Field of Science

The problem with Food Babe

The Charlotte Observer has a good article on Food Babe, a health and nutrition activist who has been responsible for many a scientist and science writer's heartburn over the last few years, and not for good reasons.

The article places Food Babe's current activism against processed food and many food ingredients in a personal context; as is often the case, her conversion was triggered by many things, but most precipitously by an epiphany:

"Sometimes when she tells her story, she says she suffered from “a serious health crisis.” It actually was appendicitis, in 2002. Although appendicitis is not often linked to nutrition, she decided hers was caused by inflammation she blamed on her diet. 
She calls it “a light-bulb moment.” She started reading about organics and changed to a diet free of processed foods. Although she isn’t a vegetarian, she gave up beef and says she only eats organic or locally raised chicken and fish."
But as often happens with epiphanies, this one was triggered for the wrong reasons, and in fact it provides a useful lens to view the flaws in her work: The fact that appendicitis is not often linked to nutrition but was still construed by her as something that must have been a result of her diet seems to be a disconnect consistent with many of the things she says on her site.

Read more here:
My problem with Food Babe is not her intentions - promoting a healthy lifestyle and diet - which are honorable but the grossly misleading mistakes she makes when she plays fast and loose with basic scientific facts, most notably chemistry. In fact several of the mistakes she makes are so consistent that they can be encapsulated in general categories, of which I will list two major ones here.

Category mistake 1: Claiming that food ingredient X must be harmful because it is used for some unrelated purpose Y. 

Thus, in one of her recent posts, azodicarbonamide which was used in Subway sandwiches was declared to be harmful because it is used in "yoga mats and shoe soles". When employing this tactic the author is using a classic psychology trick, to color someone's opinion through guilt by association. By that token, common salt should be harmful because it is used to deice roads in winter. Or as McGill chemistry professor Joe Schwarcz says, “We use water to wash our cars. Vinegar can be used to kill weeds. If she ever found out, she’d want salad dressing banned.”

The second category of mistakes made by Food Babe involves a woeful lack of understanding of basic chemistry. One of her favorite targets is a food additive named TBHQ (tertiary butyl hydroquinone). Note the word "butyl" in the name, derived from the name of the gas "butane". This word leads Food Babe to conclude that using TBHQ must be the same thing as using butane, "a toxic gas". Butane is surely a toxic and flammable gas, but one of the fundamental truths of chemistry - and of science for that matter - is that the whole is wildly different from the identity of the individual parts. The properties of TBHQ have nothing per se to do with the properties of butane. Again, guilt by association, and an ignorance of basic chemistry.

Category mistake 2: Claiming that food ingredient Y must have the same properties as the chemical ingredients it's made from.

Using Food Babe's logic, water should be considered harmful because it is made from hydrogen and oxygen, two inflammable and corrosive gases. And common salt should be even worse because it's made from a highly flammable metal (sodium) and a corrosive gas (chlorine) which is so bad that it was used as a chemical weapon in World War 1. Even Food Babe does not take her logic this far (since, presumably, being told to avoid water would be too much both for her and her readers), but the category mistake is exactly the same (incidentally her scorn for TBHQ is also part of the first category mistake: it's a preservative "derived from petroleum and used in perfumes, resins, varnishes and oil field chemicals.")

There are many other quite misleading and basic scientific howlers on her website; for instance many food additives are proclaimed to be "linked to" or "known to" cause a variety of major diseases including cancer and Alzheimer's. But there is no mention of the dose, the statistical significance, the kind of animal the additive was tested in (typically mice or rats, results on which can almost never be properly extrapolated to human beings). Science is always in the details and many health related studies can be tentative, but this is particularly so in the fields of nutrition biology and epidemiology where context matters a lot.

Sadly, most of the readers of Food Babe's website are non-scientists who are not equipped to deconstruct these fine points of epidemiological studies, so at least a few of them trust her. Her successes in causing some large corporations to phase out certain food additives from the products - which as the article notes is less a measure of her success and more a measure of a large corporation's shrewd strategy to minimize public fallout without affecting its bottom line - extends the trust. To her credit the author does allow a lot of critical comments pointing out some of these mistakes on her posts, but she has rarely responded to them and the comments don't seem to have affected her basic take on these matters.

Food Babe's goal is laudable, but in propagating these basic scientific errors and misleading opinions, she is not only ignoring fundamental facts which are not in dispute but is also performing a great disservice to her readers who are coming to her website for finding out the truth about food products. It's hard to justify getting basic facts wrong if your goal is to seek the truth.

