Is there a future for computational chemistry in drug research? Journal of Computer-Aided Molecular Design DOI: 10.1007/s10822-011-9493-2

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Autism studies among the Asian-American diaspora.

noreply@blogger.com (Wavefunction) — Tue, 15 Nov 2011 20:05:00 +0000

Last week's issue of Nature had a special section on autism research. One look at the series of articles should convince anyone how complex the determination of causal factors for this disorder is. From a time when pseudoscientific environmental factors (such as "frigid" mothers) were supposed to play a major role, we have reached a stage where massive amounts of genetic data are uncovering tantalizing hints behind Autism Spectrum Disorders (the title itself pointing to the difficulty of diagnosis and description) without a clear indication of causes. Indeed, as pointed out in the Nature articles, some researchers think that the pendulum has now swung to the other side and environmental factors need to be taken into account again.

All the articles are worth reading, but for me the most interesting was a piece describing the research of psychologist Simon Baron-Cohen (brother of the colorful actor Sacha Baron-Cohen) who believes that there is a link between autistic children and the probability of their having technically-minded parents like engineers or scientists. Baron-Cohen's hypothesis has not been validated by rigorous studies but it's extremely intriguing. He thinks that the correlation may have to do with the existence of a "systematizing" brain, one which is adept at deciphering and constructing working relationships in mechanical and logical systems, but which is simultaneously rather poor at grasping the irrational, ill-defined nature of human relationships. Baron-Cohen's hypothesis would be consistent with the lack of empathy and human understanding sometimes found among autistic individuals who also seem to have an aptitude for mathematics, science and engineering.

The moment I read the article, I immediately thought of the substantial Asian-American diaspora in the US, especially Indian and Chinese. I don't have precise statistics (although these would be easy to obtain) but I would think that the majority of Indians or Chinese who emigrated to the US in the last twenty years or so are engineers. If not engineers then they would mostly be scientists or doctors, with businessmen, lawyers and others making up the rest. Chinese and Indian engineers and scientists have always been immigrants here, but the last twenty years have undoubtedly seen a dramatic increase in their numbers.

Now most of the Asians who migrated to the US in the last few years have children who are quite young. From what I read in the Nature article, it seems to me that this Asian community, especially concentrated in places employing large numbers of technically-minded professionals like Silicon Valley and New Jersey, might provide a very good population sample to test Baron-Cohen's hypothesis between autism in children and their probability of having parents who are engineers or physical scientists. Have there been any such studies indicating a relatively higher proportion of ASDs among Asian-American children? I would think that geographic localization and a rather "signal-rich" sample to test Baron-Cohen's hypothesis would provide fertile ground. And surveys conducted with these people by email or in person might be a relatively easy way to test the idea. In fact you may even gain some insight into the phenomenon by analyzing existing records detailing the ethnicity and geographic location of children diagnosed with autism in the last two decades in the US (however, this sample may be skewed since awareness of autism among Asian parents has been relatively recent).

If the prevalence of autism among recent Chinese and Indian children turns out to have seen an upswing in the last few years (therefore contributing to the national average), it would not prove Baron-Cohen's hypothesis. There could be many other factors responsible for the effect. But the result would certainly be consistent with Cohen-Baron's thinking. And it would provide an invitation to further inquiry. That's what good science is about.

A Nobel Prize for "fundamental biology"?

noreply@blogger.com (Wavefunction) — Thu, 10 Nov 2011 15:06:00 +0000

There's a letter in this week's Nature by a William Hughes from Carleton University in Ottawa lamenting the fact that there's no Nobel Prize for "fundamental biology". It's somewhat ironic to hear a biologist complaining about a lack of a biology prize after several chemists have complained that biologist have poached the chemistry prize from them.

But the important question is, what does Hughes mean by "fundamental biology"? In his letter he makes it clear that he is talking about an award that recognizes all discoveries in biology and not just those related to medicine. But the problem is that several of the biological discoveries that were later recognized by the Nobel Prize in medicine had little to do with medicine when they were made. For instance what would you say about the discovery of telomerase (2009)? Or cell cycle control (2000)? Or any number of discoveries in molecular biology including restriction enzymes, DNA and RNA polymerase, phosphorylation or indeed, the structure of DNA itself? By any definition all of these discoveries seem to fall under the "fundamental biology" rubric and I think even Hughes would have a hard time denying this. It just happened that most of them also turned out to be important for medicine (and why not, medicine is after all grounded in biology).

