Get past Rubisco to see some other proteins!

Rubisco is a really high copy number protein in plants.  How high?  Many textbooks consider it a given that it is the most common protein on our planet.  You can imagine how much fun it is to do plant proteomics, then, right?  This is worse than albumin and transferrin in plasma samples.

Help is on the way, plant proteomics researchers!  A paper currently in press at MCP by Zhang, Gao and Xing, et al., out of Texas A&M describes Polyethyleneimine (PEI) Assisted Rubisco Cleanup (PARC) and how you can get past this pesky protein and actually see something else in your proteomics samples.

Direct link to the pre-release text here.

IonSource Daily: News on Biotech and Pharma every day

Are you interested in daily updates of what biopharma or biotech company is in cahoots with today or who as bought whom?  Then you should check out the IonSource Daily page.  Tons of information on the business side of our field in a timely manner.  You can find go directly to it here.

Global proteomic analysis of the NCI-60 cell line panels!

This is HUGE.
The NCI-60 is a compilation of, you guessed it, 60 immortalized cancer cell lines that are used by the National Cancer Institute for virtually every drug screen and subsequent analysis.  These are also, by far, the most common cancer cell lines studied by researchers world-wide.  Every major cancer type is represented.

And here, we have a brand new study from Gholami et. al., detailing the proteomes of all of them.  All.  This may be the most useful body of proteomic data every generated for oncology.  The analysis was done on an Orbitrap XL plus ETD and the data was processed with MaxQuant.

The analysis is top notch.  This team pulled out all the stops and did this right.  If you are doing any kind of cancer research in proteomics, you need to read this.  This is how you do it.

How often should you update your FASTA database?

Great question, right?  How much are these databases really changing?
During one of my postdocs, one of the staff scientists told me that I should update my FASTAs every 3 months.  In order to compare, I dug up an old complete Uniprot-SwissProt library that I downloaded in July, 2011 and I downloaded the newest version while making dinner tonight.

So!  What's the difference?  First off, I notice that the new file is about 2.5 MB bigger than my FASTA from July 2011.  That doesn't seem like a lot.

Random comparisons:
1) Burkholderia:
Old FASTA; 8206 entries
New FASTA;  8208 entries

2) Green monkey (Chlorocebus):
Old FASTA;  128 entries
New FASTA;  130 entries

3)  Malaria (Plasmodium falciparum)
Old FASTA;  298 entries
New FASTA;  300 entries

It looks like I'm making this up.  They've added exactly 2 new annotations to each of the databases?  Or I pseudo-randomly chose 3 databases that had exactly 2 new entries?  Seems made up.

Better analysis!  How different is the same file searched in the same way versus the same FASTA?

I'll parse each database on "sapiens" and run the same method against the same cell line digest ran on a 120 min gradient on an Orbitrap Elite operating in High-high mode using a Top15 method

Both FASTAs came up with 1431 unique proteins

...only one of which had changed.

What about annotations?

Here is where we see some differences.  29 Annotations in our species has been updated in the last 2 years.

What's the takeaway?  I guess it is that if we are working with a well annotated organism, such as us or mice, we probably shouldn't be too paranoid about remaking all our databases every couple of months when the new Uniprot-Swissprot is posted.

It is important to remember that this is the best annotated protein database out there.  These are manually verified.  Databases that are annotated via automated processes (like TREMBL?) are probably going to be a whole lot more dynamic than this one, so all these thoughts go out the window.  For example, the PlasmoDB (malaria) database is the complete other side of the spectrum.  It changes constantly.  (It has to, the global Plasmodium genome has probably went through millions of changes since I started writing this entry....)

Good news for Proteome Discoverer 1.4 users, the Annotation module that is linked to ProteinCenter automatically updates these annotations to the newest available for you.  It works perfectly for slowly changing databases, and does a surprisingly good job even for dynamic databases such as Plasmodium.

Caprotec Syngenta deal on plant proteomics

Caprotec and Syngenta announced yesterday a deal to work together on a plant proteomics project using Caprotec's Capture Compound MS (CCMS) technology to profile active components of Syngenta in plant systems.

CCMS works by pulling out proteins using customizable molecules with the three specific functions shown in the picture above.  One for the target for your protein, one for cross-linking after recognition and a final one for sorting or pulling down the molecule.  It's pretty cool technology and worth checking out.  You can read the article from GenomeWeb here, and learn more about CCMS here.

ABRF is seeking core labs to run this year's standard sample

ABRF is looking for core labs to participate in this year's study.  The aims are as follows:

To allow labs to evaluate their ability to measure heavy/light peptide ratios for a large number of peptides in a complex biological background
• To provide a learning opportunity for labs unfamiliar with experimental techniques and data analysis strategies for measuring heavy/light peptide ratios
• To benchmark the standard across a large number of labs

For more information, you can go to the ABRF website here, or look specifically at this year's project description.  I've ran this sample and it is pretty cool.  Very complex with isotopically labeled peptides (1,0000!) spiked in at known concentrations.

The results are due by October 11, 2013 and will be released at ABRF in 2014.

Details on the HeLa agreement

Last week, a compromise was reached between the descendents of Henrietta Lacks and the National Institute of Health.  The agreement is for controlled access to the HeLa cell line genome sequences, and you can read more about it here.

In order to obtain access to the sequence data, you must first register with the NIH dbGaP program here.  It isn't an easy process and only PIs with the consent of their organizational signing officers may apply for access.  Complete details on the application process are available here.

Surprisingly good video on malaria produced by Animal planet

An American cable channel produced a surprisingly accurate video on malaria and taxoplasmodium. Considering my country's seeming hatred to anything requiring intellect to follow, I'm always surprised (and happy!) when I see stuff like this!  Got 10 minutes?  You should check this out!

List of all upcoming Proteomics conferences in the World!

A GREAT suggestion came in from a reader the other day:  We should have all of the upcoming proteomics conferences in the world listed on this site.  Hence, there is a now a new page.  This is tough, so if you have a proteomics conference, big or small that isn't on our list -- please send me the information.  You can post it in the comments section, or send me an email:  orsburn@vt.edu.

 You can check out the list in its current state here.

Thank you!

Identification of Autocrine Growth Factors Secreted by CHO Cells for Applications in Single-Cell Cloning Media, or Orbi Velos vs. Xevo

This new paper from U Ming Lim et. al., out of the National University of Singapore is a very thorough study of one of the world's favorite model cells, the Chinese Hampster Ovary (CHO).  While it is a well carried out study with many implications for people who use these cell lines as well as autocrine researchers, it also serves to answer a question I posed quite a while ago -- head to head, which is better, and Orbi Velos or a Waters Xevo Synapt?

Readers of my blog from way back know that my department had these two instruments and I attempted to answer this question, but could not.  These researchers ran the same fractions on the Orbi Velos and on the Xevo.  In all of their analyses, the Velos positively smoked the Xevo.  You'll have to check out the paper for yourself, but in unique peptides/proteins found by only one instrument, the Velos identified as many as 10 times more than the Xevo.  It makes for some lop-sided Venn diagrams.