Ironically, the right remedy comes from her own words: At one point, when she is asked whether the name 'Food Babe' would cause people to trust her less, she says that it would only be a problem for people who look at labels and interpret them simplistically. Maybe she could apply the same logic to 'TBHQ' herself?

Read more here:

A response to recent events on Sci Am Blogs

The re-post of my Feynman post on SciAm and the note attached to it have led me to write this response. Several readers have asked me what happened, so I will endeavor to clear the air and provide what I consider to be a complete story from my perspective.

Folks are welcome to comment and criticize any of the actors involved in the story. One reason I am writing this is because I think it raises several issues that are at the heart of blogging in the age of social media and under the umbrella of a larger organization.

Here's the gist of the story:

  1. I host a guest post on women in science and later, I write a post on Wade’s controversial book (these are 2 of almost 200 posts on a variety of topics I've written for SciAm).
  2. In response to criticism of the two posts on social media, SciAm issues a public statement. The blog editor asks me to run “controversial” posts by him. No specific guidelines are discussed (something I now regret not doing).
  3. I write a post about how my perception of Feynman has changed and how we need to judge historical figures in their entirety and understand the times in which they lived. I do not think the post was "controversial" in the least and therefore do not run it by the editor.
  4. The post elicits both positive and negative responses on Twitter, blogs and email. 
  5. The post is taken down because the editors find it "controversial" and think that I should have run it by them. I am told that it would be best to part ways with the network.
  6. SciAm resurrects the post with a note containing what I would consider an accurate, but incomplete, description of events.
SciAm is a strong network and I have much respect for their bloggers and editors, particularly blog editor Curtis Brainard. In fact, I am happy to note that they published a post debating the points raised in my original piece. In addition, the piece has also resulted in a spectrum of reactions on other blogs and on social media. This is exactly the kind of diverse give and take I expect a post to stimulate, and therefore I find the foregoing turn of events to be particularly unfortunate.

This episode does, however, raise bigger questions: 

  • Should a network support the airing of controversial views? How does it decide what exactly is controversial, especially considering that the perception of controversial content can be quite subjective?
  • How much should a brand care about opinions (particularly negative ones) on social media, especially in an age when waves of such criticism can swell and ebb rapidly and often provide a transient, biased view of content? How much and how should it let these opinions influence both its internal deliberations and its public responses?
  • If a network is averse to publishing what it thinks are controversial posts, should it communicate this fact to bloggers at the time of hiring? And in this regard, since the perception of controversial content is subjective, how exactly should it word these concerns to bloggers? By explicitly declaring certain topics to be off limits? By instituting a formal process of vetting for posts that are declared to be "controversial" by majority consensus?
  • Finally, if such policies are instituted, how does a blogging network foster an environment in which bloggers have the freedom to explore topics of interest?
I would love to see a serious discussion about these topics and hear people’s views. Meanwhile, I will continue writing (and occasionally covering “controversial” topics) here, so I look forward to the conversation.

Richard Feynman, sexism and changing perceptions of a scientific icon

I fell in love with Richard Feynman when I was in middle school. That is when I discovered "Surely you're joking Mr. Feynman" in my dad's bookshelf. For the first few hours I laughed till tears were rolling out of my eyes. This was not science, it was choice entertainment of the highest order. Whether he was fixing radios by "thinking", blowing up the physics lab at Princeton to test his thoughts on a water sprinkler experiment or cracking top-secret safes at Los Alamos for pure amusement, there was no one like Feynman. This perception was shared by almost all his colleagues and millions of Feynman fans around the world. I was hooked. 

My appreciation for Feynman's quirks continued with James Gleick's brilliant biography of him as well as the more comprehensive account by Jagdish Mehra. Gelick's biography is the most accessible and evocative; Mehra's is the most scientific and complete. I also read Feynman's more philosophical takes on everything from science to religion documented through books like "The Character of Physical Law" and "The Meaning of it All". The Feynman saga continued in college when, guided by a patient professor, a friend and I painstakingly studied chapters from "The Feynman Lectures on Physics". Again, the experience was like nothing else in science that we had experienced. This was not physics, it was the physics of life. The lectures are actually not that great as a textbook in my opinion since they are quite unconventional, but you would be hard pressed to find anything comparable that really exposes you to a gut feeling for the principles of physics and its relevance to the universe around us. And unlike other texts Feynman showed you the way using colloquial, no frills language and everyday examples; he was clearly one of the earliest popularizers of science in this regard. 