Perhaps Hughes means discoveries in "non-molecular" biology including ecology, evolutionary biology. But I believe that these fields are often honored by the Crafoord Prize which has gone to thinkers like E O Wilson, Robert May and William Hamilton. Maybe the Crafoord doesn't quite satisfy biologists' Nobel cravings because only one prize is awarded every year to different fields. It's also sort of odd that the Nobel Prize in medicine was only once awarded to ethologists studying animal behavior, and perhaps they should hand out more of those. But in any case, I think there have been ample prizes awarded to fundamental biological discoveries, so it doesn't seem very meaningful to me to institute a separate prize for biology. And ultimately of course, there's not much point getting hung up over any of these prizes since the work done by these scientists speaks for itself.

But maybe having a separate biology prize would make chemists feel happier. Which may not be an entirely good thing.

Age is of course a fever chill?: Why even older scientists can make important contributions

noreply@blogger.com (Wavefunction) — Tue, 08 Nov 2011 13:32:00 +0000

A ditty often attributed to Paul Dirac conveys the following warning about doing scientific work in your later years:

Age is of course a fever chill
That every physicist must fear
He is better dead than living still
When past his thirtieth year

Dirac was of course a member of the extraordinary generation of physicists who changed our understanding of the physical world through the development of quantum mechanics in the 1920s and 30s. These men were all in their 20s when they made their revolutionary discoveries, with one glaring exception - Erwin Schrodinger was, by the standards of theoretical physics, a ripe old thirty-eight when he stumbled across the famous equation bearing his name.

When we look at the careers of these individuals we could be forgiven for assuming that if you are past thirty you will probably not make an important contribution to science. The legend of the Young Turks has not quite played out in other fields of science though. Now a paper in PNAS confirms what we suspected, that since the turn of the twentieth century there has been a general increase in the age at which scientists make their important discoveries. What is more surprising is that this increase exists more across time than across fields. The study looks at Nobel Laureates, but since many people who make Nobel Prize worthy discoveries never get the prize, the analysis applies to others too.

So what does the paper find out? It finds out that in the early years of the last century, young men and women made contributions in every field at a relatively young age, often in their twenties. This was most pronounced for theoretical physics. Einstein famously formulated special relativity when he was a 26-year-old patent clerk, and Heisenberg formulated quantum mechanics when he was 24. The same was true for many other physicists including Bohr, Pauli, Dirac and De Broglie and the trend continued into the 70s, although it was less true for chemists and biologists studied by the authors.

The reasons postulated in the paper are probably not too surprising but they illustrate the changing nature of scientific research during the last one hundred years or so. It is easiest for a brilliant individual to make an early contribution to a theoretical field like theoretical physics or mathematics, since achievement in such fields depends more on raw, innate ability than skills gained over time. In an experimental field it's much harder to make early contributions since one needs time to assimilate the large body of experimental work done and to learn the painstaking trade of the experimentalist, an endeavor where patience and perseverance count much more than innate intelligence. As the paper puts it, deductive knowledge lends itself more easily to innate analytical thinking skills visible at a young age than inductive knowledge based on a large body of existing work. This is true even for theoretical physicists where fundamental discoveries have become extremely hard and scarce, and where new ideas depend as much on integrating an extensive set of facts into your thinking process as on "Eureka!" moments. And this difference holds even more starkly for social sciences like economics and psychology where you find very few young people making Nobel Prize winning contributions. In these cases success depends as much on intellectual maturity gained from a thorough assimilation of data about extremely complex systems ("humans") as it does on precocity.

