The Curious Wavefunction

Lindau: The teachings of the savants

noreply@blogger.com (Wavefunction) — Wed, 08 Jul 2009 10:58:00 +0000

Marie Curie once said that "Science is about things, and not people". While this statement is true and profound, the fruits of science are unmistakably linked to their human origins, postmodernist relativism notwithstanding. The scientists who make discoveries are human beings, and they shoulder their share of foibles and successes, petty rivalries and forthcoming generosity, despair and triumph. Their life displays cycles that any young researcher will go through in his or her future career...

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Lindau: Who is the joke going to be on?

noreply@blogger.com (Wavefunction) — Wed, 08 Jul 2009 03:15:00 +0000

When the controversial and talented physicist Edward Teller was doing a PhD. with the great Werner Heisenberg at the University of Leipzig, the question asked at the end of every group meeting that focused on a complex sequence of problems was "Wo ist der Witz?", supposed to be translated as "What is the point"? but more correctly translated as "What is the joke?". The joke part of it consisted of turning a wry eye at the world, donning the hat of the court jester who laughs even as the fire that he predicted would engulf the world rages on. The question about global warming that we ask is also "Wo ist der Witz"? and we only hope that the joke is not upon us and we can actually still get the last laugh. Whether we might was the topic of discussion of a panel on global warming on the final day of the 59th Meeting of Nobel Laureates at Lindau...

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Lindau: From fullerenes to global education

noreply@blogger.com (Wavefunction) — Thu, 02 Jul 2009 10:24:00 +0000

When I visit my favourite restaurant for lunch or dinner, I usually order a legitimate food item from the main course. But once in a while, just to indulge, I order a sample platter of appetizers. The appetizers don't always provide the deep satisfaction that I get from eating a proper, expensive food item. But they provide me with a different kind of unique satisfaction; they give me a glimpse of what's new, what's possible. They provide a view of the diversity that can emerge in a plate of bite-sized chunks. And through their frequent novelty, they give me hope that there are new possibilities on the horizon. These appetizers constitute occasional but necessary fodder. Sir Harold Kroto's talk was one of the most satisfying platter of appetizers I have sampled, and I had not even ordered it...
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Lindau: The way dinner should be

noreply@blogger.com (Wavefunction) — Thu, 02 Jul 2009 10:18:00 +0000

When you first meet Aaron Ciechanover, he appears to have the distracted air of a man who feels slightly inconvenienced to be in whatever situation has been apparently imposed on him. But this preoccupied demeanor belies a mind which is ready to hold forth on a disparate variety of topics with infinite verve and enthusiasm and which is not reluctant to be politically incorrect, provocative and utterly honest. And it hides a broad smile which is very readily revealed at the mention of a favourite incident or fact.

If there is one word to describe the Israeli doctor, biochemist and Nobel Laureate it's passion, and this passion is pronounced no matter what the topic of discussion; from protein degradation to languages and traveling, from politics to history. Whether we were talking about protein structure or Israel-Palestine relations, Ciechanover's thoughts were always opinionated, honest, cogent, provocative and without a dull shade in them. This is the kind of stimulating person that you always want as a dinner companion...

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Lindau: the glowing joy of discovery

noreply@blogger.com (Wavefunction) — Thu, 02 Jul 2009 10:16:00 +0000

Last year's chemistry Nobel Prize was one of the most softball predictions ever made for the Nobel Prize. The Green Fluorescent Protein (GFP) has become so widely used in chemistry, biology and medicine that it is easy to forget that someone had to discover it and develop the technology. Every year Roger Tsien's name used to be on everybody's favorite candidate list along with Martin Chalfie's and Osamu Shimomura's. Then last year, he along with Shimomura and Chalfie finally put the tortuous process and spilling of ink to rest.

A post about GFP is a writer's dream for indulging in pretty pictures. I will restrict myself to two. GFP has become a poster boy for the science of biotechnology. Its barrel shaped ß-sheet structure shown above has become iconic in the scientific world. This is most emblematic in the odd and many varieties of glowing animals that now grace the covers of everything from scientific journals to websites and children's textbooks. If as some have predicted, we happen to "domesticate" biotechnology in the next few decades, it is very likely that one of the first things that our children would do would be to produce glowing pet rabbits, dogs, mice and cats. Along with a few other icons like DNA and the fruit fly, the image of glowing animals and fluorescent proteins is now deeply ensconced in our imagination as an example of what humans can do by manipulating biological systems. Perhaps one day our children can become friends with transgenic, green, glowing human beings, without the hulk-like physique and temper tantrums...

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Wine, wisdom and wish-fulfillment at Lindau

noreply@blogger.com (Wavefunction) — Tue, 30 Jun 2009 22:43:00 +0000

This cannot get any better. There's everything here; the opportunity to interact with dozens of Nobel prizewinners in a very informal setting, spectacular views of the alps bordered by three countries (Germany, Switzerland and Austria), nice bicycle rides, a charming hotel to stay in, polonaises to dance to, great banquets with varied food and drink and a festive atmosphere, really nice people to interact with (my co-bloggers are super-friendly and helpful) and dinner with small groups of students and Nobel laureates. I could not have asked for anything more. Here's me with my wunderbar fellow bloggers. I also ran into Bora and PZ Myers and had a nice walk with them around town. Both of them are attending and vigorously blogging as usual and Bora was also part of a panel discussion on open science access.

This year India is a partner country and has sent the third-largest delegation of students, about 43. Guests included the minister for human resources Kapil Sibal and the minister for science and technology S. E. Chavan. As a partner country India hosted a wonderful banquet yesterday with lots of Indian food, followed by an Indian dance performance. This was followed by a Lindau tradition; a polonaise in which the ladies and the gentlemen form lines and ascend the stage from both sides. The gentlemen pick up a flower and present it to whichever lady happens to be in front of them in the center of the stage. The polonaise then breaks into a waltz, and the dancing continues late into the night. There is purportedly ghastly photographic evidence of a certain individual trying to waltz.

Most importantly, you cannot help but be taken in by the picture of hundreds of students from every possible country interacting so enthusiastically with each other, underscoring the global nature and brotherhood of science. Indians interact with Belorussians, Americans interact with Poles, Chinese interact with Russians, Zambians interact with Germans. And Nobel Prize winners participate in the dances and interact with everyone else. The atmosphere is truly international and sparkles with verve.

Today I had the opportunity to conduct an informal interview with Prof. Peter Agre whom I had also met last year. But this year it was one-on-one for 40 mins and was truly enjoyable since Prof. Agre is an exceptionally witty and nice person. You can read about the interview here.You can find the rest at the official Lindau blog, including all my posts (my name is right below each). Updating will continue all week long. Keep watching that spot for more!

Blogging from ground zero: day zero

noreply@blogger.com (Wavefunction) — Mon, 29 Jun 2009 07:12:00 +0000

I have finally arrived in Lindau, Bavaria to offer my thoughts on the meeting of minds between 500 students, 23 Nobel Prize winners in chemistry and the handful of acting scientific journalists such as myself. The journey itself was uneventful but very long. It took me almost the same time to get from Frankfurt to this little island as it took me to get from New York City to Frankfurt. I had to change trains twice, first at Mannheim and then at Stuttgart. Plus I think I am still to savor the punctuality of German transport since my train was delayed by more than half an hour at Stuttgart and then twice more at miscellaneous stops. However I have to admit that this still beats driving or any form of personal transport.

I cannot yet offer my thoughts on the environment Lindau provides, but one thing stuck out as I passed over a bridge; a spectacular view of the Alps on the other side of the Bodensee. Again, I have yet to see around, but an island at the base of the alps which is located in Germany, Austria and Switzerland cannot exactly be dull and ugly, can it?

I have already started blogging on the Lindau blog website and I would prefer not to cross-post that material in other places. Here is the link to the website and to my first three posts:

Lindau blogs website

Exemplifying apprenticeship; The Lindau meetings

Diversity of talks; diversity of science

Surfaces, ammonia, ozone and scientific destiny

Live-blogging starts tomorrow! Here is the program for tomorrow:

"The partisans have ampicillin". Really?

noreply@blogger.com (Wavefunction) — Sun, 21 Jun 2009 21:14:00 +0000

The Russian covert antibiotic program must have been hugely successful

In an effort to stave off the boredom that inevitably accompanies adjustment to a new environment, I was watching the WW2-era movie "Defiance" yesterday. The movie is based on an astounding true story about two Jewish brothers (played by Daniel Craig and Liev Schreiber) who hide and lead a band of Jewish refugees through the forests of Belorussia for two years and thwart the Nazis' plans for their extermination. Surviving on food killed and obtained in the jungle, defending themselves with stolen small firearms and occasionally seeking the help of partisans from the Red Army, the Bielski brothers and their group provide one of the most exemplary stories of resistance against the Nazis during the war.

So far so good, and the movie is not bad at all. But during one scene my ears suddenly perked up. There is a winter epidemic of typhus threatening to wipe out the population. A nurse tells Craig that the disease is spread by lice and without medical attention the patients will certainly die. To prevent this, she says, Craig and his group must borrow ampicillin from the Red Army. "The partisans have ampicillin", she says with hope and concern.

