PTMScan Direct

The front page of MCP is back online.  One reason that this is good is that you can read about PTMScan Direct.  This paper is from Cell Signaling and describes a really good combination of using antibody enrichment and mass spectrometry.
The PTMScan technology is based on mixtures of antibodies that can be used to pull down multiple important signaling pathway proteins all at once.  The pulldowns are then analyzed by LC-MS/MS (in the paper, they describe the use of an LTQ-Orbitrap Velos).  The antibody combinations can provide you with a rapid and thorough look into what pathways are involved in your system of interest.  The technology is fully compatible with both label free discovery methods, as well as SILAC for quantification.
My one small criticism with this absolutely brilliant paper is in the settings they used in their MS/MS analysis.  I was very surprised to see that they used an MS1 mass window of 50ppm with an MS/MS window of 1 Da.  These are considerably less strict than what I use and what I am used to seeing in the literature.  When our bionformatician informed me that our false discovery rate calculations benefited from using larger mass windows, I began using 10ppm and 0.5 Da with our Velos.  I don't think I could be convinced to widen my MS1 window 5-fold.  My only thought on why they used these settings is that the Sorceror system they used for analysis benefits dramatically from having a larger window, but at this point I'm just speculating.

In summary, this technique takes immunoaffinity pull-downs to a whole new level.  I expect this technology to be in high-demand, particularly in drug mechanism studies.  If you read one paper this month, this is the one I recommend.

Metabolic Fate of Tea Polyphenols in Humans

This new paper in JPR isn't really a proteomics paper, its more of a metabolomics (metabonomics) study, but it is definitely interesting enough to warrant a quick mention.
In the study, 20 healthy men and 20 healthy women were put on a specific polyphenol-free diet (no caffeine or chocolate for 6 weeks).  The exception to this diet was the daily administration of a concentrated tea that was the equivalent of ~5 cups of commercially available tea.  The participants then submitted urine samples over a rigorous schedule, with some participants submitting samples 6 times daily.
The urine samples were analyzed with LC-MS using a Waters ACQUITY UPLC system coupled to a Micromass Q-TOF.  The samples were also analyzed by GC-MS using an Agilent 6890N GC and a Pegasus HT TOF system.
The study determined the rate of clearance of the polyphenols present in these teas as well as the appearance of compounds that resulted from metabolism of these molecules.  One interesting fact the study found was that although caffeine appearance in the urine peaked 1 hour after the tea was ingested, it didn't completely clear the body until 9 hours post ingestion.
The most interesting part of this study, in my opinion, was the use of multivariate statistics for results analysis.  I am encouraged when I see the science of statistics infiltrating the analysis of MS data.  Its interesting to me how rare this has been so far, especially considering how essential statistics is to genomics.

MCP frontpage down

If you've tried to access the new issue of MCP through the Highlights page, I'm sure you noticed that all of the links are down.  I alerted Andrew Harmon at MCP of the problem this morning, and they are working to resolve it.  Hopefully they'll get it fixed soon, they have some great stuff in this issue!

Identification of Targets of c-Src Tyrosine Kinase by Chemical Complement and Phosphoproteomics

This new paper in press at MCP by Martinez-Ferrando, et al., is a really good example of using cutting edge phosphoproteomics to open up our understanding of an incredibly interesting protein.  Although c-Src has been the focus of thousands of different papers, its amazing how much we still don't know about a protein that was discovered in the 1970s (and resulted in a Nobel prize for 2 top scientists in the 1980s).

In this study, a system was established to force the c-Src tyrosine kinase function to be active in a SILAC labeling system.  An interesting aspect of this study is that they only went for peptides phosphorylated on tyrosine residues, by use of the filter aided capture and elution (FACE) using the 4G10 antibody.  They didn't follow up with a metal or ion based phosphopeptide enrichment.

The proteomic analysis was top-notch, as you'd expect from the Pandey lab, using an Orbitrap Velos in 'high high' mode, where they used the Orbitrap exclusively, both collecting the MS1 and MS/MS fragmentation information (via HCD).  Both Proteome Discoverer 1.3 and Maxquant were employed because they often provide complementary information. My only criticism of this excellent paper is that peptide desalting was performed with C-18, which causes a loss of phosphopeptides, compared to other desalting methods such as graphite spin columns.

The results of this study are over 200 proteins that were shown to be downstream phosphorylation events that were downstream of c-Src.  The majority of these phosphorylations were previously unknown to be linked to c-Src and help explain some of the effects of this important oncogene.  They do a good job of 'validating' several of these events by western blotting and immunocytochemistry (ICC).

Summary:  A really good paper that shows how phosphoproteomics can really open our eyes, even when it comes to extremely well studied and characterized systems.  A good read for anyone in oncology research.

The Inficon Hapsite Portable Mass Spectrometer: One step closer to a Tricorder

If you have even casually observed any of the many iterations of the television series Star Trek, chances are you have seen the characters using a device known as a Tricorder, such as the one that Spock is holding below.

Tricorders allow these characters to instantly know whether the atmosphere of a planet is hospitable and helps them detect the signs of life forms and other possible dangers.  It has been humorously suggested at Mass Spectrometry conferences for the last 40 years that with miniaturization and improvement in mass spectrometry instruments, that we are actually heading toward the creation of these devices.

The Hapsite ER from Inficon is most certainly a device that is moving toward this direction.

While this device is not as small as the one that Spock is carrying, it might be almost as useful.  This single person-portable mass spectrometer can rapidly deliver qualitative and quantitative GC-MS data in as little as 10 minutes.  According to Inficon, it can detect numerous volatile organic compounds, toxic industrial materials, and chemical warfare agents in this span and has a detection limit in the Parts Per Trillion.