By the time I got into graduate school I had thus read almost everything by or about this icon, save his technical work on quantum electrodynamics. And yet by that time cracks had begin to appear in the Feynman edifice. For one thing, I was starting to feel a little irritated by the "Feynman industry" that had sprung up about him, an industry that continued to churn out reprints of his books and CDs and DVDs of his lectures, not to mention an entire fleet of merchandise comprising Feynman t-shirts and mugs; Apple even featured him on their "Think Different" posters. That industry continues unabated, and while it has kept the Feynman legend alive, it has in fact transformed the physicist into more legend than a living and breathing man, full of human foibles and triumphs. Somewhere in the bongo playing, the safecracking and the nude sketching in topless bars is lost the real Feynman. It's an unfortunate development that has in part been engendered by Feynman himself, arising as it does from his own narration of his life as part comedy routine, part almost accidental Nobel Prize winning work. 

My first foray into taking a more critical view of Feynman came from his once arch-rival and contender for most brilliant theoretical physicist in the world, Murray Gell-Mann. Unlike many others Gell-Mann was never swayed by the Feynman legend, so he provides a good conduit through which to view the latter's personality. Although dismissing his status as some kind of a physics God, Gell-Mann genuinely admired Feynman's brilliance and originality - on this count there seems to be unanimous consensus - but his take on Feynman's personal quirks is more revealing. The main thing about Feynman that really got Gell-Mann's goat was that Feynman seemed to "spend a huge amount of time generating anecdotes about himself". Now that much would be clear to anyone who does even a perfunctory reading of "Surely You're Joking..." but Gell-Mann's opinion of Feynman seems to indicate a much more deliberate effort on Feynman's part to do this. Feynman often used to portray himself as some kind of working class city slicker thrown in the middle of distinguished, Sanskrit-quoting, tea-imbibing intellectuals at Princeton or Los Alamos, but the fact was that he relished being a contrarian among these people. A more careful reading of "Surely..." makes it clear that he got into thorny situations deliberately. One suspects that much of this was simply the result of boredom, but whatever the reason, it does give credence to Gell-Mann's observation about him trying hard to generate stories about himself. 

The deliberate generation of these stories could occasionally make Feynman appear like a jerk. A case in point concerns an anecdote when he kept the tip for a meal hidden beneath an inverted glass full of water. He wanted to illustrate to the waitress a clever way of sliding the glass over to the edge of the table, collecting the water without making it spill, and retrieving the tip. But of course he did not actually tell the waitress this; he wanted to simply play a prank so he left it to her to figure it out. The incident is actually trivial and those who would complain loudly about the poor woman having to mop up the water just to get her tip are exaggerating their case, but it does capture a central thread in the Feynman narrative, the physicist's often casual habit to inconvenience other people simply to prove a point, play a prank or conduct an experiment. He did this all his life, and a longer view of his life and career gives you the feeling that most of his colleagues put up with it not because they actually enjoyed it, but because they benefited from his brilliance too much to really bother about it. 

What started bothering me more the deeper I dug into Feynman's life was something quite different: his casual sexism. The latest insight into this comes from Lawrence Krauss's book "Quantum Man" which does a great job explaining the one thing about Feynman that should matter the most - his science. But Krauss also does not ignore the warts. What startled me the most was the fact that when he was a young, boyish looking professor at Cornell, Feynman used to pretend to be a student so he could ask undergraduate women out. I suspect that this kind of behavior on the part of a contemporary professor would almost certainly lead to harsh disciplinary action, as it should. The behavior was clearly, egregiously wrong and when I read about it my view of Feynman definitely went down a notch, and a large notch at that. Feynman's apparent sexism was also the subject of a 2009 post with a sensationalist title; the post pointed out one chapter in "Surely..." in which Feynman documented various strategies he adopted for trying to get women in bars to sleep with him. Neither were Feynman's escapades limited to bars; more than one of his biographies have documented affairs with two married women, at least one of which caused him considerable problems. 