Which is all fine, except that ampicillin was not even known in 1942. It was introduced only in 1961. Even penicillin was a closely guarded secret in 1942. Plus I am not even sure if typhus is properly treated with beta-lactam antibiotics.

I was further chagrined when in order to confirm this I visited the Wikipedia page on penicillin. While it otherwise looked ok, it also said that the first total synthesis of penicillin was achieved by Woodward. Again, not true. Woodward synthesized cephalosporin. It was John Sheehan from MIT, a mentor of E J Corey, who synthesized penicillin after a mammoth effort of 15 years. The error is now rectified.

Seems the directors of Defiance and the editors of the Wikipedia penicillin page have the same problem of fact-checking.

So what exactly are force fields good for?

noreply@blogger.com (Wavefunction) — Thu, 18 Jun 2009 02:04:00 +0000

Sue Storm tries hard to use her favorite force field to counter the 1 kcal/mol barrier

Every once in a while there is a study asking what method X (X = docking, free energy calculations, molecular dynamics, force fields etc.) is good for. Such studies can be useful to take stock of a particular paradigm. So the question that Jonathan Goodman and his group ask in this paper is "Are force fields good for reproducing non-bonded interactions, especially hydrogen bonding, pi-stacking and dispersion?". He and his group compare very high-level quantum chemical ab initio data with data obtained from the most commonly used force fields, namely MM2*, MM3*, MMFFs, OPLS-2005 etc. The ab initio data used is from Pavel Hobza who has almost consummately published on these methods. The question is; how well do the force fields do compared to the gold standard? The answer is necessarily incomplete and complex and again raises many interesting questions about the enigmatic role of hydrogen bonding in chemical and biological systems.

The complexes studied include purely pi-stacked complexes, purely hydrogen bonded complexes and mixed complexes where both interactions play roles. Typical examples include alcohol-amide complexes, water oligomers and of course, the classic stacked and hydrogen bonded DNA nucleoside bases. The parameters that the authors looked at were geometries and energies, both of optimized complexes as well as crystal structures.

The results are perhaps not too surprising; the more recent OPLS-2005 and MMFFs are probably the best in reproducing known geometries and energies while MM2* and MM3* don't perform that well in general. As noted in some other studies, at least some of the results for MMFFs and OPLS compare with those obtained with high-level ab initio calculations, thus indicating the value of these cost-effective methods for geometry optimization and energy determination (let's ignore for a moment that solvation models in ab initio methods make even these less than perfect).

What is more important though is that all the force fields are generally not good for reproducing hydrogen bonded systems compared to systems where dispersion, stacking etc. are the key players. This is partly an indication of the tricky events including long-range solvation which play an important role in h-bond formation. But what is interesting is that the methods underestimate the energetics of hydrogen bonds. While I am a little puzzled by this, one of the explanations that comes to my mind regarding this curious fact is that in real systems, h-bonding is a cooperative interaction. An h-bond can pay for loss of entropy, thus making the overall free energy of the next h-bond more favourable. Of course force fields don't calculate free energy, but to a first approximation we can probably assume that the enthalpy and free energy are similar for these simple systems. To be honest, because of the complex nature of long-range dispersion interactions I would have assumed that the force fields would be worse in modeling these. I frankly don't understand why they work better for such interactions but it's an interesting observation.

But now for some general thoughts; it's always worth remembering that for molecules like proteins which are stabilized by h-bonds, the h-bonds when formed are simply swapped for similar bonds with water, thus making a relatively insubstantial contribution to protein stability. It is the large number of such interactions that can tip the balance for a protein, but the real driving force is now universally recognized as the hydrophobic effect and the burial of non-polar groups. Calculations such as those above indicate that because of the fine-tuning of h-bonds that proteins often use to achieve stability, force fields have some way to go in predicting tiny energy differences. It is still a great challenge to model the sub-angstrom geometry optimization of h-bonds that biopolymers achieve. But force fields are hardly unique in not being able to do this; so are other methods which are still trying to break the 1 kcal/mol barrier. Ironically in this study, the mean unsigned error when the hydrogen-bonded complexes are included is about 1 kcal/mol.

So are force fields good for anything at all? The short answer is yes, exemplified by the massive number of publications that regularly use force fields as well as the substantial number of people in academia and industry studying them. Obviously people think they are important, otherwise so many common programs doing everything from protein folding to drug-protein interactions would not have relied on them. I have had reasonable experience with force fields and have always kept in mind a couple of things about them that are worth reiterating:

1. Force fields are usually good at reproducing geometries, and best for reproducing sterics.
2. Force fields are usually not so good at reproducing energies since energy estimation is a function of the special parameterization and convergence criteria unique to every force field (As the Zen master says, "What the answer is depends on what question you ask"). However, relative conformational energies using a single force field for instance may be useful.
3. As a corollary, force fields can be pretty poor for dealing with molecules having a large number of polar functional groups. While this means that peptides are hard to model, modeling of peptides has also been mitigated by the fact that unlike small molecules, the chemistry to be parameterized is limited.
3. Many times the real problem is not with force fields per se but with the accompanying implicit solvation models. Admirable effort has been expended in developing these models but to be honest we still don't understand enough about that enigmatic solvent named water to do a satisfactory job. We are just scratching the surface when it comes to modeling things like solvent entropy for instance.

If you are following the field's developments, you also see an engaging and ongoing debate that pits the "science first" camp against the "parameterization first" camp. The science first camp disapproves of the other camp's efforts to improve their force fields simply by adding more parameters and optimizing against experiment; to them it is much more important to meticulously improve the methodology by incorporating as much real science as possible. The parameterization first camp argues that statistical methods have their honored place in the annals of science and that getting results fast and efficiently is important for application-oriented scientists like drug discovery people. I believe that as in other matters, both sides are right. It is an uncomfortable feeling when you don't truly understand the science behind a method and yet the method works, but at the same time it is important to have a well-parameterized and tested model that could help you in a practical sense, even if incompletely understood.

As with everything else, finally it is an astute application of force fields that takes into account their strengths and limitations which will lead to productive results. One of the most interesting things about doing science involves weighing the pros and cons of methods, techniques and algorithms and deciding what judicious combination would provide the best answer and why. It may not always work, but it could keep us from getting seduced by the dark side of the force (field)

Paton, R., & Goodman, J. (2009). Hydrogen Bonding and π-Stacking: How Reliable are Force Fields? A Critical Evaluation of Force Field Descriptions of Nonbonded Interactions Journal of Chemical Information and Modeling, 49 (4), 944-955 DOI: 10.1021/ci900009f

The anti-question, or when bias can be a good thing

noreply@blogger.com (Wavefunction) — Tue, 09 Jun 2009 10:54:00 +0000

A recent publication indicates that more bias in the form of natural product scaffolds not yet synthesized could improve hit rates in screening

Most drug discovery projects are inaugurated with some kind of screening campaign where millions of molecules are screened against a biological target. Even though the hit rate from High-Throughout Screening (HTS) can be quite low, HTS still provides one of the best starting points to discover interesting new structures that display biological activity. In spite of this, there is frequent disappointment at the low rates from HTS which could be as low as 0.05%.

But instead of focusing on the low hit rate from HTS, what if we express surprise that this hit rate is actually high? This thought takes me into a slight digression. In his remarkable book The Black Swan, the author Nassim Nicholas Taleb talks about an "anti-library", the set of all books you have not read. The anti-library is in some ways more important than your library because it really tells you what you are ignorant about.

Similarly we can define an "anti-question". The anti-question is a question opposite to one which we might usually ask. So instead of asking; "Why is this drug specific for this protein?", we could ask "Why is this drug not hitting other proteins?". The value of the anti-question is that it forces us to analyze and evaluate things that we otherwise may not and enables us to think outside the box. As the wise doctor constantly exhorts detective Sponer in "I Robot" to get to the all-important right question, so it could be important to get to the right anti-question.

In the context of HTS, the anti-question actually turns out to be logical. Instead of asking, "Why is the hit rate from HTS so low"?, one should ask "Given the number of small molecules in small-molecule space (~10*60) compared to the extremely low number typically screened in HTS campaigns (10*6), why should we get any hits from HTS at all?". Even narrowing down the unimaginably large small-molecule universe to more drug-like or lead-like entities, we still run into a numbers paradox since even this number is orders of magnitude greater than what is usually screened.

In their most recent paper, Brian Shoichet and his team ask this important anti-question, and it leads them down an interesting road. Most campaigns that screen libraries focus on readily available commercial compounds and fragments that can be synthesized by organic chemists. This bias in turn reflects what has been more or less synthetically accessible through more than a hundred years of synthesis. Compared to this, the Kyoto Encyclopedia of Genes and Genomes (KEGG) contains metabolites whose structures are untainted by the minds of organic chemists. These are scaffolds among secondary metabolites and natural products that have simply been found.

