The Baker lab at the University of Washington has been influential in the development of computational tools to model protein folding.  They are most well known for the ROSETTA package, which does a fantastic job in many cases of predicting a protein fold based on fragments of other known structures.

They have now developed a protein folding game, of all things, called FoldIt!.  The user registers for an account on the website and downloads a program which runs the game locally.  As of now they only have Windows and Mac clients, but I got the game running under Wine no problem.

FoldIt! presents the player with “puzzles” which involve resolving steric clashes in the proteins.  The easy tutorial levels teach you how to play the game (which mostly involves clicking and dragging on different parts of the protein), and you’re guided by a funny cartoon version of Dr. Baker himself.  You get points based on how “good” your solution to the puzzle is, and complete a given puzzle when you cross a certain threshold score.

It’s sort of fiendishly fun to play, although maybe that’s just because I’m a biochemist.  It also seems like it would make a GREAT learning tool for use in biochemistry courses.  You can form groups as well, so it might be fun to make a group out of your class and see how highly they can place.

I’ve got about 70 pages written on a document that I call “my thesis”. The problem is, I hate it. I’ve written it all in fits and spurts, jumping around from one section to the next. Some days I’ll write pages and pages and it seems like it’s going really well, and other days I’ll spend all day staring at emacs and not getting anything down. Lately it’s been much more of the latter.

I decided to give something a shot. I fired up OpenOffice and set it to full screen web view. This gives you a glorious screen of space to write in, without any distractions. I just started writing, trying as hard as I could to keep things like formatting, citations, paragraph structure, and above all sections out of my mind. I want to write something that flows.  Maybe it’s a crazy idea.  A thesis is a really large document to write from start to finish, and the nature of research (especially my research) makes combining the whole thing into a fluid narrative tough.  Call me stupid, but I’m going to take a stab at it.

I’m not sure what this document is yet, to be honest.  It may become part of my thesis.  It may just be a convenient way to help me organize my thoughts before I write them into the official document.  It’s just as likely (more likely in some ways) that I’ll give up on this a week from now.  Until then, I’ll be posting it here on Plausible Accuracy for you to read and comment on.  Any feedback is greatly appreciated.

I’m planning on just posting chunks whenever they get to some modiucm of “done” - that is to say more or less when one train of thought has been laid down.  I’m not editing this as I go (besides the most obvious of typos), not including citations or figures.  The idea is just to write.  I’ll post links to all the sections somewhere as I add on.

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The Protein Data Bank is one of the premier examples of free and open access to scientific data.  It was started in 1971 as a place to house structural data on proteins, and since then has grown tremendously.

This month (possibly today or tomorrow if they continue at their normal rate) they will hit 50,000 structures.  The PDB has actually seen explosive growth recently, doubling in size in less than 4 years, and is projected to triple in size again over 6 more years.  This is largely due to the nascent field of high-throughput proteomics, in which scientists attempt to solve as many structures as they can as rapidly as possible, often without worrying about what the protein actually does.

All of the structures in the PDB are free for anyone to download, and deposition is fairly straightforward as well.  They have done a good job updating the site to make searching for the molecule of interest relatively simple.  I feel like more could be done to indicate differences in cases where you have many structures of a single protein, but at some point this becomes an editorial burden that I’m not sure they want to carry.

So, congratulations to the PDB are in order.  I’ll celebrate by pointing you to the structure that really got me into biochemistry, alpha-hemolysin

But not today.  To be fair the paper I’ll be talking about today (from today’s issue of PNAS) inolves quite a few researchers from several institutions.  In it, the researchers describe sort of a new way of “solving” protein structures, although the technique they describe really sits at the boundary of solution and prediction.

In effect, the method involves the early stages of solving a protein’s structure using NMR spectroscopy.  This is a well-established method which has yielded lots of structures.  One of the nice things about NMR is that it’s relatively easy (compared to X-Ray crystallography) to get your sample - you “just” need to have a very pure, highly concentrated bit of protein.  Once you have the sample, it’s also relatively facile to collect data on it - typical NMR experiments take a few days, but pertinent information is available in minutes.  Contrast this to X-ray crystallography, in which growing the crystal might take weeks or months (even once the proper conditions are identified), and data collection takes on the order of hours.