It's not surprising to find these anecdotes disturbing and even offensive, but I believe it would also be premature and simplistic to write off Richard Feynman as "sexist" across the board. People who want to accuse him of this seem to have inadvertently cherry-picked anecdotes; the nude painting in topless bars, the portrayal of a woman in a physics lesson as a clueless airhead, the propensity to lie on the beach and watch girls. But this view of Feynman misses the big picture. While not an excuse, several of his 1950s adventures were probably related to the deep pain and insecurity caused by the death of his first wife Arlene; by almost any account the two shared a very deep and special bond. It was also during the late 40s and early 50s that Feynman was doing some of his most intense work on quantum electrodynamics, and at least a few of the situations he narrates were part of him letting off steam. 

Also importantly, while some of Feynman's utterances and actions appear sexist to modern sensibilities, it's worth noting that they were probably no different than the attitudes of a male-dominated American society in the giddy postwar years, a society in which women were supposed to take care of the house and children and men were seen as the bread winners. Thus, any side of Feynman that raises our eyebrows is really an aspect of a biased American society. In addition, Feynman's ploys to pick up girls in bars were - and in fact are - probably practiced by every American male seeking companionship in bars, whether consciously or unconsciously; what made Feynman different was the fact that he actually documented his methods, and he was probably the only scientist to do so. In fact we can be thankful that society has now progressed to a stage where both genders can practice these mate-seeking strategies on almost equal terms, although the gap indicated by that "almost" deserves contemplation as an indication of the unequal bargaining power that women still have. The point though is that, whatever his actions appear like to a modern crowd, I do not think Richard Feynman was any more sexist than a typical male product of his times and culture. The fact that society in general behaved similarly to what he did of course does not excuse the things he did, but it also puts them in perspective. I think recognizing this perspective is important partly to understand how our views on sexism have changed for the better from 1950 to 2014. The encouraging development is that actions by Feynman - and male society in general - that were considered acceptable or amusing in 1950 would quite rightly cause instant outrage in 2014. We still have a long way to go before both genders achieve parity in science, but the change in attitudes is definitely encouraging. 

However the fact that simply dismissing Feynman as sexist is problematic is ascertained by this 1999 article from the MIT Tech (by a woman) which gives us a more complete picture of his views toward women. As far as we know, there is no evidence that Feynman discriminated against women in his career; the letters he writes to women in the collection of letters edited by his daughter indicate no bias. Both male and female students admired him. His sister Joan documents how he was always supportive of her own career in physics. At one point he came to the aid of a female professor filing a discrimination suit at Caltech. In addition he was a devoted husband to his first and third wife and a loving and supportive father to his daughter who in fact tried hard to get her interested in science.

The irony thus seems to be that, just like Feynman was fond of generating cherry picked anecdotes about himself, we seem to be fond of generating skewed, cherry picked anecdotes about him that accuse him of sexism. In fact most conversations about Feynman seem to center on a few select anecdotes that showcase some side of his character, whether positive or negative, and this anecdotal reading of his life is something he himself encouraged. But a more complete view of Feynman's life and career indicates otherwise. My own perceptions of Feynman have changed, and that's the way it should be. At first I idolized Feynman like many others, but over time, as a more careful reading of his life revealed some of the unseemlier sides of his character, I became aware of his flaws. While I still love his lectures and science, these flaws have affected my perception of his personality, and I am glad they did. There are things that he said or did that are clearly wrong or questionable at the very least, but we can at least be grateful that we have evolved to a stage where even the few instances of his behavior that have been documented would not be tolerated on today's college campuses and would be instantly condemned. As a man I do not now admire Feynman as much as I did before, but I am also glad to have a more complete understanding of his life and times. 

However I think it's also important that we don't make the same mistake that the "Feynman industry" has made - focus on a part of the celebrated physicist's life and ignore many others. Feynman was a brilliant physicist, Feynman was occasionally sexist - and sometimes disturbingly so - and Feynman also supported women in science. All these facts are equally true. One reason why it's interesting to explore these contradictory sides of Feynman's personality is because he is not a scientist who is usually regarded as complicated and contradictory, but the facts indicate that he was. Feynman himself did a kind of disservice by sending a few wrong messages through the recounting of his adventures, and others have performed an equal disservice by embellishing his achievements and papering over his ugly side. But knowing his emphasis on honesty and integrity in science - one ethic that does consistently shine forth from the narrative of his life - he would almost certainly want us to do better and locate the narrative of his life in a more realistic milieu. We can condemn parts of his behavior while praising his science. And we should.

This post was originally posted on Scientific American. I am no longer blogging on that network and will be writing here.