There is another set of structures; the Generated Database (GDB), a theoretical set which contains all possible molecules containing less than 11 heavy atoms consisting of first-row elements (C, O, N, F). This number is not as large as may be imagined and amounts to about 26 million. In the study the authors essentially compare the set of purchasable or commercial KEBB compounds found in their own annotated library called ZINC with the GDB. They use a similarity measure called a Tanimoto coefficient derived from 2D fingerprint comparison to accomplish this. 2D fingerprints use different kinds of protocols for breaking up a molecule into bit strings and then compare bit strings by distances and atom types.

The comparison indicates something interesting; the compounds in the purchasable set are much more similar to the KEBB compounds than are the compounds from the rest of the GDB. In other words, purchasable compounds contain scaffolds that are biased towards those in the KEBB. This is a good thing, since metabolites are usually primed by nature to show at least some biological activity. Another noteworthy finding was that the bias also increased with molecular size, as compounds became more drug-like or lead-like in terms of size.

However, the more surprising and useful observation was that there are hundreds of scaffolds in the KEBB that are notpresent in the commercial library. The authors also do this comparison for other popular commercial libraries designed specifically for screening and find a similar result. The bottom line; while synthesized commercial libraries of molecules show a bias toward natural products and metabolites, there are also several natural product scaffolds that are not found in these libraries.

So what is the prescription? Introduce further bias! The compounds in the KEGG are more or less optimized for biological activity. If their scaffolds are not yet present in the commercial libraries, organic chemists should go ahead and focus on synthesizing these scaffolds and adding them to screening libraries. More such scaffolds could increase the hit rate in HTS by enriching libraries in biologically relevant scaffolds. Of course the usual caveats of false positives and promiscuous compounds should be kept in mind, and it's also not clear that proteins like kinases which are optimized to bind certain core scaffold structures would greatly benefit from these diverse scaffolds. But in terms of unmined drug space, introducing such further bias would be beneficial.

This study again goes to show the possibilities for finding new stars in the constellations and galaxies of the drug universe. Hopefully the universe will keep on expanding.

Hert, J., Irwin, J., Laggner, C., Keiser, M., & Shoichet, B. (2009). Quantifying biogenic bias in screening libraries Nature Chemical Biology DOI: 10.1038/nchembio.180

Overmedicated overachievers

noreply@blogger.com (Wavefunction) — Thu, 04 Jun 2009 22:05:00 +0000

Since we were on the subject of messing with brain chemistry in the last post, it's worth pointing out an interesting and a more than a little disturbing and provocative article by Margaret Talbot in the New Yorker that deals with the controversial use of stimulant drugs like Adderall and Ritalin.

Talbot especially focuses on Ivy League students who seem to be on a veritable diet of cocktails of these drugs. They not only turn them into supermen and women when it comes to writing papers and doing assignments, but "enhance" their social, romantic and personal lives. Websites on which anonymous users share their experiences with these compounds abound, and these users often are not bashful about sharing not just experiences but samples of such drugs. Nor do doctors seem to hesitate in rather liberally prescribing these medications. Talbot chronicles the experiences of several users who report on an overall enhanced sense of perception and understanding. The phenomenon is of course not limited to Ivy League students, but professors at top schools as well as their students seem to be ideal test cases, considering the pressures of academic life and the myriad ways to cope that they come up with.

So the question naturally is; is this a good thing? The bigger question I want to ask is; in twenty years, when I meet a person, do I want the sum total of his or her personality to be essentially defined by 5 magic pills that are popped into the mouth every morning, like recharging a battery? Are we going to enhance our lives with drugs so much that our intrinsic persona is only vaguely visible, if at all, under a thick blanket of smiles, appropriate social manners, and exuberant behavior that is artificially induced by medication? And of course, do we understand enough about brain chemistry to use these neuroenhancers on a regular basis? (This one's easy; the answer is a terse 'no')

Proponents of the drugs say that these molecules tickle similar receptors in the brain as caffeine. If copious quantities of stimulant black coffee are still kosher, what's wrong with minute quantities of Ritalin taken essentially for the same purpose? It's hard to make an argument against this, but from a long-term perspective I would be much more skeptical about the effects of...I don't know, amphetamines on the brain compared to coffee?. The long-term effects of both Ritalin and Adderall are not known. A related matter is that the temporary stimulation and enhancement induced by these drugs may mask the loss of deeper and important functions that may not be apparent in the short-term. Indeed, perhaps the most troubling side-effect that Talbot documents is a loss of truly creative thinking. As she says, this is not surprising. Truly creative thinking often happens when the mind is wandering, when one is not too focused on a particular task. Ritalin-like compounds that may bring about intense spells of concentration may deprive us of those strokes of insights that actually result from a scatter-brained loss of focus.

Another practical issue that these medicines pose is that of unduly ramping up competitiveness. Consider that your co-worker is on these medicines and it's apparently enhancing his or her productivity. Would you feel pressured to aid your normal faculties with a boost of these babies? Wouldn't you like to stay competitive by asking your doctor for Ritalin so that you are sure that it's you and not your co-worker who bags that lucrative contract or job position? In an era where competitiveness has becoming so mind-numbing that's it's hardly noticed, do we need more incentives for competing even harder? It's a question that is going to constantly rear its head.

In the end though, I have a problem with these enhancers for the same reason that I have a problem with antidepressants. We are in an era where ordinary problems like shyness are being presented as "disorders" that may benefit from a pill. Attention Deficit Hyperactive Disorder is of course a real, clinical manifestation. But aren't all of us attention deprived to varying extents during the day. Simply as a scientific fact, wouldn't it generally help us if all of us take Ritalin? Who wants to be a member of Ritalin nation?

But that's just my opinion. Our parents' and grandparents' generations exemplified the maxim "Where there is a will, there's a way". Maybe for us it's going to be, "Where there's no will, there is a pill". I cannot wait for the singularity.

Atypically typical: The single vs multiple compound hypothesis in schizophrenia

noreply@blogger.com (Wavefunction) — Tue, 02 Jun 2009 11:04:00 +0000

One of the most painful parts in the book "A Beautiful Mind" narrates how the brilliant mathematician John Nash was admitted to a Trenton hospital and subjected to what was then one of the most fashionable treatments for schizophrenia- insulin shock therapy. The periodic administrations of large doses of insulin to induce convulsions and coma not only was embarrassing for the future Nobel Laureate and his family but it may have possibly damaged parts of his mind- and not just brain- beyond repair. It may have scarred a beautiful mind.

But we had lobotomy, and we had insulin shock therapy. And then we evolved. The drugs chlorpromazine and reserpine revolutionized the treatment of schizophrenia in the 1950s (recall the movie "Awakenings"). Since then a variety of drugs have been used for mitigating the symptoms of this devastating disorder. However most of these drugs target what are called the "positive symptoms" of the disease, which include delusions, agitation and hallucinations. The "negative symptoms" include social withdrawal, depression and poverty of speech, symptoms not targeted by many drugs. More importantly, many of the early drugs had nasty side effects, termed "extrapyramidal symptoms" (EPS) which included involuntary twitching of facial and other muscles, part of what is termed tardive dyskinesia. A lot of focus has been put over the years on reducing these effects as well as in mitigating negative symptoms. Medicines supposed to achieve these goals have been traditionally termed "atypical antipsychotics"

Now an article co-authored by Nobel Laureate Arvid Carlsson questions this widely accepted definition of atypical antipsychotics and suggests that the definition actually hampered the development of these drugs for more than 30 years. The article contains some rather technical commentary, but what I could get from it is the following: the most widely accepted hypothesis for the etiology of schizophrenia is the so-called "dopamine hypothesis", pioneered by Carlsson himself, that contends that high levels of dopamine in the brain are associated with psychoses. Drugs like clozapine are supposed to prevent dopamine metabolism by binding especially to the D2 family of dopamine receptors. These drugs bind to other receptors too but it's their action at dopamine D2 receptors that's important in managing the symptoms of schizophrenia.

Carlsson contends that the flaw in 30 years of antipsychotic therapy lies in searching for the perfect "atypical" antipsychotic which will tackle both positive and negative symptoms of schizophrenia as well as EPS. It was believed for many years that all these effects could not be disentangled from each other and necessarily went together. This led to the search for a "magic bullet", a single compound that could hit all symptoms. Carlsson says that recent studies on the action of antipsychotics suggests different mechanisms responsible for different symptoms, including mechanisms involving novel receptors that were not implicated before. The drugs also cause different levels of occupancy for D2 receptors in different tissues and parts of the brain, and thus provide the opportunity for designing multiple compounds that hit subtypes in different places. According to Carlsson, the "atypical" compounds used to treat psychoses should actually be called "typical" since they usually do a good job of treating the positive symptoms of the disease. The bottom line is that multiple avenues for treating the symptoms of schizophrenia arising from different molecular mechanisms should be explored, instead of focusing on a single compound that would encompass all features. Different compounds should be used for targeting positive and negative symptoms.