The gist of the method described in the paper is this: you collect the “fast and easy” information on your NMR sample, then construct a model of your protein that uses this data to extract fragments of already-solved structures and put them together in a way which matches your NMR information.  It’s sort of like building a new device from LEGO parts which you’ve gotten by breaking up other devices.  Well, to extend the analogy to the breaking point, what they really do is use the amino acid seqeunce of the protein they are examining to pull out a bunch of LEGO pieces (solved structures with similar sequences), and then apply the NMR data to pick the “best” piece for that bit of the protein.  They call this CS-ROSETTA (CS for chemical shift, the NMR data; ROSETTA for the method of picking out the individual segments of structure and assembling them into a new protein).

Now that I’ve thoroughly muddled a rather efficient and clean approach, how does it do?  Pretty well, according to the authors.  The figure to the right is from the paper, and shows an overlay of the actual structures of some of the test proteins (determined by X-ray crystallography or NMR, in blue) and the lowest-energy structures from their CS-ROSETTA method (in red).  You can see that they overlap quite nicely.

They follow up the test case by using the CS-ROSETTA technique on several structures that were in the process of being solved by a proteomics consortium.  Once again, they find very good agreement between the final structures (solved after the modeling with CS-ROSETTA) and their predicted folds.

As a structural biochemist myself, I’m always interested in new ways to solve structures as quickly and efficiently as possible.  What concerns me is that methods based on simulations, no matter how accurate or elegant, will always be viewed with skepticism by the scientific community.  Although there is a fair amount of simulation already in the “standard” methods of NMR and X-ray crystallography, it seems that this can be more easily justified in the eyes of the community than the types of calculations needed to “solve” a structure with ROSETTA or other computational methods.

Perhaps it’s coming off of a relaxing weekend, perhaps it’s just because I’m in a “get things done” sort of mode, I decided to send in a job application today.  It’s for a position at the Research Collaboratory for Structural Bioinformatics (RCSB), specifically at the Protein Data Bank.  This is the main repository for protein structures, and it serves an absolutely critical and invaluable role to the research community.

I had sent in an application for the position once before.  That time, I was rapidly told that they were not hiring any additional people for the position I was interested in.  However, the opening has remained listed on the website, and I simply couldn’t take the first “no” as an answer.

The reason is that, by all appearances, the job seems to be perfect for me.  The criteria they list are more or less an itemized list of my research experience and personal interests.  The RCSB is one of the most established and well-respected repositories of structural information, they are non-profit (yay) and the information they collect is freely available (super yay).  The job is right next door to where my wife might get an offer for a post-doc, and it’s not so far away that we’d be leaving all of our friends behind.

Of course, the very perfection that this job seems to be also serves to make me especially nervous about being turned down for it.  C’est la vie, I suppose.  Wish me luck.

Viruses are really interesting.  We all know about them, mostly as those things that make us sick.  In fact, only a very small percentage of viruses lead to illness in humans.  This is a good thing, because there are lots and lots of viruses.  I’ve been told that there are about 10,000,000 in a single milliliter of ocean water.

Viruses are, generally speaking, composed of two main parts.  The capsid, or outer “skin” if you will, is made up of proteins.  Inside of the capsid is the genetic material (this can be RNA or DNA).  Scientists are interested in examining how these particles are constructed, both as a way to learn methods of combating viruses, as well as to attempt to “reverse engineer” them to do our evil bidding.

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I spent the first 4 years of my graduate program working on the NK1 receptor.  This is one of the very large and important family of G Protein-coupled Receptors which are a poorly understood and pharmaceutically interesting set of signaling proteins.

In the March 14 issue of Science, George et al report the use of NK1 antagonists - that is to say small molecules which block the signaling that normally takes place through NK1 - to treat alcohol cravings.