Molecular modeling and physics: A tale of two disciplines

The LHC is a product of both time and multiple disciplines

In my professional field of molecular modeling and drug discovery I often feel like an explorer who has arrived on the shores of a new continent with a very sketchy map in his pocket. There are untold wonders to be seen on the continent and the map certainly points to a productive direction in which to proceed, but the explorer can't really stake a claim to the bounty which he knows exists at the bottom of the cave. He knows it is there and he can even see occasional glimpses of it but he cannot hold all of it in his hand, smell it, have his patron duke lock it up in his heavily guarded coffers. That is roughly what I feel when I am trying to simulate the behavior of drug molecules and proteins.

It is not uncommon to hear experimentalists from other disciplines and even modelers themselves grumbling about the unsatisfactory state of the discipline, and with good reason. Neither are the reasons entirely new: The techniques are based on an incomplete understanding of the behavior of complex biological systems at the molecular level. The techniques are parametrized based on a limited training set and are therefore not generally applicable. The techniques do a much better job of explaining than predicting (a valid point, although it's easy to forget that explanation is as important in science as prediction).

To most of these critiques I and my fellow brethren plead guilty; and nothing advances a field like informed criticism. But I also have a few responses to the critiques, foremost among which is one that is often under-appreciated: On the scale of scientific revolutions, computational chemistry and molecular modeling are nascent fields, only just emerging from the cocoon of understanding. Or, to be pithier, give it some more time. This may seem like a trivial point but it's an important one and worth contemplating. Turning a scientific discipline from an unpolished, rough gem-in-the-making to the Kohinoor diamond takes time. To drive this point home I want to compare the state of molecular modeling - a fledgling science - with physics - perhaps the most mature science. Today physics has staked its claim as the most accurate and advanced science that we know. It has mapped everything from the most majestic reaches of the universe at its largest scale to the production of virtual particles inside the atom at the smallest scale. The accuracy of both calculations and experiments in physics can beggar belief; on one hand we can calculate the magnetic moment of the electron to sixteen decimal places using quantum electrodynamics (QED) and on the other hand we can measure the same parameter to the same degree of accuracy using ultra sensitive equipment.

But consider how long it took us to get there. Modern physics as a formal discipline could be assumed to have started with Isaac Newton in the mid 17th century. Newton was born in 1642. QED came of age in about 1952 or roughly 300 years later. So it took about 300 years for physics to go from the development of its basic mathematical machinery to divining the magnetic moment of the electron from first principles to a staggering level of accuracy. That's a long time to mature. Contrast this with computational chemistry, a discipline that spun off from the tree of quantum mechanics after World War 2. The application of the discipline to complex molecular entities like drugs and materials is even more recent, taking off in the 1980s. That's thirty years ago. 30 years vs 300 years, and no wonder physics is so highly developed while molecular modeling is still learning how to walk. It would be like criticizing physics in 1700 for not being able to launch a rocket to the moon. A more direct comparison of modeling is with the discipline of synthetic chemistry - a mainstay of drug discovery - that is now capable of making almost any molecule on demand. Synthetic chemistry roughly began in about 1828 when German chemist Friedrich Wöhler first synthesized urea from simple inorganic compounds. That's a period of almost two hundred years for synthetic chemistry to mature.

But it's not just the time required for a discipline to mature; it's also the development of all the auxiliary sciences that play a crucial role in the evolution of a discipline that makes its culmination possible. Consider again the mature state of physics in, say, the 1950s. Before it could get to that stage, physics needed critical input from other disciplines, including engineering, electronics and chemistry. Where would physics have been without cloud chambers and Geiger counters, without cyclotrons and lasers, without high-quality ceramics and polymers? The point is that no science is an island, and the maturation of one particular field requires the maturation of a host of others. The same goes for the significant developments in mathematics - multivariate calculus, the theory of Lie groups, topology - that made progress in modern physics possible. Similarly synthetic chemistry would not have been possible had NMR spectroscopy and x-ray diffraction not provided the means to determine the structure of molecules.

Molecular modeling is also constrained by similar input from other science. Simulation really took off in the 80s and 90s with the rapid advances in computer software and hardware; before this period chemists and physicists had to come up with clever theoretical algorithms to calculate the properties of molecules simply because they did not have access to the proper firepower. Now consider what other disciplines modeling is dependent on - most notably chemistry. Without chemists being able to rapidly make molecules and provide both robust databases as well as predictive experiments, it would be impossible for modelers to validate their models. Modeling has also received a tremendous boost from the explosion of crystal structures of proteins engendered by genomics, molecular biology, synchrotron sources and computer software for data processing. The evolution of databases, data mining methods and the whole infrastructure of informatics has also really fed into the growth of modeling. One can even say without exaggeration that molecular modeling is ultimately a product of our ability to manipulate elemental silicon and produce it in an ultrapure form.