But if this were purely the distinction, then we wouldn't find young people making contributions even to experimental chemistry and biology in the early twentieth century. The reason why this happened is also clear; there was a lot of low-hanging fruit to be picked. So little was known for instance about the molecular components of living organisms that almost every newly discovered vitamin, protein, alkaloid, carbohydrate or steroid could bag its discoverer a Nobel prize. The mean age for achievement was not as early as in theoretical physics, but the contrast is still clear. Even in theoretical physics, the playing field was so rife for new discoveries in the 1930s that in Dirac's words, "even a second-rate physicist could make a first-rate discovery". The paper draws the unsurprising conclusion that there is much more opportunity for a young person to discover something new in a field where little is known.

This conclusion is starkly illustrated in the case of DNA. Watson and Crick are the "original" Young Turks. Watson was only 25 and Crick was in his early thirties when they cracked open the DNA structure, although one has to give Crick a pass since his career was interrupted by the war. What's important to note is that both Watson and Crick came swinging into the field with very little prior knowledge. For instance they both knew very little chemistry. But in this case this lack of knowledge did not really hold them back and in fact freed up their imagination because they were working in a field where there were no experts, where even newcomers could use the right kind of knowledge (crystallography and model building in this case) to make important discoveries. Watson and Crick's story points to a tantalizing thought- that it may yet be possible to make fundamental contributions at a young age to fields in which virgin territory is still widely available. Neuroscience comes to mind right away.

Since this is a chemistry blog, let's look at the authors' conclusions as they apply to chemistry. Linus Pauling provides a very interesting example since he plays into both categories. The "early Pauling" made his famous contributions to chemical bonding in his twenties, and this contribution was definitely more of the deductive kind where you could indulge in much armchair analysis based on principles and approximations drawn from quantum theory. In contrast, contributions by the "late Pauling" are much more inductive. These would include his landmark discovery of the fundamental elements of protein structure (the alpha helix and the beta sheet) and the first description of a disease at a molecular level (sickle cell anemia). Pauling did both these things in his 40s, and both of them needed him to build up from an extensive body of knowledge about crystallography, chemical bonding and biochemistry. It would be hard to imagine even a Linus Pauling deducing protein structure the way he deduced orbital hybridization.

If we move to more inductive fields then the relatively advanced age of the participants is even more obvious. In fact in chemistry, in contrast to mathematics or physics, it's much harder to pinpoint a young revolutionary precisely because chemistry more than physics is an experimental science based on the accumulation of facts. Thus even exceptional chemists are often singled out more for lifelong contributions than for lone flashes of inspiration. Even someone as brilliant as R. B. Woodward (who did make his mark at a young age) was really known for his career-wide contributions to organic synthesis rather than any early idea. It's also interesting that Woodward did make a very important contribution in his late 40s - to the elucidation of the Woodward-Hoffmann rules- and although Hoffmann provided a robust deductive component, inspiration for the rules came to Woodward through anomalies in his synthesis of Vitamin B12 and his vast knowledge of experimental data on pericyclic reactions. Woodward was definitely building up from a lot of inductive knowledge.

An additional factor that the authors don't discuss is the contribution of collaborations. From a general standpoint it has now become very difficult for scientists in any field to make lone significant contributions. In fact one can make a good case that even the widely cited lone contributions to theoretical physics in the 1920s involved constant collaboration and exchange of ideas (mostly through Niels Bohr's institute in Copenhagen). This was far from the case for most of scientific history, when you had people like Cavendish, Lavoisier, Maxwell, Faraday, Kekule and Planck working alone and producing spectacular results. But things have significantly changed, especially in the case of experimental particle physics and genomics where even the most outstanding thinkers can often work only as part of a team. In such cases it may even be meaningless to talk about the young vs advanced age dichotomy since no one individual makes the most important discovery.

Finally, one rather disturbing reason that could potentially contribute to an even greater advancement of age in the context of important discoveries is left undiscussed. As the biologist Bob Weinberg lamented in an editorial a few years ago, the mean age at which new academic researchers receive their first important research grant has been advancing. This means that even brilliant scientists may be held back from making important discoveries simply because they lack the resources. While this trend has really been visible in the last decade or so, it could contribute as an unfortunate factor to the age-corrected generation of novel ideas. One only hopes that this does not make things so bad that scientists are forced to consider contributing to their fields in their 70s.