If that wasn't enough, it also has a built in GPS unit so that the precise time and location of the sample collection can be saved along with the identity and quantity of the chemicals detected.

The following chromatogram is from the a mixture of 11 different toxic compounds, clearly showing that the Hapsite has the resolution to separate them all.

On this 14 minute gradient, they had sufficient resolution to separate all 11 in under 10 minutes, with spacing between peaks of around 0.5 to 1 minute, meaning that they really didn't push the machine that hard, I think that using the same column they could increase the intake pressure and actually cut this time in half while still getting the same number of IDs.

If you'd like to read more about this device, check out the product page at Inficon.

Agilent Offgel vs SCX

Overview:
~800 ug of peptides were divided in half
1 half went to SCX on a 10mM sodium phosphate (pH 2.8) gradient with an increase to 0.6M KCl.  20 fractions were collected
1 half was separated by peptide offgel using a high resolution strip pH 3-10
The peptides were desalted by ZipTips and loaded on our Velos using a standard top 10 method.
The MS/MS spectra were analyzed using Proteome Discoverer 1.3 with both Sequest and Mascot using Peptide Validator.
The results are shown above.  Pretty one-sided.  We'll see what the repeats look like

Dropping the Science

This post is off the proteomics topic.  If you are in for a distraction, check this site I found this morning:  Dropping the Science.  It is pretty fun.

Colbert Associates Xpertex Inserts

Sometime last year it became just about impossible for me to find good autosampler vials for my Accela system.  Thermo got tired of making them, or changed the catalog number or something.  After weeks of searching and multiple calls to manufacturers, we were able to secure several boxes to set us up for the future.  Then I started my new position.

We have been making due for the last 4-5 months by using the autosampler vials for our Shimadzu HPLC with spring-loaded glass inserts from Thermo that run about $70/100 inserts.  Couple this with the price of the glass vials, and we're running a couple of bucks a sample vial.

Tonight I happened to be proof-reading my recent entries and an ad for Colbert Associates popped up.  Although I'm not supposed to click on the Adsense ads, I did in this case.  (Google, you can take back the 0.02 cents that I earned for that click, I apologize).  I don't think Colbert Associates would be too angry considering that I ordered 500 of their polypropylene inserts which run about $0.15/insert.  As long as the inserts are of good quality, we'll definitely be switching over to their autosampler vials.  I need to check the numbers, but it looks like they are substantially cheaper than the ones we are using.  It looks like we could save at least $1 per sample, which doesn't seem like much, but will definitely add up at 24 samples/day.

I'll report later on the quality of the inserts when they arrive.

PepNovo Part 2: Predicting Intensity Ranks of Peptide Fragment Ions

I'm still investigating PepNovo for performing de novo sequencing on our data sets.  The second paper from the list at CSE Bioinformatics is this 2008 paper by Ari Frank and describes the PepNovo Plus algorithm.
While a lot of the statistics are a little beyond my level, there is a lot of very useful information in this somewhat long paper.
In the introduction, Dr.Frank points out the problem with most statistical models used in bioinformatics -- that "such models tend to oversimplify the phenomenon they describe and are consequently inaccurate."
In order to address these shortcomings, the paper describes the use of a machine learning boosting algorithm to analyze a large database of low resolution MS/MS spectra.
The dataset used was >300,000 peptide spectrum pairs.
The principle of boosting "produces highly accurate prediction rules by combining many "weak" rules that, each on their own, might be only moderately accurate."
The boosting algorithm, as described here, is able to make use of a combination of over 800 possible features produced by CID fragmentation of a peptide.
Its pretty clear that this algorithm is much more complicated than simpler programs like Sequest.  Considering the amount of thought that has went into the PepNovo program, I'm expecting big things from it once I can actually get the file to run.
A handicap is that the Thermo RAW files can not be inputted directly into the software.  They must be converted before they will upload successfully.  I'm still working on that one....

PepNovo Part 1

PepNovo De novo peptide sequencing

Today's lunch time reading was an older paper.  Some of the proteins we are interested in have high variability regions.  The way we've been dealing with them is a complex FASTA file containing all known sequences of these proteins from dozens of partially sequenced field isolated.  Unfortunately, it doesn't look like we're only seeing the tip of the iceberg.  The next plan is to filter our peptide data and remove everything that matches.  What we're going to be interested in is the stuff that doesn't match any entries in our database.
The first program I've chosen to evaluate is the PepNovo software.
The following paper was cited in the link above, and its short, so I figured it was a good place to start (if first appeared in JPR in 2006).

De Novo Peptide Sequencing and Identification with Precision Mass Spectrometry

The central concept of this paper is that of  homeometric peptides, which the authors define as different peptides with similar theoretical MS/MS spectra.  The authors site a number of reasons that these can and do occur, though I'm sure they are FAR more likely when you are looking at lower resolution MS/MS spectra
The authors propose that multiple de novo sequencing outputs should be produced by the software that can be narrowed down/filtered by other means.

The big advance forward from this paper is the description of the Dancik scoring algorithm.  From what I can understand of this algorithm, in addition to normal de novo sequencers, the Dancik ranks the intensity of the fragment ions (1st most intense, 2nd, etc.,). The most intense fragments are considered to be the most likely to be b or y ions and the probability of the outputted peptide sequence takes this into consideration.

They then take this scoring algorithm and interrogate a dataset generated by a 7-tesla(!) FT machine and conclude that it is an improvement over other scoring methods.