To me this narrative reinforced what is becoming clear about CNS disorders and the accompanying therapy; that non-selective drugs targeting different mechanisms are often more beneficial than single, selective drugs targeting only one receptor, and that multiple pathways affect the development of a disease whose symptoms and side-effects may be classified into distinct categories only with deceptive convenience. The brain is the most complex structure known to man. Its manipulation and the treatment of its disorders deserves an approach that is not too less complex and nuanced.

Gründer, G., Hippius, H., & Carlsson, A. (2009). The 'atypicality' of antipsychotics: a concept re-examined and re-defined Nature Reviews Drug Discovery, 8 (3), 197-202 DOI: 10.1038/nrd2806

Presence of absence is not absence of presence

noreply@blogger.com (Wavefunction) — Sat, 30 May 2009 01:57:00 +0000

The proximate cause of my absence from this blog has been the tribulations of settling down in the most lawless state in the country. Just kidding, but two things about the Garden State are axiomatic truths:

A: The shortest route from A to B is most generally not the shortest route from B to A. Heisenberg would have been pleased. Traffic circles, one-way streets, deer roadkill on Route 202 gradually disintegrating for three days, drivers who must be thinking they are competing in Formula-1 and potholes on roads that seem like they are designed to retain 1920s charm all make the picture endearingly complete.

B: If you are in Princeton you should expect to see photos of Einstein eating ice cream, Oppenheimer licking his fingers after eating buffalo wings at Chuck's and John von Neumann balancing a paper cone filled with popcorn on his generous belly.

Ok, I made the last two up, but I did see the first one; Einstein somewhat disinterestedly licking an ice cream cone in one of those small, family-owned ice cream stores on Nassau Street whose name I will have to look up again. Actually this fact about Einstein should not surprise one at all: the man took as much pleasure in ice cream and all the simple joys of life as in tensor calculus. In the 1950s, according to his own admission, Princeton was a "quaint ceremonial village, occupied by demigods on stilts". The quaintness still somewhat lingers but the stilts have definitely given way to big cars that block traffic and pedestrian access. As for the rest of the state, what was George Merck thinking?

I hope to explore more of the village on the weekend, after I have finally moved into an apartment. I also hope to get a bite of the Apple and of some docking and chlorine-pi interactions. Work and the postdoc has started and all I can say is that it involves trying to model what are currently seeming to be unmodelable (?) proteins.

For the love of science

noreply@blogger.com (Wavefunction) — Thu, 21 May 2009 02:14:00 +0000

So I am trying to find possible groups interested in science meeting up in NJ and my friend suggests this site called meetup.com. So I type in my zip code and ask the site to find people or groups interested in "science" within 25 miles. Among the twenty or so hits are included "The NY/NJ group for parents with science careers" (11 members) and "Red Bank Life Science Discussion Group" (4 members). Good for them, but almost everything else includes things like

The Monroe Township Law of Attraction Meetup Group (28 members)
Princeton NJ ~Tantra Awakening ~The Art Of Conscious Loving (42 members)
Princeton Holistic Clinic (98 members)
The Central New Jersey Astrology Meetup Group (6 members)
The Healers' Guild (37 'adepts')

Both the predominance of these groups and their member counts indicate that the 5+ years that I spent learning and doing science in graduate school were futile after all. But at least it does seem that we are catching up with astrology.

State of the Garden

noreply@blogger.com (Wavefunction) — Sun, 17 May 2009 18:31:00 +0000

I am finally in the Garden State after a rather protracted road trip amply filled with fever and a cold brought on by all the allergies dust stirred up during packing (I think there's a Nobel Prize in the wings for discovering the true nature of allergies).

The first thing I rather surprisingly notice is how much more tiring it is to drive here compared to Atlanta where the drivers are supposed to be rather rash. Plus, the constant exits and bifurcations on several different highways, routes and streets that one needs to traverse to travel quite non-linearly between any two points is frustrating. I am also hoping to actually see some gardens in the garden state. My hundreds of books also made the journey here although I have to still check how many of them are still in pristine condition. And I am still looking for an apartment while I am comfortably imposing myself on my cousin at his place.

For now I am aiming to hang out a little at the bookstores and cafes in nearby Princeton, hoping to meet some like-minded people. If you want to pointlessly muse and pontificate and don't feel scared in meeting strange new people, drop me a line. I would also appreciate it if anyone could bring their wisdom to bear on three things important to me; good bookstores (other than the ubiquitous Borders), good movie theaters where one can especially catch off-beat or foreign movies, and good restaurants and cafes where one can stare blankly at nothingness for hours and read.

The 2009 Lindau Nobel Laureates Meeting

noreply@blogger.com (Wavefunction) — Thu, 07 May 2009 19:25:00 +0000

It is a great privilege for me to be invited to live-blog and write about the 2009 Lindau Nobel Prize Winners meeting in the scenic Bavarian town of Lindau, Germany. Since 1951, dozens of Nobel laureates have been joined every year by about 500 carefully chosen students from around the world for a full week of informal discussions, seminars, lunches and lectures where students and Nobelists mingle with each other and one can find at least one laureate on every square foot of the floor no matter what direction he looks.

This year's focus is on chemistry and an august list of no less than 22 Nobel Prize winners in the subject is going to gather in this scenic town. I am honored to be invited because of my background in chemistry and blogging and relish the opportunity like nothing else. I am supposed to be on a small team of 7 journalists and bloggers blogging the event for scienceblogs.com and scienceblogs.de. Along with Matthew Chalmers who is an editor and writer for several publications like New Scientist and the Times, I will largely be responsible for writing about the event in English for Scienceblogs.com. The writing will include both general observations about the meeting as well as descriptions of the talks and seminars. Hopefully I can bring it all together.

Nobel laureates have long been a particular interest of mine. People interested in this kind of a thing collect Nobel statistics like sports and stock market statistics; it was only when exploring facts about youngest, oldest, tallest, most awarded, famous father-son duos, and most neglected non-winners that I realised the allure of cricket or sensex figures.

Calling the list of scheduled speakers at Lindau stellar is a futile and redundant effort because every one of them has won the highest honor in his or her field. Many of the names are familiar and not only have I long admired these people, but I have even directly and indirectly used their work in my own research, as have thousands of scientists and students around the world. Now we will all experience a connection to our work like no other.

In any case, this is as magnificent a concatenation of minds as you can expect to find and I am immensely looking forward to it. The meeting is going to be held from June 28 - July 3. 22 Nobelists in one of the most beautiful parts of the world. It does not get better than this. I will naturally keep on updating.

And you wonder why atheists bristle when the religious call them 'intolerant'

noreply@blogger.com (Wavefunction) — Thu, 07 May 2009 14:23:00 +0000

A very reasonable religious woman writes in on a show to ask Pat Robertson how she could strike a middle ground between herself and her boyfriend who is an atheist. He has stuck by her for a long time and the two obviously are quite close. Do we need to guess how the Reverend Robertson responds? Remember that this guy is still worshipped by millions of people. And they say Richard Dawkins is 'intolerant' of religious people.

Drug Discovery, Models and Computers: A (necessarily incomplete) Personal Take

noreply@blogger.com (Wavefunction) — Wed, 29 Apr 2009 22:27:00 +0000

Drugs and rational drug discovery

Natural substances have been used to treat mankind’s diseases and ills since the dawn of humanity. The Middle Ages saw the use of exotic substances like sulfur and mercury to attempt to cure afflictions; most of these efforts resulted in detrimental side effects or death because of lack of knowledge of drug action. Quinine was isolated from the bark of the Cinchona tree and used for centuries to treat malaria. Salicylic acid was isolated from the Willow tree and was used for hundreds of years to treat fevers, knowledge that led to the discovery of Aspirin. The history of medicine has seen the use of substances ranging from arsenic to morphine, some of which are now known to be highly toxic or addictive.

The use of these substances reflected the state of medical knowledge of the times, when accidentally generated empirical data was the most valuable asset in the treatment of disease. Ancient physicians from Galen to Sushruta made major advances in our understanding of the human body and of medical therapies, but almost all of their knowledge was derived through patient and meticulously documented trial and error. A lack of knowledge of the scientific basis of disease meant that there were few systematic rational means of discovering new medicines, and serendipity and the traditional folk wisdom passed on through the centuries played the most important role in warding off disease.

This state of affairs continued till the 19th and 20th centuries when twin revolutions in biology and chemistry made it possible to discover drugs in a more logical manner. Organic chemistry formally began in 1848 when Friedrich Wöhler found that he could synthesize urea from simple inorganic substances like ammonium cyanate, thus dispelling the belief that organic substances could only be synthesized by living organisms (1). The further development of organic chemistry was orchestrated by the formulation of the structural theory in the late 19th century by Kekulé, Cooper, Kolbe, Perkin and others (1). This framework made it possible to start to elucidate the precise arrangement of atoms in biologically active compounds. Knowledge of this arrangement in turn led to routes for synthesis of these molecules. These investigations also provided impetus to the synthesis of non-natural molecules of practical interest, sparking off the field of synthetic organic chemistry. However, while the power of synthetic organic chemistry later provided several novel drugs, the legacy of natural products is still prominent, and about half of the drugs currently on the market are either natural products or derived from natural products (2).