Ultimately there's one thing that age brings that's hard to replace with raw brilliance, and that's the nebulous but invaluable entity called 'intuition'. As scientific problems become more and more complex and interdisciplinary, it is inevitable that intuition and experience will play more important roles in the divining of new scientific phenomena. And these are definitely a product of age, so there may be something to look forward to when you grow old after all.

Overturning hydrophobic assumptions

noreply@blogger.com (Wavefunction) — Mon, 31 Oct 2011 17:40:00 +0000

One of the most fun things about chemistry is that for every laundry list of examples, there is always a counterexample. The counterexample does not really violate any general principles, but it enriches our understanding of the principle by demonstrating its richness and complexity. And it keeps chemists busy.

One such key principle is the hydrophobic effect, an effect with an astounding range of applicability, from the origin of life to cake baking to drug design. Textbook definitions will tell you that the signature of the "classical" hydrophobic effect is a negative heat capacity change resulting from the union of two unfavorably solvated molecular entities. The nonpolar surface area of the solute is usually proportional to the change in heat capacity. The textbooks will also tell you that the hydrophobic effect is favorable principally because of entropy; the displacement of "unhappy" water molecules that are otherwise uncomfortably bound up in solvating a solute contributes to a net favorable change in free energy. Remember, free energy is composed of both enthalpy and entropy (∆G = ∆H - T∆S) and it's the latter term that's thought to lead to hydrophobic heaven.

But not always. Here's a nice example of a protein-ligand interaction where the improvements in free energy across a series of similar molecules comes not from entropy but from improved enthalpy with the entropy actually being unfavorable. A group from the University of Texas tested the binding of a series of tripeptides against the Grb2 protein SH2 domain. The exact details of the protein are not important; what's important is that the molecules only differed in the size of the cycloalkane ring in the central residue of the peptide- going from a cyclopropane to a cyclohexane. They found that the free energy of binding improves as you go from a 3-membered to a 5-membered ring but not for the reason you expect, namely a greater hydrophobic effect and entropic gain from the larger and more lipophilic rings.

Instead, when they experimentally break down the free energy into enthalpy and entropy using isothermal titration calorimetry (ITC), they find that all the gain in free energy is from enthalpy. They find that every extra methylene group contributes about 0.7 kcal/mol to the interaction. In fact the entropy becomes unfavorable, not favorable as you move up the series. There's another surprise waiting in the crystal structures of the complexes. There are a couple of ordered water molecules stuck in some of the complexes. Ordered water molecules are fixed in one place and are "unhappy", so you would expect these complexes to display unfavorable free energy. Again, you would be surprised. It's the ones without ordered water molecules that have worse free energy. The nail in the coffin of conventional hydrophobic thinking is driven by the observation that the free energy does not even correlate with decreased heat capacity, something that's supposed to be a hallmark of the "classical" hydrophobic effect.

Now it's probably not too surprising to find the enthalpy being favorable; after all as they note, you are making more Van der Waals contacts with the protein with larger rings and greater nonpolar surface area. But in most general cases this value is small, and the dominant contribution to the free energy is supposed to come from the "classical" hydrophobic effect with attendant displacement of waters. Not in this case where enthalpy dominates and entropy worsens. They don't really speculate much on why this may be happening. One factor that comes to my mind is the flexibility of the protein. The improved contacts between the larger rings and the protein may well be enforcing rigidity in the protein, leading to a sort of "ligand enthalpy - protein entropy" compensation. Unfortunately a comparison between bound and unbound protein is precluded by the fact that the free protein forms not a monomer but a domain-swapped dimer. In this case I think that molecular dynamics simulations might be able to shed some light on the flexibility of the free protein compared to the bound structures; it might especially be worthwhile to do this exercise in the absence of the apo structure

Nonetheless, this study provides a nice counterexample to the conventional thermodynamic signature of the hydrophobic effect. The textbooks probably don't need to be rewritten anytime soon, but chemists will continue to be frustrated, busy and amused as they keep trying to tame these unruly creatures, the annoying wrinkles in the data, into an organized whole.