Before using the small molecule antagonist, they first looked at mice that were mutated to remove the gene for NK1.  These mice tended to drink a lot less booze than normal mice, or even mice which were missing the NK1 gene on only one chromosome.  Based on these results, they then decided to examine the antagonist treatment in humans.

It’s a bit surprising that the authors move so quickly from animal to human experiments, but they point out that the antagonist they chose is not effective in binding to the mouse or rat versions of NK1.  There is one line in the paper that is a bit frightening:

Preclinical pharmacology, safety, and human pharmacokinetics of LY686017 will be reported separately

I hope this is only for reasons of space constraints in Science…

Regardless, the authors found that treatment with the antagonist tended to decrease alcohol cravings in the patients (who were classified as “anxious alcoholics).  It’s an interesting result, and I think merits further investigation in the form of an expanded clinical trial.

George, D.T., Gilman, J., Hersh, J., Thorsell, A., Herion, D., Geyer, C., Peng, X., Kielbasa, W., Rawlings, R., Brandt, J.E., Gehlert, D.R., Tauscher, J.T., Hunt, S.P., Hommer, D., Heilig, M. (2008). Neurokinin 1 Receptor Antagonism as a Possible Therapy for Alcoholism. Science DOI: 10.1126/science.1153813

When we want to determine the structure of a protein, we usually think of two methods. The most historically popular has been X-Ray crystallography (XRC), in which a very pure sample of the protein is induced (through a sort of mystical, poorly understood process) to form regular repeating arrays — crystals. These are then blasted with powerful X-ray beams. The way those beams scatter off of the crystal lattice allow us to work out the exact atomic structure of the components of the lattice. The second method, which has been gaining popularity and strength over the past few decades, is Nuclear Magnetic Resonance (NMR). This technique does not require crystals, so the preparation of the sample is generally considered easier than for XRC. In NMR, the sample is placed into a magnetic field and then bombarded with pulses of radio waves. These waves influence the atoms in the sample, and then by monitoring the relaxation of the atoms (and how that relaxation is influenced by nearby atoms), we can determine protein structures. NMR is still somewhat less popular than XRC mainly based on technical aspects and perhaps a bit because XRC is a little more entrenched as a technique.

These are not the only two methods for determining structural information, however. Several other methods exist, and are constantly improving. The February 28 issue of Nature (vol 451) contains a report describing the application of a technique called cryo-electron microscopy (cryo-EM) to examine the capsid of a virus. The authors (from Purdue, Baylor, and MIT), were able to obtain a structure of the capsid at 4.5 angstroms (1 angstrom is 1×10^-10 meters), a really phenomenal resolution for this method. In comparison, a great X-ray structure will be in the range of 1 angstrom, with 2 - 3 angstroms being a “good” X ray diffraction.

The structure of the capsid (shown to the left) is one of those beautifully geometric constructs that seem to show up frequently in nature. The high resolution allowed them to map the backbone of the main protein making up the 22 megadalton capsid (a dalton is the same mass as a single hydrogen atom, so a megadalton is 1,000,000 hydrogen atoms. In comparison the protein I study is 13 kilodaltons, 3 orders of magnitude smaller). In addition to mapping the known protein component of the capsid, the authors were able to identify a second protein component. Unfortunately they were not able to fully map the backbone of this one.

The authors do mention that the high resolution they were able to obtain for the main protein component was partially due to some unique aspects of the system they are studying. It remains to be seen if other researchers will be able to perform similar measurements on their own samples. In the meantime, cryo-em will only improve overall, potentially giving scientists a very powerful tool with which to study very large macromolecular assemblies such as these.

To biochemists who deal with soluble proteins, the various membranes inside of a cell can often be treated as little more than nondescript barriers which their enzyme or receptor of interest might interact with only briefly during synthesis.  Of course cell membranes are strikingly complex systems in and of themselves, and there is a fairly large field of research focused only on the study of membrane structure and organization.

In a recent (Open Access!!) paper from PNAS, the authors (from Tokyo University and JSTA) describe the “de-orphanization” of an entire family of proteins (an orphaned protein is one for which no function is known). (more…)