Success in the application of chemistry to medicine was exemplified in the early 20th century by tentative investigations of what we currently call structure-activity relationships (SAR). Salvarsan, an arsenic compound used for treating syphilis, was perhaps the first example of a biologically active substance that had been improved by systematic investigation and modification. As the same time, chemists like Emil Fischer were instrumental in synthesizing further naturally occurring substances like carbohydrates and proteins, thus extending the scope of organic synthesis into biochemistry.

The revolution in structure determination initiated by physicists led to vastly improved synthesis and studies of bioactive substances. At this point, rational drug discovery began to take shape. Chemists working in tandem with biologists made hundreds of substances which were tested for their efficacy against various diseases. Knowledge from biological testing was in turn translated into modifications of the starting compounds. The first successful example of such rational efforts was the synthesis of sulfa drugs used to treat infections in the 1930s (3). These compounds were the first effective antibiotics and were followed by the famous discovery, but this time serendipitous, of penicillin by Alexander Fleming in 1928 (4).

Rational drug discovery received a substantial impetus because of the post-World War 2 breakthroughs of structure determination by x-ray crystallography that revealed the structures of small molecules, proteins and DNA. The discovery of the structure of DNA in 1953 by Watson and Crick heralded the advent of molecular biology (5). This landmark event led in succession to the elucidation of the genetic code and the transfer of genetic information from DNA to RNA that results in protein synthesis. The first structure determination of a protein- hemoglobin by Perutz (6)- was followed by the structure determination of several other proteins, some of which were pharmacologically important. Such advances and preceding ones by Pauling and others (7) led to the elucidation of common motifs in proteins such as alpha helices and beta sheets. The simultaneous growth of techniques in biological assaying and enzyme kinetics made it possible to monitor the binding of drugs to biomolecules. At the same time, better application of statistics and the standardization of double blind, controlled clinical trials caused a fundamental change in the testing and approval of new medicines. A particularly noteworthy example of one of the first drugs discovered through rational investigations is cimetidine (8), a drug for acid reflux that was for several years the best-selling drug in the world.

Structure-based drug design and CADD

As x-ray structures of protein-ligand complexes began to emerge in the 70s and 80s, rational drug discovery received enormous benefits. The development was also accompanied by High-Throughput Screening, an ability to screen thousands of ligands against a protein target to identify likely binders. These studies led to what today is known as “structure-based drug design” (SBDD) (9). In SBDD, the structure of a protein bound to a ligand is used as a starting point for further modification and improvement of properties of the drug. While care has to taken in order to fit the structure well to the electron density in the data (10), well-resolved data can greatly help in identifying points of contact between the drug and the protein active site as well as the presence of special chemical moieties such as metals and cofactors. Water molecules identified in the active site can play crucial roles in bridging interactions between the protein and ligand (11). Early examples of classes of drugs discovered using structure-based design include Captopril (12) (angiotensin-converting enzyme inhibitor- hypertension) and Trusopt13 (carbonic anhydrase inhibitor- glaucoma) and recent examples include Aliskiren (14) (renin inhibitor- hypertension) and HIV protease inhibitors (13).

As SBDD progressed, another approach called ligand-based design (LBD) has also recently emerged. Obtaining x-ray structures of drugs bound to proteins is still a tricky endeavor, and one is often forced to proceed on the basis of the structure of an active compound alone. Techniques developed to tackle this problem involve QSAR (Quantitative Structure-Activity Relationships) (15) and pharmacophore construction in which the features essential for a particular ligand to bind to a certain protein are conjectured from affinity data for several similar and dissimilar molecules. Molecules based on the minimal set of interacting features are then synthesized and tested. However, since molecules can frequently adopt diverse conformations when binding to a protein, care has to be exercised in developing such hypotheses. In addition, it is relatively easy to be led astray by a high correlation between affinity data in the training set. It is paramount in such cases to remember the general discrepancy between correlation and causation, and overfitting of models can lead to both spurious correlations and absence of causation (16). While LBD is more recent than SBDD, it has turned out to be valuable in certain cases. Noteworthy is a recent example where an inhibitor of NAADP was discovered by shape-based virtual screening (17) (vida infra)

As rational drug discovery progressed, software and hardware capacities of computers also grew exponentially, and CADD (Computer-Aided Drug Design) began to be increasingly applied to drug discovery. An effort was made to integrate CADD in the traditional chemistry and biology workflow and its principal development took place in the pharmaceutical industries, although academic groups were also instrumental in developing some capabilities (18). The declining costs of memory and storage, increasing processing power and facile computer graphics software put CADD within the grasp of relatively untrained computational chemists or experimental scientists. While the general verdict on the contribution of CADD to drug discovery is still forthcoming, many drugs currently on the market now include CADD as an important component of their discovery and development (19). Many calculations that once were impractical because of constraints of time and computing power can now be routinely performed, some on a common desktop. Currently the use of CADD in drug design aims to address three principal problems, all of which are valuable to drug discovery.

Virtual Screening

Virtual screening (VS) is defined by the ability to test thousands or millions of potential ligands against a protein, distinguish the actives from inactives and rank the ‘true’ binders in a certain top fraction. If validated, VS would serve as a valuable complement, if not substitute, for HTS and would save significant amounts of resources and time in HTS. Just like HTS, VS has to circumvent the problem of false positives and false negatives, the latter of which in some ways are more valuable since by definition they would not be identified. VS can be either structure-based or ligand-based. Both approaches have enjoyed partial success although recent studies have validated 3D ligand-based techniques in which ligand structures are compared to known active ligands by means of certain metrics as having a greater hit rate than structure-based techniques (20). Virtual libraries of molecules such as DUD (21) (Directory of Useful Decoys) and ZINC (22) have been built to test the performance of several VS programs and compare them with each other. These libraries typically consist of a few actives and several thousand decoys, with the goal being to rank the true actives above the true decoys using some metric.

Paramount in such retrospective assessment is an accurate method for evaluating the success and failure of these methods (23,24). Until now ‘enrichment factors’ have mostly been used for this purpose (24). The EF refers to the number of ‘true’ actives that rank in a certain top fraction (typically 1% or 10%) as a function of the screened database. However the EF suffers from certain drawbacks, such as being dependent on the number of decoys in the dataset. To circumvent this problem, recent studies have suggested the use of the ROC (Receiver Operator Characteristic) curve, a graph that plots false positives vs. true positives (24,25) (Figure 1). The curve indicates what the false positive rate is for a given true positive rate and the measured variable is the Area Under the Curve (AUC). A completely random performance gives a straight line (AUC 0.5), while better performance results in a hyperbolic curve (AUC > 0.5).

Figure 1: ROC curve for three different VS scenarios. Completely random performance will give the straight white line (AUC 0.5), an ideal performance (no false positives and all true positives) will give the red line (AUC 1.0) and a good VS algorithm will produce the yellow curve (0.5 < AUC < 1.0)

Until now VS has provided limited evidence of success. Yet its capabilities are being improved and it has become a part of the computational chemist’s standard repertoire. In some cases VS can provide more hits compared to HTS (26) and in others, VS at the very least provides a method to narrow down the number of compounds actually assayed (27). As advances in general SBDD and LBD continue, the power of VS to identify true actives will undoubtedly increase.

Pose-prediction

The second goal sought by computational chemists is to predict the binding orientation of a ligand in the binding pocket of a protein, a task that falls within the domain of SBDD. This endeavor if successful will provide an enormous benefit in cases where crystal structures of protein-ligand complexes are not easily obtained. Since such cases are still very common, pose-prediction continues to be both a challenge as well as a valuable objective. There are two principal problems in pose prediction. The first one relates to the scoring of the poses obtained in order to identify the top-scoring pose as the ‘real’ pose; current docking programs are notorious for their scoring unreliability, certainly in an absolute sense and sometimes even in a relative sense. The problem of pose prediction ultimately is defined by the ability of an algorithm to find the global minimum orientation and conformation of a ligand on the potential energy surface (PES) generated by the protein active site (28). As such it is susceptible to the common inadequacies inherent in comprehensively sampling a complex PES. Frequently however, as in the case of CDK7, past empirical data including knowledge of poses of known actives (roscovitine in this case) provides confidence about the pose of the unknown ligand.

Another serious problem in pose prediction is the inability of many current algorithms to adequately sample protein motion. X-ray structures provide only a static snapshot of ligand binding that may obscure considerable conformational changes in protein motifs. Molecular dynamics simulations followed by docking (‘ensemble docking’) have remedied this limitation to some extent (29), induced-fit docking algorithms have now been included in programs such as GLIDE30, and complementary information from dynamical NMR studies may help judicious selection between several protein poses. Yet simulating large-scale protein motions are still outside the domain of most MD simulations, although significant progress has been made in recent years (31,32).