Myslinski, J., DeLorbe, J., Clements, J., & Martin, S. (2011). Protein–Ligand Interactions: Thermodynamic Effects Associated with Increasing Nonpolar Surface Area Journal of the American Chemical Society DOI: 10.1021/ja2068752

Scientific challenges in drug discovery (Part 1): We are still infants

noreply@blogger.com (Wavefunction) — Wed, 19 Oct 2011 15:09:00 +0000

In the face of a flagging pharmaceutical industry and depleted pipeline of new drugs, you will inevitably find someone who tries to put what he or she thinks is a positive spin on the whole depressing situation. That person will say something to the effect that it's probably ok if we don't have more drugs since we already have very good drugs for many disorders and reasonably good ones for most others, so we shouldn't worry too much if we don't continue to come up with miracle breakthroughs.

The recent deaths of Steve Jobs (56) and Ralph Steinman (63, who tragically died only two days before he was awarded the Nobel Prize) should put any such pseudocheerful beliefs to rest. These were people who had access to the best treatment money can buy. Jobs could have afforded any treatment anywhere in the world and Steinman was a professor at one of the world's leading medical research institutes. Yet both were mowed down in the prime of their lives and careers by pancreatic cancer, a disease for which our current pharmaceutical tools are as blunt and primitive as stones and twigs were for fighting wars. Cancer is the great equalizer, unsparing and all-encompassing enough to compete with death itself as one of the great facts of life.

Pancreatic cancer however is just one ailment which we are light years from conquering. Include many other cancers, Alzheimer's disease and ALS, antibiotic-resistant infections and diseases like malaria that are still rampant in developing countries, and it's quite clear that nobody should have to make a case for a thriving pharmaceutical and biotech industry that needs every inch of financial and societal support that we can muster. That we still have to do so is of course a tragic reflection on the state of our myopic short-term vision and misguided financial goals.

But even if we disregard this massive mass of humanity that still very much needs the fruits of pharmaceutical research, there are still enough unsolved problems and challenges on a purely scientific level that should keep researchers - both in academia and industry- hungry for more ideas and solutions. These challenges are longstanding and we can be assured that, whatever happens to drug research in the new century, they will always stick around waiting for us to tackle them. The scope and diversity of these problems widely vary, but you would be hard-pressed to find a researcher who is satisfied with their current status. In this post and the next, I hope to briefly dwell on some problems that we will surely need to solve if we want to radically improve the chances of discovering a potent organic molecule and taking it to the market as a drug.

1. We are still far from being able to predict toxic side effects for any drug

As those in the know are well aware, most drugs fail in the clinic because of unacceptable adverse effects and it's obvious that being able to predict toxic side effects will work wonders for medical science. The truth however is that it's rather shameful that, even after achieving so much sophistication in our knowledge of biology and chemistry, exuberant advertisements for drugs have to be tempered with a laundry list of side-effects (proclaimed sotto voce of course) that outnumber the benefits by at least five to one. It's rather shameful that all these years of unprecedented funding for cancer have resulted in hundreds of clinical compounds, even the best of which evidence rather horrible side effects like nausea, immune system debilitation and loss of fertility. No wonder the bar for the approval of cancer therapeutics is so low; any higher and almost nothing from our current list would make the cut. If this is really the best we can do to fight cancer, then the war on cancer has not even started. A Martian who has conquered most diseases in his own land would call us primitive savages if he saw our present arsenal of anticancer drugs.

As far as prediction goes, we are barely able to predict the effects of unknown drugs with a modest degree of success, and that too using statistical models based on empirical data. Calculating side-effects from first principles is pretty much impossible right now. We will have to develop exceedingly clever and complicated model systems approximating whole-organism physiology to do any kind of respectable toxicity prediction. Of course we don't have to predict side effects in order to minimize them, but any such kind of optimization is currently largely a matter of trial and error. Black box approaches can only get us so far and at some point we will need a healthy understanding of the interplay of various proteins and systems in our body that contributes to toxicity. Systems biology could help us pin down these interactions, but ultimately there would be no substitute for a detailed understanding of biology at a molecular level. For now doing all this is a dream, and toxicity prediction remains one of the great challenges of drug discovery that should keep researchers busy for decades.

