As much as I appreciate the mission of PLoS, I haven’t actually gotten around to covering any of their articles in this space.  The other day I happened to come across one entitled “Open Access: Taking Full Advantage of the Content“, and knew that I would have to read it.

In the paper, the authors echo the call of Science Commons to work on creating applications which can leverage open scientific content.  They describe some of the benefits and current shortcomings of producing manuscripts with XML markup (which provides for more facile machine reading and data extraction).  They then go on to argue that the only way to convince people to go through the trouble of creating the machine-readable file is to demonstrate what can be done with the current level of markup and then drawing a picture of what expanding this would do.

The authors have been involved in the development of several data mining tools (BioLit and PubNet are mentioned in this article).  I took a look at both of these, and they are definitely interesting.  My favorite was PubNet, which allows cross-referencing of PubMed queries.  For instance, I could input my name as one query and my advisor’s as another.  This can generate a map of co-authorship.  I would point out here that there are some issues, such as the fact that someone else with the same name as I (at least according to PubMed) has papers in the database.  This throws off the co-authorship map by including things which I’m not looking for.  BioLit seemed to have some problems - often I would click on a link and nothing would seem to happen.

The paper continues with more examples of applications and systems people are developing based around open access data, such as SciVee (which I’ve mentioned in the past).  They end with a “call to action” for scientists and others to engage in expanding these tools and developing new ones in order to encourage interest in the idea of Open Access.

I agree with the authors of this paper that I’d love to see more things being done with the data we currently have available.  If we are able to point out place where having more data would lead to even greater returns, so much the better.  I also appreciate that this is published in a journal, as I think this is something that professors are more likely to see (as compared to blogs, where the majority of the conversation has been held).  I do wonder if they are “preaching to the choir” a bit, in that the users of PLoS are more likely to be aware of these tools and potential for growth.  Perhaps this is partially the point - to engage those who are currently thinking “I really enjoy things like PLoS and SciVee” in a more active role.

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.

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.

(more…)

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

I had this masterful plan to write about today’s seminar from Dr. Tobias (UC Irvine), including a review of the professor’s work, direct quotes from lunch with him, and notes from his talk at the end of the day.

Unfortunately weather has kept the speaker away, so instead I think I’ll just review a recent paper!

Fortunately for us, Dr. Tobias was a co-author on a recent paper in Science discussing specific ion effects in solution.  The paper is a minireview of recent literature that goes against the conventional wisdom of what salts are doing in solution.  The seminal work on salt’s action in a solution was done in the late 1800’s by Franz Hofmeister.  The common thought is that adding salts to a solution causes widespread changes in the structure of water (water’s rapidly shifting morass of hydrogen bonds is what keeps it liquid rather than gas at room temperature - adding salt is thought to influence this network on a large scale).

Computational chemistry experiments, however, have indicated that salt effects are more local, and only influence water molecules nearby the ions of the salt.  Other biophysical measurements also seem to call the long-range bulk model of salt effects into question.  Experiments also indicate that certain ions may concentrate at the interface between two solvents (or at the air/solvent interface), while others stay away from the interface.

This work is interesting, both from purely theoretical as well as practical standpoints.  Theoretically, it demonstrates that the simplistic view of salts based on the Hofmeister series smooths over some details which may be minor but significant.  This has practical applications in the field of crystallography, where scientists often use solvents with varying salts and salt concentrations to carefully induce their proteins to crystallize.  This crystallization process is poorly understood, and it’s almost certain that these sorts of specific ion effects are at least partially involved.

Tobias, D.J., Hemminger, J.C. (2008). CHEMISTRY: Getting Specific About Specific Ion Effects. Science, 319(5867), 1197-1198. DOI: 10.1126/science.1152799

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…)

In addition to muttering about how everyone needs a little more ‘net in their lives, I’ll also be doing reviews of interesting papers that I come across in the literature.  I’ve applied to register at BPR3, so these posts should be accompanied by a nice little icon to indicate when the verbage is going to get dense.

I’m working on the first of these posts now.  I’m going to put some time into it, because I’m probably going to submit it as part of a “job interview”.