An example of how pose prediction can shed light on anomalous binding modes and possibly save the allocation of time and financial resources was experienced by the present author during his study of a paper detailing the development of inhibitors of the p38 MAP kinase (33). In one instance the authors followed the SAR data in the absence of a crystal structure and observed contradictory changes in activity influenced by structural modifications. Crystallography on the protein ligand complex finally revealed an anomalous conformation of the ligand in which the oxygen of an amide at the 2 position of a thiophene was cis to the thiophene sulfur, when chemical intuition would have expected it to be trans. The crystal structure showed that an unfavorable interaction of a negatively charged glutamate with the sulfur in the more common trans conformation forced the sulfur to adopt the slightly unfavorable cis position with respect to the amide oxygen. Surprisingly this preference was seen in all top 5 GLIDE poses of the docked compound. This example indicates that at least in some cases pose prediction could serve as a valuable timesaving complement and possible alternative to crystallography.

Binding affinity prediction

The third goal is possibly the most challenging endeavor for computational chemistry. Rank-ordering ligands in terms of their binding affinity involves accurate scoring, which as noted above is a recalcitrant problem. The problem is a fundamental one since it really involves calculating absolute free energies of protein ligand binding. The most accurate and sophisticated approaches for calculating these energies are the Free-Energy Perturbation (FEP) (34) or Thermodynamic Integration (TI) methods based on MD simulations and statistical thermodynamics. The methods involve ‘mutating’ one ligand to another in hundreds of thousands of infinitesimal steps and evaluating the binding enthalpy and entropy at every step. As of now, these techniques are some of the most computationally expensive techniques in the field. This problem typically limits their use only to evaluating free energy changes between ligand that differ little in structure. Therefore successful examples where they have found their greatest use involve cases where small substituents on aromatic rings are modified to evaluate changes in binding affinity (35). However as computing power grows, these techniques will continue to find more applications in drug discovery.

Apart from these three goals, a major goal of computational science in drug discovery is to aid the later stages of drug development when pharmacokinetics (PK) and ADMET (Absorption Distribution Metabolism Excretion Toxicity) issues are key. Optimizing the binding affinity of a particular compound to a protein only results in an efficient ligand and not necessarily an efficient drug. Computational chemistry can make valuable contributions to these later developmental stages by trying to predict the relevant properties of ligands in the early stages, thus limiting the typically high attrition of drugs in the advanced phases. While much remains to be accomplished in this context, some progress has been made (36). For example, the well-known Lipinski Rule of Five (37) provides a set of physicochemical properties necessary for drugs to have good bioavailability and computational approaches are starting to help evaluate these properties during early stages. The QikProp program developed by Jorgensen et al. calculates properties like Caco-2 cell permeability, possible metabolites, % absorption in the GI tract and logP values (38). Such programs are still largely empirical, depending on a large dataset of properties of known drugs for comparison and fitting.

Models, computers and drug discovery

In applying models to designing drugs and simulating their interactions with proteins, the most valuable lesson to remember is that these are models that are generated by computers. Models seldom mirror reality; in fact they often may succeed in spite of reality. Models are not usually designed to simulate reality but they are designed to produce results that agree with experiment. There are many approaches that produce such results. These approaches may not always encompass factors operating in real environments. In QSAR for instance, it has been shown that adding enough number of parameters to your model can lead to a good fit to the data with a high correlation coefficient. However the model may be overfitted; that is, it may seem to fit the known data very well but may fail to predict the unknown data, which is what it was designed to do (16,39). In such cases, using more advanced statistical methods and using ‘bootstrapping’ (leaving out a part of the data and looking at the resulting fit to investigate whether that part of data is predicted) can lead to improvement in results (39).

Models can also be used in spite of outliers. A high correlation coefficient of 0.85 that leads to acceptance of a model may nonetheless lead to one or two outliers. It then becomes important to be aware of the physical anomaly which the outliers represent. The reason for this is clear. If the variable producing the outlier does not constitute a part of the model building, then applying the well-trained model to a system where that particular variable suddenly becomes dominant will result in a failure of the model. Such outliers, termed ‘black swans’, can prove extremely deleterious if their value is unusually high (40). This phenomenon is known to operate in the field of financial engineering (40). In modeling for instance, if the training set for a docking model consists of largely lipophilic protein active sites, then the model may fail to deliver cogent results if applied to a set of ligands binding to a protein that has an anomalously polar or charged active site. If the value of this protein is unusually high for a particular pharmaceutical project, an inability to predict its behavior under unforeseen circumstances may lead to valuable losses. Clearly in this case the physical variable, namely the polarity of the active site, was not taken into account in spite of the fact that the model delivered a high initial correlation merely because of the addition of a large number of parameters or descriptors, none of which was related in a significant way to the polarity of the binding pocket. The difference between correlation and causation is especially relevant in this respect. This hypothetical example illustrates one of the limitations of models iterated above; that they may not bear relationship to actual physical phenomena and may yet fit the data well enough because of various reasons to elicit confidence in their predictive ability.

In summary, models of the kind that are used in computational chemistry have to be carefully evaluated, especially in the context of practical applications like drug discovery where time and financial resources are valuable. Training the model on high-quality datasets, reiterating the difference between correlation and causation and better application of statistics and bootstrapping can help to avert model failure.

In the end however, it is experiment that is of paramount importance for building the model. Inaccurate experimental data with uncertain error margins will undoubtedly hinder the success of every subsequent step in model building. To this end, generating, presenting and evaluating accurate experimental data is a responsibility that needs to be fulfilled by both computational chemists and experimentalists, and it is only a fruitful and synergistic alliance between the two groups that can help overcome the complex challenges in drug discovery.

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Demolishing the 'vacuous' argument for the RRW

noreply@blogger.com (Wavefunction) — Tue, 28 Apr 2009 16:02:00 +0000

The Reliable Replacement Warhead program (RRW) is a long-standing and controversial proposal that aims to replace aging plutonium pits and other parts in nukes to modernize the current US nuclear arsenal. Proponents of the RRW say that having nuclear weapons of dubious function and quality will defeat the basic purpose of a deterrent and therefore modernizing the arsenal and replacing worn out parts is essential for its very existence. In addition, Russia has indicated that it is modernizing its nuclear arsenal and this fact has put further pressure on implementation of the RRW. Opponents of the RRW say that by modernizing the arsenal the US will send out the wrong signals to the rest of the world, indicating that nuclear deterrence and weapons development is still an important part of US defense strategy. In any case, very recently the top government advisory group JASON which among others included Freeman Dyson) did a study on the central plutonium 'pits' in the nuclear weapons and concluded that these would last for at least half a decade if not more. Since then several arguments have continued to float around for the RRW.

In the latest issue of The Bulletin, Jeffrey Lewis and Kingston Reif do a neat and clean job in demolishing the latest argument made by a General Chilton who, of all possible reasons, bases his argument on vacuum tubes, the point being that outdated vacuum tubes in nukes necessitate replacement. The last line is priceless and is not exactly BAS-like

Firstly, vacuum tubes are not used in the physics package of a single nuclear weapon design. Vacuum tubes are used only in the radar-fuse, which tells the firing system when the bomb is at the correct altitude for detonation, in some modifications (mods) of one warhead design, the B61 gravity bomb. In total, the B61 bombs that have vacuum tubes in their radar-fuses account for only about one in ten operationally deployed warheads. (Vacuum tubes are used in the radars of three B61 mods: 3, 4, and 7. Mods 10 and 11 have newer radars that use solid-state electronics.) The fuses in these weapons are old, but perfectly functional. To reiterate, vacuum tubes are not in use in any other warhead design, including the W76 warhead, a portion of which would be replaced by the first RRW warhead, the WR1, if it ever were funded and developed.

Secondly, the Energy Department has routinely replaced radars without nuclear testing or redesigning the physics package. In fact, during the 1990s, Sandia National Laboratories scientists developed the MC4033 common radar, which uses solid-state electronics, for planned refurbishments of the B61 and B83 gravity bombs. All B83 bombs now use the common radar, though similar plans to fit a new radar on all B61s have been repeatedly deferred.

Most recently, in 2006, Sandia planned to replace the remaining B61 vacuum tube radars as part of ALT 364/365/366. The National Nuclear Security Administration, which overseas the nuclear weapons complex, canceled these latest ALTs, which would have resulted in the removal of the last vacuum tubes from the U.S. nuclear stockpile, because the U.S. Air Force preferred replacement to life extension. Due to this absurd twist, one could say that vacuum tubes remain in the U.S. nuclear arsenal in part because of the RRW, contrary to Chilton's insistence that the RRW is needed to get rid of them.

The bottom line is that vacuum tubes are used only sparingly in the U.S. nuclear arsenal and can be replaced on short notice if the need arises, independent of whether Congress funds the RRW Program. Of the many reasons that Defense and Energy officials have put forth to justify the RRW Program, the need to replace vacuum tubes is the worst and has no place in the debate about the RRW or modernizing the nuclear stockpile. General Chilton can stick that prop in his, um, pocket.