2. We still cannot accurately predict the free energy of binding of an arbitrary small molecule to an arbitrary protein from first principles

From clinical-level challenges to the most basic of problems, this one essentially being one in physical chemistry. It is a commentary on both the limitations and the opportunities inherent in computational modeling of biochemical systems that we have barely started to scratch the surface of being able to understand, let alone predict, the myriad factors that go into dictating the free energy of binding of a small molecule to a protein. Part of the problem is just the sheer number of variables involved, from the conformational complexity of ligand and protein to the behavior of solvent and solvation of the various parts (vida infra) to the plethora of energetic interactions that any two molecules have with each other.

The other problem involves a battle against nature herself. Because of the exponential dependence of the binding constant on the free energy of binding, errors as small as 1 kcal/mol can significantly under or overpredict small molecule binding. This problem itself subsumes a difficulty that is inherent to any kind of system susceptible to perturbation by minute changes- being able to predict small differences between large numbers; whether the numbers are economic statistics, variables influencing the weather or in this case, free energies of binding.

How far have we come in being able to predicting these energies? The good news is that we now understand a lot more of the physical chemistry of protein ligand binding than before. We have a rather good understanding of the statistical mechanics framework involved in calculating free energies. Using model systems, we have been able to do a reasonably good job of classifying the various factors - hydrogen bonding, the hydrophobic effect, entropy, electrostatic interactions- that encapsulate these energies. The other good news is that phenomenal improvements in hardware and software continue to allow us to push the boundaries of accuracy.

The bad news is that listing the contributing factors is like listing the genes in a human genome without understanding their function. For instance, listing "entropy" as a factor and being able to calculate the entropy of the protein are two quite different things, with the latter still being largely beyond our capabilities. As with other chemical systems, being able to predict protein ligand systems means predicting the precise contribution (even as a rough percentage) of each one these factors to a single resultant number. And even if we were able to calculate these variables in theory, implementing this theoretical framework inevitably involves patching all the factors together in a model. Using the model inevitably involves parameterizing it. And parametrization is a well-known fickle beast, subject to statistical and systematic errors. The worse news is that when it comes to complex systems subject to so many causes, model building is always going to be our best bet. So we can only hope for the time when we have essentially unlimited computing power and have been able to either parametrize our model to kingdom come (without overparameterizing it) or have been able to implement every minute part of the physics of protein-ligand binding in our model. While I would love to have the latter, I would be more than happy to settle for the former if it ever happens.

3. We understand very little about the behavior of solvents, especially water

This is a significant factor in its own right to separate it from the second point. My optimism about the prospects of computational modeling of proteins and molecules in general took an exponential leap downwards when I schooled myself about solvent behavior and realized that we lack the resources to calculate accurate solvation energies even for simple organic molecules, let alone proteins. It's one of life's enduring mysteries; that which is most familiar succumbs the least to our efforts in understanding it- in this case that elusive entity would be water.

It's become something of a cliche to say that water is an anomalous solvent essential to life and yet we understand so little of its depths, but the gloomy implication of this for drug discovery is that we have always been struggling to incorporate this essential factor in our understanding of biomolecular systems. The problem is again essentially one in physical chemistry and has withstood decades of computational and experimental assault. The earliest attempts to incorporate water into molecular simulation simply involved...ignoring it. Not exactly ignoring it, but replacing its discrete tapestry with a continuous electric field that duplicated its dielectric constant. This effort gave short shrift to both the role of discrete water molecules in mediating molecular interactions as well as the shimmering dynamic hydrogen bond network that is the soul of water's behavior. I am hoping that three decades from now we will look back and laugh at the barbaric simplicity of this approximation, but we have to be excused for attempting this feat in the absence of massive computing power (which could handle thousands of discrete water molecules). And we must confess we did it for a very simple reason - it worked. Even today, these so-called "implicit solvation" models can give us surprisingly satisfactory results for many systems (partly due to cancellation of errors, but let's not go there).