Thus, just like physics was dependent on mathematics, chemistry and engineering, modeling has been crucially dependent on biology, chemistry and computer science and technology. And in turn, compared to physics, these disciplines are relatively new too. Biology especially is still just taking off, and even now it cannot easily supply the kind of data which would be useful for building a robust model. Computer technology is very efficient, but still not efficient enough to really do quantum mechanical calculations on complex molecules in a high-throughput manner (I am still waiting for that quantum computer). And of course, we still don't quite understand all the forces and factors that govern the binding of molecules to each other, and we don't quite understand how to capture these factors in sanitized and user-friendly computer algorithms and graphical interfaces. It's a bit like physics having to progress without having access to high-voltage sources, lasers, group theory and a proper understanding of the structure of the atomic nucleus.

Thus, thirty years is simply not enough for a field to claim a very significant degree of success. In fact, considering how new the field is and how many unknowns it is still dealing with, I would say that the field of molecular modeling is actually doing quite well. The fact that computer-aided molecular design was hyped during its inception does not make it any less useful, and it's silly to think so. In the past twenty years we have at least had a good handle on the major challenges that we face and we have a reasonably good idea of how to proceed. In major and minor ways modeling continues to make useful contributions to the very complicated and unpredictable science and art of drug design and discovery. For a field that's thirty years old I would say we aren't doing so bad. And considering the history of science and technology as well as the success of human ingenuity in so many forms, I would say that the future is undoubtedly bright for molecular simulation and modeling. It's a conviction that is as realistic as any other in science, and it's one of the things that helps me get out of bed every morning. In science fortune always favors the patient, and modeling and simulation will be no different.

Crystallography and chemistry: The culture issue

Image: Charles Reynolds and ACS Med Chem Letters
As the old saying goes, beware of crystallographers bearing ligands. Charles Reynolds who is a well-known structure-based drug design expert has an editorial in ACS Medicinal Chemistry Letters touching on an issue that lies at the confluence of crystallography, medicinal chemistry and modeling: flaws in protein ligand co-crystal structures. It's a problem with major ramifications for drug design, especially since it sits at the apex of the process and has the power to influence all subsequent steps. It's also an issue that has come up many times before, but like many deep-seated issues this is one that has not quite disappeared from the palette of the structure-based design scientist.

In 2003 Davis, Teague and Gerard Kleywegt (who is incidentally also one of the wittiest conference speakers I have come across) wrote an article pointing out one simple observation: in several PDB structures of proteins co-crystallized with small molecule druglike ligands, the protein seems to be well-resolved and assigned, but the small molecule is often strained, with unrealistic bond lengths, planar aromatic ring atoms, non-planar amide bonds, rings in boat or pseudo chair conformations and clashes between protein and ligand atoms. Now the protein can also be misassigned, and so can water molecules, but it turns out that the problem looms much larger for ligands.

Reynolds's editorial takes another, 2014 look at this 2003 problem. And it seems that while some people have actually become more cognizant of issues in crystal structures, things aren't exactly rosy at this point in time. He points out a 2009 study that located 75% of the structures in the data set whose geometries could be improved by using better restraints.

The first and foremost pitfall that non-specialists fall into when taking a crystal structure at face value is is to assume that whatever they see on that fancy computer screen is...real. The fact though is that, barring any structure solved to better than 1 Å (when was the last time you saw that?) every crystal structure is a model (and while we are on the topic, Morpheus's definition of "real" may also be somewhat relevant here). The raw data is those dots that you see in the x-ray diffraction; everything after that, including the pretty picture that you visualize in Pymol, comes from a series of steps undertaken by the crystallographer that involve intuition, parameter fitting, expert judgement and the divining of complete information from incomplete data. That's potentially a lot of guesswork and approximation, and so it shouldn't be surprising that it often leads to flaws in the results.