FDA does, and should, stick to only science

noreply@blogger.com (Wavefunction) — Thu, 23 Apr 2009 15:18:00 +0000

In a welcome reversal of a key politics-driven Bush era mandate, the FDA has approved the Plan B morning-after emergency contraception pill for 17-year olds. Previously the reluctance of FDA to approve the product had led a senior official to rightly resign. Not surprisingly, this decision drew wrath from conservative groups who say that the pill would "encourage promiscuity". This statement is rather typical of conservative statements opposing abortion and promotion of contraceptive measures in school, in spite of the fact that abstinence-only programs have been shown to essentially cause no change or even an increase in "promiscuity".

But here's the thing, and it should be clear all along; the FDA should stick to science and nothing else. Just as the conservative FDA officials during the Bush era were utterly out of line opposing Plan B because of political and religious interests, so should liberals also not applaud the FDA decision as a moral value judgement. The business of the FDA is to determine the efficacy and safety of medical products, period. The moment it starts to pontificate on the moral or political value of its decision its immediately sets itself on a slippery slope.

So just like the NAS and the NCSE should stick to demonstrating the evidence for evolution and lack of evidence for ID/creationism and not pass judgement on whether science and religion are compatible, so should the FDA stick to the science behind the approval of medical products. Not making political or religious statements, either conservative or liberal, would be in the safe and best interests of both the FDA and society.

Timeless Classics

noreply@blogger.com (Wavefunction) — Wed, 22 Apr 2009 15:01:00 +0000

As someone who loves to read more than anything else, I have long also been addicted to classic textbooks. Some of the most memorable moments of my student life involved walking into ghostly libraries looking like medieval castles and dusting off inches of dust collected on tomes which I regarded as treasures, volumes of great works that had not been checked out in 25 years and were languishing in anonymity, begging to be touched and read.

Sadly very few seem to bother about these anymore and regard them as outdated. I can bet that no modern undergraduate that I can meet has browsed Pauling's classic "The Nature of the Chemical Bond", a book that is regarded by many as one of the most influential works in chemistry of all time. In my opinion these students confuse outdated with poorly written. But many of the basics of chemistry don't change, and many of these old works provide crystal clear treatments of basics that are lacking in more modern books. As far as fundamentals go these books have stood the test of time and several are still in print, although some regrettably are not. A comment by Srini about Morrison & Boyd brought back fond memories of favourite classics...

Morrison & Boyd: I have already mentioned it before. Crystal clear treatments of mechanism with an especially outstanding chapter on electrophilic aromatic substitution. For organic chemistry, I have to admit that the new book by Clayden et al. is probably the single-best book on the subject I have seen, but the elegance of explanation in M & B is still hard to beat. I also remember the book by Roberts and Caserio also being pretty good. For what it's worth, the book which compresses the most number of insights in the fewest number of words is a slim volume by Peter Sykes whose clarity in explaining mechanistic concepts in short, crisp paragraphs is unprecedented.

The Great Linus Pauling: I first saw "The Nature of the Chemical Bond" as a freshman. While I then perceived it as boring and too detailed, it was only later that I recognized its monumental significance. Many famous scientists including Max Perutz and Francis Crick have learnt chemistry from it and Pauling's other book. The number of ground-breaking concepts that Pauling invented and put into this book is staggering. Especially check out the chapters on hybridization and partial ionic character of bonds. I have browsed all three editions, and the second one is probably the best-written, although the third edition is the most up-to-date and still in print. A measure of the book's significance in the history of science can be gained from the simple fact that after publication of the first edition the volume was cited no less than 16,000 times in the next 10 years. One constantly keeps on finding new papers in journals like Science and Nature that still cite it. Pauling's "The Nature..." did for modern chemistry what the Principia did for natural philosophy; it infused its subject with logic and tied together disparate threads to formulate a comprehensive and lasting science.

As if one work were not enough, Pauling also authored "General Chemistry". Again, it's a model of simplicity and clarity (note for instance how he explains the source of the difference in the three pKa values of phosphoric acid) although its emphasis on more descriptive chemistry makes it look a little quaint. The text is still widely read and in print as a Dover reprint edition; I have a copy on my shelf for a while now and recently saw one in the Barnes & Noble@GeorgiaTech.

Finally, "Introduction to Quantum Mechanics with Applications to Chemistry" co-authored with E Bright Wilson at Harvard was the first book to explain quantum mechanics to chemists. I will admit the book is not easy to read, but with effort one can find many gems in the first few chapters, especially the treatment of the hydrogen atom. Again, a Dover reprint is available and cheap.

With these three books, the prodigious Pauling secured his place in history not only as the greatest chemist of all time, but one of the most successful and greatest scientific writers of the century.

Glasstone: Samuel Glasstone was a remarkably prolific and versatile technical writer. It was in high-school that I came across a compendium of nuclear science that he had written, "Sourcebook on Atomic Energy". It is hands down the single-best example of technical science writing that I have come across, and wherever I have been since then, I have always had a copy on my bookshelf. The book is a model for comprehensive, all-inclusive writing that is clear as water from a virgin glacier. It also satisfies the difficult condition of being extremely valuable for both laymen and scientists. No praise for the book matches the praise that then AEC chairman and Nobel Laureate Glenn Seaborg penned in the foreword. Seaborg quipped that this was "technical writing at its very best" and that Glasstone was a man who had fortuitously come along at the right time to fulfill the needs of science and technology.

The range of Glasstone's writing is amazing; multi-volume works on physical chemistry and treatments of thermodynamics, electrochemistry, nuclear reactor engineering and even a sourcebook on space sciences. An exhaustively detailed and yet comprehensible book on the effects of nuclear weapons served as a standard declassified guide for years and is still in print. Although his PChem books are now really out-of-date, they still educated me in the basics when I first found them, and I learnt a lot from his book on thermodynamics. But again, nothing beats "Sourcebook on Atomic Energy" a book against which I think every other technical work should be measured.

"Valence" by Charles Coulson: Very few people in history have had the capacity to be both fine scientists and excellent writers. Charles's Coulson's book was the first book, even before Pauling and Wilson, to make quantum chemistry comprehensible to students. When it comes to pedagogical explanation it's hard to beat the British, and this is the finest example of that. I was fortunate to secure a copy at the famous Powell's bookstore in Portland, OR.

Classic books are like old wine. They should be cherished, preserved, and sampled one concept at a time.

The Pope of Cosmology 'very ill'

noreply@blogger.com (Wavefunction) — Mon, 20 Apr 2009 23:01:00 +0000

For a 67 year old man with ALS who has already defied medical science, this is not good news at all. Remember what happened to Christopher Reeve. When you are in a condition like this, even otherwise normal ailments may become life-threatening.

I have been recently reading a lot about Hawking in Leonard Susskind's splendid book "The Black Hole War: My Battle with Stephen Hawking to Make the World Safe for Quantum Mechanics". The rather grandiose title of the book obscures a perfectly entertaining and informative romp through the world of black holes; this is about as close as possible to black hole thermodynamics, string theory and quantum mechanics that we laymen can get without being drowned in a whirlpool of math. Susskind who is a professor at Stanford tells the story of the paradox of information falling into a black hole and supposedly disappearing with lots of verve, hilarious personal anecdotes and tributes to famous physicists. Being a prime participant in the debate with Hawking on the other side, he is in a unique position to tell the story. His recounting of the way the physicist Jacob Bekenstein used high-school math to derive the formula for the entropy of black holes is astounding; very rarely has someone used such simple physics and mathematics to discover such profound relationships and the act reminded me of Bell's Theorem, another spectacular twentieth-century physics result that can essentially be derived using high-school mathematics.

But more than anyone else, it is Hawking's figure that looms large in the book. Susskind describes how his physical disability, his strange disembodied computer voice and his astonishingly brilliant and creative mind guarantees the kind of reverence and silence wherever he appears that otherwise only seems to be reserved for the Pope. Susskind vividly describes a typical Q & A session after a Hawking lecture; Hawking's physical condition means that he can compose even a "yes/no" answer only after several minutes, and what's striking is that during such times Susskind has witnessed audiences of thousands maintain stand-still silence with not a whisper spoken for sometimes fifteen minutes while the great man painfully communicates himself. Hawking may be the only living scientist whose presence provokes utter and rapt silence and attention that one would observe only during religious prayer. No wonder Hawking is compared to God by many, a comparison which only makes him uncomfortable. Susskind describes a particular time in a restaurant where a passerby went to his knees and virtually kissed Hawking's feet. Needless to say Hawking was embarrassed and galled.

In any case, we can only hope that Hawking feels better. However in one way we can rest assured; Stephen Hawking's name has been etched in the annals of science forever. That's the power of ideas. Their timelessness assures us that they remain youthful and vibrant, irrespective of the age and condition of their source. But let's all hope Hawking springs back from this illness to his mischievous, witty self.