But the implicit solvent consigned water to become the proverbial elephant in the room and researchers, especially those developing molecular dynamics (MD) techniques, strove to replace this continuum with "real" water molecules. But in the world of modeling, even "real" water molecules correspond to models with calculated point charges, dipole moments mimicking the polarizability of the water molecule and so on and these models largely ignore the special behavior that particular water molecules in a system may evidence. Nonetheless, these modeled waters are widely used today in molecular dynamics simulations and massive computing power can now allow us to routinely handle thousands of such entities.

But the elephant has not left the room. Revealing experimental approaches (mainly spectroscopic) in the last few years have painted a very different picture for water in the bulk compared to water surrounding a biomolecule like a protein. Not surprisingly, water surrounding a protein is less mobile and more viscous than that in the bulk. This allows the protein to project something like a "ghost field", a watery extension of its shape and form into the surrounding solvent. This proxy effect can cause other molecules in the vicinity to respond, although its precise effects are still not known. This also brings us to another related big elephant which we will discuss in the next post- the understanding of molecular behavior in the cell as opposed to in a test tube or on the computer. For now it suffices to say that water in cells behaves very differently from water in dilute solution. Its electrostatics, hydrophobicity, composition and hydrogen bonding network are very different. And we have barely started scratching the surface of this "crowding" that is the hallmark of cellular systems.

A related unsolved problem is the behavior of discrete water molecules in protein active sites. We already know from our knowledge of enzymatic catalysis that water can play an essential role in enzyme reactions. In addition, new evidence indicates that hydrophobic interactions can lead to "dewetting" or the sudden expulsion of water between surfaces. Crystal structures usually don't have enough of resolution to clearly pinpoint the locations of bound water molecules. But the real problem is that we still don't know how to accurately predict the thermodynamic and other features of such trapped water. There have been some notable recent advances which attempt to calculate the thermodynamics of enclosed discrete water but these are just getting started. Ultimately the problem boils down to taking theories describing average bulk behavior and using them to calculate specific instances, a problem well-known to statisticians.

It should go without saying that we cannot aim for a realistic understanding of the physics and chemistry of small molecule-protein interactions before we can understand the behavior of water and predict solvation energies of simple organic molecules. But the truth of the matter is that modeling of biomolecular systems has proceeded largely in the absence of such detailed understanding but in the presence of ever so many clever tricks, ad hoc parameterization efforts, models shored up by experimental data and a "Shut up and just use it" attitude. And all this may be justified if our end goal is to find new drugs. But we haven't discovered very many, and while this cannot mainly be blamed on not being able to model water, there is little doubt that a better understanding of solvation will help not just computational modeling but also other drug discovery related activities like formulation, dosing and storage of drug molecules.

In the next part we will look at some other fundamental scientific challenges in drug discovery. Perhaps the people who are disillusioned by the current pharmaceutical bloodbath can take perverse pleasure in the fact that, even when the last scientist has been laid off by the last pharmaceutical organization, these problems will still be standing and needing our attention. When it comes to satisfactorily solving these problems, we are still infants.

Next post: Predicting crystal structures, understanding intracellular interactions, deconstructing the drug-protein-gene connection and more. Stay tuned.

GPCR modeling: The devil hasn't left the details

noreply@blogger.com (Wavefunction) — Wed, 12 Oct 2011 00:49:00 +0000

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

noreply@blogger.com (Wavefunction) — Fri, 07 Oct 2011 12:30:00 +0000

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 a modeler's value to an organization is going to be judged on the basis of her abilities to offer practical suggestions to her colleagues in the language of their own disciplines (as well as the shared language of basic chemistry). The more organic chemistry and biology she knows, the more she will be cherished. The more she empathizes with the particular intricacies of her colleagues' disciplines, the more she 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?

noreply@blogger.com (Wavefunction) — Wed, 05 Oct 2011 16:31:00 +0000

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

noreply@blogger.com (Wavefunction) — Wed, 05 Oct 2011 02:44:00 +0000

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

noreply@blogger.com (Wavefunction) — Mon, 03 Oct 2011 14:08:00 +0000

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"

noreply@blogger.com (Wavefunction) — Mon, 03 Oct 2011 01:47:00 +0000

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.