So is this problem primarily a technology issue? Not really. Reynolds points out several programs that can now fit ligands to the electron density better and get rid of strain and artifacts; Schrodinger's PrimeX and OpenEye's AFITT are only two prominent examples. Nor is it complicated to find out in the first place whether a ligand might be strained; any scientist who has access to a good molecular mechanics energy minimization program can take the ligand structure out of the protein, minimize it to the nearest local minimum, look at the energy difference (usually > 5kcal/mol for a strained ligand), visualize steric clashes between atoms and reach a reasonable conclusion regarding the feasibility of that particular ligand conformation.

The abundance of methods for both figuring out strained ligand conformations and refining them seems to point to something other than technology as the operative factor in the misinterpretation of crystal structures. I believe the problem, in significant part, is culture. Reynolds alludes to this when he says that "Crystallographers are not chemists". When you are a crystallographer and are in hot pursuit of a protein structure, you are rightly going to experience a moment of ecstasy when that huge hulking hunk of sheets and strands finally appears on your screen. But most crystallographers don't care about that little blimp in the binding site - a small molecule that's often crystallized with the purpose of stabilizing the protein as much as for aiding drug discovery - as they do about their beloved protein. In addition, many crystallographers don't have the knee-jerk, intuitive reaction to, say, rings in boat conformations that a good medicinal chemist or a medicinal chemistry-aware modeler would have.

The unfortunate consequence of all this is that the ligand often just comes along for the ride and the protein's gory structural details are exquisitely teased apart at the expense of the ligand's. Protein love often inevitably translates into ligand hate. For an organic chemist a cyclohexane boat may be a textbook violation of conformational preferences, but for a crystallographer it's a big, hydrophobic group filling up a big, fuzzy halo of electron density. Crystallographers are not chemists.

However, an honest assessment of the problem would not unfairly pin the blame for bad ligand structures on crystallographers alone. The fact is that structure-based drug design is an intimate covenant between crystallographers, medicinal chemists and modelers and true appreciation and progress can only come from each side speaking or at least understanding the other's language. To this end, chemists and modelers need to be aware of crystallographic parameters and need to ask the right questions to the crystallographer, beginning with a simple question about the resolution (even this question is rarer than you may think). A medicinal chemist or modeler who simply plucks the provided structure out of the PDB file and starts using it to design drugs is as guilty as a chemistry-challenged crystallographer.

A typical set of questions a modeler or medicinal chemist might ask the crystallographer is: 

- What's the resolution?
- What are the R-factors and the B-factors
- Do you have equal confidence in all parts of the structure? Which parts are more uncertain?
- Are the amides non-planar? 
- Where are the water molecules located? How much confidence do you have in their placement?
- Are atoms supposed to be planar non-planar? 
- Are there any gauche or eclipsed interactions? 
- Are there boats in rings? 
- Have you looked at the strain energy of the ligand?
- How did you refine the ligand?

These questions are not meant to be posed to the crystallographer by men in dark suits in a dimly lit room with bars on the windows, but rather are supposed to provide a reality check on the fidelity of the structure and its potential utility in drug design for all three arms of the SBDD process. The questions are part of a process that allows all three departments to confer and reach an agreement; anyone can and should ask them. They are meant to bring hands together, not to point fingers.

One of the cultural problems in drug discovery is still the reluctance of one group of scientists to adopt at least parts of the cultural behavior of other groups. Organic chemists are quick to look at stereochemistry or unstable functional groups, modelers are not. Modelers are much more prone to look at conformation, organic chemists are not. Crystallographers are far more likely to bear multiple conformations of loops and flexible protein side chains in their minds, the other two parties are not.

The best way to fill these gaps is for each group to speak the language of the other, but until then the optimal solution is to have all of them look at the evidence and emphasize what they think is the most important part. But for that to happen each party has to make as many details of its own domain accessible to the others, and that is partly what is being said here.

Update: As usual, the Yoda of chemistry blogging got there first.

Phil Baran is a man of style. We, of course, knew that for a while.

Thanks to @ChemicalBiology I just came to know about a piece of news that may simultaneously help resurrect chemistry's moribund public image and disintegrate multiple damsel hearts as efficiently as heterolytic bond fission:

Phil Baran may be a seriously hotshot scientist—the recent winner of a MacArthur Fellowship, in fact—but he’s also a bit of a wise guy. The proof? The pinky ring he wears even while working in his synthetic chemistry lab at La Jolla’s internationally acclaimed Scripps Research Institute. “I really love The Sopranos,” says Baran, whose wit is nearly as impressive as his CV. The affable scientist earned his Ph.D. at 24 and trained with a Nobel laureate at Harvard. At SRI, Baran’s team finds cutting-edge and cost-effective ways to produce important pharmaceutical components. And, yes, he’s a Breaking Bad fan. “But instead of making meth, we make something useful,” laughs the resoundingly modest brainiac. His first reaction when he heard about the so-called Genius grant? “It was a vote of confidence for all the people I’ve worked with,” says Baran, who credits regular workouts and twice-weekly boxing sessions for his fit body and mind. “The blood flows to your brain, and you do better science.” Also stimulating: trips to the zoo with his two kids. “They see wonder in everything!” At home in Carmel Valley, his Spanish-born wife—a chemist who’s expecting the couple’s third child this summer—often gives him fashion tips. “She’s converted me into a human,” Baran says. “I’d be happy in a pink potato sack. But I do have style and artistry in my chemistry.”

I wish this were a triumph for the public image of chemistry and chemists, but really, I think it's just Phil Baran. Once again Phil has set up impossible standards for the rest of us.

Free Energy Perturbation (FEP) methods in drug discovery: Or, Waiting for Godot

For interested folks in the Boston area it's worth taking a look at this workshop on Free Energy Perturbation (FEP) methods in drug design at Vertex from May 19-21. The list of speakers and topics is quite impressive, and this is about as much of a state-of-the-art discussion on the topic as you can expect to find in the area.

If computational drug discovery were a series of plays, then FEP might well be the "Waiting for Godot" candidate among them. In fact I would say that FEP is a textbook case of an idea that, if it truly works, can truly transform the early stages of drug discovery. What medicinal chemist would not want to know the absolute free energy of binding of his molecules to a protein so that he can actually rank known and unknown compounds in order of priority? And what medicinal chemist would not want to know exactly what she should make next?

But that's what medicinal chemists have expected from modelers ever since modeling started to be applied realistically to drug discovery, and I think it's accurate to say that it's good they haven't held their breath. FEP methods have always looked very promising because they aim to be very rigorous, bringing the whole machinery of statistical mechanics to bear on a protein-ligand system. The basic goal is "simple": you calculate the individual  energies of the protein and the drug - in explicit water - and then you calculate the energy of the bound system. The difference is the free energy of binding. Problem solved.

Except, not really. Predicting relative free energies is still a major challenge, and predicting absolute free energies is asking for a lot. The major obstacle to the application of these methods for decades was considered to be the lack of enough computing power. But if you really thought that was the major obstacle then you were still a considerable way off. Even now there seems to be a belief that given enough computing power and simulation time we can accurately calculate the free energy of binding between a drug and a target. But that's assuming that the fundamental underlying methodology is accurate, which is a big assumption.

The "fundamental underlying methodology" in this case mainly refers to two factors: the quality of the force field which you use to calculate the energy of the various components and the sampling algorithm which you use to simulate their motions and exhaustively explore their conformations. The force fields can overemphasize electrostatic interactions and can neglect polarization, and the sampling algorithms can fail to overcome large energy barriers. Thus both these components are imperfectly known and applied in most cases, which means that no amount of simulation time or computing power is then going to be sufficient. It's a bit like the Polish army fighting the Wehrmacht in September 1939; simply having a very large number of horses or engaging them in the fight for enough time is not going to help you win against tanks and Stukas.

These problems have all been well recognized however; in fact the two most general issues in any modeling technique are sampling and energy calculation. So parts of this month's workshop are aimed exactly at dissecting the factors that can help us understand and improve sampling and scoring.

The end goal of any applied modeling technique of course is how good it is at prediction. Not surprisingly, progress on this front using FEP has been rather thin. In fact FEP is the quintessential example of a technique whose successes have been anecdotal. Even retrospective examples, while impressive, are not copious. One of the problems is that FEP works only when you are trying to predict the impact of very tiny changes in structure on ligand affinity; for instance the impact of changing a methyl group on a benzene ring to a hydroxyl group. The trouble is that the method doesn't work even for these minor changes across the board; there are projects where a CH3--->OH change will give you quantitative agreement with experiment and there are cases where it will result in error bars large enough to drive a car through them. 

But anecdotes, while not being data, are still quite valuable in telling us what may or may not work. Computing power may not solve all our problems but it has certainly given us the opportunity to examine a large number of cases and try to abstract general rules or best practices for drug discovery. We may not be able to claim consistent successes for FEP right now, but it would help quite a lot even if we know what kinds of systems it works best for. And that, to me, is as good an outcome as we could expect at this time.