Assessing the known and unknown unknowns: WYSI(N)WYG

noreply@blogger.com (Wavefunction) — Fri, 17 Apr 2009 15:32:00 +0000

Ken Dill and David Mobley from UCSF have a really nice review in Structure on computational modeling of protein-drug interactions and the problems inherent in the process. I would strongly recommend anyone interested in the challenges of calculating protein-drug binding to read the review, if not for anything else for the copious references provided. The holy grail of most such modeling is to accurately calculate the free energy of binding. For doing this we frequently start with a known structure of a protein-ligand complex. The main point that the authors emphasize is that when we are looking at a single protein-ligand complex, deduced either through crystallography or NMR, we are missing a lot of important things.

Perhaps the most important factor is entropy which is not at all obvious in a single structure. Typically both the protein and the ligand will populate several different conformations in solution. Both will have to pay complex entropic penalties to bind one another. The ligand strain energy (usually estimated at 2-3 kcal/mol for most ligands) also plays an important role. The desolvation cost for the ligand also can prominently figure. In addition both protein and ligand will have some residual entropy even in the bound state. As if this were not enough of a problem, much of the binding energy can come from the entropic gain that the release of water molecules from active sites engenders. Calculating all these entropies for protein, ligand and solvent is important for accurately calculating the free energy of protein-ligand binding. But there are few methods that can accomplish this complex task.

Among the methods reviewed in the article are most of the important methods used currently. Usually the tradeoff for each method is between cost and accuracy. Methods like docking are fast but inaccurate although they can work well on relatively rigid and well-parameterized systems. Docking also typically does not take protein motion and induced-fit effects into account. Slightly better methods are MM-PBSA or MM-GBSA which as the names indicate, combine docking poses with an implicit solvent model (PBSA or GBSA). Entropy and especially protein entropy is largely ignored, but since we are usually comparing similar ligands, such errors are expected to cancel. Going to more advanced techniques, relative free-energy calculations use molecular dynamics (MD) to try to map the detailed potential energy surfaces for both protein and ligand. Absolute free-energy perturbation calculations are perhaps the gold standard in calculating free energies but are hideously expensive. They work best for ligands that are simple.

There is clearly a long way to go before calculation of ∆Gs becomes a practical endeavor in the pharmaceutical industry. There are essentially two factors that contribute to the recalcitrance of the problem. The first factor as indicated is the sheer complexity of the problem; assessing the thermodynamic features of protein, ligand and solvent in multiple configurational and conformational states. The second problem is a problem inherent in nature; the sensitivity of the binding constant to the free energy. As iterated before, the all-holy relation ∆G = -RT ln K ensures that an error of even 1 kcal/mol in calculation will translate to a large error in the binding constant. The myriad complex factors noted above ensure that errors of 2-3 kcal/mol already constitute the limit of what the best methods can give us. Recall that an error of 3 kcal/mol means that you are dead and buried.

But we push on. One equal temper of heroic hearts. Made weak by time and fate, but strong in will. To strive, to seek, to find, and not to yield. At some point we will reach 1 kcal/mol. And then we will sail.

Reference:
Mobley, D., & Dill, K. (2009). Binding of Small-Molecule Ligands to Proteins: “What You See” Is Not Always “What You Get” Structure, 17 (4), 489-498 DOI: 10.1016/j.str.2009.02.010

Reading C P Snow and The Two Cultures

noreply@blogger.com (Wavefunction) — Thu, 16 Apr 2009 16:40:00 +0000

Over at The Intersection blog which I often read and comment on, Chris Mooney (author of "The Republican War on Science") has initiated an informal reading of C. P. Snow's "The Two Cultures". Anyone who is interested is more than welcome to read the influential and very short lecture and blog or comment on it. The schedule is listed in the post. The recommended edition is the Canto edition, with a very readable introduction by Stefan Collini. Incidentally it was this version that I read many years ago. Time now for a re-reading.

I have encountered Snow in two other interesting books. The first one- "The Physicists: A Generation that Changed the World"- was authored by him and contains clear and abundant photographs as well as recollections and insights on some of the most famous physicists of the century whom he closely knew. In this for instance I read his generous assessment of Enrico Fermi that captures the supreme greatness of the man's talents and achievements

"If Fermi had been born twenty years earlier, it is possible to envisage him first discovering Rutherford's nucleus and then discovering Bohr's atom. If this sounds like hyperbole, anything about Fermi is likely to sound like hyperbole"

Snow also thought that Robert Oppenheimer's real tragedy was not his sidelining or victimization during the 1950s witch hunts but the fact that he would have thrown away all his fame, brilliance and glory if he had the privilege to make one timeless discovery like Pauli's exclusion principle.

Another book with Snow in it is a fascinating piece of "scientific fiction" written by John Casti. "The Cambridge Quintet: A Work Of Scientific Speculation" features four famous scientists and intellectuals- Ludwig Wittgenstein, Erwin Schrödinger, J B S Haldane and Alan Turing- being invited over to Snow's house for a multi-course dinner. As the dinner unfolds, so do the conversations between these stalwarts. The topic is artificial intelligence, and the participants hold forth in myriad and fascinating ways on the subject with excursions that not surprisingly take them into avenues like the philosophy of mind and language, epistemology and metaphysical questions. Very much worth reading.

In any case, I am looking forward to reading The Two Cultures again and writing about it. Anyone who is interested is more than welcome. The entire lecture is 50 pages and could be read in a few hours of thoughtful contemplation. The topic is as relevant today as it was then, which explains the lecture's enduring appeal. The consequences though could be vastly more pronounced.

The rest is all noise: errors in R values, and the greatness of Carl Friedrich Gauss reiterated

noreply@blogger.com (Wavefunction) — Wed, 15 Apr 2009 17:03:00 +0000

One of the questions seldom asked when building a model or assessing experimental data is "What's the error in that?". Unless we know the errors in the measurement of a variable, fitting predicted to experimental values may be a flawed endeavor. For instance when one expects a linear relationship between calculated and experimental values and does not see it, it could either mean that there is a flaw in the underlying expectation or calculation (commonly deduced) or that there is a problem with the errors in the measurements (not always discussed).

Unfortunately it's not easy to find out the distribution of errors in experimental values. The law of large numbers and central limit theorem often thwart us here; most of the times the values are not adequate enough to get a handle on the type of error. But in the absence of such concrete error estimation, nature has provided us with a wonderful measure to handle error; assume that the errors are normally distributed. The Gaussian or normal distribution of quantities in nature is an observation and assumption that is remarkably consistent and spectacularly universal in its application. You can apply it to people's heights, car accidents, length of nails, frequency of sex, number of photos emitted by a source and virtually any other variable. While the Gaussian distribution is not always followed (and strictly speaking it applies only when the central limit theorem holds), I personally regard it to be as much of a view into the "mind of God" as anything else.

In any case, it's thus important to calculate the distribution of errors in a dataset, Gaussian or otherwise. In biological assays where compounds are tested, this becomes especially key. An illustration of the importance in error estimation in these common assays is provided by this recent analysis of model performance by Philip Hajduk and Steve Muchmore's group at Abbott. Essentially what they do is to estimate the standard deviations or errors in a set of in-house measurements on compound activities and look at the effect of those errors on the resulting R values during comparison of calculated activities with these experimental ones. The R value or correlation coefficient is a time-tested and standard measure of fit between two datasets. The authors apply the error they have obtained in the form of "Gaussian noise" to a hypothetical set of calculated vs predicted activity plots with 4, 10 and 20 points. They find that after applying the error, the R-value itself adopts a Gaussian distribution that varies from 0.7 to 0.9 in case of the 20 point measurement. This immediately tells us that any such measurement in the real world that gives us, say, a R value of 0.95 is suspicious since the probability of such a value arising is very low (0.1%), given the errors in its distribution.

You know what should come next. The authors apply this methodology and look at several cases of calculated R values for various calculated vs measured biological activities in leading journals during 2006-2007. As they themselves say,

It is our opinion that the majority of R-values obtained from this (small) literature sample are unsubstantiated given the properties of the underlying data.

Following this analysis they then apply similar noise to measurements for High-Throughput Screening (HTS) and Lead Optimization (LO). Unlike HTS, LO usually deals with molecules sequentially synthesized by medicinal chemists that are separated by small changes in activity. To investigate the effect of such errors, enrichment factors (EFs) are calculated for both scenarios. The EF denotes the percentage of active molecules found or "enriched" in the top fraction of screened molecules relative to random screening, with a higher EF corresponding to better performance. The observation for HTS is that small errors give large EFs, but what is interesting is that even large errors in measurement can give modest enrichment, thus obscuring the presence of such error. For LO the dependence of enrichment on error is less, reflecting the relatively small changes in activity engendered by structure optimization.

The take home message from all this is of course that one needs to be aware of errors and needs to apply those errors in quantifying measures of model assessment. God is in the details and in this case his name is Carl Friedrich Gauss, who must be constantly beaming from his Hanover grave.

Reference:
Brown, S., Muchmore, S., & Hajduk, P. (2009). Healthy skepticism: assessing realistic model performance Drug Discovery Today, 14 (7-8), 420-427 DOI: 10.1016/j.drudis.2009.01.012